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2026

Adherence to the Mediterranean diet, inflammatory biomarkers and cognitive status in older Italian adults.

Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model.

Objectives To describe long-term trends in mortality attributed to community-acquired pneumonia (CAP) in Chile from 1990 to 2021, stratified by age group, and to evaluate associations with selected socioeconomic and demographic indicators. Study design Ecological, observational, longitudinal study using national secondary data. Methods CAP mortality rates were analyzed for the total population and by age group. Associations with the Human Development Index (HDI), poverty rate, aging index, and life expectancy at birth were examined using a hierarchical analytical approach. This included Spearman's rank correlation for initial exploration, multivariable linear regression to assess adjusted associations, and Prais–Winsten generalized least squares regression to account for first-order autocorrelation and shared temporal trends. Stationarity was evaluated using augmented Dickey–Fuller tests, with supplementary analyses using first-differenced variables. Missing data were imputed using time-based regression or interpolation, with sensitivity analyses performed. Results CAP mortality declined substantially across all age groups over the study period. Strong bivariate correlations were observed between mortality and all socioeconomic indicators; however, these associations were attenuated after adjustment for confounding and temporal autocorrelation. In multivariable and time-series models, HDI and the aging index remained significantly associated with CAP mortality in children (0–9 years) and older adults (≥65 years), whereas associations in intermediate age groups were not robust after accounting for shared secular trends. Poverty and life expectancy did not demonstrate independent associations in adjusted models. Conclusions CAP mortality in Chile has decreased markedly over the past three decades. Associations with socioeconomic indicators are strongest at the extremes of age and persist after accounting for temporal structure, although the ecological design precludes causal inference. These findings highlight the importance of considering demographic and socioeconomic context in population-level analyses of infectious disease outcomes.

Association between socioeconomic and health variables and community-acquired pneumonia mortality rates in Chile from 1990 to 2021.

With the growing academic pressure and competitive educational environment, students often face mental stress, which can affect their academic performance and mental health. Its accurate and timely detection and prevention is important. Traditionally, mental stress has been reported by self-assessment, which is highly subjective and can be erroneous. With advances in neuroscience, electroencephalogram (EEG) signals have been used to study brain states more objectively. EEG-based features, including time-domain, frequency-domain, and various types of connectivity features, have been used to effectively classify stress signals. However, these individual features are only able to present one aspect of the brain under stress. Several studies have combined a distinct set of features extracted from EEG signals, including time and frequency domain features, with other peripheral signals. Stress is a complex mechanism which leads to alternation in brain dynamics, its connectivity patterns and information flow. This study proposed a feature-fusion model that can effectively combine spatial features, i.e. Microstates (MS), connectivity features like Transfer Entropy (TE) and Granger Causality (GC), which provided a new neuromarker for stress classification. These features are combined with attention fusion, which enhances the discriminant features and mitigates the individual limitations within each modality. We also extracted microstates for stress-based signals. It provided a new set of microstate topomaps to study brain networks when under stress, which was not explored previously. The proposed Attention-fusion based multi-feature set is classified using Support Vector Machine, Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) and gave a reliable accuracy of 95.47%, 98.91%, and 83.49%, respectively. To validate the proposed method, the classification results were compared with individual and binary fusion of MS, TE and GC features, which further confirmed the robustness of the framework. This proposed feature fusion provides a more robust stress classification neuromarker, which can effectively cover the brain dynamics for accurate reporting of the underlying mental state.

Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners.

Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision.

Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods: This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results: The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN–LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial–temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion: Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design enables real-time and edge-device deployment, addressing a major limitation of existing deep-learning techniques. The findings highlight the potential of CNNAttLSTM for scalable, efficient, and accurate agricultural disease monitoring and broader precision agriculture applications.

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification.

Background Large-scale armed conflicts can cause substantial environmental damage, with consequences for ecosystems, infrastructure, and population health. However, little is known about concern regarding these potential impacts among Latin American populations. Objective To determine the factors associated with concern about the potential environmental impact of a large-scale war among Latin American adults. Methods A multi-country cross-sectional survey was conducted among adults residing in Latin American countries. Concern about the environmental impact of a possible large-scale war was assessed using a study-specific question; the survey was conducted during the early weeks of the Russia-Ukraine war, but the outcome referred to concern about a possible large-scale war rather than to that conflict alone. Associations with symptoms of anxiety, depression, and stress measured with the DASS-21, used here as indicators of general psychological symptoms rather than conflict-specific effects, as well as sociodemographic characteristics, were then analyzed. Generalized linear models with Poisson distribution, log link, and robust variance were used to estimate prevalence ratios (PRs) and 95% confidence intervals (95% CIs). Results Among 2,669 respondents, 55% reported concern about the environmental consequences of a possible large-scale war. In multivariable analysis, concern was higher among participants with moderate or greater stress (PR: 1.16; 95% CI: 1.04–1.28) and among those residing in Argentina (PR: 1.79; 95% CI: 1.44–2.21). Concern was lower among men (PR: 0.78; 95% CI: 0.72–0.85) and among participants with technical education (PR: 0.81; 95% CI: 0.67–0.99). Conclusion A substantial proportion of respondents expressed concern about the potential environmental consequences of a large-scale war. This concern was associated with stress and selected sociodemographic factors. These findings provide exploratory evidence on war-related environmental risk perception in Latin America.

Concern about the potential environmental impact of a large-scale war among Latin American adults.

Concern for mpox infection in Latin America.

Consumption of ultra-processed foods is associated with cognitive status in elderly patients.

Correction: Enhancing fault detection in new energy vehicles via novel ensemble approach.

Curcumina en niños con trastorno del espectro autista: inflamación sistémica, regulación sensorial y trastornos de la interacción intestino-cerebro. Ensayo clínico aleatorizado, doble ciego y controlado con placebo (CURATEA).

Efecto de la suplementación con L-leucina, L-arginina y Lcarnitina sobre la composición corporal en hemodiálisis.

End-to-end emergency response protocol for tunnel accidents augmentation with reinforcement learning.

Enhancing fault detection in new energy vehicles via novel ensemble approach.

Enhancing smart city transportation networks: A novel particle swarm optimization and YOLO7x-based ensemble model for accurate vehicle detection.

Background Age-related structural changes in the human brain, including cortical atrophy, reductions in grey and white matter volumes, and the accumulation of small vessel–related lesions such as white matter hyperintensities (WMH) and cerebral microbleeds, represent critical biological substrates underlying cognitive decline and dementia. Fish consumption has been associated with slower cognitive decline and reduced risk of dementia, but a comprehensive evaluation of its relation with brain structures is lacking. Aims The aim of this study was to systematically review current scientific literature providing evidence of relation between fish intake and brain structures in human studies. Methods Studies indexed in two major electronic databases have been screened based on a combination of keywords and MeSH terms. Studies were eligible whether they assessed fish consumption in relation to brain structures in the adult populations. Results A total of 24 studies conducted predominantly on older adults met inclusion criteria. Most brain volume measures were obtained via magnetic resonance imaging (MRI) procedures. Higher fish consumption was associated with reduced severity of white matter hyperintensities (a biomarker of cerebral small vessel disease and white matter damage) and cerebral micro-bleed, preservation of certain brain areas volumes (i.e., hippocampus, temporal lobe and periventricle white matter) and cortical thickness of specific areas (i.e., precuneus, parietal, and cingulate grey matter), among others, compared to lower intake. Some analyses found no association and isolated findings suggested possible adverse associations that were not consistently replicated. Studies reporting null findings may underline the possible relevance of the overall diet (i.e., adherence to the Mediterranean diet). Conclusions Inclusion of fish in a healthy and balanced diet is associated with better white matter grades on MRI and slower progression of white matter hyperintensities and reduction of vascular-related lesions of the aging brain, suggesting a potential role in preventing neurocognitive deterioration. Heterogeneity across studies underscores the need for additional studies.

Fish consumption and brain structure: a comprehensive systematic review of observational studies.

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models.

A Hybrid Temporal-spectral Load Forecasting Model with Static Context Fusion for Smart Cities.

In silico prediction, molecular docking and simulation of natural flavonoid apigenin and xanthoangelol E against human metapneumovirus.

Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies.

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.

Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students.

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

Non‑pharmacological care for early-stage dementia through smart environments in Colombia: a mixed‑methods study and methodological guide for caregivers and patients.

Predicting Academic Award Recognition Across Disciplines Using Publication-Based Bibliometric Indices and SHAP-Driven Explainability.

Evaluating groundwater quality in irrigated areas is crucial for sustainable agriculture, especially as limited water resources and climate change pose significant threat to groundwater resources. Spatial information on groundwater quality is essential for effective management and utilization of water resources, particularly in intensive cropping areas such as irrigated regions in IBIS, Pakistan. However, recent advancements in machine learning (ML) techniques have highlighted that conventional groundwater quality assessment methods are costly and time-consuming, especially for developing nations. Accurate and efficient ML models can address this challenge in agricultural water management by optimally identifying the categories of water quality. This study is conducted to predict groundwater quality using an innovative ensemble-boosting methodology. The data is collected from Rahim Yar Khan’s irrigation system by the Scarp Monitoring Organization. Four irrigation water quality indicators, including sodium adsorption ratio, total dissolved solids, residual sodium carbonate, and electrical conductivity, are used to predict groundwater quality by applying four ML models. The performance of the ML models is assessed using mean squared error, correlation coefficients (r), and root mean square error measures. The proposed Gradient Boosting (GB) approach combines the advantages of interpretable tree models and boosting approaches. Experimental results validate the utility of the proposed approach with a 99% accuracy in predicting groundwater quality, compared to conventional ML techniques. Based on the proposed GB model and the inverse distance weighting interpolation technique, the groundwater quality distribution in the Hazardous Area is 17.34%, the Marginal Area is 79.36%, and the Safe Area is 3.30%. Enhancement and validation of groundwater quality index predictions are carried out using k-fold validation and hyperparameter tuning. Results indicate that the ML models have the potential to accurately delineate different groundwater quality zones for managing water resources and ensuring sustainable agriculture. Water quality assessment through the proposed approach can help managing the groundwater for the regions susceptible to deterioration of water quality thus contributing to better irrigation governance.

Prediction of groundwater quality in irrigated areas using a novel gradient boosting approach.

Psychometric revalidation of the SPRINT-E scale for assessing post-traumatic stress in Latin American populations during the COVID-19 pandemic.

Quantifying Domain-Specific Risk Signals in Lung Cancer Severity Prediction: A Multi-Domain Ablation Study Using XGBoost and SHAP.

Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model.

A Systematic Literature Review on Integrated Deep Learning and Multi-Agent Vision-Language Frameworks for Pathology Image Analysis and Report Generation.

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.

A novel approach for disease and pests detection in potato production system based on deep learning.

A scalable and secure federated learning authentication scheme for IoT.

2025

Advancing Nutritional Science: Contemporary Perspectives on Diet’s Role in Metabolic Health and Disease Prevention.

Advancing fake news combating using machine learning: a hybrid model approach.

Analysis of genetically modified foods and consumer: 25 years of research indexed in Scopus.

Antimicrobial resistant enteric bacteria are widely distributed among environmental water sources in Dhaka, Bangladesh.

Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.

Botnet detection in internet of things using stacked ensemble learning model.

Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.

Children's and adolescents' lifestyle factors associated with physical activity in five Mediterranean countries: the DELICIOUS project.

Chronotype and Cancer: Emerging Relation Between Chrononutrition and Oncology from Human Studies.

In the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively.

Client engagement solution for post implementation issues in software industry using blockchain.

Clinical epidemiology of dengue and COVID-19 co-infection among the residents in Dhaka, Bangladesh, 2021-2023: A cross-sectional study.

Correction: Yousef et al. Upholding or Breaking the Law of Superposition in Pharmacokinetics. Biomedicines 2024, 12, 1843.

Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis.

Detection of cotton crops diseases using customized deep learning model.

Diet, Eating Habits, and Lifestyle Factors Associated with Adequate Sleep Duration in Children and Adolescents Living in 5 Mediterranean Countries: The DELICIOUS Project.

Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.

Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened by various diseases, including Anthracnose and Red Rust. Although farmers try to mitigate such situations on time, early and accurate detection of mango diseases remains challenging due to multiple factors, such as limited understanding of disease diversity, similarity in symptoms, and frequent misclassification. To avoid such instances, this study proposes a multimodal deep learning framework that leverages both leaf and fruit images to improve classification performance and generalization. Individual CNN-based pre-trained models, including ResNet-50, MobileNetV2, EfficientNet-B0, and ConvNeXt, were trained separately on curated datasets of mango leaf and fruit diseases. A novel Modality Attention Fusion (MAF) mechanism was introduced to dynamically weight and combine predictions from both modalities based on their discriminative strength, as some diseases are more prominent on leaves than on fruits, and vice versa. To address overfitting and improve generalization, a class-aware augmentation pipeline was integrated, which performs augmentation according to the specific characteristics of each class. The proposed attention-based fusion strategy significantly outperformed individual models and static fusion approaches, achieving a test accuracy of 99.08%, an F1 score of 99.03%, and a perfect ROC-AUC of 99.96% using EfficientNet-B0 as the base. To evaluate the model’s real-world applicability, an interactive web application was developed using the Django framework and evaluated through out-of-distribution (OOD) testing on diverse mango samples collected from public sources. These findings underline the importance of combining visual cues from multiple organs of plants and adapting model attention to contextual features for real-world agricultural diagnostics.

Dual-modality fusion for mango disease classification using dynamic attention based ensemble of leaf & fruit images.

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2.

Efectos del entrenamiento físico -deportivo en la salud de las personas con discapacidad motriz atendidas en el Tenis Club de Portoviejo, Ecuador.

Effects of a Garlic Hydrophilic Extract Rich in Sulfur Compounds on Redox Biology and Alzheimer's Disease Markers in Caenorhabditis Elegans.

Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.

Enhanced FPGA-based smart power grid simulation using Heun and Piecewise analytic method.

Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals.

Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.

Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review.

Fear and concern about the outbreak of a world war: validation of an instrument in eight Latin American countries (Third World War scale).

Fundus image classification using feature concatenation for early diagnosis of retinal disease.

Game-based educational experience in clinical simulation and academic achievement in medical students: a retrospective study.

Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.

Hybrid Model with Wavelet Decomposition and EfficientNet for Accurate Skin Cancer Classification.

Background/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitations across heterogeneous sources. This study assessed ChatGPT-5, a general-purpose vision-language model, across four scenarios differing in the amount and type of contextual information provided, using a composite dataset to quantify accuracy for calories and macronutrients. Methods: A total of 195 dishes were evaluated, sourced from Allrecipes.com, the SNAPMe dataset, and Home-prepared, weighed meals. Each dish was evaluated under Case 1 (image only), Case 2 (image plus standardized non-visual descriptors), Case 3 (image plus ingredient lists with amounts), and Case 4 (replicates Case 3 but excluding the image). The primary endpoint was kcal Mean Absolute Error (MAE); secondary endpoints included Median Absolute Error (MedAE) and Root Mean Square Error (RMSE) for kcal and macronutrients (protein, carbohydrates, and lipids), all reported with 95% Confidence Intervals (CIs) via dish-level bootstrap resampling and accompanied by absolute differences (Δ) between scenarios. Inference settings were standardized to support reproducibility and variance estimation. Source stratified analyses and quartile summaries were conducted to examine heterogeneity by curation level and nutrient ranges, with additional robustness checks for error complexity relationships. Results and Discussion: Accuracy improved from Case 1 to Case 2 and further in Case 3 for energy and all macronutrients when summarized by MAE, MedAE, and RMSE with 95% CIs, with absolute reductions (Δ) indicating material gains as contextual information increased. In contrast to Case 3, estimation accuracy declined in Case 4, underscoring the contribution of visual cues. Gains were largest in the Home-prepared dietitian-weighed subset and smaller yet consistent for Allrecipes.com and SNAPMe, reflecting differences in reference curation and measurement fidelity across sources. Scenario-level trends were concordant across sources, and stratified and quartile analyses showed coherent patterns of decreasing absolute errors with the provision of structured non-visual information and detailed ingredient data. Conclusions: ChatGPT-5 can deliver practically useful calorie and macronutrient estimates from food images, particularly when augmented with standardized nonvisual descriptors and detailed ingredients, as evidenced by reductions in MAE, MedAE, and RMSE with 95% CIs across scenarios. The decline in accuracy observed when the image was omitted, despite providing detailed ingredient information, indicates that visual cues contribute meaningfully to estimation performance and that improvements are not solely attributable to arithmetic from ingredient lists. Finally, to promote generalizability, it is recommended that future studies include repeated evaluations across diverse datasets, ensure public availability of prompts and outputs, and incorporate systematic comparisons with non-artificial-intelligence baselines.

Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios.

Incorporating soil information with machine learning for crop recommendation to improve agricultural output.

Influencia del teletrabajo impuesto por el confinamiento derivado de la pandemia de COVID-19 en la ganancia de peso en adultos en 21 países de Iberoamérica.

Mediterranean Diet and Quality of Life in Adults: A Systematic Review.

Metaheuristic-based optimal energy assessment of hybrid multi-effect evaporator with synergy of solar and wind energy sources.

Background: The increasing complexity of basketball and the need for optimal decision-making in order to maximize competitive performance highlight the necessity of specialized training for basketball coaches. This systematic review aims to compile, synthesize, and integrate international research published in specialized journals on the training of basketball coaches and students, examining their characteristics and needs. Specifically, it analyzes the content, technical-tactical actions, and methodologies used in practice and education programs to determine which essential parameters for their technical and tactical development. Methods: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA®) guidelines and the PICOS® model until January 30, 2025, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus, and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 14,090 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 23 articles. These studies maintained a high standard of quality. This revealed data on the technical-tactical actions addressed in different categories; the profiles, characteristics, and influence of coaches on player development; and the approaches, teaching methods, and evaluation methodologies used in acquiring knowledge and competencies for the professional development of basketball coaches. Conclusions: Adequate theoretical and practical training for basketball coaches is essential for player development. Therefore, training programs for basketball coaches must integrate technical-tactical, physical, and psychological knowledge with the acquisition of skills and competencies that are refined through practice. This training should be continuous, more specialized, and comprehensive, focusing on understanding and constructing knowledge that supports the professional growth of basketballers. Additionally, training should incorporate digital tools and informal learning opportunities, with blended learning emerging as the most effective methodology for this purpose.

Methodology and content for the design of basketball coach education programs: a systematic review.

Polyphenols are naturally occurring compounds that can be found in plant-based foods, including fruits, vegetables, nuts, seeds, herbs, spices, and beverages, the use of which has been linked to enhanced brain health and cognitive function. These natural molecules are broadly classified into two main groups: flavonoids and non-flavonoid polyphenols, the latter including phenolic acids, stilbenes, and tannins. Flavonoids are primarily known for their potent antioxidant properties, which help neutralize harmful reactive oxygen species (ROS) in the brain, thereby reducing oxidative stress, a key contributor to neurodegenerative diseases. In addition to their antioxidant effects, flavonoids have been shown to modulate inflammation, enhance neuronal survival, and support neurogenesis, all of which are critical for maintaining cognitive function. Phenolic acids possess strong antioxidant properties and are believed to protect brain cells from oxidative damage. Neuroprotective effects of these molecules can also depend on their ability to modulate signaling pathways associated with inflammation and neuronal apoptosis. Among polyphenols, hydroxycinnamic acids such as caffeic acid have been shown to enhance blood-brain barrier permeability, which may increase the delivery of other protective compounds to the brain. Another compound of interest is represented by resveratrol, a stilbene extensively studied for its potential neuroprotective properties related to its ability to activate the sirtuin pathway, a molecular signaling pathway involved in cellular stress response and aging. Lignans, on the other hand, have shown promise in reducing neuroinflammation and oxidative stress, which could help slow the progression of neurodegenerative diseases and cognitive decline. Polyphenols belonging to different subclasses, such as flavonoids, phenolic acids, stilbenes, and lignans, exert neuroprotective effects by regulating microglial activation, suppressing pro-inflammatory cytokines, and mitigating oxidative stress. These compounds act through multiple signaling pathways, including NF-κB, MAPK, and Nrf2, and they may also influence genetic regulation of inflammation and immune responses at brain level. Despite their potential for brain health and cognitive function, polyphenols are often characterized by low bioavailability, something that deserves attention when considering their therapeutic potential. Future translational studies are needed to better understand the right dosage, the overall diet, the correct target population, as well as ideal formulations allowing to overcome bioavailability limitations.

Molecular mechanisms underlying the neuroprotective effects of polyphenols: implications for cognitive function.

Natural Products in Alzheimer’s Disease: A Systematic Review of Clinical Trials and Underlying Molecular Mechanisms.

Nivel de conocimiento sobre el tiburón toro (Carcharhinus leucas) como base para su conservación y gestión medioambiental en la Reserva de la Biósfera Caribe Mexicano.

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

Novel transfer learning approach for hand drawn mathematical geometric shapes classification.

Novel transfer learning based bone fracture detection using radiographic images.

Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.

Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors.

Olive Leaf Extracts With High, Medium, or Low Bioactive Compounds Content Differentially Modulate Alzheimer's Disease via Redox Biology.

Parentalidad Positiva y Prevención de la Población infanto-juvenil con relación a su Salud Mental: una Revisión Actualizada.

In the world of remote sensing, hyperspectral imaging has emerged as a powerful tool that captures incredibly detailed information about our environment. These images contain hundreds of spectral bands that reveal what the human eye cannot see, making them invaluable for applications ranging from precision agriculture to environmental monitoring. However, extracting insights from complex data requires sophisticated analytical approaches. Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We wanted to understand which approach works better under different real-world conditions that researchers and practitioners face. Through extensive experiments across five diverse public hyperspectral datasets, we discovered that the choice between these models is not straightforward, it depends significantly on your specific circumstances. When labeled data are scarce, which is a common challenge in remote sensing, SVM proves more reliable and efficient. Conversely, when abundant training data are available, SAE demonstrates impressive capabilities in learning complex patterns. One interesting finding was how active learning as a technique that intelligently selects the most informative samples for labeling, improved SAE’s performance on medium-sized datasets, potentially offering a practical solution to the data scarcity problem. The proposed approaches showed vulnerability to noise, highlighting the importance of preprocessing steps in real-world applications. Although SVM generally requires less computational resources, SAE’s potential to handle large and complex datasets makes it an attractive option when the appropriate computing infrastructure is available. The model training also achieved high accuracy, compared to other models published in the literature. The results achieved provide a practical path for researchers and practitione...

Performance Evaluation of Support Vector Machine and Stacked Autoencoder for Hyperspectral Image Analysis.

Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.

Scalable Comprehensive Automatic Inspection, Cleaning, and Evaluation Mechanism for Large‐Diameter Pipes.

Background Anterior shoulder instability is a common condition, especially among young and active individuals, often associated with both osseous and soft tissue injuries. Recent innovations have introduced various surgical options for managing critical and subcritical instability. Therefore, the primary objective of this systematic review was to collect, synthesize, and integrate international research published across multiple scientific databases on shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization (DAS), and arthroscopic Trillat techniques used in the treatment of shoulder instability. Method A structured search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the PICOS model, up to January 30, 2025, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus, and Scopus databases. The risk of bias was evaluated, and the PEDro scale was used to assess methodological quality. Results The initial search yielded a total of 964 articles. After applying the inclusion and exclusion criteria, the final sample consisted of 25 articles. These studies demonstrated a high standard of methodological quality. The review summarized the effects of ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic Trillat techniques in treating shoulder instability, detailing the sample population, immobilization period, frequency of instability episodes—including recurrent dislocations and subluxations—surgical methods, study designs, assessed variables, main findings, and reported outcomes. Conclusions Arthroscopic ligamentoplasty is advantageous in preserving the patient’s native anatomy, maintaining joint integrity, and allowing for alternative interventions in case of failure. The arthroscopic Trillat technique offers a minimally invasive solution for anterior instability without significant bone loss. The DAS technique utilizes the biceps tendon to provide dynamic stabilization, aiming to generate a sling effect over the subscapularis muscle. The Latarjet procedure remains the gold standard for managing anterior glenoid bone loss greater than 20%. Each surgical option for anterior shoulder instability carries specific implications, and treatment decisions should be tailored based on bone loss severity, capsuloligamentous quality, and the patient’s functional needs.

Shoulder ligamentoplasty, arthroscopic Latarjet, dynamic anterior stabilization, and arthroscopic trillat for the treatment of shoulder instability: a systematic review of original studies on surgical techniques.

Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.

Tensiomyography, functional movement screen and counter movement jump for the assessment of injury risk in sport: a systematic review of original studies of diagnostic tests.

Transformer-based ECG classification for early detection of cardiac arrhythmias.

Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.

Ventilator pressure prediction employing voting regressor with time series data of patient breaths.

Virtual Patient (E+DIETing_LAB).

Virtual nutritional clinic (E+DIETing_LAB).

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by persistent inflammation and oxidative stress. Ilex paraguariensis (yerba mate) contains bioactive compounds—particularly chlorogenic acids, quercetin, and rutin—with documented antioxidant and anti-inflammatory properties. Objectives: To systematically review the mechanistic and clinical evidence on Ilex paraguariensis and its main constituents in RA-relevant inflammatory, oxidative, and bone metabolic pathways. Methods: Following PRISMA 2020, PubMed/MEDLINE, LILACS, and SciELO were searched up to September 2025. Eligible studies included yerba mate preparations (last 10 years) or isolated compounds (last 5 years) assessing RA-relevant clinical, inflammatory, oxidative, or bone metabolic outcomes. Non-original studies were excluded. Owing to heterogeneity, findings were narratively synthesized, and risk of bias was evaluated using RoB 2, ROBINS-I, OHAT, and SYRCLE. Results: Twenty-three studies met inclusion criteria: 11 human (clinical or observational), 7 human-based in vitro, and 5 animal studies. Interventions with yerba mate infusions or standardized extracts suggest reductions in inflammatory markers (e.g., C-reactive protein, interleukin-6) and indicate improvements in glutathione-related oxidative balance. Evidence from isolated compounds, particularly quercetin and rutin, suggests comparable anti-inflammatory and antioxidant effects. Preclinical studies appear to indicate modulation of inflammatory and redox pathways relevant to RA. Conclusions: Yerba mate and its constituents show preliminary indications of anti-inflammatory and antioxidant effects with potential relevance to RA pathophysiology. However, in the absence of clinical trials in RA patients, conclusions remain tentative, constrained by small sample sizes, methodological heterogeneity, species differences, and internal validity concerns. Future research should include rigorously designed randomized trials and mechanistic studies using advanced human-relevant platforms, such as organoids and organ-on-chip systems.

Yerba Mate (Ilex paraguariensis) and Rheumatoid Arthritis: A Systematic Review of Mechanistic and Clinical Evidence.

Background Maternal and child undernutrition remains a major global health concern despite modest progress. Accelerating reductions in stunting and wasting will require increased investments in nutrition-sensitive interventions, which target nutrition impacts outside of the healthcare setting. This review examines the effects of four types of nutrition-sensitive interventions —cash/food transfers, nutrition-sensitive agriculture, water/sanitation/hygiene, and school nutrition— on maternal and child nutrition outcomes and dietary diversity. Methods We synthesized the evidence using an initial broad search and synthesis for nutrition-sensitive interventions, followed by targeted searches and syntheses for specific interventions and nutrition outcomes. Meta-analyses were performed to evaluate the impacts of cash transfers and agricultural interventions, while a narrative synthesis was produced for additional nutrition-sensitive interventions. Additionally, qualitative synthesis was incorporated to provide insights into the relationship between implementation context and program effectiveness. Results Our initial evidence synthesis included 260 quantitative studies, and additional targeted searches produced 72 eligible articles. Meta-analyses reveal positive impacts on dietary diversity for cash transfers without nutrition-specific components (0.14 SMD; 95% CI: 0.06–0.22), and some nutrition-sensitive agricultural interventions (0.24 SMD; 95% CI: 0.11–0.37). Cash transfers have larger effects on dietary diversity when they include behavior change communication or other nutrition-specific elements (0.41 SMD; 95% CI; 0.15–0.66), whereas agriculture programs with nutrition-specific elements do not show larger effects on dietary diversity than those without. Narrative syntheses indicate that homestead food production interventions may reduce anemia, school feeding interventions may improve anthropometric outcomes, and WASH interventions are most effective when combined with other nutrition initiatives. Conclusions We find consistent evidence that nutrition-sensitive programs contribute to dietary diversity and may have small but positive effects on nutrition outcomes, such as anthropometric outcomes and anemia. Integrating nutrition into social protection, agriculture, and education sectors is essential for addressing the underlying causes of malnutrition, such as dietary diversity.

The role of nutrition-sensitive interventions in improving nutritional outcomes: findings from a systematic review and meta-analysis.

A systematic review of deep learning methods for community detection in social networks.

2024

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis.

Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.

La dynamique et la transition démographique mondiales avec le nombre croissant des jeunes auront des effets socio-économiques variables sur les pays à faible revenu et la communauté mondiale en général. L’extrême pauvreté des jeunes particulièrement en zones rurales est encore beaucoup plus élevée comparé à la moyenne mondiale. La création d'emplois plus nombreux et de meilleure qualité pour les jeunes est donc une priorité urgente du siècle. En dépit du rôle de locomotive reconnu de l’agriculture dans la croissance économique, la création d’emploi et la réduction de la pauvreté, plusieurs évidences font étant d’un désengagement des jeunes de ce secteur. C’est ainsi que nous avons réalisé une analyse systémique de l’engagement des jeunes ruraux dans les chaines de valeur agricoles au Sud-Kivu. Pour répondre à notre question principale à savoir pourquoi les jeunes ruraux se désengagent de plus en plus de l’agriculture, nous avons conduit la recherche sur le terrain de mars 2020 à mai 2023 dont les résultats ont été analysés et discutés à la lumière des théories en lien avec l’agriculture et la croissance économique, les chaines de valeur et l’engagement. Afin de bien appréhender la complexité et la transversalité de la question, l’approche systémique a été utilisée. Les individus à enquêter ont été choisis aléatoirement par triage sans remise en s’inspirant de la technique de l’urne de Bernoulli. Les données ont été analysées grâce au logiciel SPSS, ce qui a permis de décrire les différentes variables, vérifier leur normalité et utiliser les tests de Friedman, Khi Carré, Mann-Whitney et Kruskal-Wallis pour comprendre les liens entre elles. Premièrement, l’enquête auprès de 144 acteurs a permis d’identifier 12 facteurs d’ordre institutionnel, économique, social et écologique perçus principalement comme limitant l’essor de chaines de valeur agricoles dans la zone. Une différence statistiquement significative a été observée quant à la perception de ces facteurs selon la fonction, le type de motivation, le mieux d’origine et le niveau d’éducation des acteurs. Ce qui nous a permis de confirmer notre première hypothèse. Deuxièmement, l’étude des perceptions de 456 jeunes ruraux a montré que seuls 13,4 % des jeunes aspirent à l’agripreneuriat, 71,9% ne sont pas encouragés par leur entourage à s’engager dans l’agriculture, 73,7% sont prêts à emprunter la voie de d’exode rural/migration. 53,5 % des jeunes ont affiché une attitude négative vis-à-vis de l’agriculture comme profession. Une différence statistiquement significative a été observée concernant les scores des perceptions des facteurs économiques, personnels et sociétaux vis-à-vis de l’agriculture comme profession selon le sexe, l’âge, le niveau d’étude ainsi la zone d’origine des jeunes. Nous avons ainsi confirmé notre deuxième hypothèse. L’étude de cas mobilisant 32 jeunes agripreneurs a montré que 84,4% d’entre eux ont enregistré des résultats d’exploitation annuels positifs malgré le faible accès aux facteurs de production, un climat des affaires nonincitatif et le désintérêt affiché par la société vis-à-vis du secteur agricole. Une différence significative a été observée au niveau de leurs revenus en fonction du genre/sexe, la formation professionnelle, le sous-secteur d’activité, le statut matrimonial, l’expérience, la source du capital de démarrage. Nous considérons cela comme facteurs clés de succès de jeunes agripreneurs. Nous confirmons ainsi la troisième et dernière hypothèse de la présente thèse. Pour engager durablement les jeunes ruraux dans les chaines de valeur agricoles nous recommandons de créer un environnement politique et socio-économique facilitant l’accès aux facteurs de production ; développer des stratégies programmatiques sensibles aux jeunes ruraux en tirant profit des TIC ; promouvoir un narratif positif en faveur de l’agriculture comme profession

Analyse et stratégies d’engagement des jeunes ruraux dans les chaines de valeur agricoles dans la province du Sud-Kivu en République Démocratique du Congo.

Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer’s satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Analyzing patients satisfaction level for medical services using twitter data.

Attitudes and Skills in Basic Life Support after Two Types of Training: Traditional vs. Gamification, of Compulsory Secondary Education Students: A Simulation Study.

Blockchain based transparent and reliable framework for wheat crop supply chain.

Can the Functional Physical Fitness of Older People with Overweight or Obesity Be Improved through a Multicomponent Physical Exercise Program? A Chilean Population Study.

Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.

Challenges and opportunities in engineering next-generation 3D microelectronic devices: improved performance and higher integration density.

Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.

A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.

Daily Intake of Two or More Servings of Vegetables Is Associated with a Lower Prevalence of Metabolic Syndrome in Older People.

Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.

Deep transfer learning-based bird species classification using mel spectrogram images.

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.

Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images.

Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.

DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.

Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.

Editorial: Host-bacteria interactions in fish pathogens.

Efficient deep learning-based approach for malaria detection using red blood cell smears.

Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese...

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence.

Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing.

Estudio de las relaciones entre enseñanza, planificación, metodología y evaluación de las prácticas de enseñanza en Educación Física en el Sistema Educativo uruguayo.

Evaluation of depression, anxiety, risky eating behaviors, eating habits and physical activity after the COVID-19 pandemic among adolescents in Mexico City.

Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.

This study explored the impact of varying weight percentages of TiMoVWCr high-entropy alloy (HEA) powder addition on A356 composites produced using friction stir processing (FSP). Unlike previous research that often focused on singular aspects, such as mechanical properties, or microstructural analysis, this investigation systematically examined the multifaceted performance of A356 composites by comprehensively assessing the microstructure, interfacial bonding strength, mechanical properties, and wear behavior. The study identified a uniform distribution of TiMoVWCr HEA powder in the composition A356/2%Ti2%Mo2%V2%W2%Cr, highlighting the effectiveness of the FSP technique in achieving homogeneous dispersion. Strong bonding between the reinforcement and matrix material was observed in the same composition, indicating favorable interfacial characteristics. Mechanical properties, including tensile strength and hardness, were evaluated for various compositions, demonstrating significant improvements across the board. The addition of 2%Ti2%Mo2%V2%W2%Cr powder enhanced the tensile strength by 36.39%, while hardness improved by 62.71%. Similarly, wear resistance showed notable enhancements ranging from 35.56 to 48.89% for different compositions. Microstructural analysis revealed approximately 1640.59 grains per square inch for the A356/2%Ti2%Mo2%V2%W2%Cr processed composite at 500 magnifications. In reinforcing Al composites with Ti, Mo, V, W, and Cr high-entropy alloy (HEA) particles, each element imparted distinct benefits. Titanium (Ti) enhanced strength and wear resistance, molybdenum (Mo) contributed to improved hardness, vanadium (V) promoted hardenability, tungsten (W) enhanced wear resistance, and chromium (Cr) provided wear resistance and hardness. Anticipating the potential applications of the developed composite, the study suggests its suitability for the aerospace sector, particularly in casting lightweight yet high-strength parts such as aircraft components, engine components, and structural components, underlining the significance of the investigated TiMoVWCr HEA powder-modified A356 composites.

Exploring Microstructural, Interfacial, Mechanical, and Wear Properties of AlSi7Mg0.3 Composites with TiMOVWCr High-Entropy Alloy Powder.

Exploring body composition and somatotype profiles among youth professional soccer players.

Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.

Factors associated with the perception of resentment towards the Chinese in Latin America during the first wave of the COVID-19 pandemic.

In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.

Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.

Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.

From by-products to new application opportunities: the enhancement of the leaves deriving from the fruit plants for new potential healthy products.

Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.

Human‐based new approach methodologies to accelerate advances in nutrition research.

In Vitro and In Vivo Insights into a Broccoli Byproduct as a Healthy Ingredient for the Management of Alzheimer’s Disease and Aging through Redox Biology.

Incidence of a Multicomponent Physical Exercise Program on Body Composition in Overweight or Obese People Aged 60 Years or Older from Chile.

Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria.

Introducción: El proceso de estallido social ocurrido (octubre, 2019) en Chile junto a la pandemia por COVID-19, generaron consecuencias directas en la salud mental de los funcionarios públicos del país, dando paso a ausentismo laboral, carga mental y estrés asociado al puesto de trabajo junto a una reorganización de funciones y procesos. El objetivo es determinar la influencia del clima laboral en los niveles de mobbing y burnout en funcionarios públicos de la Región del Maule. Método: Estudio descriptivo y correlacional-explicativo, no experimental y transversal que abarca una muestra de n=399 funcionarios públicos, incorporando hombres y mujeres de diferentes profesiones, cargos, edades y condición socioeconómica de la Región del Maule. La investigación presenta 7 fases: a)Aprobación pre-proyecto de tesis, b)Contacto con actores claves de cada estamento gubernamental, c)Elaboración Dossier de instrumentos en Google Forms (Listado de datos sociodemográficos, Cuestionario de Clima Organizacional Validado CCOV (Bustamante y Álvarez, 2019), Cuestionario individual sobre psicoterror, negación, estigmatismo y rechazo en organizaciones sociales (CISNEROS) (Fidalgo y Piñuel, 2004), Maslach Burnout Inventory MBI (Maslach y Jackson, 1981), d)Envío y aplicación Dossier de instrumentos, e)Análisis de datos y obtención de resultados, f)Elaboración de informe final de tesis, y g)Elaboración de informe de devolución de resultados a entidades gubernamentales. Resultados: Se observan correlaciones negativas y altamente significativas entre Clima laboral - Burnout (ρ=-0,336; p<0,001), Clima laboral - Acoso o Mobbing (ρ=-0,534; p<0,001) y Burnout - Acoso o Mobbing (ρ=-0,336; p<0,001). Se formula el Modelo Predictivo de Salud Laboral (MPSL) para el ámbito público. Conclusiones-Discusión: El clima laboral negativo repercute en la aparición de acoso/mobbing y éste genera burnout, por ello, se requiere intervenir a tiempo en cada variable de la investigación para enfrentar las demandas laborales, optimizando los recursos personales y organizacionales, alcanzando un adecuado bienestar laboral.

Influencia del clima laboral sobre la aparición de mobbing y burnout en funcionarios públicos de la Región del Maule, Chile.

Introduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field. Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings. Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments. Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems. Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.

Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.

Intention to Purchase Foods Based on Insects, Arachnids, and Arthropods, Processed by 3D Printing in Panama Consumers.

Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation.

Is It Possible to Improve the Perceived Quality of Life of Overweight or Obese Older People through a Multicomponent Physical Exercise Program?

Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?

A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model.

Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.

MLS Pedagogy, Culture and Innovation.

Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.

Esta investigación propone la producción de un dispositivo analítico grupal con el fin de incorporarlo en la formación de docentes de la provincia Río Negro. La propuesta surge a partir de una demanda originada por alumnos de cuarto año de la carrera de profesorado de primaria del Instituto de Formación Docente de El Bolsón (IFDCEB) quienes debido a la inmediatez de sus prácticas se cuestionan de manera epistémica y epistemológica el sentido real de ser docente. La formación docente actual no cuenta con un espacio propicio para la contención de situaciones especiales como la angustia y temor que surgen al enfrentar el inicio de las prácticas. Luego, ¿Cómo garantizar que estudiantes avanzados de las carreras de formación docente del IFDCEB aprendan a gestionar adecuadamente situaciones de temor frente a la inminencia de sus prácticas profesionales? Para ello, la investigación propone desarrollar el dispositivo grupal, La máquina para producir significados compartidos: Una metodología grupal para la exploración psicoanalítica, dirigido a alumnos y alumnas avanzados en las carreras de formación docente del IFDCEB, que permita la gestión adecuada de situaciones de temor frente a la inminencia de sus prácticas profesionales. Se emplea una metodología cualitativa enmarcada en el trabajo descriptivo hermenéutico. La intervención se apoya en la investigación-acción mediante técnicas cualitativas, como la observación abierta y no estructurada, la observación participante y el registro anecdótico, para realizar luego un análisis de contenido y establecer categorías de análisis apoyado este proceso en el uso del software Atlas.ti. Se obtendrán resultados teórico-metodológicos y prácticos, pues se fundamenta una concepción alternativa a la visión tradicional del psicoanálisis, se sugiere una metodología para sumar a la formación de los futuros docentes, y se ofrece una mejora al programa de formación docente actual, contribuyendo así con una futura práctica docente más eficiente en la región.

Metodología para la exploración psicoanalítica grupal de situaciones de temor en alumnos y alumnas avanzados en las carreras de formación docente del IFDC de El Bolsón.

Natural Language Processing-Based Software Testing: A Systematic Literature Review.

Novel model to authenticate role-based medical users for blockchain-based IoMT devices.

Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.

Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.

Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.

Prediction of leukemia peptides using convolutional neural network and protein compositions.

A Prospective Observational Study of Frailty in Geriatric Revitalization Aimed at Community-Dwelling Elderly.

PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.

Cardiovascular diseases are among the leading causes of mortality worldwide, with dietary factors being the main risk contributors. Diets rich in bioactive compounds, such as (poly)phenols, have been shown to potentially exert positive effects on vascular health. Among them, resveratrol has gained particular attention due to its potential antioxidant and anti-inflammatory action. Nevertheless, the results in humans are conflicting possibly due to interindividual different responses. The gut microbiota, a complex microbial community that inhabits the gastrointestinal tract, has been called out as potentially responsible for modulating the biological activities of phenolic metabolites in humans. The present review aims to summarize the main findings from clinical trials on the effects of resveratrol interventions on endothelial and vascular outcomes and review potential mechanisms interesting the role of gut microbiota on the metabolism of this molecule and its cardioprotective metabolites. The findings from randomized controlled trials show contrasting results on the effects of resveratrol supplementation and vascular biomarkers without dose-dependent effect. In particular, studies in which resveratrol was integrated using food sources, i.e., red wine, reported significant effects although the resveratrol content was, on average, much lower compared to tablet supplementation, while other studies with often extreme resveratrol supplementation resulted in null findings. The results from experimental studies suggest that resveratrol exerts cardioprotective effects through the modulation of various antioxidant, anti-inflammatory, and anti-hypertensive pathways, and microbiota composition. Recent studies on resveratrol-derived metabolites, such as piceatannol, have demonstrated its effects on biomarkers of vascular health. Moreover, resveratrol itself has been shown to improve the gut microbiota composition toward an anti-inflammatory profile. Considering the contrasting findings from clinical studies, future research exploring the bidirectional link between resveratrol metabolism and gut microbiota as well as the mediating effect of gut microbiota in resveratrol effect on cardiovascular health is warranted.

Resveratrol and vascular health: evidence from clinical studies and mechanisms of actions related to its metabolites produced by gut microbiota.

Risk Factors for Eating Disorders in University Students: The RUNEAT Study.

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.

Short-Term Effect of Biopolymer-Based Coatings on Surface Hardness and Color of Limestone Exposed to Tropical Outdoor Conditions.

Assessment of side effects associated with COVID-19 vaccination is required to monitor safety issues and acceptance of vaccines in the long term. We found a significant knowledge gap in the safety profile of COVID-19 vaccines in Bangladesh. We enrolled 1805 vaccine recipients from May 5, 2021, to April 4, 2023. Kruskal-Wallis test and χ2 test were performed. Multivariable logistic regression was also performed. First, second and third doses were administered among 1805, 1341, and 923 participants, respectively. Oxford–AstraZeneca (2946 doses) was the highest administered followed by Sinopharm BIBP (551 doses), Sinovac (214 doses), Pfizer-BioNTech (198 doses), and Moderna (160 doses), respectively. Pain at the injection site (80-90%, 3200–3600), swelling (85%, 3458), redness (78%, 3168), and heaviness in hand (65%, 2645) were the most common local effects, and fever (85%, 3458), headache (82%, 3336), myalgia (70%, 2848), chills (67%, 2726), muscle pain (60%, 2441) were the most prevalent systemic side effects reported within 48 h of vaccination. Thrombosis was only reported among the Oxford–AstraZeneca recipients (3.5-5.7%). Both local and systemic effects were significantly associated with the Oxford–AstraZeneca (p-value < 0.05), Pfizer–BioNTech (p-value < 0.05), and Moderna (p-value < 0.05) vaccination. Chronic urticaria and psoriasis were reported by 55-60% of the recipients after six months or later. The highest percentage of local and systemic effects after 2nd and 3rd dose were found among recipients of Moderna followed by Pfizer-BioNTech and Oxford–AstraZeneca. Homogenous doses of Oxford–AstraZeneca and heterogenous doses of Moderna and Pfizer-BioNTech were significantly associated with elevated adverse effects. Females, aged above 60 years with preexisting health conditions had higher risks. Vaccination with Pfizer-BioNTech (OR 4.34, 95% CI 3.95–4.58) had the highest odds of severe and long-term effects followed by Moderna (OR 4.15, 95% CI 3.92–4.69) and Oxford–AstraZeneca (OR 3.89, 95% CI 3.45–4.06), respectively. This study will provide an integrated insight into the safety profile of COVID-19 vaccines.

Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study.

Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.

StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.

Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing.

Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.

Over the last decades, the Mediterranean diet gained enormous scientific, social, and commercial attention due to proven positive effects on health and undeniable taste that facilitated a widespread popularity. Researchers have investigated the role of Mediterranean-type dietary patterns on human health all around the world, reporting consistent findings concerning its benefits. However, what does truly define the Mediterranean diet? The myriad of dietary scores synthesizes the nutritional content of a Mediterranean-type diet, but a variety of aspects are generally unexplored when studying the adherence to this dietary pattern. Among dietary factors, the main characteristics of the Mediterranean diet, such as consumption of fruit and vegetables, olive oil, and cereals should be accompanied by other underrated features, such as the following: (i) specific reference to whole-grain consumption; (ii) considering the consumption of legumes, nuts, seeds, herbs and spices often untested when exploring the adherence to the Mediterranean diet; (iii) consumption of eggs and dairy products as common foods consumed in the Mediterranean region (irrespectively of the modern demonization of dietary fat intake). Another main feature of the Mediterranean diet includes (red) wine consumption, but more general patterns of alcohol intake are generally unmeasured, lacking specificity concerning the drinking occasion and intensity (i.e., alcohol drinking during meals). Among other underrated aspects, cooking methods are rather simple and yet extremely varied. Several underrated aspects are related to the quality of food consumed when the Mediterranean diet was first investigated: foods are locally produced, minimally processed, and preserved with more natural methods (i.e., fermentation), strongly connected with the territory with limited and controlled impact on the environment. Dietary habits are also associated with lifestyle behaviors, such as sleeping patterns, and social and cultural values, favoring commensality and frugality. In conclusion, it is rather reductive to consider the Mediterranean diet as just a pattern of food groups to be consumed decontextualized from the social and geographical background of Mediterranean culture. While the methodologies to study the Mediterranean diet have demonstrated to be useful up to date, a more holistic approach should be considered in future studies by considering the aforementioned underrated features and values to be potentially applied globally through the concept of a “Planeterranean” diet.

Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet.

Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation.

Upholding or Breaking the Law of Superposition in Pharmacokinetics.

Virtual histopathology methods in medical imaging - a systematic review.

Background/Objectives: The diet quality of younger individuals is decreasing globally, with alarming trends also in the Mediterranean region. The aim of this study was to assess diet quality and adequacy in relation to country-specific dietary recommendations for children and adolescents living in the Mediterranean area. Methods: A cross-sectional survey was conducted of 2011 parents of the target population participating in the DELICIOUS EU-PRIMA project. Dietary data and cross-references with food-based recommendations and the application of the youth healthy eating index (YHEI) was assessed through 24 h recalls and food frequency questionnaires. Results: Adherence to recommendations on plant-based foods was low (less than ∼20%), including fruit and vegetables adequacy in all countries, legume adequacy in all countries except for Italy, and cereal adequacy in all countries except for Portugal. For animal products and dietary fats, the adequacy in relation to the national food-based dietary recommendations was slightly better (∼40% on average) in most countries, although the Eastern countries reported worse rates. Higher scores on the YHEI predicted adequacy in relation to vegetables (except Egypt), fruit (except Lebanon), cereals (except Spain), and legumes (except Spain) in most countries. Younger children (p < 0.005) reporting having 8–10 h adequate sleep duration (p < 0.001), <2 h/day screen time (p < 0.001), and a medium/high physical activity level (p < 0.001) displayed a better diet quality. Moreover, older respondents (p < 0.001) with a medium/high educational level (p = 0.001) and living with a partner (p = 0.003) reported that their children had a better diet quality. Conclusions: Plant-based food groups, including fruit, vegetables, legumes, and even (whole-grain) cereals are underrepresented in the diets of Mediterranean children and adolescents. Moreover, the adequate consumption of other important dietary components, such as milk and dairy products, is rather disregarded, leading to substantially suboptimal diets and poor adequacy in relation to dietary guidelines.

Youth Healthy Eating Index (YHEI) and Diet Adequacy in Relation to Country-Specific National Dietary Recommendations in Children and Adolescents in Five Mediterranean Countries from the DELICIOUS Project.

A deep learning approach for Named Entity Recognition in Urdu language.

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.

An enhanced approach for predicting air pollution using quantum support vector machine.

An improved deep convolutional neural network-based YouTube video classification using textual features.

A real-time air-writing model to recognize Bengali characters.

2023

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.

Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach.

Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.

Análisis de la innovación en los procesos y la productividad respecto al uso de TIC en las empresas panificadoras del Municipio de Campeche, México.

Aporte de las tecnologías de la información y la comunicación (TIC) para minimizar la deserción de carreras universitarias en tecnología.

The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage.

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.

Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.

Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field.

An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.

Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.

Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.

Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection?

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.

A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.

A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems.

Contextual Urdu Lemmatization Using Recurrent Neural Network Models.

Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques.

Cytotoxicity and Antimicrobial Resistance of Aeromonas Strains Isolated from Fresh Produce and Irrigation Water.

Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.

Derecho económico y desarrollo local. El Cantón Yaguachi de Ecuador.

Diagnosing Training Needs in European Tourism SMEs: The TC-NAV Project for Managing and Overcoming Virulent Crises.

Diseño de matriz como herramienta para la evaluación de requerimientos de calidad, medio ambiente y seguridad.

Diseño de un Tablero de Gestión Estratégico para el Instituto Provincial de Vivienda de Salta.

Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.

DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications.

Efectos del Kéfir Moderno sobre el Peso, Estatura, Gasto Energético y Trastornos Intestinales y Respiratorios Asociados en la Parálisis Cerebral Hipertónica.

El foro virtual como impulsor de la experiencia de aprendizaje.

Emotional Management in Journalism and Communication Studies.

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.

Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.

Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.

Evaluation of Organofunctionalized Polydimethylsiloxane Films for the Extraction of Furanic Compounds.

Exploring factors influencing the severity of pregnancy anemia in India: a study using proportional odds model.

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.

Fabrication of polypyrrole gas sensor for detection of NH3 using an oxidizing agent and pyrrole combinations: Studies and characterizations.

Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data.

Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.

Función del estilo de aprendizaje en el rendimiento académico de los estudiantes de Administración y Negocios Internacionales en una institución universitaria en Perú.

Green entrepreneurship intention among high school students: a teachers’ view.

A high penetration of renewable energy (RE) in utility grids creates the problems of power system flexibility, high transmission losses, and voltage variations. These problems can be solved using a hybrid combination of transmission network restructuring and optimal placement of distributed energy generator (DEG) units. Hence, this work investigated a technologically and economically feasible solution for improving the flexibility of power networks and reducing losses in a practical transmission utility network by implementing a restructuring of the network and optimal deployment of the distributed energy generators (DEGs). Two solutions for this network restructuring were proposed. Furthermore, a grid-oriented genetic algorithm (GOGA) was designed by combining the conventional genetic algorithm (GA) and mathematical solutions to identify optimal DEG placement. A power system restructuring and GOGA flexibility index (PSRGFI) was formulated for the assessment of network flexibility. A cost–benefit assessment was also performed to estimate the payback period for the investment required for restructuring of the network and DEG placement. The least-square approximation technique was applied for load projection for the year 2031 considering the base year 2021. It was established that minimization of transmission losses, reduction in voltage deviations, and improvement of network flexibility were achieved through hybrid application of network restructuring and DEG placement using GOGA. A network loss saving of 61.19 MW was achieved via optimal restructuring and GOGA. For the projected year 2031, the PSRGFI increased from 30.94 to 132.78 after the placement of DEGs using GOGA and optimal restructuring, indicating that network flexibility increased significantly. The payback period for the investment was very small, equal to 0.985 years. The performance of the designed method was superior to the GA-based method, simulated annealing technique, and bee colony algorithm (BCA) used for placement of DEG units in the test network. The study was completed using MATLAB software, considering data from a practical transmission network owned by Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India.

Hybrid Combination of Network Restructuring and Optimal Placement of Distributed Generators to Reduce Transmission Loss and Improve Flexibility.

Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization.

Image Watermarking Using Least Significant Bit and Canny Edge Detection.

Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.

Importancia del cumplimiento normativo en México. Implementación del compliance en las empresas mexicanas.

Indagación sistémica para la mejora continua de las herramientas de gestión de proyectos: el caso Gestiona de la Universidad Estatal a Distancia.

Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area.

An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field.

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.

IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.

Liderazgo directivo y desarrollo productivo del sector automotriz.

The Microstructure and Properties of Ni-Si-La2O3 Coatings Deposited on 304 Stainless Steel by Microwave Cladding.

Mitigating 5G security challenges for next-gen industry using quantum computing.

Mitigating Low-Frequency Oscillations and Enhancing the Dynamic Stability of Power System Using Optimal Coordination of Power System Stabilizer and Unified Power Flow Controller.

Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support.

Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del Departamento de Loreto, Perú.

Modelo de comunicación efectiva para la difusión de los programas y proyectos de inversión pública del departamento de Loreto (Perú).

Molecular Mechanisms of the Protective Effects of Olive Leaf Polyphenols against Alzheimer’s Disease.

Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review.

Multidrug resistance pattern and molecular epidemiology of pathogens among children with diarrhea in Bangladesh, 2019–2021.

Método FORTE v. 1.0: una contribución a la gestión de megaproyectos de ingeniería en Brasil.

Navigating SMEs in the tourism sector through crisis (T-CRISIS-NAV).

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient’s life may be in danger. In this work, an ensemble deep learning-based technique is proposed for COVID-19 detection that can classify the disease with high accuracy, efficiency, and reliability. A weighted average ensemble (WAE) prediction was performed by combining three CNN models, namely Xception, VGG19 and ResNet50V2, where 97.25% and 94.10% accuracy was achieved for binary and multiclass classification, respectively. To accurately detect the disease, different test methods have been proposed and developed, some of which are even being used in real-time situations. RT-PCR is one of the most successful COVID-19 detection methods, and is being used worldwide with high accuracy and sensitivity. However, complexity and time-consuming manual processes are limitations of this method. To make the detection process automated, researchers across the world have started to use deep learning to detect COVID-19 applied on medical imaging. Although most of the existing systems offer high accuracy, different limitations, including high variance, overfitting and generalization errors, can be found that can degrade the system performance. Some of the reasons behind those limitations are a lack of reliable data resources, missing preprocessing techniques, a lack of proper model selection, etc., which eventually create reliability issues. Reliability is an important factor for any healthcare system. Here, transfer learning with better preprocessing techniques applied on two benchmark datasets makes the work more reliable. The weighted average ensemble technique with hyperparameter tuning ensures better accuracy than using a randomly selected single CNN model.

A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.

O acesso à educação infantil no estado de Mato Grosso–Brasil: ao final do Plano Nacional de Educação (2014-2024).

Optimal Modeling and Feasibility Analysis of Grid-Interfaced Solar PV/Wind/Pumped Hydro Energy Storage Based Hybrid System.

Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements.

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant.

PRUS: Product Recommender System Based on User Specifications and Customers Reviews.

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations.

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree.

Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.

Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study.

Introduction: Rotavirus infection is a major cause of mortality among children under 5 years in Bangladesh. There is lack of integrated studies on rotavirus prevalence and genetic diversity during 1973 to 2023 in Bangladesh. Methods: This meta-analysis was conducted to determine the prevalence, genotypic diversity and seasonal distribution of rotavirus during pre-vaccination period in Bangladesh. This study included published articles on rotavirus A, rotavirus B and rotavirus C. We used Medline, Scopus and Google Scholar for published articles. Selected literatures were published between 1973 to 2023. Results: This study detected 12431 research articles published on rotavirus. Based on the inclusion criteria, 29 of 75 (30.2%) studies were selected. Molecular epidemiological data was taken from 29 articles, prevalence data from 29 articles, and clinical symptoms from 19 articles. The pooled prevalence of rotavirus was 30.1% (95% CI: 22%-45%, p = 0.005). Rotavirus G1 (27.1%, 2228 of 8219) was the most prevalent followed by G2 (21.09%, 1733 of 8219), G4 (11.58%, 952 of 8219), G9 (9.37%, 770 of 8219), G12 (8.48%, 697 of 8219), and G3 (2.79%, 229 of 8219), respectively. Genotype P[8] (40.6%, 2548 of 6274) was the most prevalent followed by P[4] (12.4%, 777 of 6274) and P[6] (6.4%, 400 of 6274), respectively. Rotavirus G1P[8] (19%) was the most frequent followed by G2P [4] (9.4%), G12P[8] (7.2%), and G9P[8], respectively. Rotavirus infection had higher odds of occurrence during December and February (aOR: 2.86, 95% CI: 2.43-3.6, p = 0.001). Discussion: This is the first meta-analysis including all the studies on prevalence, molecular epidemiology, and genetic diversity of rotavirus from 1973 to 2023, pre-vaccination period in Bangladesh. This study will provide overall scenario of rotavirus genetic diversity and seasonality during pre-vaccination period and aids in policy making for rotavirus vaccination program in Bangladesh. This work will add valuable knowledge for vaccination against rotavirus and compare the data after starting vaccination in Bangladesh.

Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.

Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh.

Principio de tutelaridad protección jurídica preferente ante el menoscabo al derecho del trabajo.

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.

Real Word Spelling Error Detection and Correction for Urdu Language.

Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.

SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations.

The Scope of Technostress and Care of The Self on Journalists During the Pandemic.

Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.

Software Cost and Effort Estimation: Current Approaches and Future Trends.

Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.

Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI.

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective.

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.

A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions.

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.

Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement.

Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band.

Un problema en los PPA de contratos de suministro de energía eléctrica, la indexación.

Ante los retos, cada vez mayores de la Educación superior en Colombia, se hace necesario reconocer el perfil del docente de lenguas extranjeras de los programas de licenciatura y sus necesidades de formación en aras de contribuir con las formaciones de los nuevos profesionales según los propósitos de formación planteados por las instituciones. La presente tesis doctoral nace como producto de varios años de investigación sobre formación del profesorado en el área de la enseñanza de las lenguas extranjeras. El estudio se centró en indagar la coherencia entre el perfil de los docentes formadores con el perfil de formación de maestros propuesto por los programas de licenciatura en lenguas extranjeras en Colombia a la luz de las reformas curriculares que han adelantado los programas de licenciatura en lenguas extranjeras según los lineamientos de la resolución 18583 del 2017 expedida por el Ministerio de Educación en Colombia. La investigación realiza un análisis entre las ofertas académicas de los programas de licenciatura en lenguas extranjeras, los perfiles propuestos y el perfil del docente formador de los mismos programas, haciendo una relación entre las competencias alineadas con las funciones sustantivas de la educación superior. Esta investigación de tipo mixto con un enfoque de estudio exploratorio- descriptivo empelo una muestra aleatoria de 160 profesores pertenecientes a 20 programas de licenciatura del país, se identificó que no existe una correlación directa entre el perfil del docente formador y el perfil del licenciado propuesto por la institución, lo cual lleva a realizar análisis y dar comienzo a planes de formación permanente especialmente en la formación de competencias relacionadas con la función sustantivas de investigativas y proyección social. Hasta el momento las instituciones de educación superior han centrado mayormente sus esfuerzos en fortalecer las ofertas académicas descuidando la formación permanente de los docentes formadores.

Una mirada al perfil de los docentes de programas de licenciatura en lenguas extranjeras en Colombia en coherencia con las propuestas de formación de las instituciones de educación superior.

Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.

Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive.

Utilization of Stockwell Transform, Support Vector Machine and D-STATCOM for the Identification, Classification and Mitigation of Power Quality Problems.

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.

Elite performance and sporting success are often the result of optimal integration and synergy of all components of sports preparedness (i.e., health, technical and tactical skills, bioenergetic and neuromuscular abilities and capacities, anthropometric characteristics, cognition, emotions, creativity, or personality), which evolve because of systematic long-term sports preparation. However, the relative importance of these characteristics varies between individual and team sports. While some individual sports require a high standard of bioenergetic and neuromuscular abilities and capacities, team sports performance is closely related to technical and tactical skills, which may compensate for weakness within the fitness level (1). Nonetheless, successful team sport performances seem to be much more dependent on the interaction among a wide range of factors than on the maximum development of one or two factors in isolation. In team sports, elite performance emerges from the interaction among the individual parts (2) to overcome the opponent during competition. Sports may be categorized according to the degree of predictability of the environment that they are played in (3). Team sports occur in highly unpredictable environments due to the interactions with both teammates and opponents, with performance dealing with this unpredictability. Thus, it is important to have a clear understanding of the integrative systems and the principles that rule their interactions with the environment, keeping in mind the main aim of the process: developing the diversity/unpredictability potential of athletes/teams (4) to afford the emergence of rich patterns of behavior from players to adapt quickly and effectively in dynamically changing and unpredictable environments (5). Performance in team sports is affected by several factors that affect the organization of training and competitions. These include, for example, COVID-19 cases (6), PCR tests (7), air flights and their effects prior to competition (8), injuries (9), or match-congested schedules (10). The interaction among these factors may also influence player availability. The concept of player availability is a common one in elite team sports. Available players can be considered the ones who are injury-free and ready to compete whether the head coach chooses to put them on the lineup. Thus, an available state would be when a player is fit and recovered enough to compete. On the other hand, player unavailability would be considered a state which includes injury, sanction or suspension, or other reasons that would keep a player out of match. However, this topic needs to be explored more in elite team sport environments. Considering previous enriching work, it remains important to further progress and provide academic knowledge in order to support coaches/managers, strength and conditioning coaches, sport scientists, and medical team members (e.g., doctors, physicians, and physiotherapists) in their working environments. While widely-advocated scientific groundwork is considered throughout this manuscript, the main aim of this opinion article is to provide a review of factors related to player availability and its influence on performance in elite team sports (Figure 1). Finally, some practical suggestions and recommendations are provided to deal with constant alterations in player's availability and performance fluctuations.

A commentary of factors related to player availability and its influence on performance in elite team sports.

A fuga à paternidade à luz do ordenamento jurídico angolano: um olhar atento às causas e consequências no Múnicípio do Mungo – Província do Huambo-Angola.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

A novel study on the influence of graphene-based nanofluid concentrations on the response characteristics and surface-integrity of Hastelloy C-276 during minimum quantity lubrication.

2022

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Abuso de la figura del fideicomiso como fuente de pago o garantía con cargo a participaciones federales. Caso del estado de Colima.

Adherence to the Mediterranean Diet Is Inversely Associated with the Prevalence of Metabolic Syndrome in Older People from the North of Spain.

Adherence to the Mediterranean-Style Eating Pattern and Macular Degeneration: A Systematic Review of Observational Studies.

Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.

Amyloid β-but not Tau-induced neurotoxicity is suppressed by Manuka honey via HSP-16.2 and SKN-1/Nrf2 pathways in an in vivo model of Alzheimer's disease.

Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.

Analysis of the effects of career management on occupational satisfaction within the Bank of Central African States (analyse des effets de la gestion des carrieres sur la satisfaction professionnelle au sein de la Banque des Etats de L'afrique Centrale).

An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.

Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector.

Analyzing preventive precautions to limit spread of COVID-19.

An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

Análise das competências interpessoais autocontrole e autoconsciência de alunos do 8º e 9º anos do ensino fundamental.

Análisis de la cadena productiva del café diferenciado para exportación en el departamento de Quetzaltenango, Guatemala, para la aplicación de un modelo de planificación de proyectos productivos.

Análisis de la eficiencia de un colector solar de disco con motor Stirling en la ciudad de Armenia, Colombia.

Análisis de las metodologías de enseñanza en docentes de natación aplicadas con niños en Montevideo.

La delegación es una forma legítima que tienen los Estados centralizados o seccionales de transferir capacidades jurídicas, administrativas, operacionales y de gestión en general a entidades descentralizadas del propio Estado o entes del sector privado. En el Ecuador existen varias formas de delegar competencias y gestiones financieras, administrativas y operativas a cargo de empresas estatales extranjeras, privadas nacionales o extranjeras e incluso por medio de alianzas público-privadas. Los puertos marítimos del Ecuador forman parte de los sectores estratégicos del Estado por sus condiciones de infraestructura, ubicación geopolítica y facilidades de conectividad, elementos que los hacen atractivos para los inversionistas nacionales e internacionales especializados en los negocios marítimos, situación que los transforma en aliados valiosos para el comercio internacional y el tránsito de pasajeros turísticos. La estructura jurídica fundamental que soporta la acción de concesionar o delegar áreas de sectores estratégicos del Estado ecuatoriano —en este caso los puertos marítimos— tiene sustento legal en la Constitución de la República, el Código Orgánico de la Producción Comercio e Inversiones, la Ley Orgánica de Incentivos Para Asociaciones Público Privadas, el Código Orgánico Administrativo y Ley Orgánica de la Contraloría General del Estado, entre otros cuerpos legales. El objetivo fundamental para la delegación e incluso la concesión de los puertos marítimos en el Ecuador obedece a la búsqueda de una gestión eficiente y eficaz para crear un entorno competitivo, sostenido en un ordenamiento jurídico que le permite lograr una dinámica efectiva que incentiva el comercio multilateral. El Estado ecuatoriano pretende que las ventajas comparativas y competitivas de infraestructuras y posicionamiento estratégico sirvan para generar un desarrollo sostenido basado en la capacidad de brindar servicios portuarios ágiles, seguros y con costes competitivos.

Análisis del marco constitucional y normativo para la delegación de puertos marítimos en el Ecuador.

Análisis del nivel de rendimiento de megaprojets en Camerún: caso del proyecto de construcción del segundo puente sobre el Wouri = Analysis of the performance level of megaprojects in Cameroon: the case of the second wouri bridge construction project.

Análisis y mejores prácticas proyectuales de una obra civil hidroeléctrica de Honduras.

Aplicación del método continuo variable en la planificación de las clases de bailoterapia para el mejoramiento de la resistencia de las participantes de la parroquia "grl. Pedro J. Montero" del cantón Yaguachi, Ecuador.

Application of the Gaussian Model for Monitoring Scenarios and Estimation of SO2 Atmospheric Emissions in the Salamanca Area, Bajío, Mexico.

An Artificial Neural Network Model for Water Quality and Water Consumption Prediction.

A internet, um meio privilegiado de comunicação e disseminação de conhecimentos, possibilitou cursos online, criando e expandindo as possibilidades de amplo acesso às populações remotas e outros diferentes públicos. As Tecnologias Digitais da Informação e Comunicação impactam diretamente nas relações laborais e no processo de ensino e aprendizagem, que possibilitou aos trabalhadores flexibilização, porém também contribuiu para a precarização do trabalho docente na EaD. O que faz desta investigação relevante e pertinente, para se tratar da problemática, sobre: os fatores que precarizam o trabalho docente na EaD e as características da contemporaneidade para a categoria. Enfatizando a falta de reconhecimento e a necessidade de políticas públicas que dê legitimidade aos docentes, salientando a polidocência e os meios de contratação a qual são submetidos. Este estudo quali-quantitativo, objetivou perscrutar as características, ações e implicações que delimitam as relações laborais dos professores EaD, de instituições de ensino público e privado, nas regiões nordeste e sudeste do Brasil. Participantes: 300 professores da EaD e do ensino presencial, com a finalidade de analisar as convergências e divergências entre as modalidades e à docência. Critérios de exclusão: foram excluídos aqueles que não estavam trabalhando no territorio pesquisado e nas respectivas modalidades. Instrumento de pesquisa: Utilizouse questionário criado no Google Forms e enviado por meio de correios eletrônicos. Análise de dados: utilizou-se a análise de conteúdo para as questões dissertativas e para as objetivas os softwares Excel e SPSS. Os resultados, comprovaram: que a remuneração e o plano de carreira são fundamentais para a dignidade e o reconhecimento dos professores. Considerando, a falta de programas de formação continuada, políticas de progressão salarial, infraestrutura, possibilidades de ascensão, incentivo de produções individuais e direitos autorais, a voz dos professores nas decisões da instituição, entre otros fatores que influenciam e limitam o trabalho docente na EaD.

As relações laborais na Educação a Distância: uma investigação com professores de educação básica e superior das regiões nordeste e sudeste do Brasil.

Barriers to and facilitators of adherence to pelvic floor muscle exercises and vaginal dilator use among gynecologic cancer patients: a qualitative study.

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML.

Biodisponibilidad de fósforo en alimentos y su efecto en la enfermedad renal crónica.

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices.

Cambios en el comportamiento alimentario de personas adultas con elevado nivel académico durante las diferentes etapas del confinamiento domiciliario por COVID-19 en Iberoamérica.

Changes in the Lifestyle of the Spanish University Population during Confinement for COVID-19.

¿Chile podría ser considerado un país desarrollado?

The COVID-19 pandemic has put a lot of pressure on health systems worldwide. Mass vaccination against SARS-CoV-2 has reduced morbidity and mortality worldwide. Despite their safety profiles, vaccines, as with any other medical product, can cause adverse events. Yet, in countries with poor epidemiological surveillance and monitoring systems, reporting vaccine-related adverse events is a challenge. The objective of this study was to describe self-reported vaccine adverse events after receiving one of the available COVID-19 vaccine schemes in Ecuador. A cross-sectional analysis based on an online, self-reported, 32-item questionnaire was conducted in Ecuador from 1 April to 15 July 2021. Participants were invited by social media, radio, and TV to voluntarily participate in our study. A total of 6654 participants were included in this study. Furthermore, 38.2% of the participants reported having at least one comorbidity. Patients received AstraZeneca, Pfizer, and Sinovac vaccines, and these were distributed 38.4%, 31.1%, and 30.5%, respectively. Overall, pain or swelling at the injection site 17.2% (n = 4500) and headache 13.3% (n = 3502) were the most reported adverse events. Women addressed events supposedly attributable to vaccination or immunization [ESAVIs] (66.7%), more often than men (33.2%). After receiving the first dose of any available COVID-19 vaccine, a total of 19,501 self-reported ESAVIs were informed (87.0% were mild, 11.5% moderate, and 1.5% severe). In terms of the vaccine type and brand, the most reactogenic vaccine was AstraZeneca with 57.8%, followed by Pfizer (24.9%) and Sinovac (17.3%). After the second dose, 6776 self-reported ESAVIs were reported (87.1% mild, 10.9% moderate, and 2.1% severe). AstraZeneca vaccine users reported a higher proportion of ESAVIs (72.2%) in comparison to Pfizer/BioNTech (15.9%) and Sinovac Vaccine (11.9%). Swelling at the injection site, headache, muscle pain, and fatigue were the most common ESAVIs for the first as well as second doses. In conclusion, most ESAVIs were mild. AstraZeneca users were more likely to report adverse events. Participants without a history of COVID-19 infection, as well as those who received the first dose, were more prone to report ESAVIs

A Comparative Analysis of a Self-Reported Adverse Events Analysis after Receiving One of the Available SARS-CoV-2 Vaccine Schemes in Ecuador.

Composición Nutricional.

Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.

Correction to: Systematic Review of Telemedicine and eHealth Systems Applied to Vascular Surgery.

Corrigendum to “Dietary supplementation of Bacillus velezensis improves Vibrio anguillarum clearance in European sea bass by activating essential innate immune mechanisms” [Fish Shellfish Immunol. 124 (2022) 244–253].

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud.

Cuadro de mando integral en el sector público: caso de estudio la Gendarmería Nacional Argentina.

Cualificaciones del nivel de licenciatura en trabajo social en Chile: anclaje de competencias.

Cursos abiertos masivos en línea (MOOC) y capacitación del personal: la experiencia del sistema penitenciario costarricense.

A Deep Learning-Based Intelligent Garbage Detection System Using an Unmanned Aerial Vehicle.

Desarrollo, producción y análisis de bebidas alcohólicas destiladas empleando diez tipos de frutas autóctonas ecuatorianas.

Descarte incorrecto de máscaras em tempo de pandemia de covid-19.

To address the current pandemic, multiple studies have focused on the development of new mHealth apps to help in curbing the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV-2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. The main objectives of this paper are: (1) Analyze the current status of COVID-19 apps available on the main virtual stores: Google Play Store and App Store for Spain, and (2) Propose a novel mobile application that allows interaction and doctor-patient follow-up without the need for real-time consultations (face-to-face or telephone). In this research, a search for eHealth and telemedicine apps related to Covid-19 was performed in the main online stores: Google Play Store and App Store, until May 2021. Keywords were entered into the search engines of the online stores and relevant apps were selected for study using a PRISMA methodology. For the design and implementation of the proposed app named COVINFO, the main weaknesses of the apps studied were taken into account in order to propose a novel and useful app for healthcare systems. The search yielded a total of 50 apps, of which 24 were relevant to this study, of which 23 are free and 54% are available for Android and iOS operating systems (OS). The proposed app has been developed for mobile devices with Android OS being compatible with Android 4.4 and higher. This app enables doctor-patient interaction and constant monitoring of the patient's progress without the need for calls, chats or face-to-face consultation in real time. This work addresses design and development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of existing applications have this functionality. The COVINFO app offers a novel service: asynchronous doctor-patient communication, as well as constant monitoring of the patient’s condition and evolution. This app makes it possible to better manage the time of healthcare personnel and avoid overcrowding in hospitals, with the aim of preventing the collapse of healthcare systems and the spread of the coronavirus.

Descriptive Analysis of Mobile Apps for Management of COVID-19 in Spain and Development of an Innovate App in that field.

Patient care and convenience remain the concern of medical professionals and caregivers alike. An unconscious patient confined to a bed may develop fluid accumulation and pressure sores due to inactivity and deficiency of oxygen flow. Moreover, weight monitoring is crucial for an effective treatment plan, which is difficult to measure for bedridden patients. This paper presents the design and development of a smart and cost-effective independent system for lateral rotation, movement, weight measurement, and transporting immobile patients. Optimal dimensions and practical design specifications are determined by a survey across various hospitals. Subsequently, the proposed hoist-based weighing and turning mechanism is CAD-modeled and simulated. Later, the structural analysis is carried out to select suitable metallurgy for various sub-assemblies to ensure design reliability. After fabrication, optimization, integration, and testing procedures, the base frame is designed to mount a hydraulic motor for the actuator, a DC power source for self-sustenance, and lockable wheels for portability. The installation of a weighing scale and a hydraulic actuator is ensured to lift the patient for weight measuring up to 600 pounds or lateral turning of 80 degrees both ways. The developed system offers simple operating characteristics, allows for keeping patient weight records, and assists nurses in changing patients’ lateral positions both ways, comfortably massage patients’ backs, and transport them from one bed to another. Additionally, being lightweight offers reduced contact with the patient to increase the healthcare staff’s safety in pandemics; it is also height adjustable and portable, allowing for use with multiple-sized beds and easy transportation across the medical facility. The feedback from paramedics is encouraging regarding reducing labor-intensive nursing tasks, alleviating the discomfort of long-term bed-ridden patients, and allowing medical practitioners to suggest better treatment plans

Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.

Detection of Upper Limb Asymmetries in Athletes According to the Stage of the Season—A Longitudinal Study.

Development Agencies and Local Governments—Coexistence within the Same Territory.

Digital competencies: perceptions of primary school teachers pursuing master’s degrees from eight African countries.

Diseño e implementación de un sistema de indicadores de productividad para la gestión de PyMEs colombianas.

Educación, enseñanza, escuela y educación física: sentidos, relaciones y puntos de encuentros a la luz de la praxis docente.

Educar em e para os direitos humanos, criando na escola básica a cidadanização e a socialização do futuro.

Educação ambiental popular na América Latina e Caribe e educação para o desenvolvimento sustentável: incongruências e desafios.

Efecto de un entrenamiento propioceptivo para prevenir el riesgo de caída en adultos mayores.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

El Derecho producto de la globalización: una teoría integracionista.

El derecho a la ciudad y el desarrollo en Ecuador.

El desempeño profesional de profesores de español como lengua extranjera en Jamaica.

El método MACTOR para analizar los procesos de gestión de los proyectos y programas de desarrollo en África.

Emotion Detection Using Facial Expression Involving Occlusions and Tilt.

Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center.

An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling.

Environmental Sciences and Practices.

Estrategias didácticas y desafíos de enseñanza-aprendizaje para los docentes fonoaudiólogos de Chile en la educación a distancia por contexto de pandemia COVID-19.

Estructura y secuestro de carbono en manglares urbanos, fundamentos para su conservación en Isla del Carmen, Campeche, México.

Estudio longitudinal sobre las intervenciones antiacoso para estudiantes de primaria en una escuela rural.

Estudio sobre el análisis de las estrategias empleadas por la facultad de enfermería para cumplir con la retención de los estudiantes del Programa de Enfermería.

Evaluación del rendimiento de Honeypot en redes telemáticas.

Evaluación del índice de satisfacción de los profesores participantes del curso educación emprendedora: jóvenes emprendedores. Primeros pasos de Sebrae en escuelas públicas del estado de Rondônia.

Evaluation of mechanical properties of matrices derived from fish scale collagen.

Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.

Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve.

Funcionalidad motriz, estado nutricional e índices antropométricos de riesgo cardiometabólico en adolescentes chilenos de 12 a 15 años.

A Fuzzy-Genetic-Based Integration of Renewable Energy Sources and E-Vehicles.

Genetic Resistance Determinants in Clinical Acinetobacter pittii Genomes.

Gestión de herramientas de inteligencia de negocios para el diagnóstico de la categoría comercial de inocuidad en un entorno empresarial ecuatoriano.

Gestión de proyectos empresariales en el eje de gestión de recursos humanos: impacto de las prácticas de gestión de recursos humanos en la productividad de las pymes agroalimentarias camerunesas.

Gestión de proyectos en tesis de titulación universitaria.

Gestión sostenible de residuos de construcción en el Área Metropolitana de Misiones.

Gestão escolar democrática e participativa: um olhar para as habilidades, competências, perspectivas e desafios dos directores escolares do município da Caála.

Grupo de trabajo WhatsApp para la enseñanza de lenguas extranjeras durante el Covid-19 en un país de África Subsahariana (Gabón).

An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram.

Impacto de la gamificación con TIC en la enseñanza de las ciencias sociales en estudiantes de cuarto grado de primaria.

Impacto educativo de la experimentación en ciencias naturales: estudio de caso en la Institución Educativa Distrital Andrés Bello en Colombia.

Imperative Role of Automation and Wireless Technologies in Aquaponics Farming.

Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations.

Implementation of photovoltaic energy, sustainability, economic and social development in a Higher Education Institution in Brazil.

Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities.

Incidencia de la experiencia, en la elección de la metodología de enseñanza en natación.

Angola, as with many countries on the African continent, has great inequalities or asymmetries between its provinces. At the economic, financial, and technological level, there is a great disparity between them, where it is observed that the province of Luanda is the largest financial business center to the detriment of others, such as Moxico, Zaire, and Cabinda. In the latter, despite the advantages of high oil production, from a regional point of view, they remain almost stagnant in time, in a social dysfunction where the population lives on extractivism and artisanal fishing. This article analyzes the most important events in contemporary regional history, the Portuguese occupation that was the Portuguese colonial rule over Angola (1890–1930) and the civil war that was a struggle between Angolans for control of the country (1975–2002), in the consolidation of the asymmetries between provinces. For this work, a theoretical-reflective study was conducted based on the reading of books, articles, and previous investigations on the phenomenon studied. Considering the interpretation and analysis of the theoretical content obtained through the bibliographic research conducted, this theoretical construction approaches the qualitative approach. We conclude that the deep inequalities between regions and within them, between the provinces studied, originated historically in the form of exploitation of the regions and from the consequences of the war. The asymmetries, observed through the variables studied show that the provinces historically explored and considered object regions present a lower growth compared to those that were considered subject regions in which the applied geopolitical strategy, as they are centers of primary production flows, was different. We also observe that, due to the conflicts of the civil war in the less developed regions, the inequalities have deepened, contributing seriously to a higher level of poverty and a lower development of the provinces where these conflicts took place.

Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War.

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome.

Intelligent Load-Balancing Framework for Fog-Enabled Communication in Healthcare.

An Intelligent Logic-Based Mold Breakout Prediction System Algorithm for the Continuous Casting Process of Steel: A Novel Study.

IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.

IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information.

IoT-Inspired Reliable Irregularity-Detection Framework for Education 4.0 and Industry 4.0.

La crisis de 2007 en Estados Unidos ¿Desequilibrio entre oferta y demanda o crisis de rentabilidad?

La generación de energía eléctrica de 1996 al 2017 en Guatemala.

La mortalidad de las empresas familiares ecuatorianas por falta de planificación sucesoria.

La redacción de las citas y referencias en los Proyectos de Grado: caso República Dominicana.

La regulación asociativa en el Ecuador.

Las Actividades de gestión de la innovación como determinantes explicativas del desempeño innovador de las mipymes industriales en Córdoba, Argentina. Estudio de caso 2015-2020.

Las competencias docentes en su formación de pregrado: un estudio del perfil profesional para la acción pedagógica en educación básica en la ciudad de Soacha-Colombia.

Las tic para fortalecer las inteligencias múltiples y aprender historia en secundaria.

Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT.

Los desafíos de la educación en el siglo XXI en el municipio de Bailundo Angola: una mirada a las demandas actuales utilizando las NTIC`S.

Los tratados de doble imposición tributaria y su efecto en la inversión extranjera directa en Colombia.

Loss of coastal ecosystem services in Mexico: An approach to economic valuation in the face of sea level rise.

MLS Health and Nutrition Research.

MLS Inclusion and Society Journal.

MLS Law and International Politics.

Metodología híbrida de dirección de proyectos aplicada a la industria de la construcción.

Modelo de madurez aplicado al contexto organizacional de la gestión de proyectos para la Alcaldía de Chinácota-Colombia.

Modelo estandarizado para la planificación en la ejecución de proyectos que permita mejorar el desempeño de las entidades no lucrativas.

Modelo geoespacial para priorizar los factores de riesgo ambiental de las comunidades sin alcantarillado sanitario en la cuenca del Río Grande de Loíza en Puerto Rico.

A Modified Marx Generator Circuit with Enhanced Tradeoff between Voltage and Pulse Width for Electroporation Applications.

The Molecular Weaponry Produced by the Bacterium Hafnia alvei in Foods.

Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.

Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network.

Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.

Más allá del enfoque cognitivo en la educación inicial, desde un modelo que impacta saberes y prácticas del profesorado.

NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange.

A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis.

A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization.

A Novel Large-Scale Stochastic Pushback Design Merged with a Minimum Cut Algorithm for Open Pit Mine Production Scheduling.

An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value.

Objectives: This study aimed to map the characteristics and the predominant components of clinical physical activity (PA) counseling in Saudi Arabia for adult patients and outline evidence of outcomes and prevalent barriers to its implementation. Methods: We conducted a systematic literature search of four online databases: Web of Science, PubMed, ScienceDirect, and The Cochrane Library. Each study was assessed and evaluated using the Mixed Methods Appraisal Tool (MMAT) for methodological quality. Results: A total of 120 studies were screened, and 47 studies were sought for retrieval. In total, 25 articles were eligible and were subjected to extensive review. After a detailed evaluation, only nine studies met the inclusion criteria. All included were quantitative studies that compiled descriptive and numerical data on physical activity counseling. Four studies described PA counseling information in Saudi Arabia or prescription as lifestyle modification and program structure. The programs used various techniques to motivate patients to adhere to PA protocols. In general, practitioners indicated a high perceived competence in helping patients meet PA guidelines. The most frequently stated barrier was a lack of time for PA discussions with patients, followed by a lack of training in PA counseling, and a lack of patient compliance. Significant improvements in clinical parameters and smoking, food, and exercise habits were detected in experimental trials with respective intervention programs. Conclusion: This review provides preliminary insights into the delivered intervention and standard care content, its outcomes, and clinicians’ perceived competence and barriers regarding current PA counseling approaches in Saudi Arabia. Despite the small number of studies included, this review contributes to the limited understanding of current PA counseling practices in Saudi Arabia and serves as an informational source for clinicians and policymakers and a starting point for further research.

Physical Activity Counseling in Saudi Arabia: A Systematic Review of Content, Outcomes, and Barriers.

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.

População infantil e adolescente nas ruas e estudantes estrangeiros: impactos interculturais no desenvolvimento e no acesso às escolas.

Portfólio digital docente para o desenvolvimento do aprendizado reflexivo.

Prediction β-Thalassemia carriers using complete blood count features.

Prerequis pour une integration reussie des tic dans l'enseignement/apprentissage : cas des instituts prives d'enseignement superieur au Cameroun.

Propuesta para la clasificación de los objetos virtuales de aprendizaje interactivos.

Proyecto esperanza: Desarrollo de software con realidad aumentada para enseñar danza a niños con trastorno de espectro autista.

Pruebas para el diagnóstico nutricional en pacientes con enfermedad renal crónica: una revisión narrativa.

Práctica pedagógica con las TIC: casos de los docentes de los IPES en Camerún.

Présent et futur des adaptations positives à la pandémie de COVID-19: résilience, bonnes pratiques et stratégies employées deux ans plus tard.

Psychometric Properties of the Emotional Exhaustion Scale (ECE) in Chilean Higher Education Students.

The Quality of Private Higher Education in Mexico: The Case of Culiacán, Sinaloa.

Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices.

Reciclaje de neumáticos y rentabilidad en empresas de mantenimiento y reparación de vehículos automotores de la región del Maule.

The Regular Consumption of Nuts Is Associated with a Lower Prevalence of Abdominal Obesity and Metabolic Syndrome in Older People from the North of Spain.

Relación entre funcionalidad motriz y factores antropométricos de riesgo cardio metabólico en bomberos de la región de Valparaíso, Chile.

Reliability and validity of a clinical competence test for dietitians caring patients with early chronic kidney disease.

Deep learning is used to address a wide range of challenging issues including large data analysis, image processing, object detection, and autonomous control. In the same way, deep learning techniques are also used to develop software and techniques that pose a danger to privacy, democracy, and national security. Fake content in the form of images and videos using digital manipulation with artificial intelligence (AI) approaches has become widespread during the past few years. Deepfakes, in the form of audio, images, and videos, have become a major concern during the past few years. Complemented by artificial intelligence, deepfakes swap the face of one person with the other and generate hyper-realistic videos. Accompanying the speed of social media, deepfakes can immediately reach millions of people and can be very dangerous to make fake news, hoaxes, and fraud. Besides the well-known movie stars, politicians have been victims of deepfakes in the past, especially US presidents Barak Obama and Donald Trump, however, the public at large can be the target of deepfakes. To overcome the challenge of deepfake identification and mitigate its impact, large efforts have been carried out to devise novel methods to detect face manipulation. This study also discusses how to counter the threats from deepfake technology and alleviate its impact. The outcomes recommend that despite a serious threat to society, business, and political institutions, they can be combated through appropriate policies, regulation, individual actions, training, and education. In addition, the evolution of technology is desired for deepfake identification, content authentication, and deepfake prevention. Different studies have performed deepfake detection using machine learning and deep learning techniques such as support vector machine, random forest, multilayer perceptron, k-nearest neighbors, convolutional neural networks with and without long short-term memory, and other similar models. This study aims to highlight the recent research in deepfake images and video detection, such as deepfake creation, various detection algorithms on self-made datasets, and existing benchmark datasets.

A Review of Image Processing Techniques for Deepfakes.

Revitalización lingüística de la lengua Rama en la Costa Caribe de Nicaragua.

Routing Protocol for MANET Based on QoS-Aware Service Composition with Dynamic Secured Broker Selection.

A Secure and Efficient Signature Scheme for IoT in Healthcare.

Security Verification of Social Network Model Using Improved Three-Party Authenticated Key Exchange Protocol.

Severity of protein-energy wasting and obesity are independently related with poor quality of life in peritoneal dialysis patients.

Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification.

Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.

The research will identify the role of soft powers, architecture, heritage, and culture, as building structures of cultural development and identity of Chilean society. A trilogy that contributes to a potential socio-cultural improvement, as integration platforms, applying a management model with new educational and cultural strategies, promoting the recovery of municipal infrastructure for cultural use in the neighborhoods, which favor and facilitate the interrelation of audiences at the communal level, considering in it relevance of the own, generating a greater identity thickness that allows an improvement in the quality of life of the society. Chilean society, neoliberal, considers urban spaces of mass consumption, shopping centers, and galleries, as its entertainment and “cultural” action par excellence, replacing and displacing the socio-cultural action of meeting and developing creative and cultural activities, around squares, parks, and spaces of citizen cultural infrastructure, showing a loss of cultural and identity values. Chile presents today a certain weakening in behaviors referred to as cultural attendance and consumption (low audience in theaters, libraries, museums, and art galleries), which does not mean that no time is devoted to leisure recreation, but shows deterioration and lack of massive interest in the attendance and use of spaces traditionally used as platforms for cultural expression and development, denoting, also, a growing phenomenon of fragmentation and socio-cultural stratification. As a scientific product, a typology of the project is developed, as a model of cultural management, with the recovery of existing infrastructure, which will expose a result that the soft powers are facilitators of building axes to strengthen the culture and identity of the society. As a research methodology, it is a non-experimental, interpretative and exploratory, theoretical structure with a qualitative approach. In conclusion, the constructive role achieved by soft powers, improving spaces for pa

Soft Powers and their application as potential and conditioning structures in the development and strengthening of education, culture and identity in Chile.

Software Hope: tratamiento educativo para enseñar danza a través de realidad aumentada a niños con autismo.

Spatio-temporal statistical analysis of PM1 and PM2.5 concentrations and their key influencing factors at Guayaquil city, Ecuador.

Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models.

Sustentabilidade em sistemas de segurança do trabalho na construção civil.

A Systematic Review and Meta-Analysis of Premenstrual Syndrome with Special Emphasis on Herbal Medicine and Nutritional Supplements.

Objective: The objective of this paper is to review and analyze the current state of telemedicine and ehealth in the field of vascular surgery. Methods: This paper collects the relevant information obtained after reviewing the articles related to telemedicine in vascular surgery, published from 2012 to 2022 contained in scientific databases. In addition, the results obtained are statistically studied based on various factors, such as the year of publication or the search engine. In this way, we obtain a complete vision of the current state of telemedicine in the field of vascular surgery. Results: After performing this search and applying selection criteria, 29 articles were obtained for subsequent study and discussion, of which 20 were published in the second half of the decade, representing 70% of the results. In the analysis carried out according to the search criteria used, it can be seen that using the word telemedicine we obtained 69% of the articles while with the criteria mHealth and eHealth we only obtained 22% and 9% of the results, respectively. It can be seen that the filter with the most potential content articles was “vascular surgery AND telemedicine”. In the analysis performed according to the search engine, it was observed that the Google Scholar database contains 93% of the articles found in the massive search and the relevant articles contained therein represent 52% of the total. Conclusion: An upward trend has been observed in recent years, with a clear increase in the number of publications and much lower figures in the first years. One aspect to highlight is that 47.8% of the articles analyzed focus only on postoperative treatment, which may be due to the help provided by telemedicine in detecting surgical site infections by sending images and videos, this being one of the most common postoperative complications. The analyzed works show the importance of telemedicine in vascular surgery and identify possible future lines of research. In the analysis carried out on the origin of the selected relevant papers, an important interest of the US in this topic is demonstrated since more than 50% of the research contains authors from this country, it is also observed that there is no research from Spain, so this research would be an initial step to determine the weaknesses of telemedicine in this field of medicine and a good opportunity to open a research gap in this branch.

Systematic Review of Telemedicine and eHealth Systems Applied to Vascular Surgery.

The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours and as a result it would be very difficult to monitor their body temperature. Secondly, these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield. Doing so after regular intervals is surely uncomfortable for healthcare professionals. To avoid such issues, this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield. This face shield is integrated with a built-in infrared temperature sensor. A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department. This sequence was repeated four times and as a result 40 healthcare workers participated in the study. Thereafter, feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet. Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces. The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment

Technologically Advanced Reusable 3D Face Shield for Health Workers Confronting COVID19.

Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.

Threatening URDU Language Detection from Tweets Using Machine Learning.

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.

Towards Design and Feasibility Analysis of DePaaS: AI Based Global Unified Software Defect Prediction Framework.

Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach.

A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment.

Uma breve análise da abordagem pedagógica e o uso do dua, apoiado nas TDICS como instrumento de inclusão a permanência visando a conclusão do curso de alunos com necessidades educativas especiais no ensino superior.

Una reflexión sobre la aplicación de métodos restaurativos de resolución de conflictos en la alienación parental.

Urban Solid Waste Characterization of the Misiones Metropolitan Area.

The Use of Marble Dust, Bagasse Ash, and Paddy Straw to Improve the Water Absorption and Linear Shrinkage of Unfired Soil Block for Structure Applications.

Visualization of Customized Convolutional Neural Network for Natural Language Recognition.

Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.

Willingness to change diet and exercise behavior is associated with better lifestyle in dialysis patients close to a kidney transplant.

Wireless Body Area Routing Protocols Impact Analysis on Entity Mobility Models with Static Sink Node.

A delinquência juvenil em Angola: um olhar para educação, desigualdades sociais e económicas dos jovens, caso do município do Bailundo.

A educação em Angola no século xv ao século xxi: um olhar pela organização, desenvolvimento e perspectiva do sector.

A ferramenta SWOT na gestão escolar.

A internacionalização em casa na pós-graduação na América Latina e Caribe na modalidade a distância.

A violência doméstica no município da Cela, província do Cuanza-Sul –Angola: um fenómeno que tem preocupado o governo e a sociedade.

The world’s deadliest outbreak during the COVID-19 Pandemic: A proposed analytical approach to estimate Daily Excess mortality rates in Ecuador.

2021

Adaptación de técnicas didácticas activas en la asignatura de lectura, expresión oral y escrita.

Adherencia a las intervenciones dietéticas en pacientes con diabetes mellitus tipo 2: una revisión narrativa de la literatura.

Alimentos de origen animal y Enfermedades de Transmisión Alimentaria en Costa Rica: 2015- 2020.

Alta incidencia del acto didáctico en la desmotivación por el aprendizaje del inglés, reconocida por profesores y estudiantes.

Background: To address the current pandemic, multiple studies have focused on the development of new mHealth applications to help curb the number of infections, these applications aim to accelerate the identification and self-isolation of people exposed to SARS-CoV- 2, the coronavirus known to cause COVID-19, by being in close contact with infected individuals. Objective: The main objectives of this paper are: 1)To analyze the current status of COVID-19 apps available the main virtual stores: Google Play Store and App Store, and 2)To propose a novel mobile application based on the limitations of the analyzed apps. Methods: The search for apps in this research was carried out in the main virtual stores: Google Play Store and App Store, until May 2021. After the analysis of the selected apps, a novel app is proposed whose main function will be the multiple transmission of information about the patient's symptoms from the application, without the need for phone calls or chat in real time. For its development, the flowchart shown in this session is followed. Results: The search yielded a total of 50 apps, of which 24 were relevant to this study. It is important to note that 23 of the apps analyzed are free. Of the total number of apps, 54% are available for Android and iOS operating systems. 50% of the apps have more than 5 thousand downloads. This means that Covid-19 related apps are in high demand among mobile device users today. The developed app is called COVINFO and its name comes from the union of the words COVID-19 and information, inserted in such a way that the user can get an idea of the app's functionality just by listening or reading the resulting name. The application has been created for mobile devices with Android operating system, being compatible with Android 4.4 and higher. Conclusions: Of the apps found, 37.5% only offer information about the virus and the necessary measures to avoid infection. During the analysis it was detected that 12.5% of the apps are focused on locating outbreaks and that none of them have been successful for the following reasons: not being interconnected to share data; and the request for access to the user's geolocation, generating distrust on the part of the user who, consequently, rejects them. This work addresses the development of an application for the transmission of the user's symptoms to his regular doctor, based on the fact that only 16.6% of the existing applications have this functionality. The COVINFO app offers a service that no other application on the market has: doctor-patient interaction without the need for calls or chat in real time for constant monitoring by the doctor of the patient's condition and evolution.

Analysis of mobile apps for information, prevention and monitoring of covid-19 and proposal of an innovative app in this field.

Antimicrobial Resistance Determinants in Genomes and Plasmids from Acinetobacter baumannii Clinical Isolates.

Análisis de las percepciones de los estudiantes del 2º año de educación fundamental sobre el acoso escolar.

Approach to a Project Framework in the Environment of Sustainability and Corporate Social Responsibility (CSR): Case Study of a Training Proposal to a Group of Students in a Higher Education Institution.

As metodologias por projeto nas salas de aulas do CEFET-MG, desde o ponto de vista dos docentes do ensino médio.

Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials.

Ciencia y tecnología en Ecuador. Una revisión al estado del arte.

Competencias docentes en la aplicación de estrategias de intervención de aula durante el proceso de enseñanza-aprendizaje para una educación inclusiva y con calidad: resultados preliminares.

Competências tecnológicas: uma pesquisa com professores da EaD.

Conocimiento y percepción de profesores y alumnos para el futuro tecnológico en el interior de Brasil.

A Síndrome de Münchhausen por procuração (SMP) é considerada na área clínica como uma violência infanto-juvenil por transtorno factício, por mimetizar sinais e sintomas para a produção intencional de doenças. Esta manipulação forjada pelo adulto sobre a saúde da criança e do adolescente pode desencadear desde procedimentos invasivos desnecessários até a sua morte, preocupando a equipe de enfermagem, quanto a sua identificação e formas de tratamento adequado. Este estudo tem o propósito de oferecer uma ferramenta tecnológica de usabilidade intuitiva que utiliza a proposta de consulta virtual para materializar a percepção subjetiva da visão do enfermeiro em referência a SMP, possibilitando a impressão do relatório final. Escolheu-se para delineamento a pesquisa de natureza aplicada tecnológica mista, para a construção e validação do aplicativo móvel baseados nas normativas da ABNT ISO/TR 16982, ISO/IEC 25010-1, ISO/IEC 14598-6, ISSO/IEC 25062; e para a aplicabilidade optou-se pelo estudo descritivo-exploratório para avaliação e testes de qualidade e usabilidade para profissionais de saúde, acadêmicos e docentes. Respeitou-se os critérios éticos, tendo aprovação da instituição proponente Comitê de Ética da Universidad Internacional Iberoamericana sob o n. CR-017 e da participante Comitê de Ética em Pesquisa em Seres Humanos do Centro Universitário Campos de Andrade sob o CAAE n. 94302316.6.0000.5218. Os resultados mostraram através das Heurísticas de Nielsen, ampla aceitabilidade; pela Escala de Usabilidade de Sistema obteve alto grau de satisfação; pelas métricas de diagnóstico estatísticos e da meta-análise: sensibilidade (92%), especificidade (83%), eficiência (87,5%), acurácia (88%), precisão (92%), prevalência dos casos controle (57,10%). Conclui-se portanto que, o aplicativo móvel SISMUN obteve 100% de aprovação para uso tanto no ensino-aprendizagem, como para triagem da SMP em campo laboral simulado. Está disponível gratuitamente em website restrito com certificação SSL e registro de obra n. 610144526, responsivo para smartphones

Construção e validação do aplicativo móvel SISMUN para o ensino da enfermagem forense sobre a violência infanto-juvenil do transtorno factício.

Correlación entre aumento del ciberdelito al comienzo de la pandemia Covid-19 y pocas comunicaciones institucionales asertivas emitidas para la prevención en usuarios de las TIC de Bogotá, Colombia.

Creciendo en la adversidad: la resiliencia del estudiantado con discapacidad en la Universidad Nacional, Costa Rica.

Desafíos actuales en la educación universitaria. Procesos que se (re)inventan.

Desarrollo de un sistema de gestión y control de la calidad de los aceites lubricantes para automóviles aplicable al contexto angoleño.

Design a Model-Based on Nonlinear Multiple Regression to Predict the Level of User Satisfaction when Optimizing a Traditional WLAN Using SDWN.

Design of edges in contour and half moons from edaphoclimatic parameters, for the endorrheic basin of lagunas de tajzara - ramsar site 1030.

Dirección estratégica y responsabilidad social integrada en la carrera de Contabilidad y Auditoría de la Universidad Estatal de Bolívar 2021.

Docentes que trabajan en educación a distancia: un análisis de los derechos contemplados en el CLT.

Educational interventions on breast cancer in men and women: a necessity in primary healthcare.

Efectividad de los Centros Regionales UNAH: Buenas prácticas de gestión estratégica.

Efecto del compost de Eichhornia crassipes en la calidad de plantas en vivero de Theobroma cacao.

Efectos del ejercicio físico sobre marcadores antropométricos, fuerza muscular y presión arterial en pacientes con hemodiálisis: una revisión narrativa.

El Aprendizaje emocional para convivencia, rendimiento y motivación: Una muestra de estudio en Educación preescolar y primaria.

El Blog, aprendizaje activo y en línea para reducir la reprobación en la Educación Superior.

El Sistema CHAMÍ para Asistir el Aseguramiento la Calidad de los Requerimientos Funcionales y No Funcionales en la Industria del Software.

El autodictado en la conciencia fonológica para desarrollar habilidades escritas.

El intercambio de mercancías a través de la contratación electrónica.

El uso de herramientas tecnológicas en entornos virtuales para el desarrollo de la producción oral en Inglés en el Departamento de Lenguas de la Universidad Nacional Autónoma de Honduras.

Estado del arte sobre la relación del estrés académico y el rendimiento en estudiantes universitarios, con la mediación de dos variables.

Evaluación de la eficiencia de los sistemas nacionales de innovación en los países de Iberoamérica.

Evaluation of the Analgesic Efficacy of a Bioelectronic Device in Non-Specific Chronic Low Back Pain with Neuropathic Component. A Randomized Trial.

Formación universitaria basada en la neuroeducación y la psicología positiva: percepciones de jóvenes con y sin TDAH.

Gestión del Riesgo ante Desastres Naturales o Antrópicos: su Impacto en la Sostenibilidad Empresarial en Manabí—Ecuador.

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images.

Influencia de la variable tiempo en el uso de las tecnologías de la información y comunicación (TIC) en clases por profesores: el caso de la Universidad de La Guajira, Colombia.

Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field.

La comunidad docente y las competencias digitales: la formación a lo largo de la vida.

La confianza como empoderamiento del desarrollo humano en la policía federal de México (Guardia Nacional México).

La transversalidad de estrategias interdisciplinarias en la ruralidad y la pedagogía experiencial.

Las relaciones de género y el trabajo de mujeres como factores de riesgo demográficos por la pandemia del COVID-19.

Liderazgo y éxito organizacional con el modelo ISO 9001.

MLS Communication Journal.

MLS Sport Research.

Maternal Folic Acid Intake and Methylation Status of Genes Associated with Ventricular Septal Defects in Children: Case–Control Study.

Medición de la resiliencia en adultos víctimas del conflicto: Caso Carmen de Bolívar, Colombia.

Metodología para reducir la brecha de conocimiento y desarrollo tecnológico en el personal técnico eléctrico de las empresas pesqueras de Chanduy, Ecuador.

Modelo de valuación de sostenibilidad para una empresa exportadora 4.0.

Modos de aprender em tempos de pandemia: Deficiências e importância da inclusão digital para alunos da rede pública.

The Molecular Basis of Different Approaches for the Study of Cancer Stem Cells and the Advantages and Disadvantages of a Three-Dimensional Culture.

Molecular Interactions between Dietary Lipids and Bone Tissue during Aging.

Nueva escala de medición de mentalidad fija y aprendiente: desarrollo y validación.

Nutritional Value and Preventive Role of Nigella sativa L. and Its Main Component Thymoquinone in Cancer: An Evidenced-Based Review of Preclinical and Clinical Studies.

“Oh, My God! My Season Is Over!” COVID-19 and Regulation of the Psychological Response in Spanish High-Performance Athletes.

Percepción de competencias en las prácticas profesionalizantes e inserción laboral del Técnico en Redacción de Textos.

Perceptions from mathematics leaders' faculty at a South Texas University in factors that contribute with drop, fail and withdraw rates in college algebra.

Perseguindo o inédito viável: a pedagogia freiriana, a necesssidade da linguagem inclusiva e a denúncia à neolíngua do generismo queer.

Phage Therapy as a Focused Management Strategy in Aquaculture.

Phages and Enzybiotics in Food Biopreservation.

Planteamiento de un modelo energético descriptivo aplicable a la instalación de sistemas solares fotovoltaicos interconectados a la red mediante generación distribuida: caso de estudio en Nuevo Laredo.

Propuesta de estrategias para el control de la erosión en la playa de Sabancuy, Campeche.

Psychology students' attitudes towards research: the role of critical thinking, epistemic orientation, and satisfaction with research courses.

Revolutionary Strategies Analysis and Proposed System for Future Infrastructure in Internet of Things.

SEM model for technological, ecological and inclusive teacher training in times of pandemic.

Severity of protein-energy wasting and obesity are independently related with poor quality of life in peritoneal dialysis patients.

TICE et travail collaboratif, implication des étudiants aux forums de discussion électronique dans la formation à distance au Sénégal: cas de l’université virtuelle du Sénégal (UVS).

Transferencia de conocimiento: el caso del grupo Suez y Aguas de Cartagena SAESP “Acuacar”.

WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities.

Éxito o fracaso en la desaparición de la Organización Internacional Regional de Unasur.

2020

Biorremediación de suelos contaminados con hidrocarburos a base de bacterias utilizadas como bioproductos.

COVID-19: La necesidad de un cambio de paradigma económico y social.

Calculation of Optimal Switching Angles for a Multilevel Inverter Using NR, PSO, and GA- a Comparison.

Comparative Analysis of PWM Techniques in the Set: Multilevel Inverter + Induction Motor.

Comparative analysis of PD-PWM technique in the set: Multilevel Inverter-Induction motor.

Competencias para la orientación educativa durante la formación inicial docente.

Complementariedad y articulación de las metodologías de planificación y gestión de proyectos.

Desempeño del diseño multimedia en el aprendizaje integral en Educación Primaria.

Determinación del uso del mucilago de nopal en la construcción de la época colonial (caso Convento de San Diego).

Diseño de un modelo de gestión en la preparación y evaluación de proyectos.

El dibujo de alumnos de primaria revelando sentimientos y emociones: una cuestión discutida por la inteligencia emocional.

El interés legítimo y el amparo adhesivo como innovación jurídica en México.

Emprendimiento no lucrativo y empleo: caso de estudio de Bogotá.

Evaluación de constructividad en Proyectos BIM en Brasil.

Evaluación del ayuntamiento municipal mediante una encuesta de opinión en República Dominicana. El caso del municipio de Baitoa en la provincia de Santiago.

Factores de riesgo para accidente cerebrovascular en adultos jóvenes.

Factores de éxito de MIPYMES del sector salud, ciudad de Huancayo--Perú, 2020.

Formalización del estudio de la danza en Paraguay.

Implementación de programas basados en Mindfulness con pacientes con enfermedad mental del centro de rehabilitación psicosocial Padre Menni de Torrelavega.

Importancia de la seguridad de la información en las empresas de tecnología de información corporativa.

Integral model for the resilience of historic centers in coastal cities.

La Inteligencia de Negocios en la Toma de Decisiones Empresarial.

La Interdisciplinariedad en los centros privados del Bachillerato Internacional: dificultades para su implementación desde el punto de vista de los coordinadores académicos.

La dinamización de las prácticas pedagógicas a través del trabajo colaborativo: un estudio realizado con los estudiantes de licenciatura del CAU Barranquilla.

La gestión del cambio de las empresas familiares: sus riesgos y retos en Bogotá-Colombia.

La nueva cultura de la madurez mexicana: un plan de desarrollo profesional y acciones estratégicas encaminadas a mejorar el nivel de madurez sobre los profesionistas baja californianos.

Adoptando una postura interpretativa mediante el análisis de datos cualitativos y cuantitativos recopilados en un cuestionario en línea, esta investigación de métodos mixtos tiene como objetivo explorar las prácticas y creencias que los docentes de inglés como lengua extranjera en Venezuela tienen con respecto a la retroalimentación correctiva de textos escritos. Utilizando una muestra no probabilística por conveniencia, los participantes son 173 profesores pertenecientes a la Asociación de Profesores de Inglés, VenTESOL, quienes dictan clases en diferentes institutos educativos, en todos los niveles y modalidades disponibles. En referencia a las prácticas encontradas, los resultados indican que el proporcionar al estudiante la forma correcta del error es la más usada, seguida por subrayar el error más no dar la forma adecuada. Se concluye que los docentes corrigen todos los errores a un promedio de entre uno o dos borradores. Con respecto a las creencias y perspectivas, los docentes opinan que la retroalimentación correctiva es un proceso multidisciplinario, participativo y efectivo que permite a los estudiantes entender, analizar y reflexionar sobre los errores cometidos, así como también, promueve el desarrollo de las habilidades de escritura. Adicionalmente, esta investigación encontró que la noción personal de la enseñanza, el aprendizaje y el error, la experiencia como estudiante, la formación académica universitaria, el desarrollo profesional, la experiencia docente, los lineamientos curriculares e institucionales aunados al nivel de inglés que poseen los maestros y el tiempo disponible para corregir, son los factores que influyen en los maestros y predicen sus prácticas en relación con la provisión de retroalimentación correctiva. Las implicaciones incluyen futuras investigaciones derivadas de los hallazgos de este estudio, sugerencias concretas para el perfeccionamiento de los programas de formación del profesorado, así como también propuestas para el desarrollo profesional docente.

Los docentes de inglés en Venezuela: análisis de sus creencias y prácticas pedagógicas con respecto a la corrección de textos escritos.

O mundo contemporâneo está em crise que provoca conflitos. Basta assistir a TV, escutar radio, ler jornal, as redes sociais para dar conta do que se vive na sociedade contemporânea: terrorismo, conflitos políticos, económicos, imperialismo, discriminações, preconceitos… Conflitos e violência manifestam-se com mais frequência na sociedade, na família e na escola, o que torna relevante a pesquisa sobre os conflitos escolares que se expressam de várias maneiras e os seus efeitos, por vezes devastadores, para todos os atores da comunidade escolar, assim como o risco de mergulhar a escola numa crise de legitimidade. O tema desta dissertação, “a mediação como método de resolução de conflitos escolares em Moçambique” é ambicioso e complexo tendo em vista analisar se a mediação, enquanto método alternativo de resolução de conflitos, pode ser usada nos conflitos escolares em Moçambique. Para a concretização deste objetivo geral, estabeleceram-se quatro (4) objetivos específicos: identificar a ocorrência de conflitos escolares em Moçambique; Apontar as fontes e os níveis de conflitos escolares em Moçambique; Descrever a forma como são geridos conflitos escolares em Moçambique; Sugerir a mediação como método de resolução de conflitos escolares em Moçambique. Os resultados analisados revelaram que a desmotivação, a discordância, as diferenças culturais, religiosas e determinados comportamentos de risco tendem a originar conflitos escolares e violência. Para inverter essa tendência é necessária uma filosofia educativa de congregação e de gestão positiva dos conflitos, por meio da qual se possa construir uma cultura de paz no meio escolar. A mediação escolar é concebida, nesta dissertação como um elemento importante dessa filosofia educativa e como uma oportunidade de formação pessoal e social para resolver os conflitos do dia-a-dia e promover uma cultura de paz. As conclusões deste estudo traduzem-se em várias sugestões práticas, nomeadamente a criação e implementação de um departamento de serviços de apoio ao aluno (DSAA), como espaço de convívio, onde os alunos possam ter e sentir a liberdade de expressão para resolver as suas diferenças.

Mediação como Método de Resolução de Conflitos Escolares na Cidadeda Beira, Moçambique.

Mejoras en el desarrollo de la competencia científica en estudiantes de primer año de secundaria en un liceo de Uruguay.

Metodología para desarrollar procesos óptimos de soldadura de materiales termoplásticos (EVA Y EVA/EVOH) usando el sistema de soldaduras de alta frecuencia (HF).

La población con necesidades educativas especiales ha enfrentado a lo largo de la historia dificultades en la inclusión social, cultural y también educativa. Guatemala no es la excepción por ser un país en vías de desarrollo y con grandes deficiencias de atención en el sistema educativo. Actualmente se carece de una pedagogía y didáctica que solucione esta problemática para los docentes que trabajan con alumnos con necesidades educativas especiales, como a la vez los centros educativos carecen de un adiestramiento en relación con educación especial junto a programas terapéuticos que brinden resultados para las personas con discapacidad. En esta investigación se analizó la validez del modelo pedagógico-terapéutico “Cetumismo” contra el programa “Aula Recurso” del Ministerio de Educación de Guatemala. Esto se efectuó en una muestra seleccionada de docentes que laboran en centros educativos, luego de responder un Cuestionario estandarizado para valorar la calidad de la Educación Especial en los centros educativos por medio del proceso estadístico prueba t de student, en donde se compararon las medias obtenidas en los dos momentos de evaluación, obteniendo la varianza. Los resultados obtenidos establecen que con un nivel de significancia de 0.05 se rechaza la hipótesis nula y se acepta la hipótesis alternativa; por lo que la comparación de las medias en el proceso estadístico realizado determina que, entre ambos programas, el programa que resuelve las necesidades educativas especiales en relación con la educación especial es Modelo Pedagógico Terapéutico “Cetumismo”. Los docentes evaluados a pesar de pertenecer a una escuela que utiliza el programa “Aula Recurso” impuesto por el Ministerio de Educación de Guatemala, consideran que un modelo pedagógico-terapéutico como “Cetumismo” traería mayores beneficios en las necesidades de educación especial de personas con discapacidad, con adecuaciones curriculares específicas para cada alumno, capacitaciones constantes y actualizadas para docentes, pensum diferenciado, proceso educativo inclusivo luego del alcance de las competencias propuestas por caso

Modelo Pedagógico-terapéutica para atención a la educación especial en Guatemala.

Modelo Teórico para la resolución de conflictos en las compañías navieras brasileñas a través de una constelación organizacional sistémica - una propuesta.

Modelos de gerenciamento de serviços de TIC em escolas particulares na cidade de São Paulo.

O presente artigo reflete sobre a realidade das mulheres em tempos de pandemia. A problemática que envolve o presente trabalho parte da pergunta: como tem sido a experiência das mulheres na pandemia do coronavírus (Covid-19), devido ao acúmulo dos cuidados como tarefa feminina e o grito uterino que vem dessa situação? Para responder a essa questão, buscamos referências da teologia feminista, que parte do princípio da experiência das mulheres para a análise da realidade e a reflexão teológica e que coloca a vida mesma em sua amplitude como critério hermenêutico. A metodologia utilizada é bibliográfica, a partir de artigos de revistas, entrevistas e livros. Além do mais, somos três mulheres, profissionais, afetadas também pelo home office que se mistura com o trabalho da casa e a necessidade de uma nova organização. O processo de ensino aprendizagem da pandemia tem sido cruel e tem afetado, especialmente, a vida das mulheres. A casa, que deveria ser um lugar seguro, apresenta-se para muitas como um lugar de perigo constante. Muitos trabalhos de cuidado remunerados ou não são realizados pelas mulheres. Historicamente o cuidado tem sido delegado às mulheres, sendo, por um lado, exaltado como parte do ser/fazer feminino (mãe e dona da casa) e, por outro lado, é um trabalho não remunerado ou mal remunerado (enfermeiras, assistentes sociais). Apresenta-se o artigo em três partes: a experiência das mulheres, a necessidade de reinventar a economia do cuidado e o grito uterino que ecoa com justa indignação. Evidencia-se que a pandemia visibilizou questões preexistentes: o aumento do cuidado sob os ombros das mulheres seja em casa ou nas diferentes profissões em que as mulheres estão na linha de frente, a violência contra as mulheres. A pandemia acentua a desigualdade social, racial e de gênero da sociedade brasileira, sendo que as mais atingidas são mulheres pobres, negras, pardas, idosas e com deficiência. O grito que nasce do feminismo clama por uma reinvenção do mundo que habitamos

Mulheres em tempos de pandemia: a cotidianidade, a economia do cuidado e o grito uterino!

New model, to evaluate the implementation of LMS in institutions at a higher level, through the supplies of the student.

Orientación para estudiantes de la Escuela Secundaria para crisis emocionales y acoso en el espacio escolar.

Simulation for a Dual Inverter Feeding a Three-Phase Open-End Winding Induction Motor: A Comparative Study of PWM Techniques.

Switching Angles Calculation in Multilevel Inverters Using Triangular Number Sequence –A THD Minimization Approach.

Turismo y competitividad: una aproximación desde la teoría de grafos.

Una estrategia metodológica para la optimización de procesos de producción de música POP, basada en modelos computacionales.

2019

Aplicabilidad de la criticidad en el mantenimiento de equipos.

Calidad Percibida en un Hotel de Mérida, Yucatán.

Características de los tutores para el trabajador de la generación “Y” peruano: imagen en base a un estudio de campo.

Categorización de los conflictos organizacionales en embarcaciones de apoyo marítimo brasileñas.

Correspondencia de procesos para optimizar costos en edificios multifamiliares en Perú.

Costo-efectividad de las intervenciones nutricias y dietéticas en salud.

Delimitación de tierras comunitarias y el empoderamiento de las comunidades. Caso: Regulado de Galinha, Mozambique.

Diagnóstico del nivel de autoevaluación de los alumnos del módulo de diseño-área de proyectos de FUNIBER 2018, respecto a las competencias de certificación ipma 4.0.

Effect of Sulfonic Groups Concentration on IEC Properties in New Fluorinated Copolyamides.

Escala de valores en profesionales de enfermería activos de República Dominicana.

Estrategias instruccionales y su relación en el dominio cognitivo del participante de Educación Superior.

Evaluación del contexto organizacional en la gestión del riesgo de tecnología de información con un enfoque basado en COBIT.

Factores de éxito y fracaso en la gestión de proyectos: un enfoque en las mejores prácticas.

Formación docente: la formación del profesor y la influencia sobre el aprendizaje de los alumnos.

Harvesting Scenedesmus obliquus via Flocculation of Moringa oleifera Seed Extract from Urban Wastewater: Proposal for the Integrated Use of Oil and Flocculant.

La innovación y su importancia.

La rendición de cuentas en establecimientos educativos de Guatemala.

Las bases del crecimiento económico de Perú.

Metodología de reclutamiento y selección de docentes, a partir del enfoque por competencias, acordes a particularidades institucionales: el caso del Colegio Mundo Unido, Costa Rica (UWCCR).

Papel del trabajo remunerado no agrícola en la productividad agrícola y bienestar. Evidencias de las provincias de Gaza y Maputo, al sur de Mozambique en 2015.

Percepción del riesgo en trabajos en alturas en empresas de telecomunicaciones de Ecuador y Colombia.

Programa de intervención socioeducativo para lograr un estilo de vida saludable.

Project Design and Management.

Relación entre el apego a la dieta y la educación nutricional en pacientes con diálisis del Hospital General de Atlacomulco y el Hospital General de San Felipe del Progreso, Estado de México.

2018

En el marco de la ejecución del megaproyecto minero Las Bambas, se tuvo regulada la operación industrial de suministro de combustible Diésel B-5 bajo el contrato CB-029-12 con una duración contractual de 5 años a partir del 2012. Se tienen como partes a Xstrata Copper, como entidad contratante, y a Petróleos del Perú – Petroperú, como entidad contratada. Por un lado, aparece Xstrata Copper como compañía minera global que opera en 19 países y es uno de los grandes productores de cobre; por otro lado, aparece Petróleos del Perú – Petroperú como empresa de propiedad del Estado Peruano y de derecho privado dedicada al transporte, refinación, distribución y comercialización de combustibles y otros productos derivados del petróleo. Adicionalmente, aparece Servosa Cargo como operador logístico de Petróleos del Perú – Petroperú que se dedica al transporte de combustibles líquidos y lubricantes hacia minas, obras de infraestructura y gran industria. Respecto al desempeño de la operación industrial se observa hasta un 8% de bienes no entregados entre los años 2012 y 2013, incidentes de desabastecimiento intempestivo de combustible en instalaciones del cliente, y detección de incumplimiento de la especificación del Nivel de Limpieza del combustible entre otros. Estas fallas son atribuibles a que en la relación comercial no existe una metodología de gestión de proyectos. El Project Management Institute (PMI) es una organización internacional especializada en gestión de proyectos y ha desarrollado buenas prácticas para lograr el gerenciamiento efectivo de proyectos. La presente investigación consiste en comparar las 10 áreas de conocimiento en gestión de proyectos identificadas por el PMI con aquellas aplicadas para el desarrollo del contrato CB-029-12. La aportación al estado del arte del presente trabajo es establecer una metodología a medida para el proyecto minero Las Bambas que incluya la gestión de proyectos incluyendo como una mejora la gestión de la prevención de riesgos laborales y ambientales.

Análisis de una operación industrial de suministro de combustible Diésel B-5 y desarrollo de solución de optimización basada en las metodologías del PMI, ISO 9000, ISO 14000 y OHSAS 18000.

De las ideas a las nuevas ideas: un acercamiento conceptual al proceso de innovación.

El desarrollo de nuevos productos y su impacto en la producción: caso de estudio BH Consultores.

Estudio comparativo entre metodología de aula invertida y metodología tradicional en clases de español, inglés y matemáticas.

MLS Psychology Research.

Modelo para detección y simulación de fallas bajo la gestión de mantenimiento y proyectos.

Productivity and Biodiesel Quality of Fatty Acids Contents from Scenedesmus obliquus in Domestic Wastewater Using Phototrophic and Mixotrophic Cultivation Systems.

Sensorial Quality ofBreads and Cookies Prepared with Flour from The Shells of Two Varieties of Cocoain Ecuador.

2017

Calidad en uso: Fundamental en la evaluación de proyectos para la formación ingenieril de líderes de su entorno.

A Comparative Study in Entrepreneurship Between South Korea and Colombia.

Conocimientos, actitudes y prácticas de familias de adolescentes con discapacidad cognitiva en sexualidad y afectividad.

Efecto del reflujo en la eficiencia termodinámica de la separación de una mezcla utilizando destilación discontinua.

Estudio de la influencia del ejercicio físico durante el embarazo en la prevención de las consecuencias de la ganancia excesiva de peso - EFEMBARAZO.

Lipid Accumulation in HepG2 Cells Is Attenuated by Strawberry Extract through AMPK Activation.

MLS Educational Research.

Protective Effect of Strawberry Extract against Inflammatory Stress Induced in Human Dermal Fibroblasts.

Strawberry (cv. Romina) Methanolic Extract and Anthocyanin-Enriched Fraction Improve Lipid Profile and Antioxidant Status in HepG2 Cells.

Strawberry-Based Cosmetic Formulations Protect Human Dermal Fibroblasts against UVA-Induced Damage.

2016

Associations between Sleep, Cortisol Regulation, and Diet: Possible Implications for the Risk of Alzheimer Disease.

Estrategias pedagógicas para mejorar el desempeño de los estudiantes de enfermería en el área clínica utilizando las competencias de enfermería y las destrezas educativas.

Polyphenol-rich strawberry extract (PRSE) shows in vitro and in vivo biological activity against invasive breast cancer cells.

Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease.

The Spanish General Knowledge Norms.

2015

A Pilot Study of the Photoprotective Effects of Strawberry-Based Cosmetic Formulations on Human Dermal Fibroblasts.

Plant-Based and Plant-Rich Diet Patterns during Gestation: Beneficial Effects and Possible Shortcomings.

2014

Polyphenol-Rich Strawberry Extract Protects Human Dermal Fibroblasts against Hydrogen Peroxide Oxidative Damage and Improves Mitochondrial Functionality.

2013

Aplicación de contra-inteligencia empresarial: análisis sobre la protección contra el espionaje cibernético en las organizaciones.

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