71
A
Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.
Advancement in medical report generation: current practices, challenges, and future directions.
Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field.
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.
A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective.
A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis.
A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.
An improved deep convolutional neural network-based YouTube video classification using textual features.
C
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.
Chronotype and Cancer: Emerging Relation Between Chrononutrition and Oncology from Human Studies.
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.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions.
D
Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.
Deep transfer learning-based bird species classification using mel spectrogram images.
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis.
Detection of cotton crops diseases using customized deep learning model.
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.
E
Efficient CNN architecture with image sensing and algorithmic channeling for dataset harmonization.
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.
Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence.
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.
Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.
Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.
F
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.
Fundus image classification using feature concatenation for early diagnosis of retinal disease.
H
Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.
Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.
I
Incorporating soil information with machine learning for crop recommendation to improve agricultural output.
Investigation of structural frustration in symmetric diblock copolymers confined in polar discs through cell dynamic simulation.
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?
L
Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.
M
Mediterranean Diet and Quality of Life in Adults: A Systematic Review.
Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.
Metaheuristic-based optimal energy assessment of hybrid multi-effect evaporator with synergy of solar and wind energy sources.
N
Natural Language Processing-Based Software Testing: A Systematic Literature Review.
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.
Novel transfer learning based bone fracture detection using radiographic images.
Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.
O
Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors.
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.
P
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Prediction of leukemia peptides using convolutional neural network and protein compositions.
Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.
Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.
Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.
PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.
R
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.
S
Side effects associated with homogenous and heterogenous doses of Oxford–AstraZeneca vaccine among adults in Bangladesh: an observational study.
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.
Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.
Software Cost and Effort Estimation: Current Approaches and Future Trends.
StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.
T
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.
U
Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.
Underwater Thermal Energy Harvesting: Frameworks, Challenges, Applications, and Future Investigation.
V
Virtual histopathology methods in medical imaging - a systematic review.
Y
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 class="ep_document_link" href="/17788/1/s40537-025-01167-w.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis
The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,
Raza Ur Rehman
<a class="ep_document_link" href="/17794/1/s41598-025-95836-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.
Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,
Khouili
en
close
Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography
Background: Sarcopenia, characterized by a reduction in skeletal muscle mass and function, is a prevalent complication in the Intensive Care Unit (ICU) and is related to increased mortality. This study aims to determine whether muscle and fat mass measurements at the T12 and L1 vertebrae using chest tomography can predict mortality among critically ill COVID-19 patients requiring invasive mechanical ventilation (MV). Methods: Fifty-one critically ill COVID-19 patients on MV underwent chest tomography within 72 h of ICU admission. Muscle mass was measured using the Core Slicer program. Results: After adjustment for potential confounding factors related to background and clinical parameters, a 1-unit increase in muscle mass, subcutaneous, and intra-abdominal fat mass at the L1 level was associated with approximately 1–2% lower odds of negative outcomes and in-hospital mortality. No significant association was found between muscle mass at the T12 level and patient outcomes. Furthermore, no significant results were observed when considering a 1-standard deviation increase as the exposure variable. Conclusion: Measuring muscle mass using chest tomography at the T12 level does not effectively predict outcomes for ICU patients. However, muscle and fat mass at the L1 level may be associated with a lower risk of negative outcomes. Additional studies should explore other potential markers or methods to improve prognostic accuracy in this critically ill population.
Natalia Daniela Llobera mail , Evelyn Frias-Toral mail , Mariel Aquino mail , María Jimena Reberendo mail , Laura Cardona Díaz mail , Adriana García mail , Martha Montalván mail , Álvaro Velarde Sotres mail alvaro.velarde@uneatlantico.es, Sebastián Chapela mail ,
Llobera
<a href="/17569/1/Food%20Frontiers%20-%202025%20-%20Romero%E2%80%90Marquez%20-%20Olive%20Leaf%20Extracts%20With%20High%20%20Medium%20%20or%20Low%20Bioactive%20Compounds%20Content.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Alzheimer's disease (AD) involves β-amyloid plaques and tau hyperphosphorylation, driven by oxidative stress and neuroinflammation. Cyclooxygenase-2 (COX-2) and acetylcholinesterase (AChE) activities exacerbate AD pathology. Olive leaf (OL) extracts, rich in bioactive compounds, offer potential therapeutic benefits. This study aimed to assess the anti-inflammatory, anti-cholinergic, and antioxidant effects of three OL extracts (low, mid, and high bioactive content) in vitro and their protective effects against AD-related proteinopathies in Caenorhabditis elegans models. OL extracts were characterized for phenolic composition, AChE and COX-2 inhibition, as well as antioxidant capacity. Their effects on intracellular and mitochondrial reactive oxygen species (ROS) were tested in C. elegans models expressing human Aβ and tau proteins. Gene expression analyses examined transcription factors (DAF-16, skinhead [SKN]-1) and their targets (superoxide dismutase [SOD]-2, SOD-3, GST-4, and heat shock protein [HSP]-16.2). High-OL extract demonstrated superior AChE and COX-2 inhibition and antioxidant capacity. Low- and high-OL extracts reduced Aβ aggregation, ROS levels, and proteotoxicity via SKN-1/NRF-2 and DAF-16/FOXO pathways, whereas mid-OL showed moderate effects through proteostasis modulation. In tau models, low- and high-OL extracts mitigated mitochondrial ROS levels via SOD-2 but had limited effects on intracellular ROS levels. High-OL extract also increased GST-4 levels, whereas low and mid extracts enhanced GST-4 levels. OL extracts protect against AD-related proteinopathies by modulating oxidative stress, inflammation, and proteostasis. High-OL extract showed the most promise for nutraceutical development due to its robust phenolic profile and activation of key antioxidant pathways. Further research is needed to confirm long-term efficacy.
Jose M. Romero‐Marquez mail , María D. Navarro‐Hortal mail , Alfonso Varela‐López mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Juan G. Puentes mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Jianbo Xiao mail , Roberto García‐Ruiz mail , Sebastián Sánchez mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,
Romero‐Marquez
<a class="ep_document_link" href="/17570/1/eFood%20-%202025%20-%20Navarro%E2%80%90Hortal%20-%20Effects%20of%20a%20Garlic%20Hydrophilic%20Extract%20Rich%20in%20Sulfur%20Compounds%20on%20Redox%20Biology%20and.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Garlic is a horticultural product highly valued for its culinary and medicinal attributes. The aim of this study was to evaluate the composition of a garlic hydrophilic extract as well as the influence on redox biology, Alzheimer's Disease (AD) markers and aging, using Caenorhabditis elegans as experimental model. The extract was rich in sulfur compounds, highlighting the presence of other compounds like phenolics, and the antioxidant property was corroborated. Regarding AD markers, the acetylcholinesterase inhibitory capacity was demonstrated in vitro. Although the extract did not modify the amyloid β-induced paralysis degree, it was able to improve, in a dose-dependent manner, some locomotive parameters affected by the hyperphosphorylated tau protein in C. elegans. It could be related to the effect found on GFP-transgenic stains, mainly regarding to the increase in the gene expression of HSP-16.2. Moreover, an initial investigation into the aging process revealed that the extract successfully inhibited the accumulation of intracellular and mitochondrial reactive oxygen species in aged worms. These results provide valuable insights into the multifaceted impact of garlic extract, particularly in the context of aging and neurodegenerative processes. This study lays a foundation for further research avenues exploring the intricate molecular mechanisms underlying garlic effects and its translation into potential therapeutic interventions for age-related neurodegenerative conditions.
María D. Navarro‐Hortal mail , Jose M. Romero‐Marquez mail , Johura Ansary mail , Cristina Montalbán‐Hernández mail , Alfonso Varela‐López mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Jianbo Xiao mail , Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Cristina Sánchez‐González mail , Tamara Y. Forbes‐Hernández mail , José L. Quiles mail jose.quiles@uneatlantico.es,
Navarro‐Hortal