Implementar un control interno en el proceso de Compras y su incidencia en la liquidez aplicado a la Unidad Educativa “Bautista” de la ciudad de Ambato, provincia de Tungurahua ubicado en Ecuador, periodo 2021

<a href="/15983/1/Food%20Science%20%20%20Nutrition%20-%202025%20-%20Tanveer%20-%20Novel%20Transfer%20Learning%20Approach%20for%20Detecting%20Infected%20and%20Healthy%20Maize%20Crop.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.

Producción Científica

Muhammad Usama Tanveer mail , Kashif Munir mail , Ali Raza mail , Laith Abualigah mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,

Tanveer

<a href="/10290/1/Influence%20of%20E-learning%20training%20on%20the%20acquisition%20of%20competences%20in%20basketball%20coaches%20in%20Cantabria.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

The main aim of this study was to analyse the influence of e-learning training on the acquisition of competences in basketball coaches in Cantabria. The current landscape of basketball coach training shows an increasing demand for innovative training models and emerging pedagogies, including e-learning-based methodologies. The study sample consisted of fifty students from these courses, all above 16 years of age (36 males, 14 females). Among them, 16% resided outside the autonomous community of Cantabria, 10% resided more than 50 km from the city of Santander, 36% between 10 and 50 km, 14% less than 10 km, and 24% resided within Santander city. Data were collected through a Google Forms survey distributed by the Cantabrian Basketball Federation to training course students. Participation was voluntary and anonymous. The survey, consisting of 56 questions, was validated by two sports and health doctors and two senior basketball coaches. The collected data were processed and analysed using Microsoft® Excel version 16.74, and the results were expressed in percentages. The analysis revealed that 24.60% of the students trained through the e-learning methodology considered themselves fully qualified as basketball coaches, contrasting with 10.98% of those trained via traditional face-to-face methodology. The results of the study provide insights into important characteristics that can be adjusted and improved within the investigated educational process. Moreover, the study concludes that e-learning training effectively qualifies basketball coaches in Cantabria.

Producción Científica

Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Javier Jorge mail , Kamil Giglio mail ,

Alemany Iturriaga

<a class="ep_document_link" href="/15625/1/s41598-024-74127-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

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.

Producción Científica

Tariq Ali mail , Saif Ur Rehman mail , Shamshair Ali mail , Khalid Mahmood mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Tahir Khurshaid mail , Imran Ashraf mail ,

Ali

<a href="/15979/1/nutrients-17-00026.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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

Background/Objectives. Traditional dietary patterns are being abandoned in Mediterranean countries, especially among younger generations. This study aimed to investigate the potential lifestyle determinants that can increase adherence to the Mediterranean diet in children and adolescents. Methods. This study is a cross-sectional analysis of data from five Mediterranean countries (Italy, Spain, Portugal, Egypt, and Lebanon) within the context of the EU-funded project DELICIOUS (UnDErstanding consumer food choices & promotion of healthy and sustainable Mediterranean Diet and LIfestyle in Children and adolescents through behavIOUral change actionS). This study comprised information on 2011 children and adolescents aged 6–17 years old collected during 2023. The main background characteristics of both children and parents, including age, sex, education, and family situation, were collected. Children’s eating (i.e., breakfast, place of eating, etc.) and lifestyle habits (i.e., physical activity level, sleep, and screen time) were also investigated. The level of adherence to the Mediterranean diet was assessed using the KIDMED index. Logistic regression analyses were performed to test for likelihood of higher adherence to the Mediterranean diet. Results. Major determinants of higher adherence to the Mediterranean diet were younger age, higher physical activity level, adequate sleep duration, and, among dietary habits, having breakfast and eating with family members and at school. Parents’ younger age and higher education were also determinants of higher adherence. Multivariate adjusted analyses showed that an overall healthier lifestyle and parents’ education were the factors independently associated with higher adherence to the Mediterranean diet. Conclusions. Higher adherence to the Mediterranean diet in children and adolescents living in the Mediterranean area is part of an overall healthy lifestyle possibly depending on parents’ cultural background.

Producción Científica

Alice Rosi mail , Francesca Scazzina mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Ludwig Álvarez-Córdova mail , Osama Abdelkarim mail , Achraf Ammar mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Carmen Lilí Rodríguez Velasco mail carmen.rodriguez@uneatlantico.es, Julién Brito Ballester mail julien.brito@uneatlantico.es, Lorenzo Monasta mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,

Rosi

<a href="/15198/1/nutrients-16-03859.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Carotenoids Intake and Cardiovascular Prevention: A Systematic Review

Background: Cardiovascular diseases (CVDs) encompass a variety of conditions that affect the heart and blood vessels. Carotenoids, a group of fat-soluble organic pigments synthesized by plants, fungi, algae, and some bacteria, may have a beneficial effect in reducing cardiovascular disease (CVD) risk. This study aims to examine and synthesize current research on the relationship between carotenoids and CVDs. Methods: A systematic review was conducted using MEDLINE and the Cochrane Library to identify relevant studies on the efficacy of carotenoid supplementation for CVD prevention. Interventional analytical studies (randomized and non-randomized clinical trials) published in English from January 2011 to February 2024 were included. Results: A total of 38 studies were included in the qualitative analysis. Of these, 17 epidemiological studies assessed the relationship between carotenoids and CVDs, 9 examined the effect of carotenoid supplementation, and 12 evaluated dietary interventions. Conclusions: Elevated serum carotenoid levels are associated with reduced CVD risk factors and inflammatory markers. Increasing the consumption of carotenoid-rich foods appears to be more effective than supplementation, though the specific effects of individual carotenoids on CVD risk remain uncertain.

Producción Científica

Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Thomas Prola mail thomas.prola@uneatlantico.es, Raquel Martínez Díaz mail raquel.martinez@uneatlantico.es, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es,

Sumalla Cano