Teledu: Transmedia Learning Ecosystem for People at Risk of Exclusion

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Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice

The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.

Artículos y libros

Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,

Luo

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What works in financial education? Experimental evidence on program impact

Financial education is increasingly essential for safeguarding both individual and corporate well-being. This study systematically reviews global financial education experiments using a dual-method framework that integrates a deep learning classifier with advanced multivariate statistical techniques. Our analysis indicates that while short-term improvements in financial literacy are common, such gains tend to diminish over time without ongoing reinforcement. Moreover, the limited impact of digital innovations and monetary incentives suggests that successful financial education depends on more than simply deploying technological solutions or extrinsic rewards. Overall, this review provides valuable insights into the evolving landscape of financial education in a dynamic economic context and underscores the need for sustainable strategies that secure lasting improvements in financial literacy.

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Gonzalo Llamosas García mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es,

García

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LC-MS and GC–MS analyses reveal that amino acid-induced ammoniation of EGCG in different tea types enhances its structural stability

Epigallocatechin gallate (EGCG) is the most abundant polyphenol in tea. Owing to the different fermentation degrees, differences in polyphenol composition of water extracts of green tea, white tea, oolong tea, and black tea occur, and affect health value. This study revealed that the content of EGCG decreases with the increase in the degree of fermentation. In tea with a high fermentation degree, EGCG was stably present in the form of ammoniation to yield nitrogen-containing EGCG derivative (N-EGCG). The content of N-EGCG in tea was negatively correlated with the content of EGCG. Furthermore, the content of l-serine and L-threonine in tea was positively and negatively correlated with N-EGCG and EGCG levels, respectively, suggesting that they may participate in the formation of N-EGCG as nitrogen sources. This study proposes a new fermentation-induced polyphenol-amino acid synergistic mechanism, which provides a theoretical basis for the study of the biotransformation reaction mechanism of tea polyphenols.

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Yuxuan Zhao mail , Jingyimei Liang mail , Wanning Ma mail , Mohamed A. Farag mail , Chunlin Li mail , Jianbo Xiao mail ,

Zhao

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Exploring the nexus: Hausman test application in tourism, globalization, and environmental sustainability- evidence of top 10 visited countries

Econometric analysis has long been integral to measuring sustainable environmental quality, with panel data methods, such as fixed and random effects models, becoming the focal point of modern research. Initially, such methods were used to simply investigate environmental issues, but recent years have seen a shift toward the study of random effects models, focusing on hypothesis testing and policy debates. However, several important aspects of the Hausman test have not been sufficiently investigated in the literature. This study seeks to evaluate the utility of the Hausman test using a real dataset from tourism and globalization, exploring their effects on sustainable environmental quality. Additionally, the study examines key factors contributing to environmental issues including economic growth and energy consumption, as critical explanatory variables. By investigating the relationship between tourism, globalization, economic growth, and energy use, the research focuses on the top 10 most visited economies: France, the USA, Spain, China, Turkey, Italy, Mexico, Germany, Thailand, and the UK. Using panel data and the cross-sectional random effects model for the period of 1998 to 2024, the study produces reliable estimations of these relationships. The empirical findings suggest that while the Hausman test favors the fixed effect model, the real-world characteristics of these countries point to the random effect model, highlighting the negative impact of economic growth, energy consumption, and globalization on sustainable environmental quality. It is also suggested that socio-environmental factors should be considered for each destination for sustainable environmental quality.

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Saba Nourin mail , Ismat Nasim mail , Hafiz Muhammad Raza ur Rehman mail , Elisabeth Caro Montero mail elizabeth.caro@uneatlantico.es, Mirtha Silvana Garat de Marin mail silvana.marin@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,

Nourin

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

Introduction: The rapid expansion of generated data through social networks has introduced significant challenges, which underscores the need for advanced methods to analyze and interpret these complex systems. Deep learning has emerged as an effective approach, offering robust capabilities to process large datasets, and uncover intricate relationships and patterns. Methods: In this systematic literature review, we explore research conducted over the past decade, focusing on the use of deep learning techniques for community detection in social networks. A total of 19 studies were carefully selected from reputable databases, including the ACM Library, Springer Link, Scopus, Science Direct, and IEEE Xplore. This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works. Results: Our review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies. Discussion: However, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. These issues stand out as important areas that need further attention and deeper research. This review provides meaningful insights for researchers working in social network analysis. It offers a detailed summary of recent developments, showcases the most impactful deep learning methods, and identifies key challenges that remain to be explored.

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Mohamed El-Moussaoui mail , Mohamed Hanine mail , Ali Kartit mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Helena Garay mail helena.garay@uneatlantico.es, Isabel de la Torre Díez mail ,

El-Moussaoui