Digital Transformation and the Future of Public Value

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This study examines technological readiness as a mediator in the relationship between digital transformation and public value, filling an important gap in the literature about causal pathways in developing countries. Combining theories of digital transformation, technological readiness, and public value, the research involved 200 digital service users in Malang City. Bootstrap mediation analysis showed that digital transformation significantly affects public value (β=0.4146, p<0.001), with technological readiness mediating 35.51% of the total effect (ACME=0.1497), while the direct effect remained notable (ADE=0.2650). Results indicate partial mediation, implying that digital transformation adds value through two pathways. The study proposes a dual-pathway model that highlights the importance of investing in both digital infrastructure and user literacy, guiding comprehensive digital policies and broadening understanding of digital transformation in Indonesia’s context, characterized by high technological readiness heterogeneity.

Combined Drying Technologies for Preserving the Quality of Backyard Averroha Carambola L. in Veracruz, Mexico

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Star fruit (Averrhoa carambola L.) is cultivated in Asian and Latin American countries and has high nutritional value due to its content of bioactive compounds, polyphenols, vitamins A and C, minerals, and antioxidant activity. However, its high water content makes it highly perishable, limiting its preservation and commercialization; therefore, it is necessary to develop technological alternatives that extend its shelf life without affecting its organoleptic and nutritional quality. The objective of this study was to evaluate combined power ultrasound-assisted drying technologies for obtaining star fruit flakes as a preservation alternative, analyzing their effect on physicochemical, functional, and sensory attributes. Fruit from the Postgraduate College, Veracruz Campus, was used, and osmotic dehydration (OD) curves were performed at 50 and 60 °Brix, using ultrasound (US) as a pretreatment to convective drying (CS). Drying kinetics were performed at 50 and 60 °C, and total phenolic compounds, antioxidant activity (DPPH and ABTS), oxalic acid, color (ΔE), moisture, total solids, water activity (aw), and texture in the center and periphery were evaluated, in addition to sensory analysis. The DO treatments assisted with ultrasound reached equilibrium in less time. The 60°Brix-CUS-60 °C treatment showed lower final moisture content, while the 60°Brix-CUS-50 °C treatment reached an aw of approximately 0.47, favoring microbiological stability. The 60 °Brix DO treatment with ultrasound and 60 °C surface drying showed lower moisture content and higher total solids content. The 60 °Brix DO treatment without ultrasound and 50 °C surface drying preserved phenols better, reducing oxalic acid by >50% and showing improvements in antioxidant activity. High sensory acceptability was obtained. It is concluded that the DO-US-SC combination is effective in preserving carambola with adequate quality and acceptance.

A Proposed Process Framework for Developing the Competencies of Primary School Teachers in Facilitating Game-Based Learning Oriented Towards Education for Sustainable Development

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Education for Sustainable Development (ESD) through Game-Based Learning (GBL) is a highly effective pedagogical approach, creating a simulation space that helps primary school students experience and solve practical problems. However, through a review of international literature, the article points out a paradox: although teachers support GBL, the lack of games literacy and design skills makes them passively reuse available games on gaming platforms. In order to thoroughly address this gap, the study proposes a strategic shift in training towards Learning by Design and Mentoring. The core contribution of this article is a practical 4-stage professional development process framework: Activating and enhancing game awareness, Game design and creation, Honing pedagogical and reflective skills, and Practical implementation and mentoring. This is a standardized roadmap that helps educational institutions confidently transform their teaching staff from users into designers of sustainable educational environments.

The Guangdong–SDT Paradox: Learning Climate and the Cultural Internalization of Motivation in Elite Dance Education

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This study investigates the interplay between motivational orientations, learning climate, and student engagement within elite undergraduate dance programs in Guangdong, China. While Self-Determination Theory (SDT) typically frames controlled motivation as maladaptive, this research posits a “Guangdong–SDT Paradox,” where culturally embedded obligations support persistence. Using a cross-sectional design, data were collected from 509 dance students across six institutions and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results identify learning climate as the dominant predictor of multidimensional engagement, explaining over 70% of the variance. Consistent with SDT, autonomous motivation strongly predicted engagement and partially mediated the influence of the learning climate. However, contrary to Western-centric models, controlled motivation exhibited a significant positive effect on engagement, suggesting that collectivist values such as filial piety and institutional “face” function as adaptive mechanisms in high-performance contexts. These findings support a theory of “contextualized universality,” where basic psychological needs are universal but their expression is culturally modulated. The study concludes that instructor-led autonomy support is essential for internalizing both intrinsic and culturally sanctioned motivations, offering a framework for sustaining engagement in elite arts education.

Biopsychosocial Model Analysis of Risk Factors for Suicide in Mexico: Systematic Review (2019–2024)

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 Suicidal behavior in Mexico constitutes an urgent public health problem, that requires integrating biological, psychological, and social dimensions. The objective of this systematic review was to identify and organize risk factors associated with death by suicide in Mexican population between 2019 and 2024, using the biopsychosocial model as a theoretical framework. The protocol was registered in PROSPERO (CRD420251131626) and followed the PRISMA 2020 guidelines. A structured search was conducted in PubMed, inclusion and exclusion criteria were applied; three independent authors performed a quality analysis; 110 full-text articles were evaluated, and 57 were included for the final analysis.

The findings were organized into 75 categories grouped by biopsychosocial domains. Psychological factors were the most frequent, highlighting depression, anxiety, emotional dysregulation, hopelessness, impulsivity, and self-harming behaviors. At the biological level, studies identified associations with sex, age, chronic diseases, substance use, and emerging findings in genetics, neuroendocrinology, and physiological markers. In the social dimension, interpersonal violence, adverse experiences in childhood, low social and family support, socioeconomic inequalities, and stigma predominated. Persistent methodological limitations were also observed in the studies reviewed, including the predominance of cross-sectional designs, convenience samples, heterogeneous instruments, and limited representation of structurally vulnerable populations.

The results show that Mexican research remains fragmented, focusing mainly on individual factors and providing little integrative analysis across biological, psychological, and social dimensions. So, it is required to evolve towards robust explanatory models incorporating longitudinal methodologies, a gender perspective, intersectional approaches, and multivariate frameworks that allow the understanding of individual vulnerability and structural conditions.

Building Business Resilience in Small Economies: Lessons from SMEs in the United Kingdom

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Small and medium-sized enterprises (SMEs) play a central role in the United Kingdom’s economic structure, yet they remain particularly vulnerable to systemic shocks such as pandemics, economic restructuring, and supply chain disruptions. Recent crises, including COVID-19 and post-Brexit adjustments, have highlighted the uneven capacity of SMEs to absorb, adapt to, and recover from turbulence. This study examines how UK SMEs build business resilience by analysing the combined influence of internal organisational capabilities and external institutional support mechanisms. Drawing on Resource-Based View, Dynamic Capabilities Theory, and Institutional Theory, the research adopts a mixed-methods design. Quantitative data were collected through a structured survey of 400 UK SMEs and analysed using regression and structural equation modelling, while qualitative insights were generated from in-depth case studies with SME owners, financial institutions, and business support organisations. The findings reveal that leadership agility, digital readiness, and financial management capabilities are significant predictors of resilience outcomes. External factors, including government support schemes, access to finance, and business networks, were found to moderate and strengthen the effects of internal capabilities rather than substitute for them. The study contributes a multi-level model of SME resilience that integrates firm-level and institutional dimensions and offers evidence-based implications for policymakers and practitioners seeking to enhance SME sustainability in turbulent economic environments.

Islamic Communicative Ethics and the Moral Crisis of Social Media in Nigeria

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The rapid expansion of social media in Nigeria has transformed civic engagement and public communication but has also intensified ethical challenges, including misinformation, cyberbullying, hate speech, political manipulation, and declining public civility. This study examines these developments through the normative framework of Islamic communicative ethics, grounded in Qur’ānic and Prophetic principles of truthfulness, verification, responsible speech, restraint, and harm-avoidance. Employing a multidisciplinary qualitative approach that integrates Islamic ethical theory, media studies, and analysis of Nigeria’s socio-political context, the study interrogates the moral foundations of digital misconduct. The findings indicate that persistent abuses within Nigeria’s online sphere are not merely regulatory or technological failures but manifestations of weakened moral orientation and diminished communicative responsibility. The erosion of ethical speech norms has contributed to ethno-religious tensions, political polarisation, reputational harm, and declining social trust. The study demonstrates that Islamic communicative ethics offers a coherent and contextually resonant framework for reorienting digital behaviour toward accountability, civility, and communal welfare. It concludes that embedding value-driven ethical principles in digital literacy, public discourse, and policy development is essential for fostering a healthier and more socially cohesive online public sphere in Nigeria.​

Analysis And Contemporary Perspectives of Yágodin’s Agrochemistry and its Integration with Current Environmental Principles and Challenges

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Agrochemistry is an essential discipline that studies the chemical composition and biochemical processes of soils and plants in order to optimize agricultural production under sustainability criteria, ensure adequate plant nutrition, and preserve soil fertility. In this context, the work Agrochemistry I and II, edited by B. A. Yagodin and published by Mir Publishers, represents a classical reference in the education and dissemination of this science, particularly in the Spanish-speaking sphere, by integrating theoretical foundations and practical applications. The objective of this research was to analyze the validity, coherence, and relevance of the principles and recommendations contained in both volumes by contrasting them with recent scientific evidence and current environmental regulations, in order to identify areas for improvement and propose guidelines for modern agrochemistry. The methodology included a critical and systematic content analysis, complemented by a bibliometric study of the state of the art using RStudio. A Scopus database comprising 494 publications in the field of agricultural sciences (1980–2025) was used, applying “Agroquímica B. A.” as the search criterion. The results indicate that the foundations proposed by Yagodin remain relevant and are associated with current issues such as soil chemical processes, plant nutrition, fertilizer dynamics, soil acidification, and phosphorus availability. Furthermore, convergence was observed with sustainable nutrient management approaches and emerging technologies such as biochar, controlled-release fertilizers, and digital monitoring. The bibliometric analysis highlighted Italy, France, and Mexico in terms of citation counts, and Japan, the United States, and the United Kingdom in scientific output. It is concluded that integrating Yagodin’s contributions with recent evidence strengthens an updated agrochemical approach capable of addressing contemporary environmental and productive challenges.

Multiclass Diabetes Classification using Multimodal Artificial Intelligence

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Diabetes mellitus is a prevalent metabolic disorder globally. Its primary etiologies encompass socioeconomic determinants, behavioral risk factors, and underlying comorbidities. Numerous epidemiological studies have investigated various diabetes phenotypes, impacting both sexes across the entire age spectrum. This study utilizes a dataset containing clinical profiles of 1,000 subjects assessed on multiple biometric and sociodemographic variables. The objective is to classify diabetes into type 1, type 2, and prediabetes using an array of deep learning and machine learning algorithms. Currently, artificial intelligence-driven diagnostic methods represent a state-of-the-art approach for disease stratification. This research evaluates the performance of six classification algorithms for determining glycemic status: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) network. Results demonstrate that the XGBoost classifier attained the highest predictive accuracy of 91% with a training duration of 20 seconds, surpassing the other models. These findings underscore the potential of advanced computational algorithms for precise diabetes phenotyping and risk assessment, offering significant implications for disease management and public health interventions.

Estimation of Fetal Weight by Measuring Umbilical Cord Diameter Using Ultrasonography

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Background and Objectives: Fetal weight assessment is a significant component in obstetric practices. The present study aims to estimate the fetal weight (EFW) by measuring the diameter of the umbalical cord.

Materials and Methods: The cross-sectional study included 153 pregnant women in third trimester. The sonographic evaluation was performed using mindary and Futus Fujifilm, for the examination.  Data were collected by the researcher by measuring the umbilical cord diameter. The data were analyzed using Microsoft Excel and SPSS version 16.

Results and Discussion: The current study closely aligned with findings reported by Benjamin et al. (2020) and Sarwar et al. (2018). Both studies highlighted significant positive correlations between UCD and fetal weight, reinforcing the idea that UCD is an important metric for assessing fetal growth and development. These studies emphasized using UCD as a reliable indicator, linking it effectively to fetal growth patterns, paralleling the findings of the current study in terms of correlations with fetal weight.

Conclusion: The ultrasonographic measurement of fetal weight using the umbilical cord diameter helps predict and estimate fetal birth weight and help in obstetric management. The current study provides valuable insights by demonstrating that umbilical cord diameter is significantly correlated with fetal weight.