This study used quantitative methods to examine the correlation between adaptive learning technology and cognitive flexibility in kids receiving special education. The study included a cohort of 120 kids, ages 8–12, who were diagnosed with particular learning difficulties, ADHD, or autism spectrum disorder. Cognitive flexibility was evaluated using the Wisconsin Card Sorting Test (WCST), while the utilization of adaptive learning technologies was quantified using self–report questionnaires. The data was analyzed using several statistical methods, such as independent samples t-tests, regression, Pearson correlation coefficients, ANOVA, and ANCOVA. The findings revealed a noteworthy and favorable correlation between the utilization of adaptive technology and the scores of cognitive flexibilities. This correlation remained significant even after accounting for demographic characteristics. Moreover, it was shown that the diagnostic status had a moderating effect on the correlation between the utilization of adaptive technology and cognitive flexibility. The results emphasize the capacity of adaptive learning technologies to improve cognitive flexibility abilities in kids with special needs, offering significant knowledge for educators, legislators, and technology developers.

service-learning activities are effective in higher education programmes. During the COVID-19 period, universities have implemented this methodology and students have improved their social and practical skills. The purpose of this article is to analyse the role of higher education in the process of adapting teaching based on experiences designed for students to serve the community. This research presents the results of a specific service-learning experience of 35 students from the Teamworking programme during the academic years 2020–21 (online) and 2021–22 (face-to-face), in collaboration with the San Juan de Dios Foundation in Madrid, which provides care for people with disabilities. Students evaluated the experience through a quantitative study based on a questionnaire previously developed by Folgueiras (2013), divided into four dimensions. Students also provided some feedback, explaining that this experience changed their perception of people with disabilities, considering the personal value of contributing to social inclusion through service learning. The results show that through the Folgueiras model, students have strengthened their social skills and competences, and through an applied training project that offers the opportunity to build a real relationship through different activities, where learning was at the centre of the interaction between students and young people with disabilities. In conclusion, although the evaluation was positive in terms of the students’ professional and human development, this project requires continuous improvement in the long term, since the subjectivity of human relationships follows a dynamic course with variables that are sensitive to time and individual experience.

The transition to sustainable agricultural practices is critical in the face of escalating climate challenges. Despite significant advances, the integration of green technologies within agribusiness remains underexplored. This study undertakes a comprehensive bibliometric analysis, utilizing data from the Web of Science Core Collection (1990–2023), to elucidate the integration of green technologies within agribusiness strategies. The research highlights key trends, influential authors, prominent journals, and significant thematic clusters, including biogas, biochar, biotech remediation, sustainable agriculture transition, low-carbon agriculture, and green strategies. By employing R, Bibliometrix, and VOSviewer, the study provides a nuanced understanding of the research landscape, emphasizing the critical role of strategic planning, policy frameworks, technological innovation, and interdisciplinary approaches in promoting sustainable agricultural development. The findings underscore the growing scholarly interest in sustainable practices, driven by global initiatives such as the UN’s 2030 Agenda and the Paris Agreement. This study contributes to the literature by offering qualitative insights and policy implications, highlighting the necessity for a holistic integration of green technologies to enhance the environmental and economic viability of agribusinesses.

This study develops an optimisation model to facilitate inter-facility medicine sharing in response to anticipated medicine shortages. These facilities include hospitals and medical representatives. We adopt the concept of collective response proposed in our study literature. The optimisation model is developed according to the real-world practices of inter-facility medicine sharing. We utilise case studies of particular healthcare networks to demonstrate the efficacy of the developed model. The efficacy encompasses the model’s application to real-world case studies, as well as its validity and reliability within a specific system. The results show that the developed model is able to determine which facilities should share the requested amount of medicines; and to reduce total lead times by at least one day compared to the ones obtained in the current practice. The model can be used as a decision-support tool for healthcare practitioners when responding to shortages. The study presents the managerial implications of medicine sharing at the network level and supports the development of collaboration amongst facilities in response to medicine shortages.

The rapid increase in the aging population has raised significant concerns about the living conditions and well-being of elderly residents in old communities. This study addresses these concerns by proposing a Sustainable Urban Renovation Assessment Model (SURAM) specifically designed to enhance elderly-friendly environments in Chongqing City. The model encompasses multiple dimensions, including the comfort of public facilities, service safety and convenience, medical travel services, infrastructure security, life service convenience, neighbor relations, ambulance aid accessibility, commercial service facilities, privacy protection, elderly care facilities and service supply, and medical and health facilities. By employing factor analysis, the study reduces the dimensionality of the 49 indicator factors, allowing for a more focused and comprehensive evaluation of the effectiveness of aging-friendly renovation efforts. The main factors identified in the proposed model include community infrastructure security, elderly comfort of community public facilities, completeness and convenience of surrounding living services, and security and convenience of elderly care services. The results reveal that the age-appropriate comfort of public facilities plays a significant role in achieving successful aging-appropriate renovation outcomes. The findings demonstrate that by addressing specific needs such as safety, accessibility, and convenience, communities can significantly improve the quality of life for elderly residents. Moreover, the application of SURAM provides actionable insights for policymakers, urban planners, and community stakeholders, guiding them in implementing targeted initiatives for sustainable and inclusive urban development.

This paper explores the integration of Large Language Models (LLMs) and Software-Defined Resources (SDR) as innovative tools for enhancing cloud computing education in university curricula. The study emphasizes the importance of practical knowledge in cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), DevOps, and cloud-native environments. It introduces Lean principles to optimize the teaching framework, promoting efficiency and effectiveness in learning. By examining a comprehensive educational reform project, the research demonstrates that incorporating SDR and LLMs can significantly enhance student engagement and learning outcomes, while also providing essential hands-on skills required in today’s dynamic cloud computing landscape. A key innovation of this study is the development and application of the Entropy-Based Diversity Efficiency Analysis (EDEA) framework, a novel method to measure and optimize the diversity and efficiency of educational content. The EDEA analysis yielded surprising results, showing that applying SDR (i.e., using cloud technologies) and LLMs can each improve a course’s Diversity Efficiency Index (DEI) by approximately one-fifth. The integrated approach presented in this paper provides a structured tool for continuous improvement in education and demonstrates the potential for modernizing educational strategies to better align with the evolving needs of the cloud computing industry.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

This study evaluates the effectiveness of measures aimed at reducing traffic violations, specifically focusing on wrong-way driving, at intersections in Loja, Ecuador. The high incidence of accidents at these intersections, often resulting from wrong-way driving and non-compliance with traffic regulations, underscores the critical need for effective strategies to enhance road safety. To address this issue, we adopted a multidisciplinary approach to assess the impact of two specific interventions: the implementation of official warnings and the presence of traffic officers at a selected intersection. Data collection involved recording instances of traffic violations, administering road safety surveys, and monitoring the implementation of these interventions. The post-implementation analysis sought to determine the effect of these measures on driver behavior and overall traffic safety. Our findings indicate that while the interventions succeeded in increasing awareness about traffic violations, they did not produce a significant reduction in undesirable driving behaviors. This suggests that, although the presence of warnings and traffic officers is beneficial in raising awareness, these measures alone may not be sufficient to effect substantial behavioral changes. The research provides valuable insights for the development of more comprehensive road safety strategies and emphasizes the need for further studies to explore and address the underlying causes of traffic violations.