Search Results (1,781)

Background: Thailand is rapidly transitioning into an aging society, creating an intergenerational caregiving gap that strains existing support systems. Objective: This study evaluated the effectiveness of “Young Care,” a community-based intervention designed to enhance youth knowledge, attitudes, and caregiving practices (KAP) toward older adults. Methods: A two-day structured training was conducted in Maha Sarakham Province in 2023 using a pre-post mixed-methods design. Middle and high school students participated in lectures, multimedia sessions, and experiential learning activities related to caregiving. Quantitative data were collected using validated KAP questionnaires, while qualitative insights were obtained from focus group discussions involving students, older persons, caregivers, and local leaders. Results: Post-intervention analysis revealed significant improvements in knowledge and attitudes (p < 0.001), accompanied by increased empathy, caregiving initiative, and a sense of moral responsibility among participants. Conclusions: The initiative fostered formal partnerships among schools, local governments, healthcare providers, and universities through memoranda of understanding. These collaborations enabled budgetary support and outreach to out-of-school youth, positioning “Young Care” as a scalable, youth-centered strategy to address Thailand’s long-term care challenges. Full article

In the context of the growing emphasis on sustainable development and building safety performance, wooden architecture will attract increasing attention due to its low-carbon characteristics and excellent seismic resistance. In this study, the bibliometric software Citespace is used for data visualization analysis based on the literature related to Chinese wooden architecture in the China National Knowledge Infrastructure (CNKI) and the Web of Science (WOS) databases, aiming to construct an analytical framework that integrates quantitative visualization and qualitative thematic interpretation which could reveal the current status, hotspots, and frontier trends of research in this field. The results show the following: Research on Chinese wooden architecture has shown a steady growth trend, indicating that it has received attention from an increasing number of scholars. Researchers and institutions are mainly concentrated in higher learning and research institutions in economically developed regions. Research hotspots cover subjects such as seismic performance, mortise–tenon structures, imitation wood structures, Dong architecture, Liang Sicheng, and the Society for the Study of Chinese Architecture. The research process of Chinese wooden architecture can be divided into three stages: the macro stage, the specific deepening stage, and the inheritance application and interdisciplinary integration stage. In the future, the focus will be on interdisciplinary research on wooden architecture from ethnic minority cultures and traditional dwellings. Full article

Urban expansion has a profound impact on both society and the environment. In this study, VOSviewer 1.6.16 and CiteSpace 6.3.R1 were used to conduct a bibliometric analysis of 2987 articles published during the period of 1992–2022 from the Web of Science database in order to identify the research hotspots and trends of urban expansion and its driving factors. The number of articles significantly increased during the period of 1992–2022. The spatiotemporal characteristics and driving forces of urban expansion, urban growth models and simulations, and the impacts of urban expansion were the main research topics. The rate of urban expansion showed regional differences. Socioeconomic factors, political and institutional factors, natural factors, path effects, and proximity effects were the main driving factors. Urban expansion promoted economic growth, occupied cultivated land, and affected ecological environments. Big data and deep learning techniques were recently applied due to advancements in information techniques. With the increasing awareness of environmental protection, the number of studies on environmental impacts and spatial planning regulations has increased. Some political and institutional factors, such as subsidies, taxation, spatial planning, new development strategies, regulation policies, and economic industries, had controversial or unknown impacts. Further research on these factors and their mechanisms is needed. A limitation of this study is that articles which were not indexed, were not included in bibliometric analysis. Further studies can review these articles and conduct comparative research to capture the diversity. Full article

With continuous advancements in semiconductor technology, mastering efficient designs of high-quality and advanced chips has become an important part of science and technology education. Chip performances will determine the futures of various aspects of societies. However, novice students often encounter difficulties in learning digital chip designs using Verilog programming, a common hardware design language. An efficient self-study system for supporting them that can offer various exercise problems, such that any answer is marked automatically, is in strong demand. In this paper, we design and implement a web-based Verilog programming learning assistant system (VPLAS), based on our previous works on software programming. Using a heuristic and guided learning method, VPLAS leads students to learn the basic circuit syntax step by step, until they acquire high-quality digital integrated circuit design abilities through self-study. For evaluation, we assign the proposal to 50 undergraduate students at the National Taipei University of Technology, Taiwan, who are taking the introductory chip-design course, and confirm that their learning outcomes using VPLAS together are far better than those obtained when following a traditional method. In our final statistics, students achieved an average initial accuracy rate of over 70% on their first attempts at answering questions after learning through our website’s tutorials. With the help of the system’s instant automated grading and rapid feedback, their average accuracy rate eventually exceeded 99%. This clearly demonstrates that our system effectively enables students to independently master Verilog circuit knowledge through self-directed learning. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

As digital transformation progresses, schools are increasingly confronted with psychosocial challenges such as technostress, digital overload, and unequal participation in digital (learning) environments. This article investigates the conceptual relevance of digital well-being for school development, particularly in relation to social inequality. Despite growing attention, the term remains theoretically underdefined in educational research—a gap addressed through a theory-driven review. Drawing on a systematic search, 25 key studies were analyzed for their conceptual understanding and refinement of digital well-being, with a focus on educational relevance. Findings suggest that digital well-being constitutes a multidimensional state shaped by individual, media-related, and socio-structural factors. It emerges when individuals are able to successfully manage the demands of digital environments and is closely linked to digital inequality—particularly in terms of access, usage practices, and the resulting opportunities for participation and health promotion. Since the institutional role of schools has thus far received limited attention, this article shifts the focus toward schools as key arenas for negotiating digital norms and practices and calls for an equity-sensitive and health-conscious perspective on school development in the context of digitalization. In doing so, digital well-being is repositioned as a pedagogical cross-cutting issue that requires coordinated efforts across all levels of the education system, highlighting that equitable digital transformation in schools depends on a critical reflection of power asymmetries within society and educational institutions. The article concludes by advocating for the systematic integration of digital well-being into school development processes as a way to support inclusive digital participation and to foster a health-oriented digital school culture. Full article

There are many lessons to be learned from the findings of research that would be advantageous in realising the potential of Catholic schools to contribute to the common good of a secular society. In summary, it is possible to highlight the following inferences, which were drawn from research undertaken by the writer: (1) From the perspective of Catholic school leaders, Catholic education faces considerable challenges and, sometimes, antagonism, within a secular society, which can impose significant pressures on the conduct of their work. (2) There is a need to disseminate information to individuals and groups within and outside Catholic schools of the Christian values that are promoted by the Church. (3) Catholic schools make a significant contribution towards the common good of society, which is not always generally appreciated and should be more widely publicised. (4) Catholic communities and their leaders would benefit from ongoing opportunities to engage in continuing professional development and formation that would nourish and nurture the enhancement of spiritual capital in their schools. Full article

Social media has become a platform for the rapid spread of persuasion techniques that can negatively affect individuals and society. Propaganda detection, a crucial task in natural language processing, aims to identify manipulative content in texts, particularly in news media, by assessing propagandistic intent. Although extensively studied in English, Arabic propaganda detection remains challenging because of the language’s morphological complexity and limited resources. Furthermore, most research has treated propaganda detection as an isolated task, neglecting the influence of sentiments and emotions. The current study addresses this gap by introducing the first multi-task learning (MTL) models for Arabic propaganda detection, integrating sentiment analysis and emotion detection as auxiliary tasks. Three MTL models are introduced: (1) MTL combining all tasks, (2) PSMTL (propaganda and sentiment), and (3) PEMTL (propaganda and emotion) based on transformer architectures. Additionally, seven task-weighting schemes are proposed and evaluated. Experiments demonstrated the superiority of our framework over state-of-the-art methods, achieving a Macro-F1 score of 0.778 and 79% accuracy. The results highlight the importance of integrating sentiment and emotion for enhanced propaganda detection; demonstrate that MTL improves model performance; and provide valuable insights into the interaction among sentiment, emotion, and propaganda. Full article

Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to develop machine learning models using anthropometric measurements to predict low ALMI for the diagnosis of sarcopenia. Methods: A cross-sectional study was conducted on 183 Mexican adults (67.2% women and 32.8% men, ≥60 years old). ALMI was measured using DXA, and anthropometric data were collected following the International Society for the Advancement of Kinanthropometry (ISAK) protocols. Predictive models were developed using Logistic Regression (LR), Decision Trees (DTs), Random Forests (RFs), Artificial Neural Networks (ANNs), and LASSO regression. The dataset was split into training (70%) and testing (30%) sets. Model performance was evaluated using classification performance metrics and the area under the ROC curve (AUC). Results: ALMI indicated strong correlations with BMI, corrected calf girth, and arm relaxed girth. Among models, DT achieved the best performance in females (AUC = 0.84), and ANN indicated the highest AUC in males (0.92). Regarding the prediction of low ALMI, specificity values were highest in DT for females (100%), while RF performed best in males (92%). The key predictive variables varied depending on sex, with BMI and calf girth being the most relevant for females and arm girth for males. Conclusions: Anthropometry combined with machine learning provides an accurate, low-cost approach for identifying low ALMI in older adults. This method could facilitate sarcopenia screening in clinical settings with limited access to advanced diagnostic tools. Full article

This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges. Full article

This conceptual paper explores how artificial intelligence—particularly machine learning-based algorithmic systems—is reshaping cultural transmission and symbolic power in the digital age. It argues that algorithms operate as cultural agents, acquiring a form of operative agency that enables them to intervene in the production, circulation, and legitimation of meaning. Drawing on critical pedagogy, sociotechnical theory, and epistemological perspectives, the paper introduces an original framework: Critical Algorithmic Mediation (CAM). CAM conceptualizes algorithmic agency through three interrelated dimensions—structural, operational, and symbolic—providing a lens to analyze how algorithmic systems structure knowledge hierarchies and cultural experience. The article examines the historical role of media in cultural transmission, the epistemic effects of algorithmic infrastructures, and the emergence of algorithmic hegemony as a regime of symbolic power. In response, it advocates for a model of critical digital literacy that promotes algorithmic awareness, epistemic justice, and democratic engagement. By reframing education as a space for symbolic resistance and cultural reappropriation, this work contributes to rethinking digital literacy in societies increasingly governed by algorithmic infrastructures. Full article

School development is important in society. This study investigates how questions work as an information carrier between different levels in a school organization. The questions are organized in a hierarchy, with the regional steering committee’s overarching question at the top and then distributed further on to the municipalities to interpret the questions that engage with their practice management. At the bottom of the hierarchy are the schools, and they create the final professional research questions that engage with day-to-day practice. Previous studies show that supporting and challenging each other can lead to the development of new knowledge in the organization. This is an empirical study based on documents with questions from the three levels (regional, municipal, and school). The questions were collected and the content in the questions was analyzed. The results show that it is possible to develop an organization by asking development-related questions, from top to bottom, in a hierarchical organization. However, problems arise when the developing questions require interpretation, and the interpretation leads to the shifting of the original goal. In this study, the aim at regional-level students and knowledge/learning was shifted to a focus on teachers and teaching especially at school level. Full article

Inclusive music education refers to the use of musical experiences and practices as tools for promoting participation, equity, and meaningful engagement among all learners, including those with Special Educational Needs (SEN). Music education has long been recognized not only for its value in emotional expression and cultural transmission but also for its cognitive and relational benefits. This entry examines the inclusive and transformative potential of music, highlighting how it can foster equitable, accessible, and culturally relevant learning environments. Drawing from pedagogy, neuroscience, and educational technology, the entry explores how music contributes to cognitive, emotional, and social development, with a focus on learners with SEN. It emphasizes the importance of early exposure to music, the strong connections between music and language acquisition, and the need to challenge persistent misconceptions about innate musical talent. The findings demonstrate that when supported by digital tools and educational games, music education becomes a powerful driver of inclusion, enhancing participation, relational dynamics, and cognitive engagement. The entry concludes by advocating for a reimagining of music not as a secondary subject, but as a foundational component of holistic and inclusive education, capable of building more empathetic, connected, and equitable societies. Full article

(1) Background: As digitalisation transforms society, digital competences are increasingly essential. Yet students’ digital competences often vary significantly, largely influenced by socio-economic background. Some schools—termed “resilient schools”—effectively counter these disadvantages and foster high digital competence. This study investigates the prevalence of such schools and examines how they differ from others. (2) Methods: Drawing on representative ICILS 2023 data, quantitative secondary analyses—including descriptive statistics, t-tests, and multiple regression—were conducted. Following the identification of resilient schools, in-depth analyses focus on those countries with substantial proportions of resilient schools above 10 percent (Austria, Italy, and Portugal), as these countries seem to have effective strategies to foster school resilience. (3) Results: The findings highlight considerable cross-national variation, indicating that school resilience is context-dependent. Resilient schools consistently emphasise student learning-related factors—such as ICT-related attitudes and educational aspirations—while the influence of home environments is less pronounced than in other schools. (4) Conclusions: Strengthening student learning-related factors appears to be crucial for building digital resilience. These insights can inform policy and practice aimed at fostering educational equity and closing the digital divide, particularly in socio-economically disadvantaged contexts. Full article

Water resources are strategic resources that support regional economic social development and maintain the health and stability of ecosystems. This study revealed the evolution of the coordinated development of China’s water resources–economic society–ecological environment system based on the coordination degree mode. The research was conducted by integrating machine learning with traditional mathematical methods; by setting up the status quo development scenario, water resources priority scenario, economic society priority scenario, ecological environment priority scenario and balanced development scenario; and by using the Holt exponential smoothing–feedforward neural network prediction model, the coordinated development trends under different scenarios were predicted. The results showed that, analyzed from the perspective of the coordinated evolution type of the dual systems, the dominant development system during the study period gradually transformed from water resources–economic society to water resources–ecological environment. For the coordinated development of the complex system, the coordination degree showed “stepped leap—resilient fluctuation (from 0.7242 to 0.8238)”, and “better in the southeast than in the northwest, with significant advantages in the coast”. The most significant increase in the coordination degrees were observed in the balanced development scenario and economic society priority scenarios, where it increased by an average of around 5%, confirming the effective contribution of stable economic and social development to the level of coordination. This study provides theoretical support and practical guidance for regional water resources management. Full article