Search Results (304)

With the rapid urban aging trend in China, evaluating the age-friendliness of street environments is critical for inclusive urban planning. This study proposes the Age-Friendly Environment Assessment Tool (AFEAT) to assess street-level age-friendliness in high-density urban contexts, grounded in the World Health Organization’s (WHO) Global Age-Friendly Cities: A Guide and adapted to the spatial characteristics of Nanjing’s Qinhuai District. By integrating multi-source data such as street-view image segmentation, Point of Interest (POI)-based network accessibility, kernel density estimation, Analytic Hierarchy Process (AHP)-derived indicator weights, and Random Forest regression, the study develops a comprehensive and spatialized evaluation framework. The results reveal significant spatial disparities in age-friendliness across street segments, with Safe Mobility, Healthcare Services, and Walkable Environment identified as the most influential factors for older adults. High-performing areas are concentrated in the central urban core, while peripheral zones face challenges such as poor walkability, insufficient lighting, and a lack of facilities. The study recommends strengthening a walkability-based age-friendly safety and healthcare support system and optimizing the spatial distribution of recreational and medical facilities to address mismatches between supply and demand. These findings provide practical guidance for targeted, evidence-based interventions aimed at fostering equitable and resilient urban environments for aging populations. Full article

Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks. Yet little is known about the biases that shape the destination suggestions these systems provide. This study conducts a controlled, persona-based audit of the two models, generating 6480 recommendations for 216 traveller profiles that vary by origin country, age, gender identity and trip theme. Six observable bias families (popularity, geographic, cultural, stereotype, demographic and reinforcement) are quantified using tourism rankings, Hofstede scores, a 150-term cliché lexicon and information-theoretic distance measures. Findings reveal measurable bias in every bias category. DeepSeek is more likely than ChatGPT to suggest off-list cities and recommends domestic travel more often, while both models still favour mainstream destinations. DeepSeek also points users toward culturally more distant destinations on all six Hofstede dimensions and employs a denser, superlative-heavy cliché register; ChatGPT shows wider lexical variety but remains strongly promotional. Demographic analysis uncovers moderate gender gaps and extreme divergence for non-binary personas, tempered by a “protective” tendency to guide non-binary travellers toward countries with higher LGBTQI acceptance. Reinforcement bias is minimal, with over 90 percent of follow-up suggestions being novel in both systems. These results confirm that unconstrained LLMs are not neutral filters but active amplifiers of structural imbalances. The paper proposes a public-interest re-ranking layer, hosted by a body such as UN Tourism, that balances exposure fairness, seasonality smoothing, low-carbon routing, cultural congruence, safety safeguards and stereotype penalties, transforming conversational AI from an opaque gatekeeper into a sustainability-oriented travel recommendation tool. Full article

Next-point-of-interest (POI) recommendation aims to model user preferences based on historical information to predict future mobility behavior, which has significant application value in fields such as urban planning, traffic management, and optimizing business decisions. However, existing methods often overlook the differences in location, time, and category information features, fail to fully utilize information from various modalities, and lack effective solutions for addressing users’ incidental behavior. Additionally, existing methods are somewhat lacking in capturing users’ personalized preferences. To address these issues, we propose a new method called Multi-Branch Spectral Network with Contrastive Learning (MBSCL-Net) for next-POI recommendation. We use a multihead attention mechanism to separately capture the distinct features of location, time, and category information, and then fuse the captured features to effectively integrate cross-modal features, avoid feature confusion, and achieve effective modeling of multi-modal information. We propose converting the time-domain information of user check-ins into frequency-domain information through Fourier transformation, directly enhancing the low-frequency signals of users’ periodic behavior and suppressing occasional high-frequency noise, thereby greatly alleviating noise interference caused by the introduction of too much information. Additionally, we introduced contrastive learning loss to distinguish user behavior patterns and better model personalized preferences. Extensive experiments on two real-world datasets demonstrate that MBSCL-Net outperforms state-of-the-art (SOTA) methods. Full article

Background: Artificial intelligence (AI) can improve efficiency, documentation, and diagnostic quality in ophthalmology. This study examined clinical AI adoption, institutional readiness, perceived utility, trust, ethical concerns, and educational needs among Swiss ophthalmologists and residents. Methods: In May 2025, an anonymous online survey was distributed to board-certified ophthalmologists and residents across Switzerland. The structured questionnaire addressed clinical AI use, institutional infrastructure, perceptions of diagnostic utility, trust, ethical–legal concerns, and educational preparedness. Responses were recorded on five-point Likert scales. Results: Of 106 respondents (mean age 42.4 ± 11.4 years, 48.1% female), 20.8% reported current clinical AI use. Willingness to use AI exceeded 65% across all 10 diagnostic scenarios, but active use remained ≤12.1%. Institutional readiness was low: 6.6% reported AI-related guidelines, 26.4% had access to an institutional AI contact person, and 28.3% received supervisor support (more often among residents). While 80% agreed that AI can support diagnostics, only 12.1% trusted AI recommendations as much as those from colleagues; 87.9% critically reviewed the results, and 93.9% endorsed the use of AI in an assistive but not independently decision-making role. Ethical–legal concerns included unresolved liability (74.8%), informed consent (66.7%), and data protection adequacy (49.5%). Structured AI education was supported by 77.8%, yet only 15.1% felt prepared, and two-thirds (66.7%) indicated they would use AI more with better training. Conclusions: Ophthalmologists and residents in Switzerland express strong interest in the clinical use of AI and recognize its diagnostic potential. Major barriers include insufficient institutional structures, lack of regulatory clarity, and inadequate educational preparation. Addressing these deficits will be essential for responsible AI integration into ophthalmologic practice. Full article

Urban livability and sustainable development remain major global challenges, yet the interplay between urban planning layouts and actual human activities has not been sufficiently examined. This study investigates this relationship in Beijing by integrating multi-source spatiotemporal data, including point of interest (POI), Land Use Cover Change (LUCC), remote sensing data, and the railway network. Defining urban functional units as “street + railway network”, we analyze the spatial–temporal evolution within the 6th Ring Road over the past four decades and propose targeted strategies for the urban functional layout. The results reveal the following: (1) The evolution of Beijing’s urban functions can be divided into four stages (1980–1990, 1990–2005, 2005–2015, and 2015–2020), with continuous population growth (+142%) driving the over-concentration of functions in central districts. (2) Between 2010 and 2020, the POI densities of medical services (+133.6%) and transport services (+130.48%) increased most rapidly, subsequently stimulating the expansion of other urban functions. (3) High-density functional facilities and construction land (+179.10%) have expanded significantly within the 6th Ring Road, while green space (cropland, forestland and grassland) has decreased by 86.97%, resulting in a severe imbalance among land use types. To address these issues, we recommend the following: redistributing high-intensity functions to sub-centers such as Tongzhou and Xiongan New Area to alleviate population pressure, expanding high-capacity rail transit to reinforce 30–50 km commuting links between the core and periphery, and establishing ecological corridors to connect green wedges, thereby enhancing carbon sequestration and environmental quality. This integrated framework offers transferable insights for other megacities, providing guidance for sustainable functional planning that aligns human activity patterns with urban spatial structures. Full article

Background/Objectives: Musculoskeletal disorders causing chronic pain are increasingly prevalent due to factors such as injury, overuse, and aging, leading to interest in porcine collagen injections as a potential therapeutic and conservative option. Despite promising results, evidence-based information on this treatment is scarce. To address this gap, the authors conducted an eDelphi consensus among expert Italian physicians in musculoskeletal pain to gather their perspectives on collagen injections. Methods: A Steering Committee and a Panel of 23 physicians developed the statements list (36) including the modalities, safety, and efficacy of intra- and extra-articular collagen injections. Panelists rated their agreement with each statement on a 5-point Likert scale (5 means “Strong Agreement”). Consensus was defined as when at least 75% of the panelists voted with a score of ≥4/5 after two rounds of votes. The weighted average (WA) was calculated for each statement. As control, we elaborated a Hypothetical Parametric Distribution (HPD WA equal to 3.00), where the percent of panelists is equally distributed along each Likert Scale Value (LSV). The maximum WA for 75% of the consensus is established at 3.75. Indeed, the combination of 75% having WA > 3.75 was defined as “Strong Agreement”. While, if the consensus was under 75%, the WA vs. HPD comparison was performed using the Wilcoxon Test. Significant differences among the distribution of LSVs judged the statement as “Low Level of Agreement”. Disagreement was evaluated when the WA was under the PHD. Results: The consensus was reached “Strong Agreement” after twin rounds in 29 out of 36 (8.55%). In 5 out of 36 statements (13.89%), the panelists reached the “Low Level of Agreement” by statistical tests. In the remaining two statements, there was a “Consensus of Disagreement”. All panelists unanimously agreed on crucial points, such as contraindications, non-contraindication based solely on comorbidity, and the importance of monitoring collagen’s effectiveness. Unanimous agreement was reached on recommending ultrasound guidance and associating collagen injections with therapeutic exercise and physical modalities. Substantial consensus (concordance > 90%) supported collagen injections for osteoarthritis, chondropathy, and degenerative tendinopathies, emphasizing intra- and peri-articular treatment, even simultaneously. However, areas with limited evidence, such as the combination of collagen with other injectable drugs, treatment of myofascial syndrome, and injection frequency, showed disagreement. The potential of intra-tendinous porcine collagen injections for tendon regeneration yielded mixed results. Conclusions: Clinicians experts in musculoskeletal pain agree on using collagen injections to treat pain originating from joints (e.g., osteoarthritis) and periarticular (e.g., tendinopathies). Full article

Robust statistical analysis is essential for advancing evidence-based nutrition research, particularly when investigating the complex relationships between dietary exposure and health outcomes. Multiple regression is a widely used analytical technique in nutrition studies due to its ability to control for confounding variables and assess multiple predictors simultaneously. However, the reliability, validity, and generalizability of findings from regression analyses depend heavily on having an appropriate sample size. Despite its importance, many published nutrition studies do not include formal sample size justifications or power calculations, leading to a high risk of Type II errors and reduced interpretability of results. This methodological review examines three commonly used approaches to sample size determination in multiple regression analysis: the rule of thumb, variance explained (R²) method, and beta weights approach. Using a consistent hypothetical example, rather than empirical data, this paper illustrates how sample size recommendations can differ depending on the selected approach, highlighting the advantages, assumptions, and limitations of each. This review is intended as an educational resource to support methodological planning for applied researchers rather than to provide new empirical findings. The aim is to equip nutrition researchers with practical tools to optimize sample size decisions based on their study design, research objectives, and desired power. The rule of thumb offers a simple and conservative starting point, while the R² method ties sample size to anticipated model performance. The beta weights approach allows for more granular planning based on the smallest effect of interest, offering the highest precision but requiring more detailed assumptions. By encouraging more rigorous and transparent sample size planning, this paper contributes to improving the reproducibility and interpretability of quantitative nutrition research. Full article

Recommendation systems applied to tourism are widely recognized for improving the visitor’s experience in tourist destinations, thanks to their ability to personalize the trip. This paper presents a hybrid approach that combines Machine Learning techniques with the Ordered Weighted Averaging (OWA) aggregation operator to achieve greater accuracy in user segmentation and generate personalized recommendations. The data were collected through a questionnaire applied to tourists in the different points of interest of the Special, Tourist and Cultural District of Riohacha. In the first stage, the K-means algorithm defines the segmentation of tourists based on their socio-demographic data and travel preferences. The second stage uses the OWA operator with a disjunctive policy to assign the most relevant cluster given the input data. This hybrid approach provides a recommendation mechanism for tourist destinations and their cultural heritage. Full article

Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network (GNN) has become a common approach for this task due to the capabilities of modeling relations between nodes in a global perspective. However, most existing studies overlook the more prevalent heterogeneous relations in real-world scenarios, and manually constructed graphs may suffer from inaccuracies. To address these limitations, we propose a model called Heterogeneous Graph Structure Learning for Next POI Recommendation (HGSL-POI), which integrates three key components: heterogeneous graph contrastive learning, graph structure learning, and sequence modeling. The model first employs meta-path-based subgraphs and the user–POI interaction graph to obtain initial representations of users and POIs. Based on these representations, it reconstructs the subgraphs through graph structure learning. Finally, based on the embeddings from the reconstructed graphs, sequence modeling incorporating graph neural networks captures users’ sequential preferences to make recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model. Additional studies confirm its robustness and superior performance across diverse recommendation tasks. Full article

As sustainability becomes a strategic priority across global financial services, its implementation in emerging insurance markets remains insufficiently understood. This study explores the integration of environmental, social, and governance (ESG) principles within Saudi Arabia’s insurance sector, combining content analysis of corporate disclosures with qualitative insights from industry stakeholders. The research investigates how insurers embed ESG principles into their operations, the development of sustainable insurance products, and their perceived financial and regulatory implications. The findings reveal gradual progress in ESG integration, primarily driven by governance reforms aligned with national development agendas, while social and environmental dimensions remain comparatively underdeveloped. Stakeholders identify regulatory ambiguity, data limitations, and technical capacity as persistent barriers, but also point to increasing investor and consumer interest in sustainability-aligned offerings. This study offers policy and managerial recommendations to advance ESG principle adoption, emphasizing standardized disclosures, capacity-building, and product innovation. It contributes to the limited empirical literature on ESG principles in Middle Eastern insurance markets and highlights the sector’s potential role in promoting inclusive and sustainable finance. Full article

Point-of-interest (POI) recommendation is a crucial task in location-based social networks, especially for enhancing personalized travel experiences in smart tourism. Recently, large language models (LLMs) have demonstrated significant potential in this domain. Unlike classical deep learning-based methods, which focus on capturing various user preferences, LLM-based approaches can further analyze candidate POIs using common sense and provide corresponding reasons. However, existing methods often fail to fully capture user preferences due to limited contextual inputs and insufficient incorporation of cooperative signals. Additionally, most methods inadequately address target temporal information, which is essential for planning travel itineraries. To address these limitations, we propose PSLM4ST, a novel framework that enables synergistic interaction between LLMs and a lightweight temporal knowledge graph reasoning model. This plugin model enhances the input to LLMs by making adjustments and additions, guiding them to focus on reasoning processes related to fine-grained preferences and temporal information. Extensive experiments on three real-world datasets demonstrate the efficacy of PSLM4ST. Full article

Physical activity allows people to obtain multidimensional benefits. Regular practice and following the recommendations can provide the benefits mentioned above. However, it has been evidenced that the variability in the environmental temperature is a determining factor to adhere to the regular practice of physical activity. From this point of view, it has become evident that researchers have joined criteria to explore the effects of climate change or global warming on physical activity or physical education. This study is the first bibliometric analysis of the scientific literature related to physical activity, physical education, global warming, and climate change. The objective of the present bibliometric review was to examine annual publication trends, identifying the categories, journals, and countries with the highest number of publications on this topic. In addition, the secondary objective was to identify the most productive and prominent authors, highlight the most cited articles, and determine the keywords most used by the authors. We analyzed 261 papers published in journals indexed in the Web of Science, examining the trend followed by annual publications, identifying prolific and prominent co-authors, leading countries and journals, most cited papers, and most used author keywords. The annual publications followed an exponential growth trend (R² = 90%), which means that there is great interest in the scientific community for this object of study. The Journal of Physical Activity & Health was the journal with the most published papers. M.S. Tremblay and E.Y. Lee were the most prominent co-authors, and as reference authors on the subject, M. Nieuwenhuijsen and H. Khreis were the most prominent authors. The three countries with the highest productivity are the USA, the UK, and Canada. Although a total of 29 keywords were identified, only 25 of them were commonly recurrent, with the most used being climate change and physical activity. Full article

In the face of evolving digital technologies, all industries have undergone radical changes. Similarly, the construction industry needs to apply digital technology to improve the existing Supply Chain (SC), which has problems such as the inefficient collaboration among various links, the poor ability to cope with risks, the higher costs, the waste of resources and pollution, etc., and to adapt to the development of the digital era. Prefabricated Construction (PC), with their advantages of having a high efficiency and being energy-saving, can help improve the above problems and promote the sustainable development of the construction industry. Therefore, this review uses a combination of scientific bibliometrics and a qualitative analysis to search a total of 129 works of literature on the application of digital technologies in Prefabricated Construction Supply Chain Management (PCSCM) for the period of 2015–2024 included in the Web of Science, Scopus, and PubMed databases. After visualization and analysis in Citespace v6.3.1.0 and VOSviewer v1.6.20.0, it was found that most of the literature focuses on the economic benefits of cost reduction and efficiency, while there are fewer studies on the topic of sustainable development. Therefore, this study summarizes the current status of the application of digital technologies in PCSCM, addressing the lack of attention to environmental benefits in the existing studies and the limitations of the current research. Creatively, it proposes recommendations that will help PCSCM achieve sustainable development in the future, and points out that the construction industry must break through the limitation of focusing only on its own economic interests to realize the vision of a harmonious coexistence between human beings and nature. Full article

The strength and conditioning literature examining neuromuscular physiology, bioenergetics, neuroendocrine factors, nutrition and metabolic factors, and the use of ergogenic aids, as well as physical and physiological responses and adaptations, have clearly identified the benefits of participating in regular resistance training programs for athletic populations, especially as it relates to improving muscular strength. Beyond evidence-based research, models for resistance training program implementation are of considerable value for optimizing athletic performance. In fact, several have been provided that address general to specific characteristics of athleticism (i.e., strength endurance, muscular strength, and muscular power) through resistance training over the decades. For instance, a published model known as the strength–endurance continuum that enhances dynamic correspondence (i.e., training specificity) in athletic populations by developing structural, metabolic, and neural capacities across a high-load, low-repetition and low-load, high-repetition range. Further models have been developed to enhance performance approaches (i.e., optimum performance training model) and outcomes (i.e., performance pyramid), even within specific populations, such as youth (i.e., youth physical development model). The ten, five, three, or 10-5-3 (TFT) model for strength and conditioning professionals synthesizes currently available information and provides a framework for the effective implementation of resistance training approaches to suit the needs of athletes at each stage of development. The model includes three key components to consider when designing strength and conditioning programs, denoted by the acronym TFT (ten, five, three). Over recent years, the model has gained much support from teams, coaches, and athletes, mainly due to the ability to streamline common knowledge within the field into an efficient and effective resistance training system. Furthermore, this model is distinctly unique from others as it prioritizes the development of strength–endurance, muscular strength, and muscular power concurrently. This paper explains the model itself and begins to provide recommendations for those interested in implementing TFT-based approaches, including a summary of points as a brief take-home guide to implementing TFT interventions. It is the author’s hope that this paper encourages other performance professionals to share their models to appreciate human ingenuity and advance our understanding of individualized approaches and systems towards the physical development of the modern-day athlete. Full article

In recent years, the Flipped Classroom methodology has garnered significant interest among educators due to its potential to provide students with the flexibility to learn wherever and whenever they want. It is believed that this change in teaching may enable students to self-manage, becoming more independent and autonomous. To investigate whether the use of Flipped Classroom can help students become more autonomous, a systematic review was conducted using the PRISMA method, searching through major national and international databases. A total of 38 studies (n = 2420 students) were collected and classified based on the validity of the research design employed. The analyses revealed that although Flipped Classroom can contribute to the development of students’ learning autonomy, there are certain doubts regarding this assertion, as results from studies with higher validity point to mixed outcomes. In order to obtain a more accurate understanding of reality, it is highly recommended that future studies examining the impact of the Flipped Classroom methodology on students’ learning autonomy address the limitations found in the literature, such as the scarcity of longitudinal designs with randomized control groups, the lack of studies conducted in non-university stages, and the small quantity of participants used in interventions. Full article