Search Results (6,279)

Objectives: SailGP is an international professional mixed-sex sailing competition, which uses F50 foiling catamarans capable of reaching speeds up to ~100 km/h. This seminal study assesses injuries and illnesses observed by male and female sailors during trainings and competitions in SailGP’s third season. This study aims to assess injury and illness incidence, comparing results with other professional sailing events and high-performance sports. In addition, injury and illness risk factors (sex and position) will be explored with the goal to reduce morbidity for future seasons. Materials and Methods: This retrospective cohort design analysed medical records of male and female sailors during SailGP’s third season (April 2022 to May 2023). Risk factors assessed included sailor sex, sailor position (helm, strategist, grinder, flight controller and wing trimmer), sailing venue, wind speed and mechanism of injury/nature of illness. International Olympic Committee reporting guidelines on injuries and illnesses were followed, including the STROBE-SIIS checklist. Confidence intervals were set at 95%, statistical tests were two-sided and p-values < 0.05 were considered statistically significant. Results: A total of 40 on-water injuries were reported in 32 athletes. Injury incidence was greater during competitions than trainings, with strategists and then grinders being the most frequently injured positions. Competition injury incidence was 32.6 per 1000 h and 6.42 injuries per 365 days. Training injury incidence was 2.62 injuries per 1000 h and 3.82 injuries per 365 days. Knee, ankle, hand and head injuries were most prevalent, with three concussions observed during trainings and competitions (two female and one male). Direct impacts and falls during manoeuvres caused most injuries. Overall injury incidence (IRR = 2.69 [95% CI 1.41–5.16]), risk of training injuries (RR = 3.75 [95% CI 1.59–8.83], p = 0.001), risk of competition injuries (RR = 1.79 [95% CI 0.65–4.90], p = 0.25) and overall concussion risk (RR = 10.04 [95% CI 0.91–110.46], p = 0.02) were greater in females. Ten sailors accounted for 17 illnesses. Females had a 3.33 increase in training and competition illnesses (IRR = 3.33 [95% CI 0.94–11.81]). Conclusions: Competition injury incidence was higher than previous reported sailing studies. Knee injuries were most prevalent and direct impacts caused most injuries. Female sailors reported a higher injury and illness incidence. These results may guide injury prevention efforts and the development of an IOC-equivalent consensus statement. Future studies should examine time loss. Full article

Cultural organizations have traditionally been viewed as resistant to change, often bound by legacy structures, public dependency, and non-commercial missions. However, recent advances in digital technologies—ranging from AI and VR to IoT and big data—are reshaping the operational and strategic landscape of these institutions. Despite this shift, academic literature has yet to comprehensively map how technological innovation transforms cultural organizations into practice. This paper addresses this gap by introducing the concept of the Cultural Organizational System (COS)—a holistic framework that captures the multi-component structure of cultural entities, including space, tools, performance, management, and networks. Using a PRISMA-based scoping review methodology, we analyze over 90 sources to identify the types, functions, and strategic roles of technological innovations across COS components. The findings reveal a taxonomy of innovation use cases, a mapping to Oslo innovation categories, and a quadrant model of enablers and barriers unique to the cultural sector. By offering an integrated view of digital transformation in cultural settings, this study advances innovation theory and provides practical guidance for cultural leaders and policymakers seeking to balance mission-driven goals with sustainability and modernization imperatives. Full article

Landscapes are vital systems where ecological, cultural, perceptual, and socio-economic values meet, making their quality assessment essential for sustainable development. Landscape Quality (LQ), shaped by the interaction of natural processes and human activities, remains methodologically challenging due to its interdisciplinarity and the need to integrate multiple dimensions. This challenge is particularly perceived in peri-urban areas, predominantly understudied in landscape research. This article addresses this gap in LQ assessment at peri-urban landscapes, through the case of Houch Al Oumaraa, Zahleh, a peri-urban area of patrimonial significance and agricultural landscape value. To evaluate the four spatial dimensions of LQ (structural, ecological, cultural and visual), we adopted a mixed methodology, where a pre-developed set of landscape indicators (LIs) applied within GIS and spatial technics, were supplemented by expert analysis through visual studies. Two questions framed this research: (i) is remote sensing sufficient to assess peri-urban LQ, and (ii) what are the limits of applying pre-developed LIs to diverse landscape contexts? Results show moderate fragmentation (CONTAG 61.6%), low diversity (MSDI 0.27), high density of cultural monuments (PROTAP 4.19) and average visual disharmony (FCDHI 0.49). Findings reveal that spatial dimensions alone are insufficient for assessing LQ of peri-urban landscapes, where socio-economic dimensions must also be integrated. Structural indicators (PLAND, MPA, ED, CONTAG) and MSDI proved transferable, while ECOLBAR was less applicable, cultural indicators (PROTAP, HLE) were limited to tangible heritage, and visual indicators (FCDHI, SDHI) highly context dependent. Establishing a differentiated yet standardized framework would not only enhance methodological precision but also ensure that LQ assessment remain relevant across diverse contexts, providing policymakers with actionable insights to align planning with sustainability goals. Full article

Educational reforms have increasingly shifted from teacher and content-centric approaches to prioritising the students’ needs, sometimes even extending this beyond academic success. Academic success is easily measured through standardised assessments; however, the ‘life skills’ that students require for more general success—such as self-regulation, goal-setting, and distraction control—are harder to quantify and therefore harder to implement and measure. Reflecting these challenges, this paper critiques some of the more popular frameworks which schools adopt in their aim to nurture successful students for the future. Drawing comparisons with high-performance domains such as sports, where psychological characteristics and potential are actively considered and nurtured in talent development, we highlight the limitations of current offerings in education. Through discussion, we advocate for a more research-grounded approach to learning, equipping schools with both systems and strategies for their students’ long-term life-related success. Full article

Corporate transparency is vital for sustainable governance. However, detecting financial misstatements remains challenging due to their rarity and resulting class imbalance. Using financial statement data from Korean firms, this study develops an integrated AI framework that evaluates the joint effects of sampling strategy, model choice, and interpretability. Across multiple imbalance ratios, NearMiss undersampling consistently outperforms random undersampling—particularly in recall and F1-score—showing that careful data balancing can yield greater improvements than algorithmic complexity alone. To ensure interpretability rests on reliable predictions, we apply Shapley Additive Explanations (SHAP) and Permutation Feature Importance (PFI) only to high-performing models. Logistic regression emphasizes globally influential operating and financing accounts, whereas Random Forest identifies context-dependent patterns such as ownership structure and discretionary spending. Even with a reduced feature set identified by explainable AI, models maintain robust detection performance under low imbalance, highlighting the practical value of interpretability in building simpler and more transparent systems. By combining predictive accuracy with transparency, this study contributes to trustworthy misstatement detection tools that reinforce investor confidence, strengthen responsible corporate governance, and reduce information asymmetry. In doing so, it advances the United Nations Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions) by supporting fair, accountable, and sustainable economic systems. Full article

Artificial light at night (ALAN) is now considered as a driver of evolution, possibly harmful to biodiversity, which constitutes a threat to the terrestrial and marine environment, and as such falls under Sustainable Development Goals (SDGs) 14 and 15. One way of mitigating its impact on the environment is to select an environment-friendly light spectrum, which is made more easily with current LED technologies. In this paper, we propose to adapt the spectrum of the lamps to that of the immediate environment. It makes it possible not to disturb the light environment of animals and plants at night and during the twilight period, at least from a spectral point of view, while ensuring the usual functions of lighting for humans. Apart from its own merit, the proposed concept may also contribute to SDG 13 by saving energy compared to current approaches based on long wavelengths light. The proposed idea may be implemented in various ways and deserves to be discussed in the lighting community and tested in real settings. Full article

We refine a bottom-up, quantity-clearing framework of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. This approach relies on first-principles economic supply-and-demand dynamics rather than assumptions about anticipated Bitcoin price appreciation, its price history, or its potential effectiveness in demonetizing other asset classes. We considered five key high-level factors that may affect price determination: level of market demand; intertemporal investment preferences; fiat-denominated withdrawal sensitivity; initial liquid supply; and daily withdrawal levels from liquid supply. With a goal of both increasing understanding of the impacts of price drivers and developing probabilistic forecasts, we show two models: (1) a baseline to assess the impacts of parameter changes, alone and in combination, on Bitcoin price trajectories and liquid supply over time and (2) a Monte Carlo simulation that incorporates uncertainty across a range of uncertain parameterizations and presents probabilistic price and liquid supply forecasts to 2036. Our baseline model highlighted the importance of liquid supply and withdrawal sensitivity in price impacts. The Monte Carlo simulation results suggest a 50% likelihood that Bitcoin price will exceed USD 5.17 M by April 2036. Generally, prices from the low single millions to the low tens of millions per Bitcoin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below BTC 2 M and withdrawal sensitivity is low. Our results help locate where right-tail risk and disorderly market outcomes concentrate and suggest that policy tools are available to help guide trajectories. Full article

The HIV-1 epidemic continues to challenge global public health, especially in sub-Saharan Africa. The rise in drug-resistant viruses, particularly pan-resistant strains, threatens treatment effectiveness, hindering progress toward UNAIDS viral suppression goals. This is critical in low-to-middle income countries (LMICs) like Zimbabwe, where treatment options and access to drug resistance testing are limited. This cross-sectional study analyzed 102 genotypes from patients with HIV-1 RNA ≥ 1000 copies/mL after at least 6 months on a dolutegravir (DTG)-based ART. HIV-1 genotyping and drug resistance interpretation were performed using the Stanford HIV Drug Resistance Database. Overall, 62% of genotypes harbored at least one drug resistance mutation, with 27% showing integrase strand transfer inhibitor (INSTI)-associated mutations. High-level resistance to DTG and cabotegravir was found in 14% and 23% of integrase sequences, respectively, primarily driven by G118R and E138K/T mutations. Pan-resistance was observed in 18% of complete genotypes, with one case of four class resistance. These results highlight the emergence of INSTI resistance in LMICs. The study underscores the urgent need for enhanced HIV drug resistance testing, continuous surveillance, and strategic optimization of ART regimens in resource-constrained settings to ensure effective HIV management. Full article

To overcome the challenges of conventional low-carbon retrofits for existing buildings—such as high construction volume, cost, and implementation difficulty—this study proposes a minimally invasive design and optimization method for Photovoltaic–Energy Storage–Direct Current–Flexible (PEDF) systems. The goal is to maximize energy savings and economic benefits while minimizing physical intervention. First, the minimally invasive retrofit challenge is decomposed into two coupled problems: (1) collaborative PV-ESS layout optimization and (2) flexible energy management optimization. A co-optimization framework is then developed to address them. For the layout problem, a model with multiple constraints is established to minimize retrofitting workload and maximize initial system performance. A co-evolutionary algorithm is employed to handle the synergistic effects of electrical pathways on equipment placement, efficiently obtaining an optimal solution set that satisfies the minimally invasive requirements. For the operation problem, an energy management model is developed to maximize operational economy and optimize grid interactivity. A deep reinforcement learning (DRL) agent is trained to adaptively make optimal charging/discharging decisions. Case simulations of a typical office building show that the proposed method performs robustly across various scenarios (e.g., office, commercial, and public buildings). It achieves an energy saving rate exceeding 20% and reduces operational costs by 10–15%. Moreover, it significantly improves building–grid interaction: peak demand is reduced by 33%, power fluctuations are cut by 75%, and voltage deviation remains below 5%. The DRL-based policy outperforms both rule-based strategies and the DDPG algorithm in smoothing grid power fluctuations and increasing the PV self-consumption rate. Full article

The transition toward sustainable production requires engineering and science education to adopt active, interdisciplinary, and practice-oriented teaching strategies. This article presents a comparative analysis of two educational initiatives implemented in Panama aimed at fostering sustainability competencies at the university and secondary school levels. The first initiative, developed at the Technological University of Panama, integrates project-based learning and circular economy principles into an extracurricular module focused on production planning, sustainable design, and quality management. Students created prototypes using recycled HDPE and additive manufacturing technologies within a simulated startup environment. The second initiative, carried out in two public secondary schools, applied project- and challenge-based learning through the Design Thinking framework, supporting teachers and students in addressing real-world sustainability challenges. Both programs emphasize hands-on learning, creativity, and iterative development, embedding environmental awareness and innovation in both formal and informal educational settings. The article identifies key opportunities and challenges in implementing active methodologies for sustainability education. Challenges such as limited infrastructure and rigid schedules were identified, along with lessons learned for future implementation. Students connected local issues to global goals like the SDGs and saw themselves as agents of change. These initiatives offer practical models for advancing sustainability education through innovation and interdisciplinary collaboration. Full article

Background/Objectives: Viniyoga is a person-centered approach to yoga that emphasizes individualized adaptation of breath, movement, and meditative practices to support health and well-being. This qualitative study investigates the therapeutic goals of one-to-one Viniyoga from the perspective of experienced therapists. Methods: Fourteen certified Viniyoga practitioners participated in in-depth interviews, which were analyzed using Interpretative Phenomenological Analysis (IPA). This study details how Viniyoga therapists define therapeutic Viniyoga. Results: The findings identify three core therapeutic goals that guide Viniyoga therapy: restoring balance, cultivating self-regulation, and guiding transformation. Two case studies are presented to illustrate the application of these goals in clinical contexts. Conclusions: Qualitative information provided by the interviewed Viniyoga therapists supports the positive role of individualized Viniyoga therapy in contributing to sustainable healing and supporting clients’ return to balance, self-regulation, and personal transformation. The Viniyoga therapeutic model is applicable across diverse populations and in a variety of integrative and complementary healthcare settings. Full article

Background/Objectives: Cerebral palsy (CP) is one of the most common causes of permanent motor disability in children, and its consequences for upper limb function have a significant impact on the patient’s independence and quality of life. Virtual reality is attracting increasing interest as a modern, engaging and effective method of physiotherapy for children with cerebral palsy. This systematic literature review aimed to synthesize current scientific data on the impact of virtual-reality-based physiotherapy on upper limb function in children with cerebral palsy. Methods: The review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Science Direct, Scopus, Web of Science, Research Gate and Google Scholar databases were searched for studies published between 2010 and 2025. Six original studies meeting the following criteria were included in the analysis: virtual reality therapy, population of children with cerebral palsy, physiotherapy goals related to the upper limb and availability of full text. Results: All included studies demonstrated a positive impact of virtual reality on at least one functional parameter of the upper limb, including range of motion, muscle strength, coordination and manual precision. Task-oriented training, immersive virtual reality environments and home-based therapy supported by remote monitoring proved to be the most effective. The effects were particularly noticeable in children with moderate impairment at GMFCS I–III. Conclusions: Virtual reality represents a safe and promising technology to support upper limb physiotherapy in children with cerebral palsy. It can be used both in clinical and home settings, contributing to increased exercise intensity and motivation. Further long-term studies using high-quality methodology are needed to determine the sustainability of the effects and their impact on everyday living. Full article

This study investigates hotel employees’ perceptions of AI-driven human resource (HR) management systems within the Accor Group’s properties across three major European cities: Paris, Berlin, and Amsterdam. These diverse urban contexts, spanning a broad portfolio of hotel brands from luxury to economy, provide a rich setting for exploring how AI integration affects employee attitudes and work–life balance. A total of 437 employees participated in the survey, offering a robust dataset for structural equation modeling (SEM) analysis. Exploratory factor analysis identified two primary factors shaping perceptions: AI Perceptions, which encompasses employee views on AI’s impact on job performance, communication, recognition, and retention, and balanced management, reflecting attitudes toward fairness, personal consideration, productivity, and skill development in AI-managed environments. The results reveal a complex but optimistic view, where employees acknowledge AI’s potential to enhance operational efficiency and career optimism but also express concerns about flexibility loss and the need for human oversight. The findings underscore the importance of transparent communication, contextual sensitivity, and continuous training in implementing AI systems that support both organizational goals and employee well-being. This study contributes valuable insights to hospitality management by highlighting the relational and ethical dimensions of algorithmic HR systems across varied organizational and cultural settings. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical resilience, decarbonization, and social/community engagement. The framework conceptualizes metropolitan and building scales as analytically distinct but operationally linked, allowing strategies to reflect the different systems, stakeholders, and capacities at each level. It also outlines a three-phase progression—Initial (assessment and goal setting), Readiness (planning and implementation), and Steady-State (monitoring and iterative adjustment)—to support staged, adaptive deployment. Each phase includes sample metrics and SMART goals that can be tailored to local context and tracked over time. By integrating theoretical insights with practical implementation tools, the framework offers a flexible yet rigorous approach for advancing urban sustainability. It emphasizes the importance of aligning technical interventions with institutional capacity and community participation to enhance effectiveness and equity. This model contributes to both planning theory and applied sustainability efforts by providing a structured pathway for cities to enhance climate readiness across systems and scales. Full article