Search Results (882)

In alignment with the United Nations’ Sustainable Development Goals (SDGs) and the global pursuit of net-zero emissions, higher education institutions (HEIs) are increasingly expected to demonstrate robust climate accountability and effective decarbonization strategies. This three-year longitudinal study presents a comprehensive assessment of greenhouse gas (GHG) emissions at a higher education institution, employing the ISO 14064-1:2018 framework to strengthen inventory design, boundary delineation, and data governance protocols. Findings indicate that purchased electricity constitutes the largest share; however, fugitive refrigerant leakage and Scope 3 activities—particularly commuting and business travel—represent substantial and often underestimated components of the institution’s carbon footprint. Methodological refinements, including the incorporation of updated emission factors coefficients and enhanced data verification, have revealed the sensitivity of GHG inventories to both policy reforms and behavioral changes, as well as institutional policy reforms. The study also demonstrates that targeted refrigerant management and low-carbon mobility initiatives can generate measurable mitigation effects, even under conditions of expanding campus activity. Beyond the institution-specific results, this research proposes a replicable framework that integrates ISO 14064-1 compliance with data quality assurance and digital verification tools. This framework provides HEIs globally with a structured pathway to enhance reporting credibility, develop evidence-based mitigation strategies, and accelerate progress toward carbon neutrality. These insights underline the strategic role of universities in advancing sector-wide climate leadership and contributing to sustainable development transitions. Full article

Windows play a crucial role in maintaining indoor thermal comfort, influenced by occupant behavior, passive design strategies, and advanced technologies that contribute to sustainable building practices. Despite advancements in adaptive and occupant-centric design, critical gaps remain unresolved in understanding of multi-climate adaptability, the complex interrelation between window operation and occupant behavior, and the integration of occupant roles into energy-related strategies under emerging technologies. This scoping review synthesizes peer-reviewed studies to assess the importance of window design (geometry, glazing, shading), operational strategies (manual control to AI-driven systems), and technological approaches (passive to smart systems) on thermal comfort, energy performance, and occupant behavior. Using bibliometric and scientometric analyses, the review focuses on four primary research clusters: thermal comfort and occupant behavior, window operation strategies, their impact on energy performance, and sustainability, with an emphasis on emerging trends. The findings highlight that glazing technologies, shading systems, and operational choices have a significant impact on both comfort and energy efficiency. The study develops a framework linking thermal comfort to window operation, occupant behavior, and climate context while conceptualizing a comprehensive design matrix and outlining future research directions aligned with the Sustainable Development Goals (SDG 3: health and well-being, SDG 7: clean energy, and SDG 11: sustainable cities and communities). Full article

Background: As cardiovascular medicine advances rapidly, the integration of artificial intelligence (AI) has garnered increasing attention. Although AI has been widely adopted in diagnostics, risk prediction, and decision support, its application in exercise-based cardiovascular rehabilitation is still limited, representing a new and promising research frontier. Objective: This scoping review aimed to identify and analyze original studies that have applied AI to exercise-based interventions designed to improve cardiovascular outcomes. Methods: Following the PRISMA-ScR guidelines, PubMed, Scopus, Web of Science, Embase, and IEEE Xplore were searched for articles published between January 2015 and August 2025. Eligible studies were peer-reviewed by human research employing AI (machine learning or deep learning) to deliver, adapt, or monitor an exercise intervention with cardiovascular outcomes. Reviews, diagnostic-only studies, protocols without data, and animal studies were excluded. Non-original works (reviews, protocols), animal studies, and purely diagnostic applications were excluded, ensuring a strict focus on AI applied within exercise interventions. Data extraction focused on study design, AI method, exercise modality, outcomes, and findings. Results: From 2183 records, nine studies met the inclusion criteria (two RCTs, feasibility pilots, and validation studies). Designs included feasibility pilots, randomized controlled trials (RCTs), and validation studies. AI applications encompassed adaptive step goals, reinforcement learning for engagement, coaching apps, machine learning–based exercise prescription, and continuous monitoring (e.g., VO₂ estimation). These AI methods, such as machine learning and reinforcement learning, were used to personalize exercise interventions and improve cardiovascular outcomes. Reported outcomes included blood pressure reduction, improved adherence, increased daily steps, improvement in VO₂max, continuous physiological monitoring, and enhanced diagnostic accuracy. Conclusions: Findings demonstrate that AI has the potential to significantly enhance cardiovascular rehabilitation. It can personalize exercise prescriptions, enhance adherence, and facilitate safe monitoring in home settings. However, the evidence base remains preliminary, with very few RCTs and substantial methodological heterogeneity. Future research must prioritize large-scale clinical trials, explainable AI, and equitable implementation strategies to ensure clinical translation. Full article

The need to address long-term sustainability goals while ensuring short-term resilience to unexpected disruptions is placing an increasing challenge on urban mobility systems. This study organizes an analytical framework that compares and integrates the concepts of sustainability and resilience in urban mobility. A scoping review and thematic analysis were conducted to identify and compare the definitions, dimensions, and operational features of these two paradigms. The results reveal that, although they are conceptually distinct, sustainability and resilience share subjects of analysis, including multimodality and diversity of transport modes, the impacts of climate change, and social equity issues. However, they also present tensions between the dimensions of efficiency and redundancy, speed of recovery and sustainability of implemented solutions, and new vulnerabilities introduced by sustainable technologies. These synergies and trade-offs underscore the necessity of an integrated, systemic and holistic approach to urban mobility planning. The study emphasizes that building resilient and sustainable urban mobility requires coherent policies across government levels, technical capacity, public engagement, and comprehensive indicators. Recommendations for future research include developing integrated metrics and planning tools to support evidence-based decision-making. Full article

Industry 5.0 (I5) represents a significant evolution in the trajectory of industrial development, emphasizing a human-centric approach that integrates advanced technologies with the goal of promoting sustainable growth, resilience, and enhanced human well-being. While Industry 4.0 already posed considerable challenges for industrial organizations, particularly in terms of technological integration, workforce adaptation, and strategic realignment, the shift toward Industry 5.0 has introduced additional complexities. The accelerated pace of innovation and the evolving expectations for human–machine collaboration have intensified these challenges. Large manufacturing corporations are already facing difficulties in adapting to this new paradigm; thus, the question arises: how are Small and Medium-sized Enterprises (SMEs), which typically operate with limited resources, infrastructure, and financial capacity, managing this transition? This paper presents a scoping review of 17 research papers, chosen from an initial set of 37 publications sourced from Scopus, Web of Science and ScienceDirect on the implementation of Industry 5.0 in SMEs. A comprehensive synthesis of existing research was conducted to elucidate the current state of the topic, identify the research questions addressed, and outline future directions for this emerging paradigm. Full article

The restoration of waterways is increasingly understood as an endeavor that could (and should) be beneficial for both ecosystems and people. Researchers have already explored several ways that restoration can mediate in favor of environmental justice goals, while simultaneously acknowledging the dangers of green gentrification. This paper extends the inquiries about waterway restoration and environmental justice to include a focus on one of society’s most frequently marginalized groups, unhoused people. Working inductively, we conduct a scoping study that examines published studies, news stories, and examples from the field that explore the intersection among waterway restoration and environmental justice, in the context of the interests of unhoused people. We argue that further work on the topic is necessary, and it should include both systematic investigations as well as design guidance material; this exploration represents the first step in outlining the direction of that work. Follow-on studies will center on clarifying the social dynamics in play, including identifying contested policy narratives, describing the regulatory context of the existing cases, definition(s) of what constitutes unhoused/homeless, and what impacts they have on decision-making. The future research we anticipate will develop information and propose strategies that can be used by practitioners including planners and landscape architects, in the process of organizing project work, to help advance environmental justice and human rights goals. Full article

Background/Objectives: Outpatient stroke rehabilitation is expanding as inpatient episodes shorten. Virtual reality (VR) exergaming can extend practice and standardize progression, but setting-specific effectiveness and implementation factors remain unclear. This scoping review mapped VR exergaming in outpatient stroke care and identified technology typologies and functional outcomes. Methods: Guided by the JBI Manual and PRISMA-ScR, searches of MEDLINE, Embase, CENTRAL, Scopus, and Web of Science were conducted in April 2025. The study included adults post-stroke undergoing VR exergaming programs with movement tracking delivered in clinic-based outpatient or home-based outpatient settings. Interventions focused on functional rehabilitation using interactive VR. Results: Sixty-six studies met the criteria, forty-four clinic-based and twenty-two home-based. Serious games accounted for 65% of interventions and commercial exergames for 35%. Superiority on a prespecified functional endpoint was reported in 41% of trials, 29% showed within-group improvement only, and 30% found no between-group difference; effects were more consistent in supervised clinic programs than in home-based implementations. Signals were most consistent for commercial off-the-shelf and camera-based systems. Gloves or haptics and locomotor platforms were promising but less studied. Head-mounted display interventions showed mixed findings. Adherence was generally high, and adverse events were infrequent and mild. Conclusions: VR exergaming appears clinically viable for outpatient stroke rehabilitation, with the most consistent gains in supervised clinic-based programs; home-based effects are more variable and sensitive to dose and supervision. Future work should compare platform types by therapeutic goal; embed mechanistic measures; strengthen home delivery with dose control and remote supervision; and standardize the reporting of fidelity, adherence, and cost. Full article

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. 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

The integration of artificial intelligence plays a critical role in human resource management in local governments by ensuring smooth, essential HR operations, including recruitment, performance management, and workforce planning. The current study is a systematic review focused on determining the performance management factors that should be considered when using artificial intelligence in the local government sector. Although artificial intelligence (AI) is becoming increasingly integrated into the governance and administrative systems of local governments around the world, this study raises critical questions about how performance should be managed, measured, and improved. Articles were screened based on their title, abstract, and keywords, following which the inclusion and exclusion criteria were applied. A comprehensive search was conducted in the EBSCOhost, Emerald Insight, Taylor & Francis, Scopus, and SpringerLink databases. These databases were chosen because they are prominent sources that publish various materials related to the social sciences. This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines and included 22 peer-reviewed empirical studies published between 2015 and 2025. Analysis of the identified 22 peer-reviewed articles revealed that the successful application of AI in local government performance management depends on six critical performance management factors: data quality and accessibility; strategic alignment with performance goals; evaluation criteria and metrics; ethical and legal oversight; institutional capacity and leadership; and change management and stakeholder engagement. These factors are interdependent and represent both technical and organisational dimensions of public administration. This study highlights that AI entails more than innovation; it reshapes the foundations of performance governance, requiring new capabilities, values, and institutional practices. Full article

The cement industry accounts for approximately 7–8% of global CO₂ emissions, primarily due to energy-intensive clinker production and limestone calcination. With cement demand continuing to rise, particularly in emerging economies, decarbonization has become an urgent global challenge. The objective of this study is to systematically map and synthesize existing evidence on technological pathways, policy measures, and economic barriers to four core decarbonization strategies: clinker substitution, energy efficiency, alternative fuels, as well as carbon capture, utilization, and storage (CCUS) in the cement sector, with the goal of identifying practical strategies that can align industry practice with long-term climate goals. A scoping review methodology was adopted, drawing on peer-reviewed journal articles, technical reports, and policy documents to ensure a comprehensive perspective. The results demonstrate that each mitigation pathway is technically feasible but faces substantial real-world constraints. Clinker substitution delivers immediate reduction but is limited by SCM availability/quality, durability qualification, and conservative codes; LC₃ is promising where clay logistics allow. Energy-efficiency measures like waste-heat recovery and advanced controls reduce fuel use but face high capital expenditure, downtime, and diminishing returns in modern plants. Alternative fuels can reduce combustion-related emissions but face challenges of supply chains, technical integration challenges, quality, weak waste-management systems, and regulatory acceptance. CCUS, the most considerable long-term potential, addresses process CO₂ and enables deep reductions, but remains commercially unviable due to current economics, high costs, limited policy support, lack of large-scale deployment, and access to transport and storage. Cross-cutting economic challenges, regulatory gaps, skill shortages, and social resistance including NIMBYism further slow adoption, particularly in low-income regions. This study concludes that a single pathway is insufficient. An integrated portfolio supported by modernized standards, targeted policy incentives, expanded access to SCMs and waste fuels, scaled CCUS investment, and international collaboration is essential to bridge the gap between climate ambition and industrial implementation. Key recommendations include modernizing cement standards to support higher clinker replacement, providing incentives for energy-efficient upgrades, scaling CCUS through joint investment and carbon pricing and expanding access to biomass and waste-derived fuels. Full article

With the increasing frequency and intensity of forest fires driven by climate change and human activities, efficient detection and rapid response have become critical for forest fire prevention. Effective fire detection, swift response, and timely rescue are vital for forest firefighting efforts. This paper proposes an unmanned aerial vehicle (UAV)-based forest fire inspection system that integrates a ground support system (GSS), aiming to enhance automation and flexibility in inspection tasks. A three-layer mixed-integer linear programming model is developed: the first layer focuses on the site selection and capacity planning of the GSS; the second layer defines the coverage scope of different GSS units; and the third layer plans the inspection routes of UAVs and coordinates multi-UAV collaborative tasks. For planning UAV patrol routes and collaborative tasks, a goal-driven greedy algorithm (GDGA) based on traditional greedy methods is proposed. Simulation experiments based on a real forest fire case in Turkey demonstrate that the proposed model reduces the total annual costs by 28.1% and 16.1% compared to task-only and renewable-only models, respectively, with a renewable energy penetration rate of 68.71%. The goal-driven greedy algorithm also shortens UAV patrol distances by 7.0% to 12.5% across different rotation angles. These results validate the effectiveness of the integrated model in improving inspection efficiency and economic benefits, thereby providing critical support for forest fire prevention. Full article

Aftercare plans can support breast cancer patients’ self-management after curative treatment but are often not personalized and limitedly applied by healthcare practitioners (HCPs). This study aimed to develop a tool integrating information provision and assessment of patients’ goals and needs, to support the creation and application of a personalized aftercare plan. A multidisciplinary workgroup guided the development by defining the target audience, scope and purpose. Needs of 18 patients and 15 HCPs were assessed to determine the tool’s content and format. Usability tests of a prototype among 7 patients and 10 HCPs informed improvements and finalization. The tool, called ‘Breast Cancer Aftercare Decision Aid’ (BC-ADA), provides information on potential effects of cancer and support options on five domains: physical wellbeing, emotions, relationships, regaining trust and return to daily routine. Patients can indicate which domain(s) they wish to improve, what resources they have and where additional help is needed. Based on their answers, patients can create an aftercare plan together with the HCP, including personal goals, specific actions and agreements on follow-up. Usability and acceptability were positively evaluated by both patients and HCPs. The BC-ADA seems promising in supporting personalized aftercare decision-making and is currently being tested in the NABOR-study in Dutch hospitals. Full article

Background: Emergency laparotomy (EL) carries high morbidity and mortality relative to elective abdominal surgery. While Enhanced Recovery After Surgery (ERAS) principles improve outcomes in elective care, their translation to emergencies is inconsistent. The emergency department (ED) provides a window for rapid risk stratification and pre-optimization, provided that interventions do not delay definitive surgery. Methods: We conducted a PRISMA-ScR–conformant scoping review to map ED-initiated, ERAS-aligned strategies for EL. PubMed, Scopus, and Cochrane were searched in February 2025. Eligible sources comprised ERAS guidelines, systematic reviews, cohort studies, consensus statements, and programmatic reports. Evidence was charted across five a priori domains: (i) ERAS standards, (ii) comparative effectiveness, (iii) ED-feasible pre-optimization, (iv) risk stratification (Emergency Surgery Score [ESS], frailty, sarcopenia), and (v) oncological emergencies. Results: Thirty-four sources met inclusion. ERAS guidelines codify rapid assessment, multimodal intraoperative care, and early postoperative rehabilitation under a strict no-delay rule. Meta-analysis and cohort data suggest ERAS-aligned pathways reduce complications and length of stay, though heterogeneity persists. ED-feasible measures include multimodal analgesia, goal-directed fluids, early safe nutrition, respiratory preparation, and anemia/micronutrient optimization (IV iron, vitamin B12, folate, vitamin D). Sarcopenia, frailty, and ESS consistently predicted adverse outcomes, supporting targeted bundle activation. Evidence from oncological emergencies indicates feasibility under no-delay governance. Conclusions: A minimal, ED-initiated, ERAS-aligned bundle is feasible, guideline-concordant, and may shorten hospitalization and reduce complications in EL. We propose a practical framework that links rapid risk stratification, opportunistic pre-optimization, and explicit continuity into intra- and postoperative care; future studies should test fidelity, costs, and outcome impact in pragmatic emergency pathways. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article