Search Results (5,702)

Coal serves as the primary energy source for China, with production anticipated to reach 4.76 billion tons in 2024. However, the mining process generates a significant amount of gangue, with approximately 800 million tons produced in 2023 alone. Currently, China faces substantial gangue stockpiles, characterized by a low comprehensive utilization rate that fails to meet the country’s ecological and environmental protection requirements. The environmental challenges posed by the treatment and disposal of gangue are becoming increasingly severe. This review employs bibliometric analysis and theoretical perspectives to examine the latest advancements in gangue utilization, specifically focusing on the application of computational chemistry to elucidate the structural features and interaction mechanisms of coal gangue, and to collate how these insights have been leveraged in the literature to inform its potential utilization routes. The aim is to promote the effective resource utilization of this material, and key topics discussed include evaluating the risks of spontaneous combustion associated with gangue, understanding the mechanisms governing heavy metal migration, and modifying coal byproducts to enhance both economic viability and environmental sustainability. The case studies presented in this article offer valuable insights into the gangue conversion process, contributing to the development of more efficient and eco-friendly methods. By proposing a theoretical framework, this review will support ongoing initiatives aimed at the sustainable management and utilization of coal gangue, emphasizing the critical need for continued research and development in this vital area. This review uniquely combines bibliometric analysis with computational chemistry to identify new trends and gaps in coal waste utilization, providing a roadmap for future research. Full article

Sustainability education has become a global priority in educational systems, aiming to equip learners with the knowledge, values, and skills necessary to address complex environmental and social challenges. This study specifically aims to understand the role of science education in promoting students’ awareness of sustainability and their understanding of the interconnected relationship between humans and the environment, based on the perceptions and practices of primary science teachers in Al-Ahsa, Saudi Arabia. A qualitative approach was utilized, which included semi-structured interviews complemented by classroom observations as primary data collection instruments. The targeted participants comprised a purposive sample consisting of forty-nine primary-level science instructors from the Al-Ahsa district, located in eastern Saudi Arabia. Emergent concepts from open and axial coding processes by using grounded theory were developed with the gathered data. Based on the findings, teachers perceive science teaching not only as knowledge delivery but as an opportunity to cultivate critical thinking and nurture eco-friendly actions among pupils. Classroom practices that underscore environmental values and principles of sustainability foster a transformative view of the teacher’s role beyond traditional boundaries. The data also highlighted classroom practices that integrate environmental values and sustainability principles, reflecting a transformative perspective on the teacher’s educational role. Full article

This article provides a critical review of the current design and research challenges in contemporary timber architecture. Conducted from the perspective of a designer-researcher, the review focuses on the role of wood as a material at the intersection of environmental performance, cultural meaning, and spatial practice. The study adopts a conceptual, problem-oriented approach, eschewing the conventional systematic aggregation of existing data. The objective of this study is to identify, interpret and categorise the key issues that are shaping the evolving discourse on timber architecture. The analysis is based on peer-reviewed literature published between 2020 and 2025, sourced from the Scopus and Web of Science Core Collection databases. Fifteen thematic challenges have been identified and classified according to their recognition level in academic and design contexts. The subjects under discussion include well-established topics, such as life cycle assessment and carbon storage, as well as less commonly explored areas, such as symbolic durability, social acceptance, traceability, and the upcycling of low-grade wood. The review under consideration places significant emphasis on the importance of integrating technical, cultural, and perceptual dimensions when evaluating timber architecture. The article proposes an interpretive framework combining design thinking and transdisciplinary insights. This framework aims to bridge disciplinary gaps and provide a coherent structure for understanding the complexity of timber-related challenges. The framework under discussion here encourages a broader understanding of wood as not only a sustainable building material but also a vehicle for systemic transformation in architectural culture and practice. The study’s insights may support designers, educators, and policymakers in identifying strategic priorities for the development of future-proof timber-based design practices. Full article

This research advances the integration of terrestrial laser scanning (TLS) in heritage documentation, targeting the development of holistic and practical guidance for practitioners to adopt the technology effectively. Acknowledging the pivotal role of TLS in capturing detailed and accurate representations of cultural heritage, the study emerges against a backdrop of technological progression and the evolving needs of heritage conservation. Through a comprehensive literature review, critical case studies of heritage sites in the U.S., expert interviews, and the development of a TLS for Heritage Documentation Best Practice Guide (the guide), the paper addresses the existing gaps in streamlined practices in the domain of TLS’s applications in heritage documentation. While recognizing and building upon foundational efforts such as international guidelines developed over the past decades, this study contributes a practice-oriented perspective grounded in field experience and case-based analysis. The developed guide seeks to equip practitioners with structured methods and practical tools to optimize the use of TLS, ultimately enhancing the quality and accessibility of heritage documentation. It also sets a foundation for integrating TLS datasets with other technologies, such as Building Information Modeling (BIM), virtual reality (VR), and augmented reality (AR) for heritage preservation, tourism, education, and interpretation, ultimately enhancing access to and engagement with cultural heritage sites. The paper also critically situates this guidance within the evolving theoretical discourse on digital heritage practices, highlighting its alignment with and divergence from existing methodologies. Full article

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article

Under the global context of climate governance and sustainable development, low-carbon energy transition has become a strategic imperative. As a critical force in resource allocation, the financial system’s impact on energy transition has attracted extensive academic attention. This paper presents the first comprehensive literature review on energy transition research in the context of financial development. We develop a “Financial Functions-Energy Transition Dynamics” analytical framework to comprehensively examine the theoretical and empirical evidence regarding the relationship between financial development (covering both traditional finance and emerging finance) and energy transition. The understanding of financial development’s impact on energy transition has progressed from linear to nonlinear perspectives. Early research identified a simple linear promoting effect, whereas current studies reveal distinctly nonlinear and multidimensional effects, dynamically driven by three fundamental factors: economy, technology, and resources. Emerging finance has become a crucial driver of transition through technological innovation, risk diversification, and improved capital allocation efficiency. Notable disagreements persist in the existing literature on conceptual frameworks, measurement approaches, and empirical findings. By synthesizing cutting-edge empirical evidence, we identify three critical future research directions: (1) dynamic coupling mechanisms, (2) heterogeneity of financial instruments, and (3) stage-dependent evolutionary pathways. Our study provides a theoretical foundation for understanding the complex finance-energy transition relationship and informs policy-making and interdisciplinary research. Full article

Carbon flow tracking and spatial pattern optimization at the scale of urban functional zones are key scientific challenges in achieving carbon neutrality. However, due to the complexity of carbon metabolism processes within urban functional zones, related studies remain limited. To address these scientific challenges, this study, based on the “source–sink–flow” ecosystem services framework, develops an integrated analytical approach at the scale of urban functional zones. The carbon balance is quantified using the CASA model in combination with multi-source data. A network model is employed to trace carbon flow pathways, identify critical nodes and interruption points, and optimize the urban spatial pattern through a low-carbon land use structure model. The research results indicate that the overall carbon balance in Hangzhou exhibits a spatial pattern of “deficit in the center and surplus in the periphery.” The main urban area shows a significant carbon deficit and relatively poor connectivity in the carbon flow network. Carbon sequestration services primarily flow from peripheral areas (such as Fuyang and Yuhang) with green spaces and agricultural functional zones toward high-emission residential–commercial and commercial–public functional zones in the central area. However, due to the interruption of multiple carbon flow paths, the overall carbon flow transmission capacity is significantly constrained. Through spatial optimization, some carbon deficit nodes were successfully converted into carbon surplus nodes, and disrupted carbon flow edges were repaired, particularly in the main urban area, where 369 carbon flow edges were restored, resulting in a significant improvement in the overall transmission efficiency of the carbon flow network. The carbon flow visualization and spatial optimization methods proposed in this paper provide a new perspective for urban carbon metabolism analysis and offer theoretical support for low-carbon city planning practices. Full article

Accomplishing China’s national targets of carbon peaking and carbon neutrality necessitates proactive solutions, hinging critically on fundamentally transforming rural construction models. Current construction practices in rural areas are characterized by inefficiency, high resource consumption, and reliance on imported materials. These shortcomings not only jeopardize the attainment of climate objectives, but also hinder equitable development between urban and rural regions. Using the Digital Industrial Park in Yongtai County, Fuzhou City, as a case study, this study focuses on prefabricated public buildings in regions with extreme hot–humid climate, and innovatively integrates BIM (Building Information Modeling)-driven carbon modeling with the Gaussian Two-Step Floating Catchment Area (G2SFCA) method for spatial accessibility assessment to investigate the carbon emissions and economic benefits of prefabricated buildings during the embodied stage, and analyzes the spatial accessibility of prefabricated building material suppliers in Fuzhou City and identifies associated bottlenecks, seeking pathways to promote sustainable rural revitalization. Compared with traditional cast-in-situ buildings, embodied carbon emissions of prefabricated during their materialization phase significantly reduced. This dual-perspective approach ensures that the proposed solutions possess both technical rigor and logistical feasibility. Promoting this model across rural areas sharing similar climatic conditions would advance the construction industry’s progress towards the dual carbon goals. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

Purpose: Following George Floyd’s death, the push for law enforcement accountability policies has intensified. Despite robust legislative action, challenges in enacting and implementing meaningful reforms persist. This study analyzes police accountability policies (PAP) in the U.S. from 2020 to 2022, identifying barriers and facilitators through expert perspectives in enforcement oversight, policy advocacy, and community engagement. Methods: The study used a dual approach: analyzing 226 police accountability bills from all 50 U.S. states, D.C., and Puerto Rico via the National Conference of State Legislatures database, and categorizing them into six key areas such as training, technology use, and certification. Additionally, a survey was conducted among experts to identify the challenges and drivers in passing police accountability legislation. Findings: A legislative analysis showed that although 48 states passed police accountability laws, California, New Jersey, Oklahoma, and Colorado have made significant strides by passing multiple pieces of legislation aimed at enhancing law enforcement accountability and ensuring better policing practices. The most common policies focused on training and technology, enacted by 16 and 12 states, respectively. However, crucial certification and decertification policies were adopted in just 13 states, highlighting the inconsistent implementation of measures critical for police accountability and transparency. The survey identified several barriers to passing PAP, including inadequate support from local governments (72.7%). Structural exclusion of poor and minority communities from policing resources was also a significant barrier (54.5%). Facilitators included community support (81.8%) and a cultural shift in policing towards viewing officers as “guardians” rather than “warriors” (63.6%). Conclusions: While some progress has been made in passing PAP, considerable gaps remain, particularly in enforcement and comprehensive reform. Resistance from law enforcement institutions, lack of community support, and structural inequalities continue to impede the adoption of effective PAP. Full article

Sexual health constitutes a fundamental aspect of overall well-being, with direct implications for individual development and the broader social and economic progress of communities. Promoting environments that ensure sexual experiences free from coercion, discrimination, and violence is a key public health priority. Sexuality, in this regard, should be understood as an inherent dimension of human experience, shaped by biological, cultural, cognitive, and ideological factors. Accordingly, sexual health education requires a holistic and multidimensional approach that integrates sociocultural, biographical, and professional perspectives. This study aims to examine the level of knowledge and training in sexual health among nursing students and healthcare professionals, as well as to assess the extent to which sexual health content is incorporated into nursing curricula at Spanish universities. A scoping review was conducted using the Dialectical Structural Model of Care (DSMC) as the theoretical framework. The findings indicate a significant lack of knowledge regarding sexual health among both nursing students and healthcare professionals, largely due to educational and structural limitations. Furthermore, sexual health education remains underrepresented in nursing curricula and is frequently addressed from a narrow, fragmented biomedical perspective. These results highlight the urgent need for the comprehensive integration of sexual health content into nursing education. Strengthening curricular inclusion is essential to ensure the preparation of competent professionals capable of delivering holistic, inclusive, and empowering care in this critical area of health. Full article

Alzheimer’s disease is the most prevalent neurodegenerative disorder leading to progressive cognitive decline and functional impairment. Although advanced neuroimaging and cerebrospinal fluid biomarkers have improved early detection, their high costs, invasiveness, and limited accessibility restrict universal screening. Quantitative electroencephalography (qEEG) offers a non-invasive and cost-effective alternative for assessing neurophysiological changes associated with AD. This review critically evaluates current evidence on EEG biomarkers, including spectral, connectivity, and complexity measures, discussing their pathophysiological basis, diagnostic accuracy, and clinical utility in AD. Limitations and future perspectives, especially in developing standardized protocols and integrating machine learning techniques, are also addressed. Full article

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

This research aims to identify and prioritize key planning elements for the redevelopment of such housing complexes by incorporating perspectives from both experts (supply-side) and residents (demand-side). To achieve this, a hybrid multi-criteria decision-making framework was developed by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A total of 25 planning elements were identified through Focus Group Interviews and organized into five domains: legal and institutional reforms, project feasibility, residential conditions, social integration, and complex design. The AHP was used to assess the relative importance of each element based on responses from 30 experts and 130 residents. The analysis revealed a clear divergence in priorities: experts emphasized feasibility and regulatory considerations, while residents prioritized livability and spatial quality. Subsequently, the TOPSIS method was applied to evaluate four real-world redevelopment cases. From the supply-side perspective, Seoul A District received the highest score (0.58), whereas from the demand-side perspective, Gyeonggi D District ranked highest (0.69), illustrating the differing priorities of stakeholders. Overall, Gyeonggi D District emerged as the most favorable option in the combined evaluation. This research contributes a structured and inclusive decision-making framework for the regeneration of public housing. By explicitly comparing and quantifying the contrasting preferences of key stakeholders, it underscores the critical need to balance technical feasibility with resident-centered values in future redevelopment initiatives. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article