Search Results (1,801)

With the transformation of resource-based cities and the restructuring of industrial sectors, the sustainable conservation of coal mining heritage has become a global focus. In China, coal mining heritage faces challenges such as degradation and inadequate management, highlighting the urgent need for more context-sensitive and systematic conservation approaches. This study develops an integrated, landscape-oriented analytical framework for stakeholder identification to address these challenges and to better understand stakeholder differentiation in coal mining heritage conservation. The research objectives are as follows: (1) to bring together a three-dimensional framework based on material-technical, socio-cultural, and experiential dimensions; (2) to analyse the roles and interactions of stakeholders; and (3) to explore how technical knowledge, socio-cultural memory, and daily experiences influence the protection and reuse of coal mining heritage sites. The study integrates the theoretical frameworks of landscape character assessment, historic urban landscape, and experiential landscape, using data from field observations and interviews analysed via ATLAS.ti. The findings show that the proposed framework offers a more systematic understanding of the dynamic relationships between stakeholders and heritage landscapes, thereby providing practical guidance for local governments and relevant institutions in developing inclusive and context-sensitive conservation strategies. Full article

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall. Full article

Incentive-based demand response (DR) programs rely on accurate and trustworthy quantification of customer performance to ensure fair compensation and market efficiency. Estimating the customer baseline load is an important part of this process. It shows how much electricity would be used if there were no DR occurrence. Unlike conventional load forecasting, baseline modeling is inherently unobservable, economically sensitive, and vulnerable to strategic manipulation. With the growing penetration of distributed energy resources, electric vehicles, and intelligent control technologies, traditional baseline estimation approaches face increasing limitations. This paper offers a thorough and future-oriented synthesis of baseline load estimation for incentive-based DR strategies. Current approaches are carefully classified into rule-based, statistical, probabilistic, machine learning (ML), and hybrid intelligence techniques, and their appropriateness for various DR services and client categories is rigorously evaluated. Beyond modeling accuracy, this paper emphasizes market-oriented requirements, including incentive compatibility, simplicity, transparency, privacy preservation, and deployment feasibility. Furthermore, emerging digital trust enablers such as blockchain and FL are reviewed, along with baseline-free and baseline-light alternatives for performance evaluation. Finally, open research challenges and future directions toward interpretable, robust, and market-ready baseline intelligence are discussed. Full article

Lunar quadruped robots face landing challenges including weak gravity, large mass variations, uncertain sloped terrain, and strict payload acceleration limits, requiring effective impact mitigation and rapid post-landing stabilization. This paper presents a novel end-to-end reinforcement learning-based landing controller with three key novelties: (i) a phase-structured yet implicitly encoded formulation that distinguishes contact preparation, energy dissipation, and stabilization without explicit phase switching; (ii) a terrain-agnostic state and control representation using equivalent support direction construction and contact-gated modulation to decouple normal–tangential dynamics; and (iii) an extremum oriented learning strategy that directly captures peak impact suppression and buffering sufficiency, addressing limitations of cumulative rewards in hybrid, peak-dominated tasks. A hybrid control model for lunar quadruped landing dynamics is established, incorporating variable mass, low impact, and full stroke as key constraints during training. Simulation and full-scale experimental prototypes are built to validate the controller. Simulation results demonstrate robust landing buffering and stability control under varying mass, landing velocity, and slope conditions, with favorable robustness against parameter variations. Experimental verification is conducted under diverse conditions including different masses (200 kg, 250 kg), vertical/horizontal landing velocities (0.8 m/s, 0.2 m/s), and slopes (0, 8). The deviation between simulation and experimental results does not exceed 30%, confirming the effectiveness and transferability of the proposed approach. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

Meat quality serves as a pivotal determinant of consumer purchasing behavior and of the economic viability of the livestock industry; as such, research into its regulatory mechanisms is of critical significance for the development of modern agriculture. Traditional investigations into meat quality have predominantly centered on sensory and physicochemical assessments of ultimate phenotypic traits, thereby facing inherent limitations in systematically deciphering the intricate molecular regulatory networks underlying meat quality formation. By contrast, an integrated analysis of the transcriptome and metabolome effectively connects the cascade of “gene transcription—metabolic regulation—phenotypic determination,” which has emerged as a core methodological paradigm in contemporary research on the molecular mechanisms governing meat quality. This review systematically delineates the evolutionary trajectory and principal technological frameworks of meat quality evaluation systems, with a focused synthesis of recent advances achieved through combined transcriptomic and metabolomic analyses in the field of meat quality regulation. The scope of this review encompasses core transcriptional regulatory networks associated with meat quality attributes, pivotal metabolic pathways, signal transduction mechanisms, and protein degradation dynamics. Furthermore, the regulatory impacts exerted by genetic variation among breeds, nutritional modulation, rearing environments, and stress responses on meat quality characteristics are comprehensively elucidated. Integrative analysis reveals that combined transcriptome–metabolome approaches transcend the inherent limitations of single-omics investigations, systematically unraveling the hierarchical regulatory mechanisms governing fundamental meat quality traits, such as muscle fiber type differentiation, postmortem glycolytic progression, intramuscular fat deposition, and flavor compound accumulation. Such integrative strategies have facilitated the identification of functional genes and metabolic biomarkers with potential utility for the early prediction of meat quality outcomes. Concurrently, this review acknowledges persistent challenges confronting the field, including the absence of standardized protocols for multi-omics data integration, insufficient functional causal validation, and a discernible disconnect between research discoveries and practical industrial implementation. Building upon this comprehensive assessment, prospective directions for future multi-omics research in meat quality are proposed, accompanied by the formulation of an integrated end-to-end improvement framework spanning fundamental research, technological innovation, and industrial application. Collectively, this review provides a systematic theoretical foundation for the in-depth elucidation of mechanisms that determine meat quality and the precision-oriented regulation of quality-determining traits in livestock production practices, thereby offering substantial scientific guidance for quality improvement initiatives within the animal husbandry sector. Full article

Urban areas are pivotal to achieving the Sustainable Development Goals (SDGs), yet sustainability monitoring at the municipal level remains fragmented, difficult to operationalize, and weakly comparable across cities. Although the SDGs provide a comprehensive global agenda and ISO 37120 offers a standardized set of city indicators, municipalities still face practical barriers in translating global targets into actionable, jurisdiction-sensitive, and measurable metrics aligned with local responsibilities and available data. This study addresses this gap by presenting the design of an integrated, target-level urban sustainability assessment framework grounded in SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) principles and explicitly tailored to municipalities in developed-country contexts. The framework contributes (i) a structured procedure for disaggregating and reallocating SDG targets according to municipal responsibilities, (ii) a six-dimension architecture that consolidates SDG targets and ISO 37120 themes into a coherent, governance-oriented structure (Government and Economic Development; Civic & Social Infrastructure; Environment and Climate; Infrastructure and Urban Planning; Health; Urban Living Conditions), and (iii) a SMART-based indicator screening logic that prioritizes feasibility, data availability, and benchmarking potential, thus supporting the green transition in Urban Areas. The framework is empirically examined through validation against sustainability reporting practices of the Porto City Council, quantifying indicator coverage, assessing alignment with municipal mandates, and identifying systematic gaps—particularly in cross-cutting areas such as governance transparency, equity monitoring, and long-term climate adaptation. Overall, the results indicate that the proposed approach strengthens coherence, measurability, and comparability in urban sustainability assessment, supporting evidence-based municipal decision-making, performance benchmarking, and more strategically aligned SDG localization. Full article

Ice–snow tourism has become an important development strategy in northern China, but its contribution to urban-rural integration remains uneven. Taking Inner Mongolia as a comparative qualitative case, this study examines how ice-snow tourism can move beyond enclave-oriented development and support inclusive regional development. The analysis draws on policy and planning documents, official reports, media materials, and published secondary studies, and compares Hulunbuir and Tongliao through four common dimensions: space, economy, governance, and culture. On this basis, the paper develops a community-field perspective and connects it with an institution–space–human/land coupling lens. The findings show clear differences in developmental tendency rather than two pure types. Hulunbuir exhibits stronger event-led agglomeration, urban service concentration, and branding capacity, but weaker community benefit capture. Tongliao shows stronger village-level benefit retention, collective participation, and cultural subjectivity, but faces limits in scale linkage and resilience. The paper argues that ice-snow tourism should not be understood as a simple trade-off between efficiency and equity. Instead, a coordinated “pole-community-network” pathway is needed to connect regional growth poles, community-centered governance, and networked collaboration across urban and rural nodes. The study contributes to tourism-led regional development research by clarifying how the community field mediates spatial organization, benefit sharing, and local agency in cold-resource regions. Full article

Rural territories continue to face persistent structural challenges related to depopulation, limited economic diversification, and unequal access to specialized knowledge. Although scientific research and applied expertise are widely recognized as critical resources for addressing these challenges, their effective transmission to local actors remains fragmented. In recent years, digital platforms have emerged as potential mechanisms to bridge this gap; however, their role within rural development frameworks remains conceptually underdeveloped. This paper proposes a conceptual framework for knowledge transmission platforms oriented towards rural development, integrating scientific research, applied analysis, and structured dissemination within a unified operational architecture. Drawing on a structured review of the literature on rural development, knowledge transfer, and digital platforms, the framework identifies key functional dimensions and design principles that shape platform-based knowledge intermediation. The framework is illustrated through a qualitative case study of CreandoTuProvincia, a Spanish platform focused on territorial analysis and rural knowledge transmission. The findings highlight the relevance of hybrid platforms that combine scientific rigour, accessibility, and territorial embeddedness, offering a scalable model for strengthening evidence-informed rural development strategies. By conceptualizing platforms as structured knowledge intermediaries, this study contributes to the emerging literature on knowledge-based rural development and provides practical insights for policymakers, researchers, and platform designers. Full article

University students face sustained academic, employment, and social pressures. Campus pathways, as central linear spaces in daily routines, hold significant potential to influence well-being, yet existing research has largely overlooked how their environmental characteristics affect multidimensional health. Using Shenyang Jianzhu University as a case, this study identified frequently used pathways through GPS tracking and surveys, and quantitatively analyzed how environmental features affect walking willingness, emotional experience, and social interaction. By comparing high- and low-benefit groups, the key environmental thresholds were identified to inform health-oriented design. Beyond verifying some established understandings (e.g., daily commuting paths prioritize efficiency, while leisure paths focus on experiential quality), the study further revealed several mechanisms through quantitative analysis. For example, “road accessibility”—an indicator of convenience—showed a significant negative correlation with emotional experience. The study established quantifiable prediction models and identified design thresholds for campus pathways. A high aesthetic greenery was key to achieving high overall benefits, while low building enclosure and vegetation complexity promoted social interaction. This achievement transforms health-oriented campus pathway design from qualitative principles into a measurable and optimizable scientific practice, thus providing an empirical basis and practical guidance for the construction of health-supportive campus environments. Full article

Background: ChatGPT is a large language model (LLM) online chatbot developed by OpenAI and launched in November 2022. Early adoption studies have shown high readiness to use this technology for health-related questions and self-diagnosis. However, the quality and clinical adequacy of health-related responses remain incompletely characterized. This study aimed to explore responses generated by ChatGPT-3.5 and ChatGPT-4.0 to common patient questions regarding scoliosis. Methods: Ten scoliosis-related frequently asked questions (FAQs) were selected from a larger pool of over 250 patient-facing questions compiled from 17 publicly available FAQ webpages and informed by a Google Trends analysis. Questions were harmonized, grouped by theme, and then reduced by rule-based expert review to a final set intended to represent common patient concerns. Results: The median ratings of ChatGPT-3.5 and ChatGPT-4.0 responses ranged from satisfactory, requiring minimal (2) to moderate clarification (3). Across the ten matched questions, no statistically detectable difference was found between models in this study setting (W = 8.0, p = 0.59; Cliff’s δ = −0.12 [95% CI −0.58, 0.40]); however, given the small question set, unblinded rating process, and poor inter-rater reliability, this should not be interpreted as evidence of equivalence, non-inferiority, or comparable model performance. The results apply only to the 10–15 April 2024, online snapshots of ChatGPT-3.5 and ChatGPT-4.0 and should not be generalized to later model iterations. Conclusions: This study should be interpreted as a clinically oriented observational report, intended to inform physician awareness and patient-physician communication rather than validate chatbot accuracy or safety. In this 10–15 April 2024, sample, both model outputs frequently required clinician clarification. Given the small FAQ set, low inter-rater reliability, unblinded design, and single-sample outputs, the findings do not establish equivalence or superiority and apply only to the specific 10–15 April 2024, model snapshots and evaluated questions. Full article

Project-based organizations (PBOs) face persistent challenges in managing workload fluctuations that influence performance, competitiveness, and resource sustainability. Although previous research has explored bidding strategies and project inflows and outflows, few studies have systematically modeled workload-capacity dynamics or assessed policy responses to manage them effectively. To address this gap, this study develops a system dynamics (SD) model that integrates both pre-award and post-award project phases with internal and external organizational processes. Data for model development were drawn from the literature, industry reports, and expert interviews, resulting in the identification of 28 variables organized into subsystems covering demand, capacity planning, work execution, competitiveness, and financial performance. The model was validated through dimensional and structural tests, expert review, and further examined using social network analysis (SNA) and sensitivity analysis. The SNA results identified workload, production rate, and organizational capacity as the most influential variables. Sensitivity analysis conducted through Monte Carlo experiments, employing screening, regression, and ANOVA (analysis of variance) methods, revealed that capacity adjustment flexibility, minimum capacity, and demand level are critical factors influencing organizational stability. The validated model was then applied to evaluate policy alternatives under two distinct market conditions. Findings indicate that in lowest-price environments, a competitive, market-share-oriented policy enhances utilization and responsiveness, whereas in average-price markets, a stable capacity policy yields more sustainable outcomes. These results demonstrate how project-based organizations can strategically adjust bidding and capacity policies to stabilize workload dynamics and improve long-term operational resilience under different market conditions. The study contributes theoretically by extending the application of SD modeling to workload-capacity management in PBOs and contributes practically by offering a decision-support tool that helps managers assess capacity strategies, reduce risks, and align organizational policies with long-term sustainability objectives. Full article

The purpose of this study is to determine the most suitable Destination Management Organization (DMO) model for the sustainable development of the Rize destination. Approached from the perspective of Complex Adaptive Systems (CAS), the research is of strategic importance in order to overcome systemic entropy threats, such as coordination deficiencies and unplanned growth, faced by the destination through a scientific model. Methodologically, a sequential exploratory mixed method integrating qualitative and quantitative methods was adopted. In the qualitative phase, system bottlenecks were identified through interviews with 15 strategic stakeholders; in the quantitative phase, Analytical Hierarchy Process (AHP) and Quality Function Deployment (QFD) analyses were applied with 271 participants. Key findings indicate that the most critical factors disrupting the system’s homeostatic balance are weak inter-institutional coordination and inadequate infrastructure. AHP results confirm that market diversification, sustainable planning, and quality standards are priority activities. The final analysis conducted using the QFD decision matrix identified the PPCP (Public–Private–Community Partnership) model, which synchronizes public oversight with private sector innovation and integrates community-based feedback mechanisms, as the most effective structure for enabling resource integration and value co-creation among actors. The model’s adaptive architecture further accommodates emergent stakeholder dynamics, including the growing role of tourists as co-creators of destination experiences through digital platforms. The study contributes to the literature by offering a rational decision support mechanism for complex system management through AHP-QFD integration and proposes a three-phase evaluation framework to ensure results-oriented governance adaptation. Full article

Background: Maternity care for vulnerable pregnant women presents a particular challenge within midwifery practice. In Germany, maternity services lack standardized frameworks to adequately address the specific needs of individuals who have experienced, among other factors, sexualized violence, poverty, female genital mutilation/cutting (FGM/C), or discrimination. Limited access to healthcare among these populations contributes to increased maternal and neonatal morbidity and mortality. Emerging evidence indicates that comprehensive medical and psychosocial support provided by midwives can substantially improve obstetric outcomes for marginalized pregnant women. Methods: An elective course, Antenatal Care for Vulnerable Women, was offered in the sixth semester of the Bachelor’s program in Midwifery Science at the University of Tübingen in 2025. The course provided insights into the psychosocial challenges faced by vulnerable pregnant women and prepared students for these specific aspects of midwifery practice. The curriculum incorporated foundational lectures and innovative teaching formats aimed at cultivating constructivist approaches to problem-solving. All sixth-semester midwifery students were asked to assess their knowledge and skills across five vulnerability categories: asylum-seeking, FGM/C, intimate partner violence, trauma, and racism. A pilot pre–posttest analysis using a 6-point Likert scale (1 = very good, 6 = poor) was conducted as hypothesis-generating and curriculum-guiding. The pretest included 38 respondents. The posttest included 11 respondents who attended the course. Results: Students who attended the course demonstrated observable gains in knowledge and skills across all categories, with the greatest improvements in asylum-seeking, median of 5 (IQR 4–5) vs. 2 (2–3); FGM/C, 5 (4–5) vs. 2 (2–3); and racism, 5 (3–5) vs. 2 (2–3). Conclusions: Innovative teaching methods may contribute to preparing midwifery students for targeted care of vulnerable pregnant women. Findings from the pre- and posttests provide preliminary insight into the potential value of experiential learning and may inform the further development of practice-oriented teaching methods. Full article

Thick-film organic photovoltaics (OPVs) are key for scalable manufacturing, but increasing active-layer thickness usually lowers power conversion efficiency (PCE) due to charge recombination and limited carrier extraction. We report high-efficiency thick-film OPVs fully processed in air by doctor blading using non-halogenated solvents (o-xylene with 3.5% tetralin) for two non-fullerene acceptor systems: PM6:ITIC-4F and PTQ-10:ITIC-4F. Active layers (100–500 nm) were fabricated by adjusting the coating speed while keeping the ink concentration and gap constant. Under mild drying (40 °C, 2 min), both systems exhibited significant efficiency losses at 1 sun (AM1.5G) as the thickness increased, whereas performance was largely preserved under indoor LED illumination (200 lx and 1000 lx), enabling high performance for thick films. Short thermal post-annealing (80–140 °C, 2 min) further improved PCE by reducing bimolecular recombination and enhancing nanostructure. Optimized PM6:ITIC-4F devices reached 10.2% (300 nm) under 1 sun and 14.78% at 200 lx; PTQ-10:ITIC-4F achieved 11.3% (500 nm) under 1 sun and up to 15.71% at 200 lx. Morphological and structural analysis indicates that the superior thick-film performance of PTQ-10:ITIC-4F is linked to favorable phase behavior, polymer-rich surface composition, and preferential face-on molecular orientation, promoting charge collection. These results demonstrate that low-cost PTQ-10 and non-halogenated air processing can enable industrially relevant, high-performance thick-film OPVs. Full article