Search Results (694)

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival. Full article

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience. Full article

Background: Mobility limitation is a pivotal but under-documented dimension of disability in Saudi Arabia. Leveraging the 2017 National Disability Survey, this cross-sectional study provides a population-wide profile of mobility-related physical difficulty. Objectives: Five research aims were pursued: (1) estimate national prevalence and severity by sex; (2) map regional differentials; (3) examine educational and marital correlates; (4) characterize cause, duration, and familial context among those with multiple limitations; and (5) describe patterns of assistive-aid and social-service use. Methods: Publicly available aggregate data covering 20,408,362 Saudi citizens were cleaned and analyzed across 14 mobility indicators and three baseline files. Prevalence ratios and χ² tests assessed associations. Results: Overall, 1,445,723 Saudis (7.1%) reported at least one functional difficulty; 833,136 (4.1%) had mobility difficulty, of whom 305,867 (36.7%) had mobility-only impairment. Severity was chiefly mild (35% of cases), with moderate (16%) and severe (7%) forms forming a descending pyramid. Prevalence varied more than threefold across the thirteen regions, peaking in Aseer (9.4%) and bottoming in Najran (2.9%). Mobility difficulty clustered among adults with no schooling (36.1%) and widowed status (18.5%), with sharper female disadvantage in both domains (p < 0.001). Among those with additional limitations, chronic disease dominated etiology (56.3%), and 90.1% had lived with disability for ≥25 years; women were overrepresented in the longest-duration band. Aid utilization was led by crutches (47.7%), personal assistance (25.3%), and wheelchairs (22.6%), while 83.8% accessed Ministry rehabilitation services, yet fewer than 4% used home or daycare support. Conclusions: These findings highlight sizeable, regionally concentrated, and gender-patterned mobility burdens, underscoring the need for education-sensitive prevention, chronic-care management, investment in advanced assistive technology, and distributed community services to achieve Vision 2030 inclusion goals. Full article

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article

In the intersecting field of energy consumption and human well-being, many macro-level studies link national energy use with well-being. These studies often rely on aggregate data, however, limiting insights into intra-national inequities and diverse well-being outcomes. To bridge this gap, this study used a single Nordic survey that allowed for the calculation of consumption-based energy footprints alongside well-being measures, focusing on Icelandic participants. A factor analysis of well-being responses identifies four factors: Eudaimonic, Financial, Housing/Local, and Health-related well-being. We found that well-being in Iceland largely remains decoupled from energy footprints across income and consumption groups, except for financial well-being. However, these groups differ significantly in consumption lifestyles and associated footprints, with only a small fraction of consumers maintaining energy use within global sufficiency thresholds. Most exceed these levels, suggesting that Iceland could reduce energy consumption without significantly harming well-being. Future research should explore strategies to lower consumption without triggering negative social reactions or declines in well-being. Full article

Ostryopsis davidiana shrubs, widely distributed in northern China, have been significantly affected by global warming. Based on the current geographical distribution data of O. davidiana in China, this study used climate data, soil data, topographic data, human activity data, and the “biomod2” integrated model to conduct an integrated study on the suitable habitat of O. davidiana under the current scenario and three future climate scenarios (SSP126, SSP370, and SSP585). The results showed the following: (1) The suitable habitats of O. davidiana are mainly concentrated in the northwest and north China regions, accounting for about 9.09% of the national area, centered in Shanyin County, Shuozhou City, Shanxi Province. (2) The suitable habitats of O. davidiana are mainly influenced by temperature and precipitation, with precipitation of wettest quarter (Bio16), isothermality (Bio3), and maximum temperature of warmest month (Bio5) being the key driving factors, with contribution rates of 25.69%, 24.31%, and 14.45%, respectively. (3) Under the three future climate scenarios, the suitable habitats of O. davidiana are expected to contract significantly, with only the low suitability areas expanding, while the rest would be contracting, showing a trend of losing most of their original habitat. The centroid of the suitable habitat would be shifting westward, and the suitable habitats would be generally migrating to higher elevation areas. (4) Climate change reduces the aggregation of O. davidiana, leading to gradual habitat fragmentation. This study provides a theoretical basis for the conservation of O. davidiana. Full article

Intraoperative cell salvage (ICS) is a blood conservation technique utilized in major surgery, yet its application in oncologic procedures remains debated. Concerns persist about the theoretical risk of metastasis through reinfusion of tumor cells, despite the established disadvantages of allogeneic blood transfusion (ABT), such as transfusion-related reactions and immunosuppression. In this review, we discuss the historical development of ICS, the technical processes of ICS including leukocyte depletion filtration and irradiation, and experimental and clinical data regarding its safety and efficacy. In vitro studies suggest that tumor cells undergo significant structural alterations during ICS processing, and additional filtration further reduces cell load, although complete removal is not always achieved. Observational studies of predominantly moderate quality, aggregated in multiple systematic reviews, consistently report no increased recurrence rates or reduced disease-free and overall survival in patients receiving ICS. Accordingly, national and international guidelines endorse the use of ICS during oncologic surgery. Although high-quality data—preferably from randomized controlled trials—are lacking, and certainty of available evidence from observational studies is low, ICS appears to be effective and safe. The broader adoption of its use during oncologic surgery may be warranted to minimize reliance on ABT and its associated risks. Full article

Reliable short-term forecasts of hepatitis B incidence are indispensable for sizing national vaccine and antiviral procurement. However, predictive modelling is complicated when surveillance streams experience reporting delays and episodic under-reporting, as has occurred in Ukraine since 2022. We address this challenge by training a deliberately compact two-layer long short-term memory (LSTM) network on 72 monthly observations (January 2018–December 2023) drawn from the Public Health Center electronic registry and evaluating performance on a strictly held-out 12-month horizon (January–December 2024). Grid-search optimisation selected a 12-month sliding input window, 64 hidden units per layer, 0.20 dropout, the Adam optimiser, and early stopping. Walk-forward validation showed that the network attained mean squared errors of 411 for acute infection and 76 for chronic infection on the monthly series. When forecasts were aggregated to the cumulative scale, the mean absolute percentage error remained below 1%. This study presents the first peer-reviewed hepatitis B forecasts calibrated on Ukraine’s registry during a period of pronounced reporting instability, demonstrating that robust accuracy is attainable without missing-value imputation. Full article

Italy supplies about one-seventh of the European Union’s total glass production, and the sector’s sizeable resource demands make it a linchpin of national industrial strategy. With growing environmental regulations and the push for resource efficiency, Material Flow Accounting has become essential for companies to stay compliant and advance sustainability. The investigation concentrates on Italy’s glass industry to clarify its material requirements, ecological footprint, and overall sustainability performance. STAN software v2, combined with an Economy-Wide Material Flow Accounting (EW-MFA) framework, models the national economy as a single integrated input–output system. By tracking each material stream from initial extraction to end-of-life, the analysis delivers a cradle-to-grave picture of the sector’s environmental impacts. During the 2021 production year, Italy’s glass makers drew on a total of 10.5 million tonnes (Mt) of material inputs, supplied 76% (7.9 Mt) from domestic quarries, and 24% (2.6 Mt) via imports. Outbound trade in finished glass removed 1.0 Mt, leaving 9.5 Mt recorded as Domestic Material Consumption (DMC). Within that balance, 6.6 Mt (63%) was locked into long-lived stock, whereas 2.9 Mt (28%) left the system as waste streams and airborne releases, including roughly 2.1 Mt of CO₂. At present, the post-consumer cult substitutes only one-third of the furnace batch, signalling considerable scope for improved circularity. When benchmarked against EU-27 aggregates for 2021, Italy registers a NAS/DMI ratio of 0.63 (EU median 0.55) and a DPO/DMI ratio of 0.28 (EU 0.31), indicating a higher share of material retained in stock and slightly lower waste generated per ton of input. A detailed analysis of glass production identifies critical stages, environmental challenges, and areas for improvement. Quantitative data on material use, waste generation, and recycling rates reveal the industry’s environmental footprint. The findings emphasise Economy-Wide Material Flow Accounting’s value in evaluating and improving sustainability efforts, offering insights for policymakers and industry leaders to drive resource efficiency and sustainable resource management. Results help scholars and policymakers in the analysis of the Italian glass industry context, supporting in the data gathering, while also in the use of this methodology for other sectors. Full article

Background/Objectives: European healthcare systems are increasingly adopting automation technologies to improve efficiency. This study evaluates the economic viability of hospital automation and medication management digitalization. Methods: An economic evaluation was based on a standardized hospital model comprising 561 beds, representative of an average acute care hospital across EU27 + UK. For each technology, several cost items were estimated using country-specific parameters such as labor costs, medication error rates, healthcare expenditure, and money discount rate. The financial metrics (Return On Investment—ROI, Net Present Value—NPV, Payback Time—PBT) were first calculated at the hospital level. These results were then extrapolated to the national level by scaling the per-hospital estimates according to the total number of hospital beds reported in each country. Finally, national results were aggregated to derive the overall European impact. Results: The analysis estimated a total European investment of EUR 3.55 billion, with an average PBT of 4.46 years and annual savings of 1,96 billion. ROI averaged 167%, and the total NPV was 8.21 billion. A major saving driver was the reduction in Medication Administration Errors that has an impact of 37.2% on the total savings. Payback times ranged from 3 years in high-GDP countries, to 7 years in lower-GDP nations. Conclusions: These findings demonstrate how providing structured data on hospital automation benefits could support decision-making processes, highlighting the organizational and economic feasibility of the investment across different European national contexts. Full article

This study conducts a macro-level comparative analysis of the eco-efficiency in the agricultural sectors of the European Union (EU) member states and Türkiye from 2003 to 2022. By treating countries as decision-making units, this research offers a holistic overview of how national-level inputs and outputs shape the aggregate performance, focusing on the trade-offs between economic value generation and environmental pressures. An input-oriented Data Envelopment Analysis (DEA) model, based on Variable Returns to Scale (VRS), was employed. The model employs three inputs—compensation of employees (COE), energy consumption (EC), and gross fixed capital formation (GFC)—and two outputs—agricultural gross domestic product (GDP) and GHG emissions (GGEs). All variables were normalized by agricultural land area per country to account for scale differences. The findings reveal significant disparities in the eco-efficiency across countries and over time. Notably, Türkiye consistently demonstrated a high performance, frequently serving as a benchmark. In contrast, several Eastern European countries exhibited lower scores, suggesting significant room for structural improvement at the national level. The results point to the considerable potential for reducing energy and labor inputs in many countries. Instead of offering specific policy prescriptions, this study provides a diagnostic tool that identifies national-level performance gaps, informs policy discussions on resource allocation, and highlights priority areas for more detailed investigation. Full article

Cropland abandonment (CA) has become a significant threat to agricultural sustainability, particularly in metropolitan suburbs where urban expansion and cropland preservation often conflict. This study examines the Chengdu Directly Administered Zone of the Tianfu New Area in Sichuan Province, China, as a case study, utilizing high-precision vector data from China’s 2019–2023 National Land Survey to identify abandoned croplands through land use change trajectory analysis. By integrating kernel density estimation, spatial autocorrelation analysis, and geographically weighted regression modeling, we quantitatively analyzed the spatiotemporal patterns of CA and the spatial heterogeneity of driving factors in the study area. The results demonstrate an average annual abandonment rate of approximately 8%, exhibiting minor fluctuations but significant spatial clustering characteristics, with abandonment hotspots concentrated in peri-urban areas that gradually expanded toward urban cores over time, while exurban regions showed lower abandonment rates. Cropland quality and the aggregation index were identified as key restraining factors, whereas increasing slope and land development intensity were found to elevate abandonment risks. Notably, distance to roads displayed a negative effect, contrary to conventional understanding, revealing that policy feedback mechanisms induced by anticipated land expropriation along transportation corridors serve as important drivers of suburban abandonment. This study provides a scientific basis for optimizing resilient urban–rural land allocation, curbing speculative abandonment, and exploring integrated “agriculture + ecology + cultural tourism” utilization models for abandoned lands. The findings offer valuable insights for balancing food security and sustainable development in rapidly urbanizing regions worldwide, particularly providing empirical references for developing countries addressing the dilemma between urban expansion and cropland preservation. Full article

The Netherlands has established climate-adaptive strategies shaped by its long history of water-related climate events, such as the floods in 1421 and 1953. UNESCO World Heritage (WH) properties in The Netherlands reflect centuries of human intervention and natural processes to adapt and mitigate climate challenges, including spatial design and hydraulic engineering. The Dutch Climate Research Initiative also highlights cultural heritage as an integral component in preparing for the 2026 National Climate Adaptation Strategy. This article aims to unveil climate-adaptive World Heritage management strategies (CAWHMSs), using WH properties in The Netherlands as a case study. It collects textual data from Statements of Outstanding Universal Value, State of Conservation Reports by the State Parties and management plans. Through qualitative coding and keywords aggregation of the documents, the visualised results of a Sankey diagram and two semantic networks confirmed two CAWHMSs: conservation and developing WH properties as collaborative knowledge hubs. Conservation supports regulating urban climate and sustainable water management. As collaborative knowledge hubs, multidisciplinary sectors explore opportunities to align WH properties with broader sustainable development initiatives. They also deepen younger generations’ awareness of cultural and natural significance relevant to mitigating climate threats. The results emphasise WH as a contributor to climate adaptation. Cross-sectoral stakeholders can advance holistic climate adaptation efforts using CAWHMSs. Full article

The sole function of cultivated land of agricultural production is insufficient to meet the diverse demands of modern agriculture. To address land-use conflicts and achieve the United Nations Sustainable Development Goals (SDGs) of zero hunger and reduced carbon emissions by 2030, this study introduces the theory of land sparing and sharing, uses landscape indices to identify spatially fragmented areas, employs a four-quadrant model to assess the matching status of functional supply and demand, and applies correlation analysis to determine the trade-off/synergy relationships between functions. The results indicate the following: (1) Zhengzhou’s farmland landscape exhibits characteristics of low density, low continuity, and high aggregation, with separation zones and sharing zones accounting for 77% and 23% of the total farmland area, respectively. (2) The multifunctional supply (high in the northeast, low in the southwest) and demand (high in the west, low in the east) of farmland show significant mismatches, with PF and EF exhibiting the most pronounced supply–demand mismatches. The “LS-LD and HS-LD” types of farmland account for the largest proportions, at 39% and 35%, respectively. (3) The study area is divided into four primary types: “PCZ, RLZ, BDZ, and MAZ” to optimize supply–demand relationships and utilization patterns. This study enriches the application of land sparing and sharing in related fields, providing important references for policymakers in optimizing land-use allocation and balancing food and ecological security. Full article

The United States national identity is changing as the non-religious population is growing and fewer Americans follow traditional Christian faiths. When Alexis De Tocqueville visited the United States, he found that the national government gained legitimacy and support from the popular national religion. This faith was nominally Christian but lacked any meaningful theological content. The national creed was a simple monotheism that was supported through the public’s integration of a Cartesian methodology. This national religion was critical in providing the foundation for American economic growth and identity. Today, fewer Americans identify as Christians than at any point in its history, and more citizens have no religious preference or creed. The dominant religious culture is changing, and to understand the United States future, it is important to identify the political preferences of the non-religious population. This paper looks at the most recent Cooperative Election Survey and assesses the non-religious population’s political participation and its aggregate support for U.S. military aid to Ukraine. The data show that the non-religious population is less politically active and more opposed to miliary aid to Kyiv. United States is becoming more secular. The division between the traditional religious and the growing secular populations is generating a cultural conflict—one that has a fundamental consequence for the American national identity. Full article