Search Results (22,225)

Large-scale hydropower development provides substantial socio-economic and energy benefits but simultaneously introduces complex ecological and environmental challenges that require comprehensive scientific assessment. This study systematically evaluates the effects of the leading reservoir (Longpan hydropower station, referring to the uppermost and principal flow-regulating dam in the cascade) in the middle reaches of the Jinsha River’s operation on the water environment of the mainstream Yangtze River, China, with the aim of clarifying its water quality responses and supporting evidence-based basin management. Based on an analysis of the current water quality conditions of the Yangtze River and a comparative review of the operational experience of the Three Gorges Reservoir, this research explores the mechanisms through which large reservoirs alter hydrological and ecological processes. These mechanisms include reduced flow velocity, prolonged water residence time, weakened pollutant dispersion, and increased risk of algal blooms in tributaries. To quantitatively assess these impacts, an improved river dilution–mixing model was developed and applied to simulate the water quality response during the dry season (February–April) under different discharge scenarios. Key downstream monitoring sections were examined. The modeling results indicate that the operation of the Leading reservoir can moderately reduce dry-season concentrations of key pollutants (e.g., total phosphorus, permanganate index) at downstream sections by approximately 2–5% on average, with spatially heterogeneous effects. Although the overall improvement magnitude remains limited, the combined effects of sediment deposition and in situ degradation may yield more pronounced real-world benefits. The findings underscore the importance of optimizing the regulatory function of the Longpan Reservoir through coordinated operation within the cascade reservoir system. It is recommended to integrate water resource allocation, water quality management, and aquatic ecosystem protection, alongside enhanced pollution control and ecological restoration in key zones. The methodology and findings provide a referenced framework for assessing the water-environmental implications of large-scale reservoir regulation in other major river systems. Full article

The expansion of the invasive species Pedicularis kansuensis threatens the ecological integrity of alpine wetlands, particularly in the Bayinbuluke, northwestern China. However, operational monitoring remains challenging. Conventional indices often lack specificity in heterogeneous alpine backgrounds, while deep learning models are typically too data-intensive to support consistent, multi-year mapping. To develop a rapid, reliable, and operational method for monitoring this invader, we proposed a novel, species-specific spectral index, the Pedicularis kansuensis Index (PKI), using the blue, green, and red-edge bands of high-resolution (3 m) PlanetScope imagery. The PKI constructs a robust target signal by integrating distinct spectral features derived from in situ hyperspectral measurement with a grayscale morphological opening (GrMO) refinement to suppress background noise. A comprehensive validation against seven established benchmarks indices (e.g., NDVI, RI, and ARI) demonstrated the superior performance of PKI across the central alpine wetlands of Bayinbuluke (2841 km²). It achieved the highest separability with an M-statistic of 1.36. Furthermore, the index attained an overall accuracy of 93.52% (95% CI: 92.3–94.7%), and an F1-score of 93.28% (95% CI: 92.0–94.5%), effectively minimizing confusion with co-occurring native vegetation and background. Applying this framework to a five-year time series (2021–2025) revealed a distinct cycle of outbreaks and relaxation. Specifically, the invaded area increased to 2168 ha in 2022, then decreased to 160 ha in 2025. Spatial analysis further identified stable invasion hotspots of 161.6 ha, highlighting key targets for long-term containment. Meanwhile, 94.4% of the invaded area was transient, lasting only one year (4824.7 ha). These results confirm that the PKI is a physically interpretable, accurate, and computationally efficient tool for monitoring invasive species in heterogeneous alpine environments. It facilitates timely and targeted ecosystem management. Full article

Road traffic accidents remain a major public health concern, and effective safety management requires interpretable tools that integrate multiple causal dimensions. This study proposes a Traffic Accident Causation Index (TACI) to provide a holistic representation of severity-related drivers by combining six theoretically grounded domains: Accident Infrastructure, Driver, Pedestrian, Road Condition, Emergency and Response, and Severity. Using a national police-reported dataset from Türkiye (N = 13,639), operational variables are mapped to normalized risk scores, aggregated into domain indices, and combined into a 0–100 composite TACI score. To assess the robustness and compatibility of the proposed index framework, we develop ensemble machine learning models (Random Forest, Gradient Boosting, LightGBM, XGBoost, and CatBoost) under two feature configurations: an Extended Feature Set (EFS) with the original variables and a Core Feature Set (CFS) consisting of the six domain indices. The results indicate that domain-level aggregation improves predictive stability, and the best-performing boosting models (XGBoost/CatBoost) achieve near-perfect agreement with the constructed index (test R² > 0.99) and very high classification performance (AUC > 0.999). SHAP-based explainability highlights pedestrian exposure and vulnerability as the dominant contributors, followed by lighting/visibility conditions, road surface quality, and adverse road–environment factors, whereas emergency-response and infrastructural attributes show comparatively indirect effects. Overall, the proposed framework supports interpretable, domain-oriented evidence for prioritizing safety interventions and monitoring high-risk accident conditions. Full article

Safe Beyond Visual Line of Sight (BVLOS) operations are increasingly required for wildlife monitoring and conservation, yet existing regulatory frameworks are rarely tailored to protected areas characterised by low population density and limited infrastructure. This paper presents a field-based use case illustrating how the Specific Operations Risk Assessment (SORA) methodology can be applied to conservation-oriented BVLOS missions under Kenyan airspace conditions, including coordination within military-controlled airspace. We evaluate three population-density estimation approaches (qualitative, bottom-up, and top-down) against available ground truth, and compare tabulated and analytical SORA methods for deriving the Ground Risk Class. The work illustrates how SORA 2.5 structures ground and air risk reasoning in a conservation context, while retrospective review identifies limitations in containment, Operational Safety Objectives, and tactical mitigation performance requirements. Field trials involved five concurrent teams and 30 personnel conducting over 260 flights and more than 60 h of UAS activity across the Ol Pejeta Conservancy, providing insights into multi-team coordination under field conditions. Field implementation revealed areas of misalignment between prescribed safety requirements and operational realities, prompting iterative adaptation of workflows and procedures. Observed outcomes included reductions in team size (25–50%) and procedural steps (18%), derived from retrospective comparison of field procedures. A lightweight Uncrewed Traffic Management prototype was also trialled, revealing practical limitations in conservancy environments. Finally, we present a ten-step framework for developing field-ready safety procedures to support risk-informed decision-making in non-standard operational contexts. The findings provide empirically grounded guidance on applying SORA principles to conservation UAS missions, without proposing a new risk framework or generalised operational model. Full article

Public buildings and open spaces form key elements in an exchange system of both tangible resources (energy, water, physical spaces) and intangible assets (services, skills, time). This study presents an innovative protocol (AGAPE—Automatic GIS Assessment Protocol for Energy and environment) to regenerate metropolitan suburbs by managing common resources and support sustainable communities. It tackles energy poverty by integrating urban planning, environmental design, and economics into geographic information science. This expedites public well-being by redesigning public facilities to enhance community connections and improve bioclimatic resilience. The model test site is a peripheral suburban area, Melito di Napoli, within the Metropolitan City of Naples (Italy), characterized by high population density and ongoing suburban expansion. The protocol evaluates temporal scenarios for implementing multi-purpose solutions, supporting public agencies in strategic intervention assessments, optimizing funding allocation and community benefits. The modeling of redesigned community assets reveal key outcomes: renewed land-use opportunities, reduced spatial inequities, and increased climate change resilience. The transformation of public buildings and facilities into multi-benefit community cores catalyzes virtuous urban regeneration processes. The model AGAPE provides a replicable decision framework to transform existing settlements and to drive the transition towards more sustainable, equitable urban communities. Full article

The world is experiencing incredible biodiversity loss, including the decline of iconic species, such as elephants. The species faces an uncertain future due to habitat loss, human-elephant conflict, poaching and climate change, reminding us of the urgency of acting on a local and global scale. Art has historically been a powerful medium for expressing ideas and emotions, fostering deep connections for people. Therefore, this paper explores the impact of the sharing of scientific content through dance on conservation values in young people. Understanding conservation needs and analyzing what drives people to gain an emotional affinity towards the environment has shown the potential to support and innovate traditional education. The work presented here uses a dance piece, performed through a choreographic process with dance students, to educate an audience about the importance and behavior of the African savannah elephant and the threats to its survival. Our findings indicated differences between the level of knowledge and opinion of the audience throughout the different phases of the methodology explored here, revealing that dance (and artistic) education can provide knowledge and stimulate more empathy for species conservation. Full article

The governance of the rural water environment is essential for improving the quality of life of rural residents and advancing the construction of ecological civilization. However, the current governance system faces issues such as fragmented governance entities and low collaborative efficiency. Therefore, in this study, we focus on the intergovernmental collaborative governance mechanism for rural water environments. Drawing on complex network theory and evolutionary game theory, we employ complex network analysis and construct a complex network evolutionary game model among government departments, and we further conduct numerical simulations to examine the evolutionary dynamics of intergovernmental collaboration in rural water environmental governance. The findings show the following: (1) The reward and punishment mechanism, collaborative gain coefficient, and loss intensification trend coefficient all positively influence the participation rates of local governments. When these parameters exceed certain thresholds, they can rapidly and stably increase the proportion of participating nodes. (2) Nodes with stronger environmental preferences respond more directly to the collaborative gain coefficient, while the loss intensification trend coefficient promotes cooperation by amplifying the cost of non-cooperation. (3) The heterogeneity in economic preferences of local governments affects the stability of cooperation. Governments with stronger environmental priorities are more inclined to form the core of cooperation, whereas those driven by stronger economic priorities are more vulnerable to parameter fluctuations, leading to instability in overall participation levels. Reducing or eliminating this heterogeneity can improve both participation rates and the stability of cooperation. These findings offer theoretical support for designing intergovernmental collaborative governance mechanisms for rural water environments and provide practical guidance for calibrating reward–punishment schemes, identifying key coordinating departments, and stabilizing cross-departmental participation, thereby facilitating an efficient transition in rural water environmental governance models. Full article

Building height is a fundamental parameter for characterizing urban three-dimensional structure and supporting applications such as urban planning, population estimation, and energy assessment. However, traditional shadow-based height inversion methods often suffer from occlusion, shadow overlap, and orientation inconsistencies when applied to heterogeneous urban environments. This study proposes a single-image building height estimation method that explicitly incorporates spatial distribution characteristics to enhance robustness and estimation accuracy. Shadow lengths are first robustly extracted using a fishnet–Pauta strategy, followed by a multi-scenario scaling coefficient model accommodating different sun–sensor geometric configurations. Urban areas are then subdivided into high-rise, mid-to-high-rise mixed, and dense low-rise zones using DBSCAN clustering and a composite indicator system. For each spatial type, tailored optimization strategies—including neighborhood-weighted correction, similarity-constrained local regression, and median smoothing—are applied to suppress systematic biases and local outliers. Experiments on 11,168 buildings across 13 Chinese cities demonstrate strong overall performance, achieving an MAE of 2.07 m, an RMSE of 2.56 m, and an R² of 0.99. The proposed method outperforms existing approaches and remains highly stable across diverse urban morphologies, providing a scalable solution for large-area building height mapping from single high-resolution imagery. Full article

(1) The bird colouration is the result of adaptation to environmental conditions, predator–prey relationships, and sexual selection (intraspecific competition and signalling of quality). Only a few studies have explicitly explored the plumage colouration of birds at the level of species communities. (2) Methods: We combined data with bird plumage colours and their spatial distribution at a large spatial scale in Italy, exploring the relationship between community colours and different types of habitats and landscape heterogeneity. (3) Results: Overall, we found that the more representative colours of avian communities were grey, white, black, and brown. The percentage of black colour in the community was smaller in close habitats (e.g., forests). A high percentage of brown was observed in forests and shrublands, whereas a high percentage of white was found in wetlands, water bodies, and urban areas. The percentage of yellow was relatively low overall, but it was slightly higher in deciduous forests. Land use richness increased the percentage of brown, green, rufous, and yellow, while negatively affecting other pigments (black and grey = melanins, purple = structural, and red = carotenes). The community colour inequality decreased when the species and land use richness increased, while it increased when the weighted edge density of surrounding landscapes increased. Finally, we found that bird communities that are made up of closely related species show a wider variety of colours (e.g., lower colour inequality). This supports the idea that closely related species that live together develop different features to improve species recognition. (4) Conclusions: We found that the colours of bird communities are related to the type of environment. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

Cities play a central role in shaping societal responses to the climate crisis, concentrating both on climate risks and institutional capacity to address them. While climate impacts are widely distributed, they are experienced unevenly, with marginalized populations facing disproportionate exposure to economic disruption and environmental stress, particularly in urban environments. This article examines how cities can enhance climate resilience while supporting a just transition to a post-carbon economy. It addresses three interrelated questions: how vulnerable urban populations can be better prepared for green employment; how transformations in work and commuting can promote compact, mixed-use, and transit-friendly urban districts; and how such districts can be designed to protect residents from urban heat and improve walkability through shade and nature-based solutions. The analysis synthesizes findings from recent empirical studies and applied policy initiatives, including a municipal green-employment pilot in Tel Aviv-Yafo, the “Reinventing Paris” office-to-housing program, and urban heat and pedestrian-behavior research. Together, these cases illustrate how physical adaptation strategies interact with labor-market dynamics and social policy. The article concludes that effective urban climate resilience requires integrating infrastructural and spatial interventions with labor-market transformation, social protection, and inclusive governance, positioning cities as key operational units for advancing equitable climate action. Full article

Schistosomiasis elimination is increasingly constrained less by the technical efficacy of single interventions than by systemic dynamics in coupled human–animal–environment settings, including nonlinear feedback, spatial heterogeneity, and cross-sectoral govern frictions. We conducted a systematic methodological review (search date: 1 January 2026) across PubMed, Web of Science, Scopus, EconLit, and CNKI to identify studies that (i) addressed schistosomiasis control, (ii) used explicit system-based, causal, or network-oriented analytical structures, and (iii) incorporated economic evaluation with multi-domain outcomes. We synthesized modeling architectures, economic methods, and approaches to trade-offs and uncertainty, and applied an evidence-informed systemic causality framework to assess decision-analytic adequacy. The literature grouped into three related strands: transmission and system dynamics models that capture feedback processes and rebound risks; economic evaluations dominated by cost-effectiveness analyses; and cross-sectoral or surveillance-oriented decision models optimizing implementation under resource constraints. Across strands, elimination-stage investments such as surveillance, environmental management, and coordination exhibit strong externalities and quasi-public-good properties that are systematically undervalued in single-sector, single-metric frameworks. We argue that decision-relevant evaluation should be reframed as a multi-objective resource allocation problem that integrates systemic modeling with economic valuation, explicitly addresses uncertainty, and applies multi-criteria decision analysis to support long-horizon, cross-sectoral decision-making. Full article

Surface fouling remains a critical challenge for medical devices and chemosensor systems operating in biological environments, where nonspecific adsorption of proteins, cells, and microorganisms can lead to signal drift, reduced sensitivity, and shortened device lifetime. Conventional antifouling strategies rely primarily on synthetic hydrophilic polymer coatings, such as polyethylene glycol and polyvinylpyrrolidone, which are effective but face limitations related to long-term stability, thickness, and compatibility with surface-sensitive sensing modalities. In this review, we focus on hydrophobins derived from mushroom-forming and filamentous fungi as a bio-based alternative for antifouling and anti-wetting surface modification. Mushroom-derived hydrophobins are small amphiphilic proteins capable of spontaneous self-assembly into nanometer-scale films that modulate surface energy, wettability, and interfacial friction without requiring covalent functionalization. The current state of research on hydrophobin structure, classification, and self-assembly is reviewed, followed by a synthesis of reported antifouling and tribological behaviors relevant to medical and sensor-adjacent surfaces. Representative experimental observations are discussed to illustrate trends consistent with the literature, without establishing new performance benchmarks. The implications of mushroom-derived hydrophobin coatings for chemosensors and biosensors are examined, particularly with respect to signal stability, surface accessibility, and durability. Limitations and future research directions are outlined to support translation into practical sensing technologies. Full article

A support vector machine based on AdaBoost algorithm (SVM-AB) is proposed for complicated underground mine tunnel modeling. This method accurately predicts the nonlinear propagation characteristics of electromagnetic waves in complex environments in the case of small samples. Firstly, an electromagnetic wave propagation loss model is established by analyzing complex factors including tunnel geometry, wall roughness, tilt, dielectric properties, and multipath effects. Secondly, the complex factors and measured signal strength serve as inputs of the SVM model to establish a nonlinear mapping for preliminary prediction. Furthermore, the AdaBoost algorithm is applied to dynamically correct the SVM prediction errors, further enhancing accuracy. Finally, the measured experiments are carried out in complex underground mine tunnels to verify the proposed theoretical model. The experimental results demonstrate that the proposed SVM-AB model achieves a fitting accuracy of over 99.92%. In addition, compared with the traditional support vector machine, its Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by about 84.76% and 92.61%, respectively. The proposed tunnel model has important application value for optimizing the layout of communication system of underground mine tunnel. Full article

The rapid expansion of telecommunication infrastructure in developing countries has increased the demand for sustainable strategies to deploy field engineers in tower maintenance operations. Traditional approaches often neglect spatial factors, resulting in inefficient workforce allocation, excessive travel, and higher carbon emissions. This study develops an applied geospatial deployment framework that integrates spatial analysis with sustainable supply chain management (SSCM) principles to support operational decision-making in resource-constrained telecommunication maintenance environments. Using publicly available tools, tower and homebase coordinates were mapped and analyzed through Haversine-based geodesic distance calculations, with a comparative assessment against Euclidean approximation, while incorporating operational constraints such as service time per tower, available personnel, and work-hour limitations. The results indicate that the existing two-homebase deployment strategy leads to unbalanced workloads and unnecessary travel distances. By introducing a cluster-based restructuring using k-means to identify four sub-homebases, the proposed approach reduces total round-trip travel distance from 9120 km to 5913 km per maintenance cycle, representing a 35.2% reduction. This distance reduction corresponds to an estimated saving of approximately 593 kg of CO₂ emissions per maintenance cycle, representing an operational-scale reduction in travel-related emissions based on distance-derived fuel consumption modeling and assuming typical fuel efficiency for service vehicles. In addition, the optimized spatial configuration enables a more equitable distribution of engineers and reduces travel-related fatigue. These findings demonstrate the value of integrating geospatial optimization with sustainable supply chain management by aligning operational efficiency with quantifiable environmental and social sustainability outcomes. The proposed framework offers a replicable, low-cost, and data-driven solution for telecommunication infrastructure providers seeking to enhance the sustainability of field service operations in resource-constrained environments. Full article