Search Results (2,477)

Activity space represents the spatiotemporal interaction between individuals and their environment. While most studies measure potential activity space using short-term data, few have defined or measured its actual internal structure. This study introduces “anchor regions” as the core areas where daily activities are concentrated, and conceptualizes the structure of an individual’s activity space by incorporating the concept of regular locations, anchor regions, potential regular activity space, and potential activity space. Using three months of mobile signaling data from 10,848 residents in Shenzhen, we detected anchor regions via a weighted density-based spatial clustering for applications with noise (DBSCAN) method and categorized individuals into six typical activity space structures based on a rule-based taxonomy. We also figured out the intra- and inter-anchor region mobility pattern of each type. Our results show the following: (1) A total of 80% of activities and 87% of time are concentrated in just 26% of locations, forming anchor regions—with 95% of individuals having no more than five such regions. (2) The total area of anchor regions is merely 0.1% of the potential activity space. (3) Six typical structures of activity space are derived with different combinations of several functional anchor regions, including home, weekday anchors, and daily activity anchors. (4) The spatial patterns of the six types are different, while intra-anchor region mobilities dominate daily movement in all six types. This study provides a region-based, instead of a point-based, perspective interpretation of the anchor points theory, helping to better understand the regularities and internal structure of human activity space. Our conceptual framework and methodology have the potential to help urban and transportation planning practice and policy making. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

The Weishan rare earth element (REE) deposit, located in western Shandong, North China Block, is a typical carbonatite REE deposit and constitutes the third largest light REE resource in China. Its mineralization is closely related to the multi-stage evolution of a carbonatite magma–hydrothermal system. However, the mechanisms governing REE enrichment, migration, and precipitation remain insufficiently constrained from a mineralogical perspective, which hampers the understanding of the ore-forming processes and the establishment of predictive exploration models. Apatite is a pervasively developed REE phase in the Weishan deposit which occurs in multiple generations, and thus represents an ideal recorder of the magmatic–hydrothermal evolution. In this study, different generations of apatite hosted in carbonatite orebodies from the Weishan deposit were investigated using cathodoluminescence (CL), electron probe microanalysis (EPMA), and in situ LA-ICP-MS trace element analysis. Three types of apatite were identified. In paragenetic sequence, Ap-1 occurs as polycrystalline aggregates coexisting with calcite, is enriched in Na, Sr, and LREEs, and shows high (La/Yb)_N ratios, suggesting crystallization from an evolved carbonatite magma. Ap-2 and Ap-3 display typical replacement textures: both contain abundant dissolution pits and dissolution channels within the grains, which are filled by secondary minerals such as monazite and ancylite, and thus exhibit characteristic features of fluid-mediated dissolution–reprecipitation during the hydrothermal stage. Ap-2 is commonly associated with barite and strontianite, whereas Ap-3 is associated with pyrite and monazite and is characterized by relatively sharp grain boundaries with adjacent minerals. From Ap-1 to Ap-3, total REE contents decrease systematically, whereas Na, Sr, and P contents increase. All three apatite types lack Eu anomalies but display positive Ce anomalies. Discrimination diagrams involving LREE-Sr/Y and log(Ce)-log(Eu/Y) indicate that apatite in the Weishan REE deposit formed during the magmatic to hydrothermal evolution of a carbonatite, and that the dissolution of early magmatic apatite, followed by element remobilization and mineral reprecipitation, effectively records the progressive evolution of the ore-forming fluid. Full article

Fish passes are essential hydraulic structures that maintain longitudinal connectivity in regulated rivers, but their hydraulic performance may be affected by debris accumulation at chamber openings. This study investigates the influence of partial and total inlet blockage by plant debris on flow conditions within a semi-natural fish pass under field conditions. Hydraulic measurements were conducted at multiple locations along the fish pass, and the effects of debris covering were evaluated using statistical and mixed-effects modeling approaches. Field measurements demonstrated that the Froude number decreases systematically with increasing distance from the inlet, indicating progressive longitudinal dissipation of flow energy along the chamber sequence. Partial debris accumulation caused only marginal changes in the Froude number, remaining close to the threshold of statistical significance. In contrast, mean flow velocity decreased markedly with increasing inlet blockage, by approximately 17% at 50% covering and by about 36% under full blockage, indicating that debris primarily acts as a hydraulic damper rather than inducing a change in flow regime. The highest variability in hydraulic conditions was observed in chambers associated with changes in flow direction and local geometry. These results highlight the dominant role of longitudinal layout and chamber geometry in shaping hydraulic conditions in semi-natural fish passes, while moderate debris accumulation affects local velocities without fundamentally compromising hydraulic functionality. From an ecological perspective, transition zones with elevated hydraulic variability may represent critical locations influencing the swimming effort and passage efficiency of migrating fish. Full article

Many children with cancer referred to physical therapy (PT) do not attend the service. We conducted a pilot study, comprising a cross-sectional survey and interviews with parents of children with acute lymphoblastic leukemia. The survey explored parents’ (1) views on PT service delivery for their child, (2) perspectives on barriers and facilitators, (3) preferred timing to introduce PT, and (4) views on virtual services. Questions were designed based on the Theoretical Domains Framework, and responses were mapped onto the Capability, Opportunity, Motivation–Behavior Change Model. Twenty parents participated in the survey. Although all parents would consider their child accessing PT if deficits were present, access depended on a convenient location (70%) and availability of virtual delivery (45%). While half of the parents preferred PT treatment to be introduced during the maintenance phase of chemotherapy, findings also support earlier introduction during the consolidation phase when services are framed as part of standard care. While most parents perceived that it would be manageable to support home-based PT, barriers included a lack of child’s motivation without therapist support. Seven parents participated in semi-structured interviews. They identified time constraints, distance, and costs as common barriers. Most parents responded positively to hybrid PT models and connections with community locations to mitigate these challenges. Full article

Head and neck cancer (HNC) encompasses tumors located within the oral cavity, sinonasal cavity, pharynx, and larynx. It is the sixth most common cancer worldwide. Current treatment methods in HNC patients involve radical surgery, radical radiotherapy, and concomitant chemoradiotherapy, along with adjuvant and induction therapies. Accumulating trials examine the role of immunotherapy in patients with HNC. The results of the CheckMate-141 and KEYNOTE-048 trials demonstrated the benefits of using immunotherapy in patients with metastatic or recurrent HNC. Subsequently, numerous other immunotherapy-based protocols have been evaluated. Then, KEYNOTE-689 successfully implemented immunotherapy in patients with locally advanced disease. This review aims to comprehensively present the landscape of immunotherapy opportunities in patients with HNC. It summarizes completed key clinical trials that led to the approval of immunotherapy in HNC and presents currently performed trials with highly expected results. Furthermore, it discusses methods to improve immunotherapy outcomes in the cohort of HNC patients, describes the current role of immunotherapy in HNC, and presents future perspectives of this type of treatment. Full article

The rapid expansion of wind energy in Southeast Europe has raised concerns about its long-term impacts on bird populations, particularly through collisions with wind turbines. Here, we analyze systematic collision monitoring data collected between 2010 and 2024 within the Integrated System for Protection of Birds in the Kaliakra Protected Area (northeast Bulgaria). Monitoring covered 52 wind turbines until 2017 and 114 turbines from 2018 onwards, using daily carcass searches within standardized 200 × 200 m plots around each turbine. Collision rate was analyzed using effort-normalized statistical models and spatial (GIS-based) analyses to assess temporal trends and habitat context derived from land-cover data. Effort-normalized analyses indicate that collision rate per turbine varied over time and exhibited a pronounced long-term decline, together with clear spatial heterogeneity. Turbines located in open steppe landscapes were associated with consistently higher collision rates compared to turbines situated in other habitat types. These results provide long-term empirical evidence from an operational wind farm area, contributing robust baseline information for cumulative impact assessment and spatial planning. From a sustainability perspective, long-term, effort-standardized collision monitoring represents a critical tool for balancing renewable energy expansion with biodiversity conservation. By providing empirical evidence on how collision occurrence evolves under sustained operational conditions, this study supports adaptive mitigation, cumulative impact assessment, and spatial planning frameworks essential for the sustainable development of wind energy in ecologically sensitive regions. Full article

Automated livestock monitoring in wide-area grasslands is a critical component of smart agriculture development. Devices such as Unmanned Aerial Vehicles (UAVs), remote sensing, and high-mounted cameras provide unique monitoring perspectives for this purpose. The high-resolution images they capture cover vast grassland backgrounds, where targets often appear as small, distant objects and are extremely unevenly distributed. Applying standard detectors directly to such images yields poor results and extremely high miss rates. To improve the detection accuracy of small targets in high-resolution images, methods represented by Slicing Aided Hyper Inference (SAHI) have been widely adopted. However, in specific scenarios, SAHI’s drawbacks are dramatically amplified. Its strategy of uniform global slicing divides each original image into a fixed number of sub-images, many of which may be pure background (negative samples) containing no targets. This results in a significant waste of computational resources and a precipitous drop in inference speed, falling far short of practical application requirements. To resolve this conflict between accuracy and efficiency, this paper proposes an efficient detection framework named CAMS-AI (Clustering and Adaptive Multi-level Slicing for Aided Inference). CAMS-AI adopts a “coarse-to-fine” intelligent focusing strategy: First, a Region Proposal Network (RPN) is used to rapidly locate all potential target areas. Next, a clustering algorithm is employed to generate precise Regions of Interest (ROIs), effectively focusing computational resources on target-dense areas. Finally, an innovative multi-level slicing strategy and a high-precision model are applied only to these high-quality ROIs for fine-grained detection. Experimental results demonstrate that the CAMS-AI framework achieves a mean Average Precision (mAP) comparable to SAHI while significantly increasing inference speed. Taking the RT-DETR detector as an example, while achieving 96% of the mAP_50–95 accuracy level of the SAHI method, CAMS-AI’s end-to-end frames per second (FPS) is 10.3 times that of SAHI, showcasing its immense application potential in real-world, high-resolution monitoring scenarios. Full article

This paper investigates the planning problem of AC-integrated wind–photovoltaic–hydro–storage (WPHS) bundled transmission systems. To effectively capture the uncertainty and interdependence in renewable power outputs, a Copula-enhanced distributionally robust optimization (DRO) framework is developed, enabling a unified treatment of stochastic and correlated renewable generation within the system planning process. First, a location and capacity planning model based on DRO for WPHS generation bases is formulated, in which a composite-norm ambiguity set is constructed to describe the uncertainty of renewable resources. Second, the Copula function is employed to characterize the nonlinear dependence structure between wind and photovoltaic (PV) power outputs, providing representative scenarios and initial probability distribution (PD) support for the construction of a bivariate ambiguity set that embeds coupling information. The resulting optimization problem is solved using the column and constraint generation (C&CG) algorithm. In addition, an evaluation metric termed the transmission corridor utilization rate (TCUR) is proposed to quantitatively assess the efficiency of external AC transmission planning schemes, offering a new perspective for the evaluation of regional power transmission strategies. Finally, simulation results validate that the proposed model achieves superior performance in terms of system economic efficiency and TCUR. Full article

With the enactment of the 2013 government mandate, Indian corporations meeting specific criteria no longer have the discretion to forgo CSR expenditures. Previous studies have reported negative capital market reactions to this regulatory intervention. In contrast, our study offers a long-term perspective on the impact of the CSR law on firms’ investment efficiency. Using a difference-in-differences framework, this study examines publicly listed Indian firms from 2011 to 2018, capturing a clean pre- and post-mandate window that isolates the structural impact of the CSR law while excluding confounding and shocks such as the COVID-19 crisis. Thus, the paper focuses on identifying the long-term institutional and structural effects of CSR rather than short-term cyclical fluctuations. We find that the CSR law leads to an increase in the investment efficiency of affected firms, driven primarily by reductions in agency conflicts and information asymmetry. This effect is more pronounced among firms with a strong presence of active monitoring groups, such as Hindu-owned promoters and institutional investors. Improved efficiency is also profound among firms located in areas with a lower Human Development Index (HDI) and Gender Diversity Index (GDI). Our findings demonstrate the positive impact of mandatory CSR law on capitalism and present insights for policymakers for regulators as ESG and CSR mandates are increasingly debated and adopted. Full article

Small-scale artisanal mine production processes are characterized by significant environmental and human health impacts, especially in countries with ineffective economic resources and policies. This study accurately quantifies the impacts of artisanal gold production processes, identifying the dominating hotspots in a holistic perspective. The life cycle assessment (LCA) methodology was applied to perform an environmental sustainability appraisal for Doré and Cyanidation Gold (CyG) extraction from Mina Nueva, a small-scale artisanal mine managed by the local population, located in Asociación Campesina del Valle del río Cimitarra, near the city of Segovia in the department of Antioquia in Colombia. The obtained single-score LCA results showed a total damage of 4.99 × 10⁰² Pt, of which 55.2% was associated with the cyanidation process, 34.4% with the whole-ore amalgamation phase, and 10.4% with mine construction. A sensitivity analysis was also performed to study the potential effects of particulate emissions generated by the mine construction phase. Full article

Enhancing corporate green innovation is a critical component of advancing sustainable transformation and addressing escalating climate-related risks. From a cognition-to-behavior perspective, this paper constructs a climate risk perception index based on annual report texts from Chinese A-share listed firms from 2003 to 2023 and examines its impact on corporate green innovation, as well as the underlying mechanisms. The study finds that stronger climate risk perception significantly promotes both the quality and quantity of corporate green innovation. Mechanism analyses show that this effect operates through alleviating financing constraints, increasing research and development (R&D) investment, and improving environmental, social, and governance (ESG) performance. Heterogeneity tests further indicate that the positive impact is more pronounced among firms located in eastern China and among state-owned firms. Regarding scale heterogeneity, climate risk perception boosts the quantity of green innovation more effectively in large firms and boosts the quality of green innovation more effectively in small firms. This study provides micro-level evidence and theoretical insights into corporate green transformation behaviors under climate uncertainty. Full article

In the context of urban development transforming from external expansion to internal improvement, identifying the patterns and characteristics of urban industrial land shrinkage and proposing response strategies are crucial for achieving high-quality and sustainable urban development. Unlike previous studies that focused on the expansion of industrial land based on absolute changes in land area, we propose a formation mechanism for urban industrial land shrinkage from the perspectives of both absolute and potential shrinkage. We quantitatively identified the Chinese cities that experienced shrinkage between 2006 and 2020, and developed a comprehensive indicator system to investigate the changes in the structural and functional characteristics of industrial land use during this process. The results indicated that urban industrial land shrinkage has become a widespread phenomenon nationwide. Absolute shrinkage was predominantly attributed to resource depletion and a lack of economic development, while potential shrinkage was mainly influenced by high-quality development and a lack of economic development. Cities exhibiting potential shrinkage experienced more severe structural deterioration, while cities with absolute shrinkage faced greater functional degradation. Extensive land use remains a serious challenge that must be addressed in industrial land redevelopment. Finally, we propose that more attention should be given to the utilization of stock urban industrial land, in particular in cities with relatively low administrative levels, small populations, and remote locations. Urban land redevelopment projects need to be conducted in accordance with the principle of intensive land use to promote the sustainable development of cities. Full article

Groundwater vulnerability assessments serve as essential tools for sustainable groundwater management, particularly in regions with intensive anthropogenic activities. However, improving the objectivity and predictive reliability of vulnerability assessment frameworks remains a critical scientific challenge in groundwater science, especially for coastal aquifer systems characterized by strong heterogeneity and complex hydrogeological processes. The traditional DRASTIC model is a widely recognized method but suffers from subjectivity in assigning parameter ratings and weights, often leading to arbitrary and potentially inaccurate vulnerability maps. This limitation also restricts its applicability in areas with complex hydrogeological conditions. To enhance the accuracy and adaptability of the traditional DRASTIC model, a hybrid PSO-BP-DRASTIC framework was developed and applied it to a coastal city in China. Specifically, the model employs a backpropagation neural network (BP-NN) to optimize indicator weights and integrates the particle swarm optimization (PSO) algorithm to refine the initial weights and thresholds of the BP-NN. By introducing a data-driven and globally optimized weighting mechanism, the proposed framework effectively overcomes the inherent subjectivity of conventional empirical weighting schemes. Using ten-fold cross-validation and observed nitrate concentration data, the traditional DRASTIC, BP-DRASTIC, and PSO-BP-DRASTIC models were systematically validated and compared. The results demonstrate that (1) the PSO-BP-DRASTIC model achieved the highest classification accuracy on the test set, the highest stability across ten-fold cross-validation, and the strongest correlation with the nitrate concentrations; (2) the importance analysis identified the aquifer thickness and depth to the groundwater table as the most influential factors affecting groundwater vulnerability in Yantai; and (3) the spatial assessments revealed that high-vulnerability zones (7.85% of the total area) are primarily located in regions with intensive agricultural activities and high aquifer permeability. The hybrid PSO-BP-DRASTIC model effectively mitigates the subjectivity of the traditional DRASTIC method and the local optimum issues inherent in BP-NNs, significantly improving the assessment accuracy, stability, and objectivity. From a scientific perspective, this study demonstrates the feasibility of integrating swarm intelligence and neural learning into groundwater vulnerability assessment, providing a transferable and high-precision methodological paradigm for data-driven hydrogeological risk evaluation. This novel hybrid model provides a reliable scientific basis for the reasonable assessment of groundwater vulnerability. Moreover, these findings highlight the importance of integrating a hybrid optimization strategy into the traditional DRASTIC model to enhance its feasibility in coastal cities and other regions with complex hydrogeological conditions. Full article

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan towns located on the coast and in the pre-coastal region from 1950 to 2023, using reanalysis-based indicators of air temperature, humidity, and perceived heat as a first exploratory step prior to incorporating in situ meteorological records. Specifically, the Heat Index (HI) and the Universal Thermal Climate Index (UTCI) were computed for the typical event window (21:00–23:00 local time) to assess changes in human thermal comfort. Results reveal a clear and statistically significant warming trend in most pre-coastal locations—particularly Reus, El Vendrell, and Vilafranca—while coastal cities such as Barcelona exhibit weaker or non-significant changes, likely due to maritime moderation. The frequency and intensity of positive temperature anomalies have increased since the 1990s, with a growing proportion of events falling into “caution” or “moderate heat stress” categories under HI and UTCI classifications. These findings demonstrate that correfocs are now celebrated under markedly warmer night-time conditions than in the mid-twentieth century, implying a tangible rise in thermal discomfort and potential safety risks for participants. By integrating climatic and cultural perspectives, this research shows that rising night-time heat can constrain attendance, participation conditions, and event scheduling for correfocs, thereby directly exposing weather-sensitive form of intangible cultural heritage to climate risks. It therefore underscores the need for climate adaptation frameworks and to promote context-specific strategies to sustain these community-based traditions under ongoing Mediterranean warming. Full article