Search Results (85)

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

Background: Pain is a disabling issue in athletes, with significant impact on performance and career longevity. Many randomized clinical trials (RCTs) have explored interventions to reduce pain, leading to multiple systematic reviews with meta-analysis, but their methodological rigor and clinical applicability remain unclear. Objective: To provide an overview of systematic reviews with meta-analysis on interventions aimed at alleviating pain intensity in athletes, identifying knowledge gaps and appraising methodological quality. Methods: CINAHL, Embase, Epistemonikos, PubMed, Scopus, SPORTDiscus, and Cochrane Library were searched from inception to February 2025. Systematic reviews with meta-analysis of RCTs evaluating interventions to manage pain in athletes were considered. Athletes without restrictions in terms of sports, clinical, and sociodemographic characteristics were included. Overlap between reviews was calculated using the corrected covered area. Results: Twelve systematic reviews met inclusion criteria. Physical exercise modalities (e.g., gait retraining, hip strengthening), acupuncture, photo biomodulation, and topical medication showed potential benefits in reducing pain intensity. Other interventions, such as certain manual therapy techniques, platelet-rich plasma, or motor imagery, did not show consistent effects. All reviews focused solely on pain intensity, with minimal stratification by sport or clinical condition which may affect the extrapolation of meta-analyzed findings to the clinical practice. Methodological quality was often low, with flaws in reporting funding sources, lists of excluded studies, and certainty of evidence (was mostly rated as low/very low). Overlap was variable across the interventions. Conclusions: Given low/sparse certainty and minimal sport-specific analyses, no strong clinical recommendations can be made; preliminary signals favor proximal hip strengthening, gait retraining, photo biomodulation (acute soreness), and topical NSAIDs pending higher-quality syntheses. Future reviews should consider mandatory GRADE; pre-registered protocols; sport- and condition-specific analyses; and core outcome sets including multi-dimensional pain. Full article

Coastal public spaces in large Euro-Mediterranean cities represent critical zones of negotiation between land and sea, where ecological fragilities, infrastructural pressures and social demands intersect. Grounded in the concept of the urban amphibious, this study explores the spatial-functional complexity of city-sea interfaces through the development of CITI4SEA (City-Sea Interface Typological Indicators for Spatial-Ecological Assessment), an original multidimensional framework for the evaluation of coastal public spaces. The methodology builds on a geo-database of 149 coastal municipalities in eight EU Member States and applies a set of indicators to seven major cities (with populations over 500,000 and comprehensive port infrastructure). Through a structured evaluation grid applied to 23 coastal public spaces, the framework enables a cross-comparative analysis of spatial configurations, ecological qualities, and patterns of public use. Results reveal the emergence of transnational clusters based on shared planning logics and degrees of socio-environmental integration, rather than geographic proximity. The study also identifies asymmetries in accessibility, environmental performance and equipment provision. Beyond mapping spatial disparities, the contribution offers a replicable tool for assessing littoral transformations within the broader framework of Integrated Coastal Zone Management (ICZM) and Maritime Spatial Planning (MSP), supporting context-specific strategies for resilient and inclusive coastal governance. Full article

Rapid urbanization has intensified the systemic inequities of resources and infrastructure distribution in informal settlements, particularly in the Global South. Digital Twin Modeling (DTM), as an effective data-driven representation, enables real-time analysis, scenario simulation, and design optimization, making it a promising tool to support urban resilience. This study introduces the Integrated Modification Methodology (IMM), developed by Politecnico di Milano (Italy), to explore how DTM can be systematically structured and transformed into an active instrument, linking theories with practical application. Focusing on Paranoá (Brasília), a case study developed under the NBSouth project in collaboration with the Politecnico di Milano and the University of Brasília, this research integrates advanced spatial mapping with comprehensive key performance indicators (KPIs) analysis to address developmental and environmental challenges during the regeneration process. Key metrics—Green Space Diversity, Ecosystem Service Proximity, and Green Space Continuity—were analyzed by a Geographic Information System (GIS) platform on 30 m by 30 m sampling grids. Additional KPIs across urban structural, environmental, and mobility layers were calculated to support the decision-making process for strategic mapping. This study contributes to theoretical advancements in DTM and broader discourse on urban regeneration under climate stress, offering a systemic and practical approach for multi-dimensional digitalization of urban structure and performance, supporting a more adaptive, data-based, and transferable planning process in the Global South. Full article

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

Current tourism route recommendation systems often overemphasize popular destinations, thereby overlooking geographical accessibility between attractions and the experiential coherence of the journey. Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial–Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are defined and fused: spatial proximity, resonance correlation, and thematic-sentiment similarity, forming a Tourist Attraction Multidimensional Association Network (MAN-SRT). This network enables precise characterization of complex inter-attraction dependencies. Building upon MAN-SRT, the Tourism Sentiment Chain (TSC) model is proposed that incorporates geographical accessibility, associative resonance, and thematic-sentiment synergy to optimize the selection and sequential arrangement of attractions in personalized route planning. Results demonstrate that SSIM-TAR effectively captures the integrated attributes and experiential quality of tourist attractions, while MAN-SRT reveals distinct multidimensional association patterns. Compared with popular platforms such as “Qunar” and “Mafengwo”, the TSC approach yields routes with enhanced spatial efficiency and thematic-sentiment coherence. This study advances tourism route modeling by jointly analyzing multidimensional experiential quality through spatial–semantic feature fusion and by achieving an integrated optimization of geographical accessibility and experiential coherence in route design. Full article

Urban climate resilience is shaped by both direct exposure to environmental risks and cognitive, socioeconomic and institutional factors. This study investigates climate change risk perception (CCRP), psychological distance (PD) and adaptive capacity (AC) across five districts of Istanbul: Beşiktaş, Kadıköy, Kağıthane, Şişli and Üsküdar, using a structured survey (sample size = 500) and advanced multivariate statistical modeling to explore the factors influencing adaptive behavior. To evaluate perceptual and behavioral responses to climate threats, the study constructs both equal-weighted indices and indices derived through principal component analysis (PCA). ANOVA and chi-square tests reveal significant district-level differences in risk perception and adaptation engagement. PCA results validate the internal structure of the indices by identifying latent dimensions such as institutional confidence, emotional proximity and self-efficacy. Correlation and regression analyses confirm that CCRP and PD significantly predict AC in theoretically meaningful patterns. Structural equation modeling (SEM) demonstrates both direct and indirect pathways linking climate risk perception to adaptive capacity, highlighting the complex interplay of these variables. Mediation analysis shows that PD partially mediates the CCRP–AC relationship, accounting for 39.7% of the total effect. Cluster analysis identifies distinct cognitive profiles where proactive adaptation behaviors are more common in affluent districts while disengagement is more prevalent in low-income areas. These findings underscore the importance of localized communication efforts, institutional credibility and financial equity in shaping effective climate adaptation. By integrating perceptual and structural dimensions, the study advances a multidimensional understanding of urban climate readiness and offers empirical guidance for socially equitable resilience policy design. Full article

As consumer behavior increasingly shifts toward hyperlocal, digitally mediated retail journeys, community unmanned stores have emerged as a transformative model that integrates smart technologies with community proximity services. These fully automated stores offer convenient, contactless shopping and hybrid digital–physical interactions, playing an increasingly important role within broader omnichannel digital retail ecosystems. However, there remains a lack of validated instruments to assess customer experience in such autonomous and locally embedded retail formats. This study develops and validates an ECUS-scale (an experience in community unmanned store scale), a multidimensional measurement tool grounded in qualitative research and refined through exploratory and confirmatory factor analysis. The scale identifies nine key dimensions—convenient service, smooth transaction, preferential price, good quality, safe environment, secure payment, comfortable space, comfortable interaction, and friendly image—across 36 items. These dimensions reflect the technological, spatial, and emotional–social aspects of customer experience in unmanned retail settings. The findings demonstrate that the ECUS-scale offers a robust framework for evaluating consumer experience in low-staffed, tech-enabled community stores, with strong relevance to omnichannel digital retail strategies. Theoretically, it advances the literature on smart retail experience by capturing underexplored dimensions such as emotional engagement with technology and perceptions of safety in staff-free environments. Practically, it serves as a diagnostic tool for businesses to enhance experience design and optimize customer engagement across digital and physical touchpoints. Full article

Urban regeneration is pivotal to sustainable development, requiring innovative strategies that align social dynamics with spatial configurations. Traditional paradigms increasingly fail to tackle systemic challenges—neighborhood alienation, social fragmentation, and resource inequality—due to their inability to integrate human-centered spatial governance. This study addresses these shortcomings with a novel multidimensional framework that merges social perception (life satisfaction) analytics with spatial quality (GIS-based) assessment. At its core, we utilize geospatial and machine learning models, deploying an ensemble of Gradient Boosted Decision Trees (GBDT), Random Forest (RF), and multiscale geographically weighted regression (MGWR) to decode nonlinear socio-spatial interactions within Suzhou’s community environmental matrix. Our findings reveal critical intersections where residential density thresholds interact with commercial accessibility patterns and transport network configurations. Notably, we highlight the scale-dependent influence of educational proximity and healthcare distribution on community satisfaction, challenging conventional planning doctrines that rely on static buffer-zone models. Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. Air quality emerges as a critical determinant, while factors such as proximity to educational institutions, healthcare facilities, and public landmarks exhibit nonlinear effects across spatial scales. (2) Housing price growth in Suzhou displays significant spatial clustering, with a Moran’s I of 0.130. Green space coverage positively correlates with price appreciation (β = 21.6919 ***), whereas floor area ratio exerts a negative impact (β = −4.1197 ***), highlighting the trade-offs between density and property value. (3) The MGWR model outperforms OLS in explaining housing price dynamics, achieving an R² of 0.5564 and an AICc of 11,601.1674. This suggests that MGWR captures 55.64% of pre- and post-pandemic price variations while better reflecting spatial heterogeneity. By merging community-expressed sentiment mapping with morphometric urban analysis, this interdisciplinary research pioneers a protocol for socio-spatial integrated urban transitions—one where algorithmic urbanism meets human-scale needs, not technological determinism. These findings recalibrate urban regeneration paradigms, demonstrating that data-driven socio-spatial integration is not a theoretical aspiration but an achievable governance reality. Full article

Intercity cooperation networks are critical for addressing regional imbalances and advancing sustainable regional development, yet existing studies typically focus on specific functional domains, rather than the overall intercity cooperation network. To bridge this gap, this study examines the intercity cooperation network in the Pearl River–Xijiang Economic Belt (21 cities, 2014–2023), analyzing its spatio-temporal evolution and influence mechanisms through Social Network Analysis (SNA) and partial least squares structural equation modeling (PLS-SEM). The results reveal the following: (1) the network has undergone three policy-driven development stages: initial–accelerated–steady; (2) a spatial pattern of “east—dominant, west—weak” has emerged, shaped by the radiating influence of core cities; and (3) institutional proximity and cooperation investment are key drivers of network formation, while geographical and organizational proximity exhibit negative impacts. These findings underscore the need for related regional development strategies to foster a more vital and open cooperation network. Overall, this study deepens the understanding of intercity cooperation by revealing its macro-level patterns and influence mechanisms, and provides practical implications for policymakers committed to promoting sustainable regional development. Full article

KRAS mutations are major drivers of human cancers, yet how distinct mutations rewire protein interactions and metabolic pathways to promote tumorigenesis remains poorly understood. To address this, we systematically mapped the protein interaction networks of wild-type KRAS and three high-frequency oncogenic mutants (G12C, G12D, and G12V) using TurboID proximity labeling coupled with quantitative proteomics. Bioinformatic analysis revealed mutant-specific binding partners and metabolic pathway alterations, including significant enrichment in insulin signaling, reactive oxygen species regulation, and glucose/lipid metabolism. These changes collectively drive tumor proliferation and immune evasion. Comparative analysis identified shared interactome shifts across all mutants: reduced binding to LZTR1, an adaptor for KRAS degradation, and enhanced recruitment of LAMTOR1, a regulator of mTORC1-mediated growth signaling. Our multi-dimensional profiling establishes the first comprehensive map of KRAS-mutant interactomes and links specific mutations to metabolic reprogramming. These findings provide mechanistic insights into KRAS-driven malignancy and highlight LZTR1 and LAMTOR1 as potential therapeutic targets. The study further lays a foundation for developing mutation-specific strategies to counteract KRAS oncogenic signaling. Full article

With the rapid development of research on Wireless Radar Sensor Networks (WRSNs), security issues have become a major challenge. Recent studies have highlighted numerous security threats in WRSNs. Given their widespread application value, the operational security of WRSNs needs to be ensured. This study focuses on the problem of malware propagation in WRSNs. In this study, the complex characteristics of WRSNs are considered to construct the epidemic VCISQ model. The model incorporates necessary factors such as node density, Rayleigh fading channels, and time delay, which were often overlooked in previous studies. This model achieves a breakthrough in accurately describing real-world scenarios of malware propagation in WRSNs. To control malware spread, a hybrid control strategy combining quarantine and patching measures are introduced. In addition, the optimal control method is used to minimize control costs. Considering the robustness and adaptability of the control method, two model-free reinforcement learning (RL) strategies are proposed: Proximal Policy Optimization (PPO) and Multi-Agent Proximal Policy Optimization (MAPPO). These strategies reformulate the original optimal control problem as a Markov decision process. To demonstrate the superiority of our approach, multi-dimensional ablation studies and numerical experiments are conducted. The results show that the hybrid control strategy outperforms single strategies in suppressing malware propagation and reducing costs. Furthermore, the experiments reveal the significant impact of time delays on the dynamics of the VCISQ model and control effectiveness. Finally, the PPO and MAPPO algorithms demonstrate superior performance in control costs and convergence compared to traditional RL algorithms. This highlights their effectiveness in addressing malware propagation in WRSNs. Full article

The abandonment of agricultural land is now considered one of the primary land use changes driven by complex interactions between social, economic, and environmental factors. To understand and manage this process, a holistic approach that integrates multidimensional methodologies and interactions is essential. This study examines the key driving factors behind agricultural land abandonment in Lithuania using two methodological approaches. First, seventeen highly qualified land management experts were surveyed, and their insights were analysed using in-depth qualitative interviews, focusing on agricultural land abandonment and its underlying factors. Second, the development of agricultural land abandonment in a representative Lithuanian municipality was modelled using Markov chain models, incorporating freely available geographic data as factors influencing land use transformation. Actual areas of abandoned agricultural land were mapped using orthophotos from 2012, 2018, and 2021, for both model development and validation. The importance of predictors in the model was then assessed in relation to their significance as drivers of agricultural land abandonment. The findings indicate that natural factors, such as the proximity of forests and topographical constraints, play a significant role in explaining land abandonment processes. Additionally, agricultural land abandonment is influenced by social, economic, and legal factors, including land ownership structures, migration, and infrastructure accessibility. The importance of soil quality, productivity, and the presence of nearby arable land was found to vary depending on data accuracy and local environmental conditions, highlighting the complexity of agricultural land use patterns. The chosen mixed-method approach, combining qualitative surveys with numerical spatial modelling, demonstrates potential for identifying critical land use areas and providing insights to improve land management policies and decision making. Full article

Due to the current climate situation, it is necessary to apply new methods that support environmental, social, and energy challenges to respond to global emergencies. In this regard, Living Labs have increased their popularity as a strategic tool to promote innovation from the local level and proximity. This study aims to detect the patterns, trends, and coherence of the so-called Living Labs. For this purpose, a characterisation of Living Labs has been undertaken that focuses on energy, environmental, and social issues in Europe to support urban transition. It concludes that Living Labs do not have a single solution. They are highly influenced by the current European trends and promoted topics. They can be multi-dimensional (digital and physical), multi-scale (from small, such as the product, to large, such as the territory) and multi-purpose. It is determined that there is an absence of working in real-life environments and some of the Living Lab’s principles. Among the topics, the most common ones are social inclusion, environment, energy, health, and IoT. The implemented scale in a building and the product on the application scale are the most frequent ones. Urban Living Labs are identified as a niche opportunity because of their low current development. Full article

In recent years, significant progress has been achieved in the study of the spatial and temporal distribution of ancient sites and their influencing factors. As an integral component of the pluralistic unity of Chinese civilization, the ancient Shu civilization exhibits unique cultural characteristics and historical significance, rendering the Sichuan region a critical area for exploring the origins and development of Chinese civilization. However, existing research lacks a comprehensive compilation of archaeological sites across the entirety of Sichuan Province, and analyses of spatial and temporal distribution characteristics and their influencing factors often lack multi-scale and multi-dimensional perspectives. This study systematically compiles site data from Sichuan Province and employs GIS spatial analysis methods to examine the distribution characteristics of sites and their relationship with natural geographical factors from a geographical spatial perspective. The findings reveal that site distribution in Sichuan Province exhibits significant clustering, predominantly concentrated near rivers and in higher elevation areas. Factors such as altitude, slope, and proximity to water significantly influence site distribution. In terms of altitude, the elevation of sites’ distribution generally declined from the Stone Age to the Warring States period, dropping below 1000 m during the Shang to Spring and Autumn periods before rising again. Regarding proximity to water, a substantial proportion of sites across all periods are located within a 1 km buffer zone, with approximately 50% during the Stone Age and Warring States period, and up to 70% during the Shang to Spring and Autumn periods, indicating a preference for areas close to water for settlement and production. In terms of slope, most sites across historical periods are located in areas with slopes below 15°, with the highest number of sites during the Shang to Spring and Autumn periods. The evolution of human–environment relationships demonstrates a trend of site concentration shifting from plateau to basin areas from the Stone Age to the Warring States period, reflecting changes in population movement, economic development patterns, and socio-political structures. The research provides new insights into the evolution of human–environment relationships in the region and offers valuable references for related studies. Full article