Search Results (1,495)

Building-Integrated and Building-Attached Photovoltaic (BIPV/BAPV) systems are increasingly being adopted in metropolitan areas worldwide, driven by international commitments to reduce greenhouse gas emissions and the declining cost of PV technology. A promising application involves the vertical integration of PV panels into building facades, which offers architectural and energy benefits, particularly in urban environments with limited roof space. This study experimentally evaluates the energy behavior of 12 vertically mounted 5 W PV panels (model SP005P) installed on university buildings in Ibarra, Ecuador, across four azimuth orientations (−135° SE, −45° NE, 45° NW, 135° SW). A continuous 8-month monitoring campaign was conducted using a custom-designed Arduino-based data logger, validated with multimeter measurements (error < 5%). The dataset was used to develop MATLAB version 2025b forecasting models based on Sum-of-Sine functions, achieving R² values between 0.83 and 0.98 and RMSE values between 0.024 and 0.082 W. The 45° (NW) orientation achieved the highest annual energy yield of 48% STC, reaching up to ≈440 kWh/kWp in the best-performing facade, while 135° (SW) also exhibited favorable performance compared with the northeast and southeast orientations. These findings provide significant evidence for facade-integrated PV design in equatorial latitudes, offering performance benchmarks and validated forecasting tools that can support architectural planning, BIPV feasibility analysis, and urban solar-energy strategies in regions with similar conditions. Full article

This study examines the impact of family multidimensional poverty on cognitive function among older adults in China using the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Filling a critical gap in the existing literature, we construct a multidimensional poverty index (MPI) based on the Alkire-Foster methodology to evaluate cognitive decline within the context of China’s post-poverty-eradication landscape. Utilizing quantile regression analysis, our findings demonstrate that multidimensional poverty exerts a significant, negative effect on cognitive function, which is more pronounced among individuals at lower cognitive quantiles, consistent with the cumulative disadvantage theory. Furthermore, we identify substantial urban–rural and regional disparities, revealing unique socio-economic inequalities. By linking multidimensional poverty to elderly cognitive health through psychosocial pathways, this study provides empirical evidence that reducing multidimensional deprivation among older adults is integral to achieving both SDG1 and SDG3 in China’s post-eradication context, demonstrating that income-based metrics alone are insufficient to capture the full burden of poverty on elderly cognitive health. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

As typical natural disasters in coastal areas, node failure and link interruption caused by typhoons directly threaten the operation stability of the freight multimodal transportation network (FMTN) in urban agglomerations. Such disruptions, in turn, restrict the sustainable development of the regional transportation and logistics system. In order to scientifically assess the FMTN resilience level in coastal area urban agglomerations under typhoon disturbances, this study constructs a resilience assessment method that integrates structural performance and functional performance. The Spatial Local Failure model and the Monte Carlo method, combined with fragility curves, are used to dynamically simulate the damage process of FMTN nodes and links by different typhoons intensities. By constructing FMTN resilience performance function, the resilience ratio is used to quantitatively assess the damage resistance and resilience maintenance level of FMTN under disturbances. This study also analyzes the resilience difference between FMTN and its sub-networks. The Typhoon Bebinca case is applied to validate the application of FMTN assessment method. The results show that FMTN exhibits stronger invulnerability and robustness under typhoon disturbances, and its resilience is significantly better than that of sub-networks. Specifically, when a strong typhoon hits, the FMTN resilience ratio only decreases by 0.13, while the resilience ratio of each sub-network decreases significantly by 0.21, 0.42, 0.46 and 0.57, respectively. FMTN resilience under typhoon disturbances is further assessed through an example analysis. And it verifies not only the comprehensive advantage of FMTN under typhoon disturbances but also the rationality and practicability of the assessment method. The findings can provide an important theoretical basis and technical support for resilience assessment, disaster prevention, mitigation planning, and the sustainable development of FMTN in coastal area urban agglomerations. It is of great practical significance to promote the efficient operation of China’s FMTN. Full article

Africa reported lower COVID-19-related morbidity and mortality compared to other continents, despite widespread SARS-CoV-2 transmission and limited vaccine access. Proposed immunological explanations include potential pre-existing immunity such as cross-reactive humoral or cellular responses from earlier coronavirus exposures. However, functional immune responses to SARS-CoV-2 in African populations remain poorly characterized. To address this gap, we assessed post-pandemic neutralizing antibody responses against the SARS-CoV-2 D614G variant. We analyzed plasma samples from 989 participants in a cross-sectional survey in Ghana’s Eastern and Greater Accra regions. A live virus neutralization assay using Vero E6 TMPRSS2 cells was employed to quantify SARS-CoV-2 D614G-specific neutralizing antibodies. Responses were assessed across collected demographic data. Urban participants exhibited higher median neutralizing antibody titers than rural counterparts, in both vaccinated and unvaccinated groups (p < 0.0001). Among unvaccinated individuals, median neutralizing antibody titers were comparable across age groups in urban settings. Vaccinated individuals showed elevated median titers across all age groups, with urban residents demonstrating stronger responses. Significant sex-based differences in neutralizing titres were also identified. Our findings reveal marked disparities in functional antibody responses between urban and rural populations, likely shaped by differences in SARS-CoV-2 exposure and vaccination. Continued surveillance and immunological profiling remain key for informing vaccine strategies and future pandemic preparedness. Full article

Dispersed archaeological landscapes are often rich in heritage value but weakly integrated into regional tourism systems. This creates difficulties in visitor orientation, interpretive continuity, and conservation-sensitive tourism planning. In response to this problem, this study examines the Adana–Kozan–Anavarza axis in southern Türkiye and proposes a spatial corridor framework for organising tourism development within a dispersed archaeological landscape. The research integrates spatial accessibility assessment, service-capacity evaluation, field observation, and sequential route design in order to establish a hierarchical gateway–transition–anchor configuration. Anavarza, one of the largest archaeological complexes of Cilicia, represents a monumental urban heritage site and a biocultural landscape situated within a Mediterranean ecological zone historically associated with Pedanius Dioscorides. Although current visitor volumes remain moderate, official statistics indicate a substantial increase in annual entries between 2022 and 2024, reflecting rising destination visibility. This emerging growth trajectory underscores the need for proactive spatial governance mechanisms prior to the onset of congestion and environmental degradation pressures. The findings suggest that Adana can function as a metropolitan gateway, Kozan as an intermediate staging node, and Anavarza as the archaeological anchor within a realistic multi-day visitor sequence. In this configuration, visitor functions are distributed across multiple nodes, while the ecological and archaeological sensitivity of the anchor landscape is more cautiously managed through spatial sequencing. Rather than proposing a predictive model, the study develops and assesses a context-responsive spatial planning framework grounded in accessibility, infrastructural feasibility, and conservation-sensitive visitor distribution. Beyond the local case, the study offers a transferable hierarchical staging logic for corridor-based heritage planning. Full article

Urban green infrastructure plays a crucial role in enhancing environmental resilience in cities, particularly in arid regions characterized by water scarcity, soil salinity, and high climatic stress. However, arid coastal cities remain insufficiently studied with regard to spatially explicit assessments of the structure and dynamics of green infrastructure. This study evaluates the state and spatial organization of urban green infrastructure in Aktau, Kazakhstan, over the period 2015–2025, with the most recent satellite observations obtained in June 2025. Sentinel-2 satellite imagery was used to calculate seasonal Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) values, and zonal statistics were applied to assess intra-urban differentiation across functional zones. In addition, inventory-based indicators—Green Planting Density (GPD), Structural Composition of Greenery (SCG), and Protective Green Infrastructure (PGI)—were integrated to complement the remote sensing analysis. The results indicate a moderate overall increase in mean NDVI values (from 0.21 to 0.28), with the most significant growth observed in central and coastal areas (ΔNDVI = +0.12; ΔSAVI = +0.21), while industrial and newly developed zones exhibit only limited changes. Despite these localized improvements, the spatial configuration of green infrastructure remains fragmented, reflecting a persistent center–periphery asymmetry in urban greening. These results underline the importance of irrigation practices and spatially targeted greening strategies for improving vegetation conditions in arid urban environments. The proposed integrated approach combining satellite-derived vegetation indices and inventory-based indicators can serve as a useful tool for monitoring urban green infrastructure and supporting evidence-based planning in arid coastal cities. Full article

Rapid urbanization in the Global South has intensified the formation of mega-urban regions, where conventional urban–rural classifications often fail to capture the complexity of peri-urban systems. In the Jakarta–Bandung Mega-Urban Region (JBMUR), rapid land-use change and socio-economic transformation have produced hybrid landscapes that challenge binary zoning approaches. This study aims to delineate urban, peri-urban, and rural spatial structures using a spatially constrained clustering framework and to evaluate the performance of the Rustiadi Quantitative Zoning Method-2 (RQZM-2) compared with conventional non-spatial clustering (Non-RQZM). Built-environment, accessibility, environmental, and socio-economic indicators derived from remote sensing and spatially disaggregated statistical data were analyzed using grid-based K-Means clustering. Comparative validation using internal metrics, stability analysis, spatial coherence diagnostics, and statistical differentiation tests indicates that RQZM-2 produces more stable, spatially coherent, and interpretable clusters than conventional clustering. The validated four-cluster solution identifies compact urban cores, extensive peri-urban transition belts, and two distinct rural sub-types, revealing a functionally differentiated regional structure across the JBMUR. These findings demonstrate that incorporating spatial contextualization into clustering improves the empirical representation of peri-urban spatial continuity and provides a robust analytical basis for spatial zoning and regional planning in rapidly urbanizing mega-urban regions. Full article

The Dammam Metropolitan Area (DMA) has been experiencing tremendous growth driven by increasing population and the oil industry. This has culminated in the development of the DMA, where the transportation system is reliant on automobiles, wide arterials, and a disjointed pedestrian environment. With the increasing progression of the Vision 2030 initiative, the Kingdom of Saudi Arabia (KSA) is focusing on livability and sustainable mobility. However, despite the massive efforts, the concepts of humanizing active mobility corridors remain insufficiently developed across Saudi cities. The paper will discuss the conceptual framework for developing the active mobility corridors of the DMA, an initiative of walkability, livability, and sustainable mobility with specific regard to the study region’s climatic and cultural environment. The methodology relies on qualitative desktop research supported by a structured and iterative literature synthesis using snowballing techniques. The resulting framework positions active mobility not merely as a transport function, but as a multidimensional system that promotes inclusion, comfort, and environmental resilience. Offering design and policy principles tailored to hot-arid Gulf contexts that contributes to national efforts to advance Quality of Life objectives under Vision 2030. Ultimately, this framework aims to contribute in human-centered mobility across the KSA and similar urban areas. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Building extraction is a core task in the semantic segmentation of satellite remote sensing imagery. Conventional pixel-level segmentation methods often prioritize texture over geometric structure, resulting in suboptimal performance in complex scenes affected by illumination variations, shadows, and scale changes. In this article, an innovative object-level building extraction approach is introduced to better capture the geometric structure of buildings, which incorporates superpixel segmentation to represent images as a set of adjacent regions. The proposed model consists of a cascade multi-scale fusion module (CMSFM) that progressively integrates contextual information across different receptive fields, along with a boundary-assisted loss function designed to enhance edge delineation and improve object-level accuracy. The experimental results on the WHU building dataset and the Massachusetts Buildings Dataset show that the proposed method notably outperforms other representative semantic segmentation approaches, such as FCN, UNet, DeepLab V3, and SETR. On the WHU dataset, MRLNet achieves the largest MIoU of 90.14% and the highest F1 score of 92.47%. On the Massachusetts Buildings Dataset, MRLNet attains the best MIoU of 83.14% and the highest F1 score of 90.46%. In addition, our building extraction model achieves a substantial performance improvement after the addition of the CMSFM module and the boundary-assisted loss function, demonstrating the effectiveness of these two enhancements used in our proposed model. It is expected that this research can provide a promising tool for the accurate extraction of buildings using satellite remote sensing images, which is indispensable in urban planning, disaster assessment, and other fields. Full article

Coastal cities are complex spatial systems shaped by intertwined economic, environmental, demographic, and governance pressures. This study develops a multidimensional comparative framework to analyze coastal cities in the Black Sea basin across five dimensions: physical–morphological structure, demographic scale, economic–functional profile, transportation and accessibility, and urban quality–governance. To address cross-country data heterogeneity, an ordinal (0–1–2) indicator system is employed and analyzed through multiple multivariate techniques, including Gower dissimilarity, NMDS, Ward hierarchical clustering, MCA, Spearman rank correlation, network analysis, and rank-transformed PCA. Findings indicate that Black Sea coastal cities do not form a single homogeneous typology but cluster around distinct structural patterns. A major axis of differentiation separates port–industrial production-oriented cities from tourism–service-oriented cities, while a considerable group of multifunctional and transitional cities exhibits moderate values across several dimensions. Results show that city typologies are shaped less by national planning regimes than by structural dynamics such as port scale, economic specialization, accessibility, and spatial pressure. By integrating non-metric and metric approaches, the study proposes a context-sensitive and multi-criteria comparative methodology. The findings highlight the need for multi-scalar and multidimensional planning perspectives to better understand structural differentiation in coastal urban systems within semi-enclosed marine regions such as the Black Sea. Full article

In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal. Full article

Urban sprawl promotes significant changes in land use and occupation by interfering with the dynamics of functional ecosystems. Among other things, it encourages forest fragmentation, the degradation of woodland edges, and altered habitat integrity. This study aims to propose a simplified and low-cost methodological framework that integrates open data and open-source tools to monitor the potential of ecosystem services (ESs) at the municipal scale. Guided by the hypothesis that rapid suburbanization leads to measurable declines in ecological integrity, the InVEST Habitat Quality model was used as a proxy to analyze the landscape’s capacity to support ES. The procedure included data acquisition and organization, land use reclassification, and scores for the threats and sensitivities, implemented through the InVEST software 3.14.2. Results indicated that urban areas more than doubled between 1985 and 2005, while habitat quality scores declined across Campinas, reflecting a decrease in the potential for ES provision. Urban expansion, mainly concentrated in the central region, occurred at the expense of agricultural and pasture areas. Forest remnants, which currently occupy only 8.5% of the municipal territory, are small and fragmented, intensifying edge effects and reducing the potential capacity to provide regulatory ES. Fragmentation and adjacent land use changes limit these habitats’ capacity to provide ES. The proposed methodology demonstrates the potential for simple and reproducible monitoring of ecosystem services at the municipal scale, providing support to local governments with limited financial and technical capacity in geospatial data processing. This framework enables municipalities to incorporate environmental indicators into planning tools, offering a scalable approach for monitoring ecosystem dynamics in urbanized regions. Full article

Accurate urban green space (UGS) mapping is essential for assessing urban ecosystem health and supporting sustainable development planning. However, deep learning-based UGS segmentation from Red–Green–Blue (RGB) remote sensing imagery faces two major challenges. First, the absence of near-infrared (NIR) information in RGB imagery hinders the ability to discriminate spectrally similar classes, such as vegetation and non-vegetation. Second, conventional convolutions with fixed receptive fields struggle to model the complex and irregular boundaries characteristic of UGS. To address these challenges, this study combined the Normalized Green–Red Difference Index with the Deformable Convolutional Network Lab (NGRDI-DCNLab) model, a semantic segmentation model tailored specifically for RGB-only imagery. Based on the DeepLabV3+ framework, the model introduced three core improvements: (1) The Normalized Green–Red Difference Index (NGRDI) was incorporated to compensate for the absence of NIR information, enhancing the spectral separability of vegetation pixels. (2) Standard convolutions in the decoder were replaced with deformable convolutions, enabling the network to more effectively adapt to irregular boundaries of UGS. (3) An NGRDI-weighted loss function was designed to assign higher weights to challenging samples and uncertain boundary regions, guiding the model toward more accurate edge delineation. Comprehensive evaluations on two public high-resolution datasets—the Wuhan Dense Labeling Dataset (WHDLD) and the Beijing subset of the Urban Green Space-1m dataset (UGS-1m_Beijing)—demonstrated that the NGRDI-DCNLab model outperformed existing popular deep learning models (like Unet++, etc.). Specifically, the deformable convolution effectively enhances the feature modeling capability for irregular boundaries; incorporating the NGRDI vegetation index as a fourth channel strengthens spectral feature representation and improves the distinction between vegetation and non-vegetation; and adding the dynamic NGRDI-weighted loss enables targeted learning for challenging samples. Through the synergistic effect of these three modules, the model achieves mean Intersection over Union (MIoU) scores of 84.77% and 77.66%, as well as F1-scores of 91.75% and 87.27%, on the WHDLD and UGS-1m_Beijing datasets, respectively. Furthermore, the model exhibited certain generalization capability on the unmanned aerial vehicle (UAV) dataset, the Urban Drone Dataset 6 (UDD6), attaining an MIoU of 87.43%. Our results confirm that high-precision UGS extraction is achievable using only RGB remote sensing imagery, providing a cost-effective and practical technical solution for refined urban governance and ecological monitoring. Full article