Search Results (77)

Seismic disasters pose major challenges to urban resilience, particularly in high-density cities where the concentration of people, buildings, and infrastructure amplifies disaster risk. This study establishes a GIS-based analytical framework to evaluate the spatial distribution and fairness of seismic emergency shelters in Seoul, using built-up neighborhoods (called dongs in Korean) as the basic analytical unit. Three dimensions are assessed: (1) 500 m walking accessibility based on the road network; (2) redundancy, representing the number of shelters simultaneously reachable; and (3) fairness analysis, integrating spatial and population-based dimensions to reveal disparities between shelter provision and population demand. The results indicate that overall accessibility in Seoul is relatively high, with more than 50% of dongs achieving coverage levels above 50%. However, distinct spatial disparities remain. Central and mountainous areas, such as Jung-gu, Jongno-gu, and southern Seocho-gu, show coverage rates below 20%, while districts in the southwest and northeast exhibit higher redundancy. Fairness analysis further reveals inequality in shelter capacity relative to population: excluding null values, the median coverage ratio is 0.92 and the mean is 1.29, with only 44.97% of dongs achieving sufficient or surplus capacity (coverage ≥ 1). Notably, 44 dongs fall into the Low–High category, representing areas with large populations but limited shelter access, mainly concentrated in Jungnang-gu, Gangbuk-gu, and Yangcheon-gu. These dongs should be prioritized in future planning. Policy implications highlight strengthening shelter provision in high-population but low-coverage zones, incorporating evacuation functions into urban redevelopment, promoting inter-district resource sharing, and improving public awareness. The proposed framework provides a transferable model for optimizing seismic shelter systems in other high-density urban contexts. Full article

Urbanicity is a recognized determinant of mental health, yet conventional measures such as population density or the rural–urban divide often fail to capture the complex realities of informal settlements in low- and middle-income countries. This paper conceptualizes neighborhood effects through the lived experiences of young women in Kampala, Uganda, drawing on participatory research from the NIH-funded TOPOWA study. Using community mapping and Photovoice, participants identified neighborhood features that shape wellbeing, including sanitation facilities, drainage systems, alcohol outlets, health centers, schools, boda boda stages (motorcycle taxis), lodges, religious institutions, water sources, markets, and recreational spaces. These methods revealed both stressors—poor waste management, flooding, violence, gendered harassment, crime, and alcohol-related harms—and protective resources, including education, places of worship, health centers, social networks, identity, and sports activities. We argue that urbanicity in slum contexts should be understood as a multidimensional construct encompassing deprivation, fragmentation, exclusion, and resilience. This reconceptualization advances conceptual clarity, strengthens the validity of mental health research in low-resource settings, and informs interventions that simultaneously address structural risks and promote community assets. The case of Kampala demonstrates how participatory evidence can reshape the understanding of neighborhood effects with implications, for global mental health research and practice. Full article

In the field of communication maintenance, Augmented Reality (AR) applications are critical for enhancing operational safety and efficiency. However, deploying the required multimodal models on resource-constrained terminal devices is challenging, as traditional cloud or on-device strategies fail to balance low latency and energy consumption. This paper proposes a Cloud-Edge-End collaborative inference framework tailored to multimodal model deployment. A subgraph partitioning strategy is introduced to systematically decompose complex multimodal models into functionally independent sub-units. Subsequently, a fine-grained performance estimation model is employed to accurately characterize both computation and communication costs across heterogeneous devices. And, a joint optimization problem is formulated to minimize end-to-end inference latency and terminal energy consumption. To solve this problem efficiently, a Hybrid Genetic Algorithm for DNN Partitioning (HGA-DP) evolved over 100 generations is designed, incorporating constraint-aware repair mechanisms and local neighborhood search to navigate the exponential search space of possible deployment combinations. Experimental results on a simulated three-tier collaborative computing platform demonstrate that, compared to traditional full on-device deployment, the proposed method reduces end-to-end inference latency by 70–80% and terminal energy consumption by 81.1%, achieving a 4.86× improvement in overall fitness score. Against the latency-optimized DADS heuristic, HGA-DP achieves 41.3% lower latency while reducing energy by 59.9%. Compared to the All-Cloud strategy, our approach delivers 71.5% latency reduction with only marginal additional terminal energy cost. This framework provides an adaptive and effective solution for real-time multimodal inference in resource-constrained scenarios, laying a foundation for intelligent, resource-aware deployment. Full article

5G technology represents a transformative shift in mobile communications, delivering improved ultra-low latency, data throughput, and the capacity to support huge device connectivity, surpassing the capabilities of LTE systems. As global telecommunication operators shift toward widespread 5G implementation, ensuring optimal network performance and intelligent resource management has become increasingly obvious. To address these challenges, this study explored the role of advanced clustering methods in optimizing cellular networks under heterogeneous and dynamic conditions. A systematic literature review (SLR) was conducted by analyzing 40 peer-reviewed and non-peer-reviewed studies selected from an initial collection of 500 papers retrieved from the Semantic Scholar Open Research Corpus. This review examines a diversity of clustering approaches, including spectral clustering with Bayesian non-parametric models and K-means, density-based clustering such as DBSCAN, and deep representation-based methods like Differential Evolution Memetic Clustering (DEMC) and Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE). Key performance outcomes reported across studies include anomaly detection accuracy of up to 98.8%, delivery rate improvements of up to 89.4%, and handover prediction accuracy improvements of approximately 43%, particularly when clustering techniques are combined with machine learning models. In addition to summarizing their effectiveness, this review highlights methodological trends in clustering parameters, mechanisms, experimental setups, and quality metrics. The findings suggest that advanced clustering models play a crucial role in intelligent spectrum sensing, adaptive mobility management, and efficient resource allocation, thereby contributing meaningfully to the development of intelligent 5G/6G mobile network infrastructures. Full article

The exponential growth of the Internet of Things (IoT) has made it increasingly vulnerable to cyberattacks, where malicious manipulation of network and sensor data can lead to incorrect data classification. IoT data are inherently heterogeneous, comprising sensor readings, network flow records, and device metadata that differ significantly in scale and structure. This diversity motivates transforming tabular IoT data into image-based representations to facilitate the recognition of intrusion patterns and the analysis of spatial correlations. Many deep learning models offer robust detection performance, including CNNs, LSTMs, CNN–LSTM hybrids, and Transformer-based networks, but many of these architectures are computationally intensive and require significant training resources. To address this challenge, this study introduces an evolutionary-driven framework that mathematically formalizes the transformation of tabular IoT data into image-encoded matrices and optimizes feature selection through metaheuristic algorithms. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Variable Neighborhood Search (VNS) are employed to identify optimal feature subsets for Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers. The approach enhances discrimination by optimizing multi-objective criteria, including accuracy and sparsity, while maintaining low computational complexity suitable for edge deployment. Experimental results on benchmark IoT intrusion datasets demonstrate that VNS-XGBoost configurations performed better on the IDS2017 and IDS2018 benchmarks, achieving accuracies up to 0.99997 and a significant reduction in Type II errors (212 and 6 in tabular form, reduced to 4 and 1 using image-encoded representations). These results confirm that integrating evolutionary optimization with image-based feature modeling enables accurate, efficient, and robust intrusion detection across large-scale IoT systems. Full article

In Ecuador, the transmission of dengue has steadily increased in recent decades, particularly in coastal cities like Manta, where the conditions are favorable for the proliferation of the Aedes aegypti mosquito. The objective of this study was to model the spatial distribution of dengue transmission risk in Manta, a coastal city in Ecuador with consistently high incidence rates. A total of 148 georeferenced dengue cases from 2018 to 2021 were collected, and environmental and socioeconomic variables were incorporated into a maximum entropy model (MaxEnt). Additionally, climate and social zoning were performed using a multi-criteria model in TerrSet. The MaxEnt model demonstrated excellent predictive ability (training AUC = 0.916; test AUC = 0.876) and identified population density, sewer system access, and distance to rivers as the primary predictors. Three high-risk clusters were identified in the southern, northwestern, and northeastern parts of the city, while the coastal strip showed lower suitability due to low rainfall and vegetation. These findings reveal the strong spatial heterogeneity of dengue risk at the neighborhood level and provide operational information for targeted interventions. This approach can support more efficient surveillance, resource allocation, and community action in coastal urban areas affected by vector-borne diseases. Full article

The management of modern water supply systems requires a detailed analysis of consumption patterns in order to optimize pump operation schedules, reduce energy costs, and support the development of intelligent management systems. Traditional clustering algorithms are applied for these tasks; however, their limitation lies in the need to predefine the number of clusters. The aim of this study was to develop and validate a non-parametric method for clustering daily water consumption profiles based on a modified DBSCAN algorithm. The proposed approach includes the automatic optimization of neighborhood radius and the minimum number of points required to form a cluster. The input data consisted of half-hourly water supply and electricity consumption values for the water supply system of Gomel (Republic of Belarus), supplemented with the time-of-day factor. As a result of the multidimensional clustering, two stable regimes were identified: a high-demand regime (6:30–22:30), covering about 46% of the data and accounting for more than half of the total water supply and electricity consumption, and a low-demand regime (0:30–6:00), representing about 21% of the data and forming around 15% of the resources. The remaining regimes reflect transitional states in morning and evening periods. The obtained results make it possible to define the temporal boundaries of the regimes and to use them for data labeling in the development of predictive water consumption models. Full article

Barriers to cancer care, including transportation and Internet insecurity, are of special concern in low-resource communities. A patient-centered, telehealth-based, barrier-focused lay navigator program may mitigate such barriers. We share insights from a quality improvement project wherein we developed and delivered a lay navigator program in a low-resource community in the Mojave Desert. We identified 68 patients scheduled for lung cancer detection/management at our institution, 55 of whom completed a barrier assessment, enrolled in the program, and could be evaluated. Participants were predominantly older (76%), White (84%), had a cancer diagnosis at enrollment (69%), and lived in socioeconomically disadvantaged neighborhoods. Thirty-three (60%) patients had ≥1 barrier, the most common being transportation (31%), Internet (24%), and financial (24%) concerns. These barriers were more frequent among patients with a lung cancer diagnosis at enrollment. Crisis-focused and after-hours encounters were more frequently initiated by older and advanced cancer patients. Transportation and Internet concerns were significantly associated with missed appointment rates. While the scope of our findings is limited, the delivery of a telehealth-based, barrier-focused lay lung navigator program in this low-resource setting was feasible. Neighborhood context and barrier resource planning are important for the implementation of similar programs within our institution’s clinical practice network. Full article

The optimal allocation of Wireless Smart Concentrators (WSCs) in low-voltage (LV) distribution networks poses significant challenges due to signal attenuation caused by varying building densities and vegetation. This paper proposes a Variable Neighborhood Search (VNS) algorithm to optimize the placement of WSCs in LV distribution networks. To comprehensively assess the proposed approach, both linear and nonlinear mathematical formulations are considered, depending on whether the distance between meters and concentrators is treated as a fixed parameter or as a decision variable. The performance of the proposed VNS algorithm is benchmarked against both exact solvers and metaheuristics such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Tabu Search (TS). In the linear formulation, VNS achieved the exact optimal solution with execution times up to 75% faster than competing methods. For the more complex nonlinear model, VNS consistently identified superior solutions while requiring less computational effort. These results underscore the algorithm’s ability to balance solution quality and efficiency, making it particularly well-suited for large-scale, resource-constrained utility planning. Full article

Territorial tourism has brought new development opportunities for historic and cultural neighborhoods. However, an insufficient understanding of the spatial distribution and influencing mechanisms of neighborhood vitality continues to constrain effective revitalization strategies. This study takes the Shuitingmen Historical and Cultural Neighborhood in Quzhou, China, as a case study and develops a multi-dimensional vitality evaluation framework incorporating point-of-interest (POI) data, location-based service (LBS) heatmaps, street network data, historical resources, and environmental perception indicators. The Analytic Hierarchy Process (AHP) is applied to assign indicator weights and calculate composite vitality scores across 19 streets. The results reveal that (1) comprehensive evaluation corrects the bias of single indicators and highlights the value of integrated assessment; (2) vitality is higher on rest days than on weekdays, with clear temporal patterns and two types of daily fluctuation trends—similar and differential; and (3) vitality levels are spatially uneven, with higher vitality in central and western areas and lower performance in the southeast, often related to low accessibility and functional monotony. This study confirms a strong positive correlation between street vitality and objective spatial factors, offering strategic insights for the micro-scale renewal and sustainable revitalization of historic neighborhoods. Full article

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay. Full article

Urban parks are essential public resources that contribute significantly to residents’ well-being. However, disparities in the spatial distribution and social benefits of urban parks remain a pressing issue. This study focuses on the central urban area of Shanghai, a representative high-density megacity, and its findings hold significant reference value for similar cities, systematically evaluating urban park services from the perspectives of accessibility, spatial equity, and social equity. Leveraging multi-source big data and enhanced analytical methods, this study examines disparities and spatial mismatches in park services. By incorporating dynamic data, such as actual visitor attendance and residents’ travel preferences, and improving analytical models, such as an enhanced Gaussian two-step floating catchment area method and spatial lag regression models, this research significantly improves the accuracy and reliability of its findings. Key findings include (1) significant variations in accessibility exist across different types of parks, with regional and city parks offering better accessibility compared to pocket parks and community parks. (2) Park resources are unevenly distributed, with neighborhoods within the inner ring exhibiting relatively low overall accessibility. (3) A spatial mismatch is observed between park accessibility and housing prices, highlighting equity concerns. The dual spatial-social imbalance phenomenon reveals the prevalent contradiction in rapidly urbanizing areas where public service provision lags behind land development. Based on these results, this study proposes targeted recommendations for optimizing urban park layouts, including increasing the supply of small parks in inner-ring areas, enhancing the multifunctionality of parks, and strengthening policy support for disadvantaged communities. These findings contribute new theoretical insights into urban park equity and fine-grained governance while offering valuable references for urban planning and policymaking. Full article

Analyzing the population’s access to ecosystem services offered by urban greening constitutes a measure of environmental justice, as it directly affects the quality of life and health of the population living in cities. This article is committed to proposing a geoenvironmental model in a geographic information system (GIS), envisaged to estimate the share of urban forests and green spaces in territorial planning units (TPUs), corresponding to neighborhoods of a pilot city, using high-spatial-resolution images of the China–Brazil Earth Resources Satellite (CBERS-4A) and the normalized difference vegetation index (NDVI). These data were combined by means of a Boolean analysis with social vulnerability indicators assessed from census data related to income, education, housing, and sanitation. This model ultimately aims to identify priority areas for urban afforestation in the context of environmental justice and is thus targeted to improve the inhabitants’ quality of life. The municipality of Goiânia, the capital of Goiás state, located in the Brazilian Central–West Region, was chosen as the study area for this experiment. Goiânia presents 19.5% of its urban territory (82.36 km²) covered by vegetation. The analyses indicate an inequity in the distribution of urban forest patches and green areas in this town, where 7.8% of the total TPUs have low priority, 28.2% have moderate to low priority, 42.2% have moderate to high priority, and 21.8% have high priority for urban afforestation. This urban greening imbalance is particularly observed in its most urbanized central nuclei, associated with a peripheralization of social vulnerability. These findings are meant to support initiatives towards sound territorial planning processes designed to promote more sustainable and equal development to ensure environmental justice and combat climate change. Full article

Urban green spaces are essential for public health and well-being, emphasizing the importance of their equitable distribution in urban development. Despite efforts to expand green spaces, however, significant disparities persist between their spatial and social allocation. This study classified urban green spaces into community parks, urban parks, and country parks, and examined the relationship of their green coverage and park accessibility to neighborhood property prices in Hangzhou. We then assessed the urban green space equity using Gini coefficients. We found that (1) urban green space inequities occurred in both green coverage and accessibility; (2) high-priced neighborhoods occupied more green resources, especially green coverage and community park accessibility, but exhibited less green equity; and (3) low-priced neighborhoods and urban villages had the lowest green resources but more equity for country parks. This study highlights the relationship between property price (as a proxy for income) and urban green space equity at the neighborhood scale. The results offer guidance for policymakers and planners aiming to promote green equity and sustainable development in cities. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article