Search Results (6,421)

Traffic sign detection is a crucial task for autonomous driving perception systems, as it directly impacts vehicle path planning and safety decisions. Existing algorithms face challenges such as feature information attenuation and model lightweighting requirements in the detection of small traffic signs at long distances. To address these issues, this paper proposes a dual-pooling dynamic grouping (DPDG) module. This module dynamically adjusts the number of groups to adapt to different input features, combines global average pooling and max pooling to enhance channel attention representation, and uses a lightweight 3 × 3 convolution-based spatial branch to generate spatial weights. Based on a hierarchical optimization strategy, the DPDG module is integrated into the YOLOv10n network. Experimental results on the traffic sign dataset demonstrate a significant improvement in the performance of the YOLO-DPDG network: Compared to the baseline YOLOv10n model, mAP@0.5 and mAP@0.5:0.95 improved by 8.77% and 10.56%, respectively, while precision and recall were enhanced by 6.16% and 6.62%, respectively. Additionally, inference speed (FPS) increased by 11.1%, with only a 4.89% increase in model parameters. Compared to the YOLOv10-Small model, this method achieves a similar detection accuracy while reducing the number of model parameters by 64.83%. This study provides a more efficient and lightweight solution for edge-based traffic sign detection. Full article

The purpose of this study is to propose and critically assess a sustainable urban regeneration model for the redevelopment of the former K-2 military airbase in Daegu, Korea. Large-scale idle military sites pose significant challenges in terms of ecological remediation, social integration, and economic transformation, but also offer opportunities for redefining urban identity and global competitiveness. To address this, we develop the concept of the “Global Contents City,” a planning framework that integrates cultural exchange, creative industries, education, and tourism within a sustainable urban ecosystem. The research employs a qualitative methodology that combines theoretical review, comparative analysis of international precedents (e.g., Munich-Riem, Tempelhof, Stapleton, and Toronto), and design-oriented masterplanning. The findings highlight design strategies that spatially interconnect cultural, educational, industrial, and ecological functions while reinforcing low-carbon infrastructure and green open space. By situating the Daegu K-2 case in an international context, the study demonstrates how lessons from post-military redevelopments can be adapted to Korea, contributing to both scholarly debates and practical frameworks for sustainable city-making. Full article

Against the background of global warming, the carbon emission of cities accounts for more than 70%, and its carbon sink increase and emission reduction have become the research focus. The water bodies and green spaces in the urban blue–green space have a synergistic carbon sequestration effect, but current research pays less attention to the small and medium scales. Therefore, taking the waterfront green space on both sides of Qinhuai New River in Nanjing as the research object, this paper explores the impact of the synergy between water and greenery on the carbon sequestration efficiency of green space. The study first estimates the carbon sequestration efficiency of green spaces by integrating measured Leaf Area Index (LAI) data with the mean carbon sequestration rate per unit leaf area for typical tree and shrub species. It then constructs a set of water–green spatial relationship indicators and applies a random forest regression model to identify the key factors influencing carbon sequestration efficiency. Finally, multiple scenario models are developed to simulate the effects of green spaces on CO₂ reduction, thereby validating the roles of the identified influencing factors. The study found that waterfront green spaces tended to exhibit slightly higher carbon sequestration efficiency compared with non-waterfront green spaces. The proportion of 10 m forest land area and the proportion of 10–20 m forest land area had a higher impact on the carbon sequestration capacity of waterfront green space; that is, the closer the distance between the green space and the water, the better the carbon sequestration capacity. In order to improve the carbon sequestration efficiency of the waterfront area, the green space should be arranged along the water bank as much as possible, the depth of the green space should be increased, the proportion of the forest land area should be increased, the arbor and shrub should be planted evenly, and ribbon planting should be avoided. The study confirmed the synergistic effect of water and greenery in carbon sequestration benefits, providing data support and theoretical reference for the optimization and renewal of urban waterfront green space, and contributing to the realization of urban waterfront green space planning, design, and renewal with the goal of a high carbon sink. Full article

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

How to keep inter-regional gaps in higher-education development within a reasonable range is a shared global challenge, yet much of the literature still treats zero gap as the benchmark. Grounded in the education-led development perspective, we posit that a gap is “reasonable” when education disparities are smaller than contemporaneous economic disparities. Based on this concept, this study develops a Development Equilibrium Index (DEI) framework. Using panel data for 31 Chinese provinces from 2003 to 2020, we find the following: (i) N-DEI is positive in most years, indicating that, overall, education gaps are smaller than economic gaps—consistent with higher education’s leading role in compressing economic gaps and supporting sustainability; (ii) At the provincial level, only 4 of 31 provinces (12.9%) show negative P-DEI, while the vast majority are positive, suggesting more supportive conditions to sustainable development. Furthermore, a median-based quadrant analysis for 2020 groups the 31 provinces into seven descriptive types, helping to interpret the variations in P-DEI signs and magnitudes and to inform targeted policy recommendations. The DEI thus reframes assessment from “narrowing gaps per se” to the goal of keeping education gaps below economic ones, providing a concise diagnostic tool for planning cohesive, resilient higher-education systems. Full article

This study explores older passengers’ attitudes, behavior, and evaluations of digital air travel, as well as the impact of digital technologies on this demographic, using China as a case study. The findings of this study offer valuable insights for air transport companies to develop sustainable operational strategies, increase passenger satisfaction, and potentially achieve long-term viability. A structured questionnaire survey was conducted targeting this subgroup, applying the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) as the primary analytical frameworks. While the study’s sample is skewed towards digitally literate individuals, this subgroup remains highly relevant for analyzing digital impact trends, as they are the most likely to interact with and be influenced by digital air travel tools. This study suggests that older passengers, particularly young-old passengers, in China have a generally positive attitude towards the use of digital air travel tools, with time saving, convenience, and cost saving identified as the top three perceived benefits. Over 80% of participants indicated that digital technology influenced their decision to continue choosing air travel, highlighting a link between digital engagement and sustainable passenger behavior. However, as this study is limited to digitally literate “young-old” passengers in China, the findings should be interpreted as exploratory and context-specific rather than globally generalizable. Future studies are needed with broader age groups and mixed methods to verify these results. Full article

In light of escalating global environmental deterioration, studies on pro-environmental intention and behaviour with the ultimate goal of identifying contributing factors to minimise environmental issues are common. Theory of Planned Behaviour (TPB) is widely used to study environmental intentions and behaviours. However, how quality of life (QoL) influences these intentions and interactions among TPB’s own variables within a single research framework has not been thoroughly explored. Therefore, this study extends TPB by incorporating the four dimensions of QoL, as measured by the Control, Autonomy, Self-Realisation, and Pleasure (CASP-19) scale, to understand pro-environmental intentions from Malaysian viewpoints. In this study, quantitative approach was applied, and the data were collected from Malaysians aged 18 and above (N = 182) in Klang Valley, Malaysia. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), a two-step approach was employed to assess the measurement and structural models. The findings confirmed Theory of Planned Behaviour (TPB) is a robust model for environmental studies showing that subjective norm and perceived behavioural control significantly influence attitudes toward pro-environmental behaviour, ultimately leading to pro-environmental intention. Interestingly, this study found no relationship between QoL dimensions and pro-environmental intention. Lastly, both theoretical and managerial implications were discussed, and research limitations and suggestions for future research directions were put forward. Full article

Introduction: Despite global improvements in immunization, major gaps persist. By 2024, an estimated 14.3 million infants, predominantly in low- and middle-income countries (LMICs), remained zero-dose (ZD), never having received even the first DTP vaccine. In 2022, 33 million children missed their measles vaccination (22 million missed the first dose, 11 million missed the second dose), highlighting entrenched structural, behavioral, and systemic barriers that continue to exclude marginalized populations. Addressing these inequities requires innovative, context-adapted approaches that strengthen primary health care (PHC) and extend services to the hardest-to-reach populations. Objectives: This study aims to document and synthesize implementation research (IR) projects on immunization programs in LMICs, identifying key enablers and effective strategies that reduce inequities, improve outcomes, and support efforts to reach ZD children. Methods: We conducted a retrospective multiple-case study of 36 IR projects across 13 LMICs, embedded within an evidence review framework and complemented by policy analysis. Data were drawn from systematic document reviews and validation discussions with project leads. A total of 326 strategies were extracted, coded using a structured codebook, and mapped to the WHO–UNICEF PHC Levers for Action. Descriptive analysis synthesized patterns across service delivery and policy outcomes, including coverage gains, improved microplanning, community engagement, and system integration. Results: Of the 326 immunization strategies identified, most (76.1%) aligned with operational PHC levers, particularly monitoring and evaluation (19.3%), workforce development (18.7%), and models of care (12%). Digital technologies (11.7%) were increasingly deployed for real-time tracking and oversight. Core strategic levers comprised 23.9% of strategies, with community engagement (8.9%) and governance frameworks (7.7%) emerging as critical enablers, though sustainable financing (4%) and private-sector engagement (0.9%) were rarely addressed. While the majority of projects focused on routine immunization (n = 32), only a few directly targeted ZD children (n = 3). Interventions yielded improvements in both service delivery and policy outcomes. Improvements in microplanning and data systems (23.5%) reflected the increased uptake of digital dashboards, GIS-enabled tools, and electronic registries. Community engagement (16.2%) emphasized the influence of local leaders and volunteers in building trust, while health system strengthening (15.7%) invested in cold chain, supervision, and workforce capacity. Coverage gains (10.6%) were achieved through delivery innovations, though sustainable financing remained a critical problem (3.4%). Conclusions: Reaching ZD children requires equity-driven strategies that combine digital innovations, community engagement, and resilient system planning. Sustained progress depends on strengthening governance, financing, and research. Embedding IR in immunization programs generates actionable evidence, supports context-specific strategies, and reduces equity gaps, offering practical insights that complement health system research and advance the Immunization Agenda 2030. Full article

We address issues in existing house layout generation methods, including chaotic room layouts, limited iterative refinement, and restricted style diversity. We propose Graph-RWGAN, a generative adversarial network based on a multi-relational graph attention mechanism, to automatically generate reasonable and globally consistent house layouts under weak constraints. In our framework, rooms are represented as graph nodes with semantic attributes. Their spatial relationships are modeled as edges. Optional room-level objects can be added by augmenting node attributes. This allows for object-aware layout generation when needed. The multi-relational graph attention mechanism captures complex inter-room relationships. Iterative generation enables stepwise layout optimization. Fusion of node features with building boundaries ensures spatial accuracy and structural coherence. A conditional graph discriminator with Wasserstein loss constrains global consistency. Experiments on the RPLAN dataset show strong performance. FID is 92.73, SSIM is 0.828, and layout accuracy is 85.96%. Room topology accuracy reaches 95%, layout quality 90%, and structural coherence 95%, outperforming House-GAN, LayoutGAN, and MR-GAT. Ablation studies confirm the effectiveness of each key component. Graph-RWGAN shows strong adaptability, flexible generation under weak constraints, and multi-style layouts. It provides an efficient and controllable scheme for intelligent building design and automated planning. Full article

Global climate change and rapid urbanization have intensified urban heat risks, particularly in cities such as Jinan that face pronounced heat-related environmental challenges. This study takes Jinan’s main urban area as a case example, integrating the Local Climate Zone (LCZ) framework with the Hazard–Exposure–Vulnerability–Adaptability (HEVA) model to develop multi-temporal heat risk maps. The results indicate the following: (1) High-risk zones are primarily concentrated in the densely built urban core, whereas low-risk areas are mostly located in peripheral green spaces, water bodies, and forested regions. (2) Heat risk shows clear diurnal patterns, peaking between noon and early afternoon and expanding outward from the city center. (3) LCZ6 (open low-rise), despite its theoretical advantage for ventilation, exhibits unexpectedly high levels of heat hazard, exposure, and vulnerability. (4) SHAP-based analysis identifies land surface temperature (LST), floor area ratio (FAR), impervious surface area ratio (ISA), housing value, building coverage ratio (BCR), and the distribution of cooling facilities as the most influential drivers of heat risk. These findings offer a scientific foundation for developing multi-scale, climate-resilient urban planning strategies in Jinan and hold significant practical value for improving urban resilience to extreme heat events. Full article

As climate variability intensifies, its impacts are increasingly visible through disrupted agricultural systems and rising food insecurity, especially in climate-sensitive regions. This study explores the complex relationships between environmental stressors, such as rising temperatures, erratic rainfall, and soil degradation, with food insecurity outcomes in selected districts of Uttarakhand, India. Using the Fuzzy DEMATEL method, this study analyzes 19 stressors affecting the food supply chain and identifies the nine most influential factors. An Environmental Stressor Index (ESI) is constructed, integrating climatic, hydrological, and land-use dimensions. The ESI is applied to three districts—Rudraprayag, Udham Singh Nagar, and Almora—to assess their vulnerability. The results suggest that Rudraprayag faces high exposure to climate extremes (heatwaves, floods, and droughts) but benefits from a relatively stronger infrastructure. Udham Singh Nagar exhibits the highest overall vulnerability, driven by water stress, air pollution, and salinity, whereas Almora remains relatively less exposed, apart from moderate drought and connectivity stress. Simulations based on RCP 4.5 and RCP 8.5 scenarios indicate increasing stress across all regions, with Udham Singh Nagar consistently identified as the most vulnerable. Rudraprayag experiences increased stress under the RCP 8.5 scenario, while Almora is the least vulnerable, though still at risk from drought and pest outbreaks. By incorporating crop yield models into the ESI framework, this study advances a systems-level tool for assessing agricultural vulnerability to climate change. This research holds global relevance, as food supply chains in climate-sensitive regions such as Africa, Southeast Asia, and Latin America face similar compound stressors. Its novelty lies in integrating a Fuzzy DEMATEL-based Environmental Stressor Index with crop yield modeling. The findings highlight the urgent need for climate-informed food system planning and policies that integrate environmental and social vulnerabilities. Full article

Corporate ESG (Environmental, Social, and Governance) reporting is increasingly envisioned as evidence of accountability in the energy transition, yet persistent gaps remain between commitments and practices. This study applied the Global Reporting Initiative (GRI) framework—specifically indicators 302 (Energy) and 305 (Emissions)—to evaluate the credibility, scope, and strategic depth of disclosures by 20 Wisconsin (WI) firms in the energy, manufacturing, food, and service sectors. Guided by accountability and legitimacy theory, a comparative content analysis was conducted, complemented by Spearman correlation to examine associations between firm size and disclosure quality. Results show that while firms consistently report basic metrics such as total energy consumption and Scope 1 emissions, disclosures on Scope 3 emissions, renewable sourcing, and energy-efficiency achievements remain partial and selectively framed. Third-party assurance is inconsistently applied, and methodological transparency—such as external audit and coding protocols—is limited, weakening credibility. A statistically significant negative correlation was observed between annual revenue and disclosure quality, indicating that greater financial capacity does not necessarily translate into greater transparency. These findings highlight methodological and governance shortcomings, including reliance on generic ESG frameworks rather than climate-focused standards such as Task Force on Climate-related Financial Disclosures (TCFD). Integrated reporting approaches are recommended to improve comparability, credibility, and alignment with Wisconsin’s Clean Energy Transition Plan. Full article

Promoting economic prosperity, social justice, and ecological sustainability requires the rapid decarbonization of our global energy system in favor of renewable sources of energy. Recent news analysis estimates that 15% of counties across the US have banned wind turbines, solar fields, and other green energy developments. We answer two overarching research questions: (1) How do regulations of wind facilities vary at the county level? And (2) what factors appear to explain the variation in local wind regulations? We created a GIS database of energy regulations for all 105 counties in Kansas, a top state for wind potential and a recent hotbed of local actions. We coupled descriptive statistics, mapping, and regression modeling to describe the variation in local policy approaches and identify factors driving the variation. We find counties using at least five different policy approaches to enable or block wind regulations. Factors driving variation include a combination of infrastructure capacity, demographic characteristics that shape local planning capacity, and the apparent reliance on large farming operations for local economic output but not partisan voting patterns or underlying wind capacity. Our findings provide vital insights for policymakers at the federal, state, and local levels, as well as providing a foundation for future scholarship on planning for a just energy future. Full article

Background: Despite evidence discouraging contralateral prophylactic mastectomy (CPM) in average-risk patients, its use is increasing globally. While well-studied in Western settings, little is known about the factors influencing CPM decisions in the Middle East and North Africa (MENA) region. This study explores clinical, psychosocial, and communication-related factors associated with CPM choices among women with early-stage breast cancer. Methods: We conducted a retrospective study of 253 early-stage breast cancer patients who underwent mastectomy, with or without CPM, at the American University of Beirut Medical Center. Clinical and demographic data were extracted from medical records, and decision-making factors were assessed through tailored patient questionnaires. Associations were analyzed using chi-square tests and multivariable logistic regression. Results: Of the 253 women included in the study, 37 underwent CPM, while 216 had unilateral mastectomy (UM). Compared to the UM group, women who chose CPM were more likely to have a college education (96.9% vs. 57.6%, p < 0.001), be employed (69.7% vs. 41.3%, p = 0.002), and report a family history of breast cancer (55.6% vs. 30.2%, p = 0.003). Immediate reconstruction was significantly more common among CPM patients (67.6% vs. 16.4%, p < 0.001), and the 30-day rehospitalization rate was also higher (16.2% vs. 6.1%, p = 0.031). Women in the CPM group were more likely to prioritize extending life (84.6% vs. 56.7%, p = 0.007) and achieving peace of mind (80.8% vs. 49.3%, p = 0.003). Although all CPM patients cited risk reduction as a primary motivator, only 46.2% believed they had a lower recurrence risk than their peers (vs. 20% of UM patients, p < 0.001). Decisions to undergo UM were more frequently influenced by physicians’ recommendations (95.3% vs. 53.8%, p < 0.001), whereas CPM decisions appeared to be more patient-driven. Additionally, CPM patients reported more negative expectations and higher dissatisfaction with pain (57.7% vs. 32.0%, p = 0.012) and reconstructive outcomes (54.5% vs. 27.5%, p = 0.035). Conclusions: In this first study from the MENA region exploring CPM decision-making, choices were largely driven by personal preferences rather than clinical risk. These findings highlight the need for improved risk communication, shared decision-making, and broader integration of genetic counseling in surgical planning. Full article

Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and infrared). To address these challenges, we proposed a novel multi-modal segmentation framework, DyFuseNet, which features dynamic adaptive windows and cross-scale feature fusion capabilities. This framework consists of three key components: (1) Dynamic Window Module (DWM), which uses dynamic partitioning and continuous position bias to adaptively adjust window sizes, thereby improving the representation of irregular and fine-grained objects; (2) Scale Context Attention (SCA), a hierarchical mechanism that associates local details with global semantics in a coarse-to-fine manner, enhancing segmentation accuracy in low-texture or occluded regions; and (3) Hierarchical Adaptive Fusion Architecture (HAFA), which aligns and fuses features from multiple modalities through shallow synchronization and deep channel attention, effectively balancing complementarity and redundancy. Evaluated on benchmark datasets (such as ISPRS Vaihingen and Potsdam), DyFuseNet achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 80.40% and 80.85%, surpassing MFTransNet by 1.91% and 1.77%, respectively. The model also demonstrated strong robustness in challenging scenes (such as building edges and shadowed objects), achieving an average F1 score of 85% while maintaining high efficiency (26.19 GFLOPs, 30.09 FPS), making it suitable for real-time deployment. This work presents a practical, versatile, and computationally efficient solution for multi-modal image analysis, with potential applications beyond remote sensing, including smart monitoring, industrial inspection, and multi-source data fusion tasks. Full article