Search Results (851)

Against the backdrop of carbon reduction and sustainable development, cities play a central role in carbon emissions. These emissions are interconnected through economic, demographic, technological, and other factors, forming a complex network. This study investigates the structural characteristics and driving factors of carbon emission linkages among Chinese cities, with the aim of providing theoretical support and practical guidance for the development of sound regional carbon reduction policies. An improved gravity model was used to measure both the presence and intensity of linkages between cities. Social Network Analysis (SNA) was applied to examine network features such as density, centrality, and hierarchical structure. In addition, the Quadratic Assignment Procedure (QAP) was employed to test the effects of geographical proximity, economic disparities, demographic differences, and technological gaps on carbon emission linkages. Based on these methods, the study constructs the Chinese Carbon Emission Correlation Network (CECN), which shows high connectivity, a clear hierarchical structure, and a strengthened role of core cities. Cities with extensive linkages are mainly located in the eastern coastal region and political centers, forming a spatial pattern with stronger connections in the east than in the west, and more along the coast than inland. The network can also be divided into five distinct sub-groups. Moreover, geographical proximity, population differences, economic affluence, and technological disparities were found to significantly shape the spatial correlation of carbon emissions. These findings offer valuable guidance for designing targeted carbon reduction policies, which are essential for fostering regional coordination and advancing sustainable urban development. Full article

Accurate, real-time, and cost-effective detection and counting of pre-weaning piglets are critical for improving piglet survival rates. However, achieving this remains technically challenging due to high computational demands, frequent occlusion, social behaviors, and cluttered backgrounds in commercial farming environments. To address these challenges, this study proposes a lightweight and attention-enhanced piglet detection and counting network based on an improved YOLOv8n architecture. The design includes three key innovations: (i) the standard C2f modules in the backbone were replaced with an efficient novel Multi-Scale Spatial Pyramid Attention (MSPA) module to enhance the multi-scale feature representation while a maintaining low computational cost; (ii) an improved Gather-and-Distribute (GD) mechanism was incorporated into the neck to facilitate feature fusion and accelerate inference; and (iii) the detection head and the sample assignment strategy were optimized to align the classification and localization tasks better, thereby improving the overall performance. Experiments on the custom dataset demonstrated the model’s superiority over state-of-the-art counterparts, achieving 88.5% precision and a 93.8% $mA{P}_{0.5}$. Furthermore, ablation studies showed that the model reduced the parameters, floating point operations (FLOPs), and model size by 58.45%, 46.91% and 56.45% compared to those of the baseline YOLOv8n, respectively, while achieving a 2.6% improvement in the detection precision and a 4.41% reduction in the counting MAE. The trained model was deployed on a Raspberry Pi 4B with $ncnn$ to verify the effectiveness of the lightweight design, reaching an average inference speed of <87 ms per image. These findings confirm that the proposed method offers a practical, scalable solution for intelligent pig farming, combining a high accuracy, efficiency, and real-time performance in resource-limited environments. Full article

Historically, traditional transportation planning has promoted public policies focused on building and maintaining infrastructure for private cars to improve travel efficiency. This approach presents a significant challenge for cities in the Global South due to their unique socioeconomic conditions and urban development patterns. Dedicated public transport infrastructure can make better use of the road network by moving more people and reducing congestion. Beyond its environmental benefits, it also provides the population with greater accessibility, creating new development opportunities. This study uses Mexicali, Mexico, a medium-sized city with dispersed urban growth and a high dependence on cars, as a case study. The goal is to identify the relationship between the supply of public bus routes and actual work-related commuting patterns. The methodology considers that, given the scarcity of economic resources and prior studies in the Global South, using Geographic Information Systems (GIS) for the spatial analysis of travel is a key tool for redesigning more inclusive and sustainable public transport systems. Specifically, this study utilized origin–destination survey data from 14 urban areas to assess modal coverage, work-related commuting patterns, and the spatial distribution of employment centres. The findings reveal a marked misalignment between the existing public transport network and the population’s travel needs, particularly in marginalized areas. Users face long travel times, multiple transfers, low service frequency, and limited connectivity to key employment areas. This configuration reinforces an exclusionary urban structure, with negative impacts on equity, modal efficiency, and sustainability. The study concludes that GIS-based spatial analysis generates sufficient evidence to redesign the public transport system and reorient urban mobility policy toward sustainability and social inclusion. Full article

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature dataset was established using remote sensing imagery and meteorological observations. Spatial features were extracted through a convolutional neural network (CNN), while the temporal evolution of sandstorms was modeled using a bidirectional long short-term memory (BiLSTM) network. A random forest algorithm was employed to assess the relative importance of meteorological and geographical factors. The results indicate that the proposed CNN-BiLSTM model achieved strong performance at prediction intervals of 1, 6, 12, 18, and 24 h, with overall accuracy, F1-score, and AUC all exceeding 0.80. The 24 h prediction yielded the best results, with evaluation metrics of 0.861, 0.878, and 0.898, respectively. Compared with the individual CNN and BiLSTM models, the CNN-BiLSTM model demonstrated superior performance. The findings suggest that the model provides high predictive accuracy and stability across different time steps, thereby offering strong support for dust storm path prediction on the Mongolian Plateau and contributing to the reduction of disaster-related risks and losses. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

Organic farming is attributed to environmental, economic, and social benefits, which is why its expansion is anchored in policy objectives on various scales. Its development is typically assessed in terms of number of farms or production volume. We argue that the importance of comprehensive spatial assessments of various actors in the adjacent value chain is being overlooked. This study addresses this gap by using data from EU organic certificates to map the spatial distribution of the organic sector in Bavaria, Germany. By analyzing the distribution at the district level, we uncover different patterns and reveal the uneven presence of actor groups across the region. Our findings illustrate the complexity of the sector, highlighting the need for multi-actor analysis to capture the interwoven dynamics and factors influencing the successful development of the organic sector and the benefits attributed to it. The resulting maps point to different networks of actors, indicating a heterogeneous local development potential. In addition, we examined cross-actor relationships at the district level. Correlation and ratio analyses show strong clustering among downstream actors (processors, trade, importers), marked rural–urban asymmetries, and a close alignment of producer and processor densities once normalized by agricultural area. These insights move beyond descriptive mapping and provide an analytical basis for assessing interdependencies in the organic value chain. They enable the identification of development potentials and shortcomings so that more targeted measures in rural and environmental policies can be implemented. Further research on interactions and the potential for influence through multi-scalar politics and regional planning appears of great value. Full article

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning. Full article

Long-term and accurate prediction of air pollutant concentrations can serve as a foundation for air pollution warning and prevention, which is crucial for social development and human health. In this study, we provide a model for predicting the concentration of air pollutants based on big data spatiotemporal correlation analysis and deep learning methods. Based on an improved temporal convolutional network (TCN) and a bi-directional gated recurrent unit (BiGRU) as the fundamental architecture, the model adds two attention mechanisms to improve performance: Squeeze and Excitation Networks (SENet) and Convolutional Block Attention Module (CBAM). The improved TCN moves the residual connection layer to the network’s front end as a preprocessing procedure, improving the model’s performance and operating efficiency, particularly for big data jobs like air pollution concentration prediction. The use of SENet improves the model’s comprehension and extraction of long-term dependent features from pollutants and meteorological data. The incorporation of CBAM enhances the model’s perception ability towards key local regions through an attention mechanism in the spatial dimension of the feature map. The TCN-SENet-BiGRU-CBAM model successfully realizes the prediction of air pollutant concentrations by extracting the spatiotemporal features of the data. Compared with previous advanced deep learning models, the model has higher prediction accuracy and generalization ability. The model is suitable for prediction tasks from 1 to 12 h in the future, with root mean square error (RMSE) and mean absolute error (MAE) ranging from 5.309~14.043 and 3.507~9.200, respectively. Full article

Rapid population aging in developing countries has intensified demand for accessible nursing home services, yet spatial disparities in service distribution remain insufficiently examined in secondary cities. This study investigates spatial distribution and multi-dimensional accessibility of nursing homes in Kunming, China, using comprehensive spatial analytical methods to inform sustainable urban development. We analyzed 205 nursing homes with 47,600 beds, evaluating spatial distribution patterns, economic accessibility, and spatial accessibility across different transportation modes. Our analysis reveals a pronounced monocentric pattern with nursing resources concentrated within central urban districts, creating a “primary core-multiple satellite” structure and spatial mismatch between service supply and older adult population needs. A distinct institutional dichotomy exists between publicly and privately operated facilities, establishing a dual-track system with different accessibility implications for social equity. Economic accessibility analysis demonstrates significant barriers in central urban and tourism-oriented districts dominated by higher-priced private facilities, where minimum prices frequently exceed average monthly pension. Spatial accessibility remains inadequate across all transportation modes, with only 24.3% of communities achieving normal or higher accessibility via private car, 21.5% via public bus, and merely 13.9% via walking. These limitations primarily stem from insufficient service capacity (34 beds per 1000 older adults) relative to demographic needs rather than transportation constraints. We recommend three sustainable interventions: implementing demand-based planning mechanisms, establishing progressive pricing policies, and developing older adult-friendly transportation networks. This framework supports sustainable urbanization by promoting spatial equity and efficient resource allocation, providing valuable insights for secondary cities pursuing sustainable development goals. Full article

Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions. Full article

The Parrot Optimization Algorithm (PO) represents a contemporary nature-inspired metaheuristic technique formulated through observations of Pyrrhura Molinae parrot behavioral patterns. PO exhibits effective optimization capabilities by achieving equilibrium between exploration and exploitation phases through mimicking foraging behaviors and social interactions. Nevertheless, during iterative progression, the algorithm encounters significant obstacles in preserving population diversity and experiences declining search effectiveness, resulting in early convergence and diminished capacity to identify optimal solutions within intricate optimization landscapes. To overcome these constraints, this work presents the Adaptive Differentiated Parrot Optimization Algorithm (ADPO), which constitutes a substantial enhancement over baseline PO through the implementation of three innovative mechanisms: Mean Differential Variation (MDV), Dimension Learning-Based Hunting (DLH), and Enhanced Adaptive Mutualism (EAM). The MDV mechanism strengthens the exploration capabilities by implementing dual-phase mutation strategies that facilitate extensive search during initial iterations while promoting intensive exploitation near promising solutions during later phases. Additionally, the DLH mechanism prevents premature convergence by enabling dimension-wise adaptive learning from spatial neighbors, expanding search diversity while maintaining coordinated optimization behavior. Finally, the EAM mechanism replaces rigid cooperation with fitness-guided interactions using flexible reference solutions, ensuring optimal balance between intensification and diversification throughout the optimization process. Collectively, these mechanisms significantly improve the algorithm’s exploration, exploitation, and convergence capabilities. Furthermore, ADPO’s effectiveness was comprehensively assessed using benchmark functions from the CEC2017 and CEC2022 suites, comparing performance against 12 advanced algorithms. The results demonstrate ADPO’s exceptional convergence speed, search efficiency, and solution precision. Additionally, ADPO was applied to wind power forecasting through integration with Long Short-Term Memory (LSTM) networks, achieving remarkable improvements over conventional approaches in real-world renewable energy prediction scenarios. Specifically, ADPO outperformed competing algorithms across multiple evaluation metrics, achieving average R² values of 0.9726 in testing phases with exceptional prediction stability. Moreover, ADPO obtained superior Friedman rankings across all comparative evaluations, with values ranging from 1.42 to 2.78, demonstrating clear superiority over classical, contemporary, and recent algorithms. These outcomes validate the proposed enhancements and establish ADPO’s robustness and effectiveness in addressing complex optimization challenges. Full article

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

Urban sustainability poses a deeply interdisciplinary challenge, spanning technical fields like data science and environmental science, design-oriented disciplines like architecture and spatial planning, and domains such as economics, policy, and social studies. While numerous advanced tools are used in these domains, ranging from geospatial systems to AI and network analysis-, they often remain fragmented, domain-specific, and difficult to integrate. This paper introduces a semantic framework that aims not to replace existing analytical methods, but to interlink their outputs and datasets within a unified, queryable knowledge graph. Leveraging semantic web technologies, the framework enables the integration of heterogeneous urban data, including spatial, network, and regulatory information, permitting advanced querying and pattern discovery across formats. Applying the methodology to two urban contexts—Thessaloniki (Greece) as a full implementation and Marine Parade GRC (Singapore) as a secondary test—we demonstrate its flexibility and potential to support more informed decision-making in diverse planning environments. The methodology reveals both opportunities and constraints shaped by accessibility, connectivity, and legal zoning, offering a reusable approach for urban interventions in other contexts. More broadly, the work illustrates how semantic technologies can foster interoperability among tools and disciplines, creating the conditions for truly data-driven, collaborative urban planning. Full article

China’s rapid urbanization has presented challenges for sustainably revitalizing the historic and cultural heritage within its urban villages. Often, these efforts overlook the crucial roles of community ties and cultural values. This study focuses on 15 representative urban villages in Guangzhou (2019–2024). It tests the core idea that the physical layout of these spaces reflects underlying community structures and cultural values shaped by specific policies. Integrating this understanding into landscape planning can significantly improve revitalization outcomes. We used a mixed-methods approach: (1) Extended fieldwork to understand community networks and cultural practices; (2) Spatial analysis to measure how building density relates to land uses; (3) Sentiment analysis to reveal how people perceive cultural symbols; (4) A coordination model to link population influx with landscape suitability. Key findings reveal different patterns: Villages with strong clan networks maintained high cultural integrity and public acceptance through bodies like ancestral hall councils. Economically driven villages showed a split—open for business but culturally closed, with very low tenant participation. Successful revitalization requires balancing three elements: protecting physical landmarks in their original locations; modernizing cultural events; and reconstructing community narratives. Practically, we propose a planning framework with four approaches tailored to different village types. For instance, decaying villages should prioritize repairing key landmarks that hold community memory. Theoretically, we build a model linking social and spatial change, extending the cultural value concepts of Amos Rapoport to the context of fast-growing cities. This provides a new methodological perspective for managing urban–rural heritage in East Asia. Full article

Temporary architecture, as an expression of the concept of impermanence, offers adaptable and time-sensitive spatial interventions that promote community engagement and encourage experimentation within the urban environment. Beyond its physical and functional qualities, this architectural approach acts as a social mediator, fostering dialogue, networking, and the exchange of ideas between local communities and professionals, while contributing to the development of a socio-cultural common ground. This paper explores the Greenfeel Architecture wooden pavilion as a case study of small-scale architecture embedded within a landscape dedicated to urban agriculture and community-driven activities. The design process was guided by the need to balance functional requirements—providing shelter from the sun and rain and facilitating social interactions—with the protection of the existing vegetation and the enhancement of local biodiversity, with particular emphasis on supporting bee populations. In line with sustainable construction principles, the pavilion was built through the reuse of recovered materials, including used bricks for pavement, wooden slabs for the facade and roof, and several structural components sourced from previous building projects. Since its completion, the pavilion has acted as an urban acupuncture point within the surrounding area and has become a host for various outdoor activities and educational workshops aimed at diverse groups, including children, adults, professionals, and laypersons alike. The duality between the scale of the pavilion and the scale of its social, cultural, or ecological influence highlights the potential of temporary architecture to become a tool for both physical and socio-cultural sustainability in an urban environment. Full article