Understanding the coupling relationship between discrete road networks and continuous settlement space is a key challenge in rural geography. This study proposes and implements a novel graph-field coupling workflow to bridge this methodological gap. By constructing a complete analytical chain from road network extraction and harmonic centrality computation to Gaussian-kernel-based potential field modeling and adaptive village identification, this work establishes a reproducible framework for quantifying the "structure-space" relationship. Applying this workflow to a multi-temporal case study (2013-2025) in the Lower Yangtze region reveals a contextual tripartite typology of rural settlements: Local Network & Gateway, Inter-Village Linked & Dispersed, and Core Development Area types. The analysis further uncovers that spatial restructuring events (splits and merges) are primary drivers of type transitions, with a conversion rate of 48.4% and 45.7%, respectively, compared to only 6.2% for continuous one-to-one evolution. These findings illustrate that rural spatial evolution is a process of dynamic reconfiguration among functional components (cores, connectors, gateways), driven more by changes in connectivity than intensity. The workflow provides a transferable methodological blueprint for complex spatial system analysis and offers a typology-based, process-sensitive evidence base for differentiated rural planning and governance.

The purpose of this paper is to assess Chinese rural infrastructure development by quantitative method in view of multiple official perform indicators. Researches done so far mainly concentrates on microcosmic index and locations. This investigation builds an evaluation system using classical case based reasoning (CBR) method integrated subjective and objective weight values. National official provides the basic database. Combining database and perform indicators, a regression model is built to infer whole country infrastructure condition under high coefficient. Final result shows that the construction of sewage treatment and shower facilities in China's major villages is relatively high, but the use of clean energy and cold chain facilities in rural areas are still relatively poor. The northeast area is the region with the worst infrastructure construction, and the east, midland, southwest and south regions have best infrastructure performance. At last, the conclusions indicate that regions and thermal zones significantly affect the rural construction performance. Future government should pay close attention to comprehensively develop northeast infrastructure and northwest clean energy building.

Under the whole-county rooftop PV development initiative, large-scale distributed photovoltaic (DPV) integration has imposed significant pressure on distribution network operation and power quality. The intermittency of DPV output and the high cost of energy storage systems (ESS) make joint planning challenging. This paper proposes a joint DPV-ESS planning model aimed at minimizing the daily operating cost. The model is formulated using a convexified DistFlow representation and incorporates both electrical network and ESS operational constraints. Validation on a modified IEEE 33-bus system yields a minimum daily operating cost of 24,447.3 yuan, confirming the economic effectiveness of the proposed joint planning framework in enhancing PV accommodation and alleviating network congestion.

To address the issue that transient voltage cannot be stabilised solely by the system's own reactive power regulation capability after faults occur at some weak nodes in high-penetration wind-photovoltaic (PV) hybrid grid-connected system, this paper proposes a dynamic reactive power planning method that accounts for transient voltage stability. Based on the system's own reactive power regulation capability, this method constructs a reactive power planning benchmark scenario using the typical output of wind power (WP) and PV power and determines the installation locations and compensation capacities of the dynamic reactive power compensation devices through multiple power flow calculations. First, the key fault nodes are identified using the transient voltage stability recovery index (TVRI) to preliminarily determine the system's weak nodes. On this basis, the sensitivity index (SI) is used to determine the installation nodes of dynamic reactive power compensation devices. Subsequently, an optimisation model for dynamic reactive power planning is established with the objective of enhancing system transient voltage stability while minimising the investment cost of dynamic reactive power compensation devices. The compensation capacities at reactive power compensation nodes are optimised using a particle swarm optimisation algorithm based on differential evolution (DE-PSO). Finally, the effectiveness and superiority of the proposed method in improving the transient voltage stability and economic performance of the system are verified by the improved IEEE 39-bus system.

Decarbonization of built environments is important for climate change mitigation, while most studies have been contextualized in urban areas. In China, village population accounts for about one-third of national population, demonstrating large potential of decarbonization. Focusing on the relationship between village carbon emissions and built form, this study aims to develop data-driven estimation models of village electricity carbon emissions and explore the contributions of built form factors. The models were developed upon linear regression, random forest, eXtreme gradient boosting, artificial neural networks, and deep neural network algorithms, based on the built form factors of 120 villages in Huaiyuan county, in Anhui Province, China. The results verified that the deep neural network had the best estimation capacity for village electricity carbon emissions. This model was further adopted to estimate village electricity carbon emissions in Huaihe River Basin, China. The results showed that the total annual village electricity carbon emissions in Huaihe River Basin were 633,753 ktCO2, and the average annual village electricity carbon emissions were 2224 ktCO2. Moreover, Shandong had the largest proportion of villages belonging to the primary carbon reduction villages with the highest carbon reduction needs. Land area was one of the key factors affecting village electricity carbon emissions. Overall, this study helps clarify village carbon emission in Huaihe River Basin and enables the formulation of village planning and design strategies for decarbonization.

Rural road networks are vital for rural development, yet narrow alleys and occluded segments remain underrepresented in digital maps due to irregular morphology, spectral ambiguity, and limited model generalization. Traditional segmentation models struggle to balance local detail preservation and long-range dependency modeling, prioritizing either local features or global context alone. Hypothesizing that integrating hierarchical local features and global context will mitigate these limitations, this study aims to accurately segment such rural roads by proposing R-SWTNet, a context-aware U-Net-based framework, and constructing the SQVillages dataset. R-SWTNet integrates ResNet34 for hierarchical feature extraction, Swin Transformer for long-range dependency modeling, ASPP for multi-scale context fusion, and CAM-Residual blocks for channel-wise attention. The SQVillages dataset, built from multi-source remote sensing imagery, includes 18 diverse villages with adaptive augmentation to mitigate class imbalance. Experimental results show R-SWTNet achieves a validation IoU of 54.88% and F1-score of 70.87%, outperforming U-Net and Swin-UNet, and with less overfitting than R-Net and D-LinkNet. Its lightweight variant supports edge deployment, enabling on-site road management. This work provides a data-driven tool for infrastructure planning under China’s Rural Revitalization Strategy, with potential scalability to global unstructured rural road scenes.

With the deepening implementation of China's rural revitalization strategy, village electricity consumption (VEC) prediction has become a crucial component of energy planning. However, traditional methods face challenges including data acquisition difficulties and slow prediction speeds. This study focuses on construction land and develops a deep learning framework integrating morphology and socioeconomic data for VEC prediction. Using 285 villages in cold regions of China as research subjects, a CNN-DNN dual-model framework was developed: CNN automatically extracts nine morphology parameters from high-resolution satellite imagery, combines with three socioeconomic data to construct feature sets, and DNN realizes VEC prediction. This method not only avoids the time and cost limitations of traditional field surveys, but also solves the data acquisition challenges in rural areas, making large-scale VEC assessment possible. SHAP analysis reveals that residential land area (RLA) and permanent population (PP) are the dominant predictive factors, exhibiting nonlinear threshold effects. The model demonstrates excellent performance (R2 = 0.938, RMSE = 815,821 kWh), identifying three types of VEC patterns: high-consumption villages (15 %), medium-consumption villages (64 %), and lowconsumption villages (21 %), showing significant spatial differentiation characteristics. This framework provides scientific tools and methods for rapid assessment of VEC and formulation of differentiated electricity policies in cold regions of China.

Aiming at the problems that the uncertainty modeling process of price-based demand response is oversimplified, inconsistent for change pattern of error, and the reliability in user’s comprehensive satisfaction is neglected in the optimal scheduling for power system under multiple uncertainties, integrated factors of economy, environment and society, a multi-objective robust fuzzy optimal scheduling model considering power demand response and user’s comprehensive satisfaction with electricity under multiple uncertainties is proposed. On the basis of analyzing the operation mechanism of the multi-source system and the characteristics of multiple uncertainty sources, the robust theory is used to construct power output model of wind and photovoltaic (PV), and the fuzzy theory is used to construct power demand response model. Taking the lowest comprehensive operation cost as the economic objective, the smallest emissions of CO2 and atmospheric pollutants as environmental objective and the largest user’s comprehensive satisfaction with electricity as the social objective, based on the robust fuzzy theory, the multi-objective uncertainty optimal scheduling model is constructed, which is transformed into deterministic model and then solved by intelligent optimization algorithm. Based on the improved IEEE-39 node system to verify the validity and superiority of the price-based demand response uncertainty modelling method and multiple uncertainty modelling method in this paper, as well as the reasonableness and necessity for considering the reliability in the user’s comprehensive satisfaction with electricity and the multiple uncertainties in power system optimal scheduling.

[Objective]Dimensional management is crucial in manufacturing and construction.However,compared to the manufacturing industry,the construction industry has seen considerably less research investment in this area.Traditional construction relies on passive and inefficient dimensional management methods that focus on tolerance specifications and compliance measurements,which fail to meet the demands of rapid assembly in prefabricated buildings.This adversely affects both assembly quality and efficiency.This study establishes a dimensional management process for prefabricated buildings based on the information delivery manual(IDM)standards developed in building information modeling(BIM).It specifies the information exchange processes across different disciplines,providing a standardized implementation pathway and technical guidance for proactive dimensional management starting from the project design stage onward.[Methods]This study conducts a survey and analysis of the current state and deficiencies in dimensional management mechanisms in prefabricated buildings,following which it proposes improvements via integration of the design,production,and construction processes of prefabricated buildings and introduction of tolerance analysis methods derived from the manufacturing industry.To effectively combine the enhanced dimensional management process with BIM,a dimensional management process is developed as per the IDM standards specified by ISO 29481-1-2016.This study follows four key steps:(1)Identification of reference processes.(2)Creation of process diagrams.(3)Defining of exchange requirements and business rules.(4)Development of functional parts.[Results]The newly developed dimensional management process comprised four stages:(1)Preliminary design stage,early in this stage,a dimensional management team was formed,comprising experts,designers,production personnel,and construction technicians.This team determined the tolerance levels for key components on the basis of the owner's requirements.(2)In the detailed design stage,the dimensional management team,working as per the BIM model,identified potential deviation risks that could affect the project's appearance,functionality,quality,and constructability.In addition,critical dimensions requiring deviation accumulation prediction were specified by the team.(3)In the design optimization stage,deviation issues were predicted and simulated by means of the tolerance analysis functional part.Through the analysis results,the design was optimized,and reasonable tolerance and measurement plans were formulated.(4)In the production and construction stage,prefabricated components were manufactured and assembled onsite following the tolerance and measurement plans,with deviation reports generated via the digital compliance measurement functional part.In addition,this study clarified the exchange requirements and business rules involved in the process,facilitating the integration of dimensional management with the existing BIM systems.[Conclusions]Through the survey results and improvement of the current dimensional management mechanisms in prefabricated buildings,this study develops a standard process for collaborative dimensional management across various disciplines,with reference to the IDM standards.In addition to traditional methods focusing on tolerance specifications and compliance measurements,the process emphasizes the establishment of a dedicated dimensional management team during the design stage.This team is responsible for the selection of tolerance levels,identification of deviation risks,prediction of deviation accumulation,and further guidance of design optimizations toward manufacturing and assembly.This proactive dimensional management approach aims for higher assembly precision and quality.In addition,this study specifies the exchange requirements and functional parts of the"prefabricated building dimensional management"IDM,introduces tolerance analysis techniques from the manufacturing industry and provides an information framework for future integration of dimensional management with BIM.

Significance The construction industry in China is a major contributor to carbon emissions, creating substantial environmental challenges. In response, the construction sector is intensifying efforts to reduce its carbon footprint. Among the various strategies implemented, building information modeling (BIM) technology has emerged as a key digital tool with transformative potential to lower building-related carbon emissions. BIM technology enhances design precision and operational efficiency while enabling comprehensive analysis and optimization of building systems. This capability facilitates carbon emission reductions throughout the lifecycle of a building. However, there remains a notable lack of systematic documentation and synthesis on effectively leveraging BIM technology for carbon emission control in construction. This gap is further exacerbated by the lack of comprehensive analyses of potential future research directions and practical application scenarios for BIM in carbon reduction. Progress Therefore, the present study investigates the specific application of BIM to reduce carbon emissions across the design, production, and operation phases of a building's lifecycle. Through bibliometric methods that entail quantitative analysis of published research, the study seeks to identify key technologies and emerging trends within this domain. This research is organized into two main components. First, a comparative literature review combined with a market survey is conducted to map advancements in BIM-based research related to the whole life cycle carbon emissions of buildings. This comprehensive review aims to consolidate existing knowledge while identifying gaps or inconsistencies within the current body of research. Second, a detailed examination is conducted, focusing on the stages that have the most significant impact on carbon emissions, including building design, production, and operation. This analysis aims to identify major achievements and ongoing challenges within current research efforts and practical implementations and highlight potential directions for future advancements. Conclusions and Prospects The findings reveal several key insights. BIM technology has focused primarily on the whole life cycle carbon emission analysis and design phase of buildings. While these contributions are noteworthy, research targeting the production and operational phases remains comparatively underdeveloped. This imbalance is partly due to the limited exploration of BIM's application scenarios in these later stages of a building's lifecycle. Specifically, BIM's potential to optimize building production processes and enhance operational efficiency through real-time data analytics and predictive modeling has not been completely realized or integrated into practical projects. Therefore, future research should prioritize broadening BIM's application to cover all phases of a building's lifecycle comprehensively. This involves developing innovative BIM tools and methodologies that seamlessly integrate with building management systems to enable real-time monitoring and control of carbon emissions. Furthermore, fostering collaboration among academia, industry stakeholders, and policymakers is essential for advancing BIM-based carbon reduction strategies and ensuring their effective implementation in practical scenarios. By addressing these research and implementation gaps, the construction industry can fully leverage BIM technology to achieve substantial reductions in carbon emissions, thereby contributing to global sustainability efforts.

The imperative to mitigate carbon emissions has gained a global consensus, and environmental regulation is widely recognized as a pivotal policy instrument for decarbonization. However, existing studies predominantly employ single-tier analyses, providing limited insight into the complexities of multilevel governance and the dynamics between provincial regulatory frameworks and their implementation at subregional levels over time. To address this gap, this study investigates how the intensity of provincial environmental regulation influences carbon emissions from rural residential buildings, while accounting for socioeconomic heterogeneity across regions and temporal variations in policy stringency. Hierarchical linear modeling is applied to panel data covering 105 municipalities across 26 provinces from 2015 to 2021. The analysis reveals three key findings. First, provincial environmental regulation intensity functions as a moderating factor rather than a direct determinant of rural residential carbon emissions. Second, the influence of regulation varies significantly across regions: in eastern coastal areas, stringent environmental policies amplify the decarbonization benefits of housing retrofit initiatives, whereas in central and western regions, such policies can be counterproductive due to infrastructural mismatches. Third, policy stability emerges as a decisive factor. Provinces that maintain consistent regulatory frameworks achieve significantly superior emission reductions than those with volatile enforcement. This outcome underscores the need for spatially adaptive governance models that align policy design with regional capacities and prioritize policy coherence over uniformity. Overall, the study provides a practical framework for aligning decarbonization objectives with regional development realities, offering critical insights for policymakers aiming to optimize climate action in multilevel governance contexts.

Hyperspectral imaging offers extensive spectral and spatial information. However, effectively utilizing this data for accurate classification remains a challenge. This study introduced the CASSX-Net, a novel framework designed to capture both short- and long-range dependencies in HSI data for land cover classification. The network combined a dual spectral-spatial feature extraction mechanism with a multi-head cross-attention module to leverage local and global feature interactions. By combining convolutional layers for short-range feature extraction with cross-attention mechanisms for long-range dependencies, the CASSX-Net addressed the intricate spectral-spatial correlations often missed by traditional CNNs. In addition, the maximal correlation fusion strategy optimally integrated the features from various pathways, improving the ability of the model to distinguish between classes with similar spectral signatures. The rigorous evaluation of four benchmark HSI datasets, including Pavia University, Pavia Centre, Salinas, and Houston 2018, demonstrated that the proposed framework consistently achieved the state-of-the-art performance, surpassing the existing methods in terms of classification accuracy and advancing the HSI land cover classification.

In the realm of urban development, the precise classification and identification of land types are crucial for improving land use efficiency. This article proposes a land recognition and classification method based on data sparsity and improved Soft Hirschfeld-Gebelein-R & eacute;nyi (Soft-HGR) under multimodal conditions. First, a sparse information processing module is designed to enhance information accuracy and quickly obtain data sample features. Then, to solve the problem of information independence in single mode and lack of fusion in multimodal mode, an improved SoftHGR module is developed. This module incorporates covariance and trace constraints, enhances machine learning efficiency by stabilizing output and addressing dimensionality and variance issues in HGR, and speeds up land classification by cross-fusing multimodal features to deepen the understanding of diverse information interconnections. Based on this, a multimodal MI-SoftHGR fusion network is constructed, which can achieve cross-correlation sharing and collaborative extraction of feature information, thereby realizing accurate remote sensing image recognition and classification under multimodal conditions. Finally, empirical evaluations were conducted on Berlin, Augsburg, and MUUFL datasets, and the proposed method was compared with state-of-the-art algorithms. The results fully validate the efficacy and significant superiority of the proposed method.

The purpose of this research is to build a workflow discovering building time-series energy usage valuable information decreasing carbon emission efficiently and establish a program realizing intelligent decision. Research done so far mainly concentrates on total power utilization instead of time-series data. In this paper, a k-shape time-series algorithm is harnessed for clustering, while some regression algorithms are utilized building classifier model. The studied object regards Chinese Yushan island as the database sample. The result shows that all village carbon emission patterns derived by electricity consist of V, M and line. Each mode corresponds different inhabitants. Thereinto, fisherman illustrates classical islet energy consumption feature and several carbon lesson methods could be used in their vacant houses during fishing period. At last, the conclusion indicate that this studied workflow could efficiently identify the energy usage characteristic and provide targeted carbon reduction strategies for users quickly.

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident’s time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision.

Tolerance management in the Architecture, Engineering, and Construction (AEC) sector faces challenges due to a lack of systematic scientific methods and tools. Quality issues in construction, coupled with inefficiencies, rework, and waste arising from deviation problems, hinder the sustainable development of the AEC sector. Current deviation control methods in the AEC field rely on compliance inspections and on-site rework, which are reactive and costly. In contrast, the manufacturing industry, closely linked to AEC, has developed highly automated methods for tolerance management. Although prefabricated buildings are manufactured off-site with high precision, the on-site assembly precision remains low, failing to fully leverage manufacturing industry capabilities. This research proposes a framework for predicting assembly accuracy of prefabricated buildings during the design and early construction stages. The framework, based on tolerance analysis methods from the manufacturing industry, integrates Building Information Modeling (BIM) and Terrestrial 3D Laser Scanning (TLS) technologies. This innovative approach offers a computer-aided method for conducting tolerance analysis in prefabricated buildings. A completed prefabricated building project serves as a case study, utilizing the framework to predict variations in critical dimensions, and the predictions align with measured results, demonstrating the feasibility of the proposed framework.

This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data.