The rapid development of urbanization has increasingly intensified the urban heat island (UHI) effect, becoming a global environmental issue. Urban green spaces (UGS) have a mitigating effect on the UHI. However, there has been limited analysis on how the spatial distribution pattern of UGS influences the UHI effect. Therefore, we combined spatial autocorrelation and buffer zone analysis to determine the cooling distance of UGS based on Google Earth Engine. It also explores the impact of the spatial distribution pattern of UGS on the UHI effect using landscape pattern indices. The results show that: (1) UGS have the most significant cooling effect on the UHI effect during summer, with evergreen coniferous forests providing the greatest cooling effect and grasslands the weakest. (2) The cooling distance of UGS is 300 meters, with the best cooling effect observed within the 200-meter buffer zone. (3) When the spatial aggregation of UGS exceeds the threshold range of 85-89, the cooling effect increases significantly, while below this threshold range, the cooling effect is not significant. This study provides a basis for future development planning that utilizes UGS to mitigate the UHI effect, thereby alleviating the negative impacts of UHI and enhancing the comfort of residents' lives.

The intensification of global climate change and human activities has exacerbated the supply-demand imbalance of urban water-soil ecosystem services (WSES). However, the compounded impacts of climate change and human-induced functional space transformation remain unclear. It is necessary to examine how dual pressures restructure the production, transfer and consumption of future WSES. Therefore, this study proposed an integrated supply-flow-demand framework to simulate the spatiotemporal WSES evolution under future composite scenarios. Through cross-scenario comparative analysis, the proposed framework examined the independent contributions and interactive effects of climate change (CC) and functional space change (FSC) on WSES supplydemand, and employed network analysis to elucidate the structural properties of WSES flow networks. Taking 69 cities in the Yellow River Basin as a representative case, CC contributed over 65.28% to the internal supplydemand variations, while FSC's influence strengthened, doubling for water yield and rising about 6.5-fold for soil retention, emerging as a key regulator. CC-FSC interactions exhibited antagonistic effects across 65.75% of the basin due to geographical heterogeneity. Moreover, extreme climate increases inter-urban WSES flows, raising water yield by 4.40-5.06 billion m3 and soil retention by 0.83-0.85 billion tons, partially alleviates internal supply-demand imbalances. The biophysical differences drive contrasting WSES network responses: water yield flow network become centralized and fragile, while soil retention flows gain redundancy. From the perspectives of internal supply-demand balance and external flow networks, this study highlights the critical role of ecosystem service flows in mitigating supply-demand imbalances across urban agglomerations, providing a scientific basis for precision spatial planning and climate-adaptive strategies.

Robust environmental perception is fundamental for the safe and reliable deployment of autonomous driving systems, particularly in complex and variable real world scenarios. However, the generalization capability of perception models remains a critical challenge due to environmental changes, sensor heterogeneity, and domain shifts. Despite significant progress in deep learning–based multimodal perception, existing approaches often struggle to maintain robustness and adaptability in unseen environments. This paper presents a systematic survey of recent advances in generalization techniques for autonomous driving perception. We first analyze the key factors leading to performance degradation under distribution shifts and summarize the limitations of current methods in novel scenarios. Then, we organize existing approaches into three complementary pathways: data-level strategies, model-level generalization methods, and evaluation-level practices. Finally, we discuss open challenges and highlight future research directions toward dynamic world modeling and trustworthy autonomous perception. This survey aims to provide a structured reference and practical insights for advancing generalization in real-world autonomous driving systems.

Urban redevelopment is not merely a spatial intervention, but a complex, multi-scalar institutional process shaped by diverse property rights regimes and a wide array of actors. In this review, property regimes are understood in an extended sense: not only as legal-institutional frameworks, but as multi-scalar systems through which property rights are allocated and exercised via context-specific property arrangements. While existing scholarship has addressed urban transformation and property institutions separately, few studies have systematically integrated these domains through a unified analytical lens. Anchored in the conceptual tension between politico-economic sanction and social entitlement, the review reconceptualizes urban property regimes through three types of cross-scalar institutional frictions: (1) Transformation—between legal clarity and historical ambiguity; (2) Instrument—between capital efficiency and socio-spatial dispossession; and (3) Indicator—between top-down redevelopment rationales and bottom-up claims for justice. Within the Indicator dimension, the review introduces the concept of Property Inclusiveness as a normative-evaluative lens for diagnosing exclusionary redevelopment practices and envisioning more equitable institutional pathways. To formalize these insights, the study constructs a Cross-Scalar Friction Matrix, a novel analytical tool for mapping how institutional frictions unfold vertically across scales. By integrating fragmented literatures and repositioning Global South practices as sources of theoretical innovation rather than contextual exceptions, this review advances a more adaptive and scale-sensitive understanding of property regimes in urban redevelopment. It calls for future research to explore how institutional adaptability and digital co-governance tools can be embedded into formal structures to reconcile capital logic with spatial justice.

Light pollution, characterized by excessive or misdirected artificial light at night (ALAN), poses a significant threat to human health and environmental sustainability. Numerous studies examine light pollution’s health impacts, yet often overlook nighttime light (NTL) remote sensing’s critical role in elucidating these effects. This review synthesizes current applications of satellite-derived NTL data in monitoring human health impacts, highlighting its potential to elucidate ALAN-health relationships. From four databases (Web of Science, ScienceDirect, PubMed, and Embase), we included 173 research articles, published up to May 2025, that utilize NTL satellite remote sensing data for human health studies for this review. Our review reveals epidemiological studies using NTL data increasingly link ALAN exposure to adverse health outcomes—including sleep disorders, obesity, depression, mental disorders, and cancer. However, a core limitation for NTL remote sensing is that health impacts stem mainly from indoor lighting, but NTL data measure only outdoor exposure. Additional challenges involve coarse spatial resolution, temporal mismatches, and limited spectral response. Future research must prioritize high-resolution multi-spectral NTL sensors, integration with medical-geospatial models, and methods to deduce indoor lighting patterns from satellite-derive NTL data.

Nighttime illuminated urban roads are a vital indicator of a city's economic activity, energy consumption, and public safety. Nighttime light (NTL) data acquired by the SDGSAT-1 provide superior spatial and spectral resolution compared to conventional satellite-derived NTL products, thereby offering significantly enhanced potential for delineating and mapping illuminated urban roads. This study proposes a novel local cloth simulation filtering-based (L-CSF) method for illuminated road extraction from SDGSAT-1 NTL data. L-CSF converts the SDGSAT-1 NTL data into a three-dimensional "NTL point cloud", transforming the illuminated road extraction task into a ground filtering problem in point cloud. By employing an adaptive filtering strategy to dynamically adjust CSF parameters, the method achieves robust illuminated road extraction across heterogeneous lighting conditions. The proposed method was validated in eight Chinese cities with different levels of development and lighting characteristics, utilizing RGB, panchromatic, and PAN-RGB fused data. The results indicate that L-CSF achieves an average precision of 76.45% on RGB composite bands, with the F1 score rising to 80.44% in well-lit cities, confirming its stable performance across diverse urban contexts. PAN-RGB fusion helps reduce road fragmentation in bright, saturated urban cores. Furthermore, SDGSAT-1 NTL data can serve as a valuable complement to conventional road networks, particularly for less-developed areas. We further benchmarked L-CSF against six mainstream methods and found it outperforms the comparison methods by up to 2.61-16.04% in F1 score, showing advantages in preserving road continuity and structural integrity. The proposed method demonstrates significant potential for urban lighting infrastructure assessment and supports evidence-based sustainable urban planning.

Road traffic safety assessment is critical for mitigating traffic accidents, safeguarding human life and property, and fostering socioeconomic development. Existing methods, which rely on the statistical analysis of historical traffic accidents and conflicts as well as the evaluation of road design parameters, play a pivotal role in assessing road traffic safety. Driving visibility acts as a critical indicator of the driver's field of view and serves as a significant supplement to these methods. Consequently, this study proposes a method for quantifying 3D driving visibility utilizing LiDAR point cloud data. The approach establishes a computational framework for 3D visible space from the driver's perspective and introduces a novel Driving Visibility Index (DVI) to enable visibility-based safety evaluation. The proposed method consists of three primary components: road point cloud acquisition and preprocessing, driving visibility field computation, and DVI computation. We validated the proposed method along Yixian Avenue at Sun Yat-sen University's Zhuhai Campus, generating a driving safety map. The results revealed that the overall DVI for bidirectional travel on Yixian Avenue ranges from 0.2 to 0.6, indicating suboptimal safety conditions. Further comparative analysis with field-collected data subsequently confirmed the robustness of our proposed method. The proposed method's objective and intuitive quantification of 3D visible space from the driver's perspective provides a novel basis for traffic management, with significant applications spanning road design, traffic facility layout, and the validation of intelligent transportation networks.

Globally, cities frequently encounter challenges in improving public access to open green spaces (OGS). In China, the promotion of shared open green spaces aligns with its eco-urbanization strategy. However, practical implementation has revealed structural contradictions due to disconnections among multi-scale governance systems. This study employs Jinan as a case study and develops a district-unit-site (DUS) multi-scale analytical framework, incorporating the Dagum Gini coefficient and the coupling coordination degree model to assess the spatial sharing of open green space in multi-scale governance. The findings, as indicated by the Dagum Gini coefficient, underscore disparities in the current sharing of OGS, which are primarily attributable to imbalanced patterns, with the most pronounced differences at the district level. Based on these insights, OGS sharing is categorized into four types: imbalance-sufficiency, imbalance-insufficiency, balance-insufficiency, and balance-sufficiency. The coupling coordination model reveals a 'dumbbell-shaped’ distribution: the city core exhibits a higher sufficiency level in OGS sharing but experiences collaborative imbalance, while the eastern and western wings demonstrate balance yet lack sufficiency. Corresponding gradient governance solutions are proposed for different areas, offering replicable approaches for enhancing the spatial sharing of OGS in metropolitan areas.

Contract risk is a major source of uncertainty in construction projects. Contract risks are still identified primarily through manual review by experts, often under tight time constraints. This process is laborious and prone to error. To automate the analysis of large volumes of contract documents, this study presents Contract-Seek, a locally deployable decision support system that combines instruction tuning for the contract domain with retrieval-augmented generation (RAG). Five experts developed a risk register containing 327 items, and 8,112 instruction instances were constructed from risk annotations, coding of three contract functions, and question answering data derived from contract clauses. Contract-Seek was evaluated on 148 questions through blinded scoring by three contract experts and compared with GPT-4o, DeepSeek-R1-671B, and QwQ-32B. It achieved average scores of 9.39 for contract clause alignment, 8.44 for risk identification precision, and 8.96 for linguistic proficiency. In the controlled ablation, adding RAG to QwQ-32B improved clause alignment and risk identification precision by 5.01 and 4.17 points, respectively. Adding instruction tuning increased linguistic proficiency by a further 2.21 points and also improved the other two dimensions. A separate evaluation using 48 clauses from actual contracts supported its performance in project-specific review. By combining reasoning adapted to the contract domain with controlled access to local knowledge, Contract-Seek provides a reproducible approach for privacy-conscious AI deployment in knowledge-intensive industrial workflows.

Research on coastal recreational activities has grown substantially, yet studies focusing on user perceptions of these spaces—critical for optimizing tourism experiences and management—remain fragmented and underdeveloped. This study addresses this gap by examining tourist sentiment in Xiamen, a renowned coastal city in China, using social media data. Text mining tools were utilized to process the Weibo contents through text segmentation, frequency analysis and cluster analysis. The Two-way Neural Network Fusion Model Based on the BERT (TNNFMB) deep learning approach was employed using transfer learning for sentiment analysis, while the Latent Dirichlet Allocation (LDA) model was used to uncover latent thematic patterns. Sentiment polarity analysis revealed that positive comments constituted 56.47%, negative comments only 16.3%, and neutral comments 27.2%, confirming a generally positive perception of visitors’ coastal experiences. Tourists’ social media posts primarily revolve around five core themes in coastal areas: coastal waters, waterfronts, adjacent environments, culture and creativity, and reputation and expectation. The spatial and temporal changes in sentiment scores were discovered. Areas emphasizing sea–land landscapes, cultural theme reinforcement, and open public activities generally achieved high and stable sentiment scores. Natural and natural–artificial mixed coastlines experienced significant seasonal variations in sentiment. The recommendations of this study, generated from a sentiment perspective, include shaping a harmonious coastal environment by improving coastal management and support services to enhance the comfort of the tourist experience. This study advances understanding of user-centric coastal tourism dynamics, providing evidence-based tools for managers to enhance tourist experiences and spatial quality.

With rapid global urbanization development, impermeable surface increase, urban population growth, building area expansion, and rising energy consumption, the urban heat island (UHI) effect is becoming increasingly serious. However, the spatial distribution of the UHI cannot be accurately extracted. Therefore, we focused on Luoyang City as the research area and combined the Getis-Ord-Gi* statistic and the greenest image to extract the UHI based on the Google Earth Engine using land surface temperature–spatial autocorrelation characteristics and seasonal changes in vegetation. As bare land considerably influenced the UHI extraction results, we combined the greenest image with the initial extraction results and applied the maximum normalized difference vegetation index threshold method to remove this effect on UHI distribution extraction, thereby achieving improved UHI extraction accuracy. Our results showed that the UHI of Luoyang continuously expanded outward, increasing from 361.69 km2 in 2000 to 912.58 km2 in 2023, with a continuous expansion rate of 22.95 km2/year. Furthermore, the urban area had a higher UHI area growth rate than the county area. Analysis indicates that the UHI effect in Luoyang has increased in parallel with the expansion of the building area. Intensive urban construction is a primary driver of this growth, directly exacerbating the UHI effect. Additionally, rising temperatures, population growth, and gross domestic product accumulation have collectively contributed to the ongoing expansion of this phenomenon. This study provides scientific guidance for future urban planning through the accurate extraction of the UHI effect, which promotes the development of sustainable human settlements.

Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI’s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology.

Addressing the imbalance between supply and demand for urban parks necessitates an assessment of their service accessibility and spatial equity. This study integrates multi-source geographic data, uses multiple data sources to generate a population distribution with high spatial resolution, and constructs park service areas with multiple time thresholds based on travel preference surveys. The network analysis method is used to evaluate the supply–demand ratio and spatial equity by using location entropy, Lorenz curves, and the Gini coefficient to identify the optimal location. The results reveal a significant difference in the supply–demand ratio of parks. Within the 5 min time threshold, only 14.68% of the pixels in the park supply area meet the needs of residents, while the proportions for the 15 min and 30 min time service area expands to 71.74% and 86.34%, respectively. The distribution of parks exhibits apparent spatial inequity. Equity is highest for the 15 min service area (Gini coefficient = 0.25), followed by the 30 min area (Gini coefficient = 0.27) and 5 min areas (Gini coefficient = 0.37). Among the 80 streets in the study area, the per capita green space location entropy of 11 streets is zero. A targeted site selection analysis for areas with park supply deficiencies led to the proposed addition of 11 new parks. After this optimization, the proportion of regions achieving supply–demand balance or better reached 80.38%, significantly alleviating the supply–demand conflict. This study reveals the characteristics of park supply–demand imbalance and spatial equity under different travel modes and time thresholds, providing a scientific basis for the precise planning and equity enhancement of parks in high-density cities.

Despite the growing body of literature on the built environment and social capital, there remains a significant gap in understanding the mediating role of subjective residential satisfaction. Examining how residents perceive and experience their environment offers a fresh angle for understanding the intricate relationship between physical spaces and social dynamics. Our study assessed social capital and subjective residential satisfaction through an extensive questionnaire survey conducted across 60 communities, involving 1684 participants in Tianjin’s metropolitan area, China. We evaluated the elements of the built environment using the ‘5D’ framework, and the pathways of influence were examined using a multilevel structural equation model. Our results reveal a notable mediating effect, with subjective residential satisfaction being a key factor in the intricate process by which the built environment affects social capital. The results also indicate that land use diversity negatively impacts social capital, while population density, access to facilities and services, and public transport density have positive effects. These insights offer practical guidance for fostering social capital and community development by considering subjective perceptions. The results enhance our understanding of effective strategies for building social capital through improved socio-spatial interventions.

Building community resilience is essential for ensuring that communities can not only survive but also thrive in the face of various challenges and uncertainties. However, existing research has deficiencies in the comprehensive evaluation framework and systematic analysis of different types of urban communities within high-density Chinese cities. This study constructed a comprehensive urban community resilience assessment system (UCRA) that covers four dimensions: environmental, service, social, and governance resilience. In a case study of the Chinese megacity of Tianjin, urban communities were categorized into three physical types and three regional categories. The UCRA contained 40 detailed indicators, and the weighting of indicators was was determined using a mixed approach combining the AHP and entropy methods. The findings revealed that tower apartments in urban Chinese communities demonstrated relatively high resilience, whereas older residential complexes exhibited the lowest resilience performance. Furthermore, central urban communities generally displayed high resilience, in contrast to peripheral urban areas, where low levels of resilience were often discovered. Building upon these findings, this study discusses the characteristics and challenges associated with the resilience of various community types. By establishing a theoretical basis for creating intelligent assessment and monitoring systems, we advocate for targeted community development strategies, thereby promoting smart transformation of community resilience.

Africa's rapid urbanization and infrastructure expansion are driving demand for road lighting across the continent. However, the current knowledge regarding road lighting coverage is fragmented. Here, we employed high-resolution nighttime SDGSAT-1 satellite data, OpenStreetMap road network data, and population datasets to quantify rural road lighting coverage and disparities across scales. The results show that, on average, only 3.52% of rural roads are lit across the continent. Northern Africa (14.65%) exhibits markedly higher rural road lighting coverage than Central Africa (1.28%). Furthermore, the West, East, Central, and Southern African regions have rural road lighting coverage rates below 2.2%. At the national scale, Egypt has the highest road lighting rate at 22.81%, whereas Sierra Leone and Burundi have rates below 0.20%. Overall, our study reveals substantial inadequacies in rural lighting infrastructure across Africa and demonstrates the value of high-resolution nighttime data for monitoring infrastructure.

The 15-min city model promises hyper-local accessibility to essential services, yet its resilience faces unprecedented challenges during systemic crises. This study investigates how COVID-19 pandemic policies disrupted public service dynamics in Tianjin, China—an early adopter of the 15-min city framework—through a mixed-methods approach combining policy analysis, GIS spatial mapping, and questionnaire surveys across pandemic phases. The diminished accessible ranges of a 15-min walking distance and quantity of available services were largely observed. Changes in open areas and operating hours of facilities influenced service capacity. The factors of accessibility, quantity, and capacity together caused a cascading effect of service supply alterations. The results reveal essential life and health care services were universally overwhelming demands during lockdowns, but attention needs to be paid to the vulnerabilities of groups and areas where potential demand–supply imbalances and variations may have occurred. The pandemic exposed the 15 min city model’s spatial disparities and inflexibility in service elasticity. We argue that future 15-min cities must integrate: 1) Hybrid digital-physical service delivery to maintain access during mobility restrictions, 2) Equity-centered crisis governance prioritizing high-risk areas and groups 3) Adaptive planning protocols enabling rapid conversion of spaces. Consequently, we advocate for further institutional adaptations to ensure that the existing 15-min city can effectively adapt and transform to enhance system resilience in response to epidemics or emergencies.

The prevalence of urban parks serves as a crucial indicator for assessing a city’s vitality and livability. However, limited research has revealed the key characteristics of parks that attract large and stable numbers of visitors. In this study, we established intensity and stability indicators to trace fine-granular flow of visitors in parks. Visitor flows was analyzed using mobile phone signaling data with an accuracy of hourly data, which also represented the spatiotemporal patterns of park visitation. We delineated the patterns of 96 parks in Jinan, a prominent city in North China. Patterns of visitation exhibit diverse features of peak gatherings. The intensity and stability of park visitation are both subject to typical weekday-weekend variations, as measured by the accumulated visitor flow and the Shannon entropy. The effects of environmental factors on park visits were examined by using regression models. Factors that significantly influence the intensity and stability of park visits are the surrounding transportation facilities and the distance from the city center. The factors of surrounding population density and allocated construction land exert additional influences on various temporal periods. Understanding the spatiotemporal patterns of park visitations provides insights into advanced strategies for optimizing park resources and refining park management. This study explored the spatiotemporal characteristics of urban park visitation using intensity and stability indicators. We investigated 96 parks in Jinan, China, and fine-granular hourly-based visitor flow was extracted using mobile phone signaling data. Park visitations typically displayed characteristics of peak-gathering and exhibited regular fluctuations in and stability on weekdays and weekends, which were assessed using the accumulated visitor flow and Shannon entropy method. Six city- and park-related factors were found to affect park visits. The intensity of park visits is influenced by similar factors on both weekends and weekdays; however, the stability of park visits is influenced by distinct variables during these two time periods.

Sustainable Development Science Satellite 1 (SDGSAT-1), an innovative satellite engineered by the Chinese Academy of Sciences, focuses on achieving sustainable development goals (SDGs) by using high-resolution low-light imaging for urban planning and environmental monitoring. Although its potential to provide substantial information relevant to various SDG indicators has been demonstrated across multiple domains, a comprehensive analysis of the potential of SDGSAT-1 glimmer imagery for urban road detection has not yet conducted. To address this gap, we aimed to evaluate the applicability of SDGSAT-1 glimmer imagery in road detection across five cities with well-developed road networks in China, namely Chengdu, Guangzhou, Hangzhou, Shanghai, and Wuhan. Watershed segmentation and optimal thresholding algorithms were applied to extract the road network data of these five cities. We observed that the large connecting roads, including highways (Motorway), urban expressways (Trunk), urban main roads (Primary), urban secondary roads (Secondary), and urban tertiary roads (Tertiary), were visible in the SDGSAT-1 glimmer data. Watershed segmentation and optimal thresholding algorithms demonstrated a remarkable precision of approximately 80% for road extraction across all cities. Approximately, 64% of urban roads were detectable within the SDGSAT-1 glimmer imager data. Our study suggests that the SDGSAT-1 glimmer data have great potential for extracting urban roads.

In order to analyze the drying and kinetic characteristics of sludge, drying experiments were designed and carried out in this paper. The changes of each key indicator in the drying process were investigated under drying temperature of 40-80 degrees C, relative humidity of drying air of 7 similar to 35 %, and sludge thickness of 1-9 mm, respectively. The results showed that the drying time was shortened by 12.63 % for each 10 degrees C increase in the range of 40-70 degrees C. However, when the temperature was increased to 80 degrees C, the drying time reduced significantly, and the drying rate of the sludge increased by 25.53 %. Reducing relative humidity shortened the drying time, and when the relative humidity was below 14 %, the relative humidity had no significant effect on the drying time. The reduction of sludge thickness decreased the drying time significantly, and the maximum drying rate of 1 mm thick sludge was 86.73 % higher than that of 9 mm. When the sludge thickness was 5 mm, the drying energy consumption per unit mass reached the minimum value of 49.63 kJ/g. The effective diffusion coefficient was positively correlated with temperature and thickness, while the opposite existed with relative humidity. The activation energy of drying under 3 mm thickness is 15.293 kJ/mol, and the pre-exponential factor is 8.951E-08 m(2)/s. The sludge drying followed the Midilli model, where the model parameters a and n were mainly affected by constants, and the R-2 of model parameters b and k were 0.628 and 0.891, respectively. The results can provide reliable parameter guidance for strengthening sludge drying performance and reducing drying energy consumption.