Search Results (14,019)

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control. Full article

Construction industry in Afghanistan is crucial for economic and social advancement, particularly after years of instability. However, the construction industry has been already confronting huge time overruns, affecting all stakeholders. This research aims to identify the various risks associated with time overruns in construction projects within Afghanistan and to explore effective risk management strategies to mitigate these challenges. To address time overruns, this study employed Monte Carlo simulations using RiskPert to assess time overruns by combining expert judgment with historical data. This study assesses construction project historical data from 2002 to 2023, emphasizing the political and economic circumstances of that period using a literature review and an examination of 74 construction project reports, in addition to semi-structured interviews with industry experts to determine schedule-related risks and their frequent causes. This research found 29 distinct risk indicators classified into eight categories, facilitating a methodical integration of risks into the simulation model. The Monte Carlo Simulations conducted with @RISK software (version 8.0, Palisade Corporation, New York, NY, USA) assessed the influence of these risks on project performance over 10,000 iterations, demonstrating a robust association with actual project results and a standard deviation of ±15% in durations. Time overruns in projects are linked to socio-political, organizational, and financial risks. The findings emphasize the significance of these factors on project outcomes and recommend strategies for their mitigation to improve decision-making and ensure project management success. Full article

Phytoremediation is an eco-friendly and in situ solution for remediating heavy metal-contaminated soils, yet practical application requires timely and accurate effectiveness evaluation. However, conventional chemical analysis of plant parts and soils is labor-intensive, time-consuming and limited for large-scale monitoring. This study proposed a rapid sensing framework integrating laser-induced breakdown spectroscopy (LIBS) with deep transfer learning and spectral indices to assess phytoremediation effectiveness of Sedum alfredii (a Cd/Zn co-hyperaccumulator). LIBS spectra were collected from plant tissues and diverse soil matrices. To overcome strong matrix effects, fine-tuned convolutional neural networks were developed for simultaneous multi-matrix quantification, achieving high-accuracy prediction for Cd and Zn (R²_test > 0.99). These predicted concentrations enabled calculating conventional phytoremediation indicators like bioconcentration factor (BCF), translocation factor (TF), plant effective number (PEN), and removal efficiency (RE), yielding recovery rates near 100% for TF and PEN. Additionally, novel spectral indices (SIs)—directly derived from characteristic wavelength combinations—were constructed to bypass intermediate quantification. SIs significantly improved the direct evaluation of Zn removal and translocation. Finally, a decision-level fusion strategy combining concentration predictions and SIs enhanced Cd removal assessment accuracy. This study validates LIBS combined with intelligent algorithms as a rapid sensor tool for monitoring phytoremediation performance, facilitating sustainable environmental management. Full article

Accurate prediction of lithium battery remaining useful life (RUL) is crucial for battery management systems to monitor battery health status. However, RUL prediction remains challenging due to capacity non-stationarity caused by capacity regeneration phenomena. Therefore, this study proposes a novel RUL prediction framework that combines a two-stage decomposition strategy with a parallel Informer-LSTM architecture. First, STL decomposition is employed to decompose the capacity sequence into trend, seasonal, and residual components. The VMD method further refines the residual component from STL, extracting the underlying multiscale subsignals. Subsequently, a parallel dual-channel prediction network is constructed: the Informer branch captures global long-range dependencies to prevent trend drift, while the LSTM branch models local nonlinear dynamics to reconstruct fluctuations associated with capacity regeneration. Experiments on the NASA dataset demonstrate that this framework achieves an MAE below 0.0109, an RMSE below 0.0160, and an R² above 0.9950. Additional validation on the Oxford battery dataset confirms the model’s robust generalization capability under dynamic conditions, with an MAE of 0.0017. This further demonstrates that the proposed RUL prediction framework achieves significantly enhanced prediction accuracy and stability, offering a reliable solution for battery health status detection in battery management systems. Full article

It is well documented that road construction is a pillar of well-balanced socioeconomic development worldwide. The first decades of the new millennium have witnessed unprecedented development in road construction activities in emerging economies and the Global South. At the same time, the construction industry is widely considered to be a rather conservative one, based on traditional technologies and materials. However, the development of materials science increases the possibilities and volumes of by-products from various technologies, and the selective collection of garbage necessitates innovation in the road construction sphere. The goal of this paper is to provide a broad overview of innovations in road construction. Based on a bibliometric approach, the article analyses the various trends in round construction, where the increasing pressure to reduce costs and the environmental footprint drives deep-rooted innovation through the use of new materials and the optimisation of technologies and management methods. Our results highlight the potential for significant improvements in road construction efficiency, environmental impact, and cost-effectiveness through the adoption of these technologies and methodologies, as well as a trend towards more efficient, sustainable, and technologically advanced road construction practices, with a focus on overcoming traditional inefficiencies and environmental concerns. Future research should continue to focus on addressing these challenges and developing comprehensive, adaptable solutions for the road construction industry, while leveraging the latest findings in this area. Full article

Organizational culture is widely recognized as an important contextual factor shaping career development and long-term human resource sustainability. Although prior research has examined organizational culture, career development, and sustainable HRM, these constructs have often been studied separately and predominantly within Western contexts. This study addresses this gap by analyzing their interrelationships within a transitional economy. Grounded in sustainable human resource management and sustainable careers perspectives, the study examines how organizational culture typologies influence career development and HR sustainability. Career development is operationalized through organizational career growth and career competences. Survey data were collected from 542 employees across 23 IT and manufacturing companies in Serbia and analyzed using factor analysis and multiple regression. The findings show that organizational culture significantly shapes career growth opportunities and career competences and is also directly related to HR sustainability. Person-oriented cultures are associated with more favorable career development conditions and higher levels of HR sustainability, while power- and role-oriented cultures are linked to weaker outcomes. Career growth and career competences further emerge as key mechanisms supporting long-term workforce sustainability. This study contributes to the literature by integrating organizational culture, career development, and HR sustainability into a single analytical framework within a transitional economy context and provides practical insights for managers aiming to foster sustainable careers and long-term HR sustainability. Full article

Current agricultural irrigation management practices are often extensive, and traditional soil moisture content (SMC) monitoring methods are inefficient, so there is a pressing need for innovative approaches in precision irrigation. This study proposes a Multi-Feature Fusion Network (MFF-Net) for SMC inversion. The model uses a designed Channel-Changeable Residual Block (ResBlockCC) to construct a multi-branch feature extraction and fusion architecture. Integrating the Channel Squeeze and Spatial Excitation (sSE) attention module with U-Net-like skip connections, MFF-Net inverts root-zone SMC from summer maize leaf images. Field experiments were conducted in Zhengzhou, Henan Province, China, from 2024 to 2025, under three irrigation treatments: 60–70% θ_fc, 70–90% θ_fc, and 60–90% θ_fc (θ_fc denotes field capacity). This study shows that (1) MFF-Net achieved its smallest inversion error under the 60–70% θ_fc treatment, suggesting the inversion was most effective when SMC variation was small and relatively low; (2) MFF-Net demonstrated superior performance to several benchmark models, achieving an R² of 0.84; and (3) the ablation study confirmed that each feature branch and the sSE attention module contributed positively to model performance. MFF-Net thus offers a technological reference for real-time precision irrigation and shows promise for field SMC inversion in summer maize. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

Unsafe behaviors at construction sites often originate from cognitive failures such as lapses in memory and attention. This study proposes a novel, hybrid framework to systematically identify and predict the key contributors of cognitive failures among construction workers. First, a detailed literature review was conducted to identify 30 candidate factors related to cognitive failures and unsafe behaviors at construction sites. Thereafter, 10 construction safety experts ranked these factors to prioritize the most influential variables. A questionnaire was then developed and field surveys were conducted across various construction sites. A total of 500 valid responses were collected from construction workers involved in residential, highway, and dam projects in Pakistan. The collected data was first analyzed using conventional statistical analysis techniques like correlation analysis followed by multiple linear and binary logistic regression to estimate factor effects on cognitive failure outcomes. Thereafter, machine-learning models (including support vector machine, random forest, and gradient boosting) were implemented to enable a more robust prediction of cognitive failures. The findings consistently identified fatigue and stress as the strongest predictors of cognitive failures. These results extend unsafe behavior frameworks by highlighting the significant factors influencing cognitive failures. Moreover, the findings also imply the importance of targeted interventions, including fatigue management, structured training, and evidence-based stress reduction, to improve safety conditions at construction sites. Full article

Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance ($f$) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance $f$ and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R² increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems. Full article

Australia’s National Housing Accord aims to deliver 1.2 million new dwellings between mid-2024 and mid-2029, representing 240,000 annual completions—a 37% increase above the 2024 baseline of 175,000. This study employs a comprehensive system dynamics model with 79 equations (10 stocks, 69 auxiliary variables) to analyze whether this target is structurally achievable, given construction industry capacity constraints. The model integrates builder population dynamics, workforce capacity, construction cost inflation, material supply constraints, and financial market conditions across a ten-year simulation horizon (2024.5–2035). Three policy scenarios test the effectiveness of interventions, including capacity expansion (±10–15%), cost inflation management (±15–20%), planning reforms (+5–15% efficiency), and workforce development programs (+1000–4000 annual graduates). Model validation against Australian Bureau of Statistics data from 2015 to 2024 demonstrates strong empirical foundations. Results show that structural capacity constraints—driven by three simultaneous bottlenecks in material supply, workforce availability, and financing—create a supply ceiling of around 180,000–195,000 annual completions. Even under optimistic policy assumptions, the model projects cumulative completions of 880,000–920,000 dwellings over the Accord period, falling 23–27% short of the 1.2 million target. Critical findings include the following: (1) builder insolvencies exceeding entry rates by 15–25% annually under stress conditions, (2) capacity decline trends of 0.6–0.8% per year due to productivity losses, infrastructure bottlenecks, and regulatory burden, (3) system efficiency degradation from 100% to 96% over the projection period, and (4) non-linear capacity utilization, showing saturation above 82% baseline levels. The analysis reveals that demand-side policies cannot overcome supply-side structural limits, suggesting that policymakers must either substantially reduce targets or implement transformative capacity-building interventions beyond current policy contemplation. Full article

Digital twin (DT) technology is recognized for its transformative potential to enhance efficiency in the construction process. However, the full potential of DT in construction practices remains largely unrealised. Moreover, few studies explore how DT success factors affect efficiency improvement (EI), energy optimization (EO), and cost control (CC) in the context of economic strength (ES). The study applied a hybrid research method to examine the impact of key DT success factors on EI, EO, and CC under the moderation of ES. After a critical literature review, five key DT success factors were identified. Then, 490 valid questionnaires were analyzed with the Partial Least Squares Structural Equation Model (PLS-SEM) to assess how success factors affect DT effectiveness. This is complemented using extreme gradient boosting (XGBoost) to assess prediction accuracy and understand which factors most influenced EI, EO, and CC. Research shows that ES exerts a significant positive influence on the relationships between most success factors and performance outcomes. High levels of ES enhance the contribution of success factors to performance in EI, EO, and CC. Resource management (RM) has a strong influence on EI and EO, but a weaker influence on CC; process optimization (PO) has the strongest influence on EO, a moderate influence on CC, and the weakest influence on EI; real-time monitoring (R-Tm) primarily affects EI; sustainable design (SD) has a comprehensive and significant regulatory effect on EI, EO, and CC; and predictive maintenance (PM) has a strong influence on both EI and CC. In practice, it offers practical guidance for implementing DT and supports policy and resource planning for building stakeholders. Full article

Cultural ecosystem services (CESs) represent a critical link between ecosystems and human well-being and constitute a core foundation for regional sustainable development. The balance between CES supply and demand directly affects the coordination efficiency between ecological conservation and socio-economic development, making it a key prerequisite for ecosystem management, conservation planning, and policy formulation. This study focuses on the circum-Taihu region and integrates multi-source data to assess public perceived demand and spatial supply capacity of CESs. Supply–demand matching relationships are examined across three dimensions, namely, scenic beauty, cultural heritage, and recreation, through the construction of a region-specific CES quantitative indicator system. The impacts of multiple environmental factors on CES supply–demand dynamics are further explored to provide scientific support for coordinated ecological, cultural, and economic sustainability at the regional scale. The findings demonstrate the following: (1) the proposed methodology effectively quantifies CES perception and supply capacity in the circum-Taihu region. Scenic beauty exhibits the highest perception levels, whereas cultural heritage and recreation show lower perception. Cultural heritage displays the strongest supply capacity, whereas scenic beauty and recreation exhibit weaker supply. (2) Significant spatial imbalances exist between CES perception levels and supply capacity across the circum-Taihu region. Areas exhibiting mismatches constitute the largest proportion for cultural heritage CESs, followed by scenic beauty, with recreation displaying the smallest amounts of imbalance. (3) Environmental drivers exert differentiated effects on CES supply–demand relationships. Slope, road network density, and elevation have significant positive effects, whereas the normalized difference vegetation index (NDVI), distance to water bodies, and distance to roads exhibit significant negative effects. Distance to roads imposes the strongest inhibitory influence on CES perception, whereas elevation emerges as the most influential driver of public perceived CES levels. Full article

As contemporary global supply chains have become interconnected and exposed to diverse escalating cyber threats, Cyber Supply Chain Risk Management (C-SCRM) has rapidly evolved as a managerial imperative to safeguard security, robustness, and resilience, and hence ensure organizational sustainability and growth. While the concept of C-SCRM has recently received much attention among scholars, practitioners, and policymakers as an emerging field of study, its conceptual utility and theoretical foundation remain undeveloped. To address this gap, this paper provides a systematic literature review of C-SCRM using a hybrid approach that integrates bibliometric and concept evaluation analysis to ensure the goodness of the concept. A total of 175 relevant peer-reviewed scholarly articles from the Web of Science (WOS) Core Collection were collected and analyzed. The review reveals that the concept has many strengths, in terms of its interdisciplinary conceptual foundation and growing managerial relevance, but it also suffers from conceptual diffusion, overlapping terminology, and limited construct operationalization that inhibits theory development, hinders empirical accumulation, and limits practitioners’ ability to operationalize C-SCRM as a strategic resource. This review contributes to the C-SCRM literature by providing (1) a historical overview and intellectual structure of C-SCRM; (2) a synthesis and comparative analysis of the existing definitions; (3) an evaluation of the conceptual adequacy and theoretical relevance that underpin C-SCRM research based on established criteria and (4) conceptual and empirical research directions as well as an integrative framework. Based on the insights, our review might facilitate the improvement of multidimensional construct clarity and validation in future empirical studies and could be a useful tool for managers to benchmark C-SCRM maturity in practice. Full article

Diabetic wounds are often accompanied by severe inflammation, which is unfavorable for vascular growth and wound repair. Therefore, promoting the healing of diabetic wounds is of great significance. In this study, carboxymethyl chitosan (CMCS) was grafted with 4-formylphenylboronic acid (FPBA) and then crosslinked with oxidized sodium alginate (OAlg) to form a dual-dynamic covalent hydrogel (CPOA) based on borate ester bond and Schiff base bonds. Mesenchymal stem cells’ exosomes (Exos) were incorporated into the CPOA to construct CPOA@Exos for diabetic wound healing. Owing to the dual-dynamic covalent crosslinking network, the CPOA hydrogel showed good injectability and self-healing ability. In addition, the hydrogel displayed reactive oxygen species (ROS) responsive properties, enabling both scavenging of multiple free radicals and on-demand release of Exos in the ROS-rich wound microenvironment. A diabetic wound model was established on C57 mice, and treatment with CPOA@Exos demonstrated that it could promote the polarization of macrophages toward the M2 phenotype, enhance cellular proliferation in the wounded area, and thereby accelerate the healing of diabetic wounds. In conclusion, this study provides a new hydrogel wound dressing that can inhibit inflammation for the management of diabetic wounds. Full article