Renewable Energy Communities (RECs) play an increasingly important role in decentralized energy systems by improving local renewable energy utilization, enhancing energy flexibility, and supporting low-carbon energy transitions. However, the integration of distributed energy resources (DERs), flexible electrical loads, and energy sharing mechanisms continues to create operational and management challenges for REC-based systems. This study investigates the energy management and optimization of a residential REC in Italy composed of photovoltaic (PV) generation, battery storage systems, and flexible air-conditioning loads. A detailed optimization framework was developed to coordinate DERs and flexible demand with the objective of maximizing shared energy utilization and related economic incentives while maintaining user comfort and avoiding additional electricity costs. The regulatory framework and operational structure of RECs in Europe and Italy were also examined to support the development of the proposed management strategy. The optimization process was conducted under different operating conditions to evaluate the influence of coordinated load management on REC performance. The results showed that the coordinated control of battery storage systems and air-conditioning units improved shared renewable energy utilization and increased the economic return associated with energy sharing. The optimized operation strategy also reduced electricity costs for users while improving the operational efficiency of the community energy system. The findings indicate that advanced energy management and load coordination strategies provide an effective approach for enhancing the performance of distributed renewable energy systems and supporting the practical implementation of REC-based energy infrastructures.
Tourism provides considerable economic advantages; however, it also imposes environmental challenges, especially in coastal regions where unmanaged waste poses a threat to long-term sustainability. This research seeks to examine the behavioral and spatial elements that affect tourists’ willingness to pay (WTP) for circular waste management in eight coastal destinations in Southern Yogyakarta, Indonesia. Employing the Contingent Valuation Method (CVM), primary survey data were gathered from 984 visitors and analyzed using Ordinary Least Squares (OLS) regression, K-Means clustering, and spatial mapping techniques with geomap orange data mining. The analysis investigates how socio-economic factors such as age, income, gender, education level, and travel costs influence WTP, with behavioral theory serving as the interpretive framework. The findings indicate that younger and more educated tourists demonstrate a higher WTP, while age and travel costs negatively and significantly impact their WTP. The estimated average WTP of IDR 13,840 surpasses the official waste retribution fee, reflecting a considerable level of environmental concern among visitors. Additionally, spatial and cluster analyses uncover diversity in visitor segments across coastal areas, implying that standardized waste management policies may not be effective. In summary, the results underscore the necessity of merging economic valuation with spatially informed and behaviorally conscious policy tools, illustrating the potential of WTP as a funding mechanism for sustainable and circular waste management in coastal tourism regions.
This study aims to analyze the traffic noise levels at three locations in Makassar City and to compare them with the established noise quality standards. Measurements were conducted over a one-week period at specific times using a sound level meter, a vehicle speed measurement device, and a counting application to classify vehicle types into heavy vehicles (HV), light vehicles (LV), and motorcycles (MC). The observation sites included an educational area, a hospital area, and a residential area. Correlation analysis using Statistical Package for the Social Sciences (SPSS) was employed to examine the relationships between HV, LV, MC, and vehicle speed with the equivalent continuous sound level (Leq). The results indicated that noise levels at all three locations exceeded the standard threshold of 55 decibels (dB). The correlation analysis showed significant relationships between Leq and HV (0.834), LV (0.782), MC (0.787), and vehicle speed (-0.680). The effective contribution to noise was highest for HV (40.44%), followed by MC (13.35%), LV (12.68%), vehicle speed (10.38%), and other factors (23.15%), including human activity, construction noise, road surface type, road gradient, and surrounding environmental conditions. Recommended mitigation measures include restricting the operating hours and rerouting of HV in sensitive areas, as well as enforcing noise emission testing and regulations on illegal exhaust modifications.
In mobility-aware scenarios such as vehicular networks, mobile augmented reality (AR)/virtual reality (VR) services, and other latency-sensitive Multi-access Edge Computing (MEC) applications, continuous user movement leads to frequent migrations of service function chains (SFCs). Traditional approaches typically rely on global deployment comparisons, which fail to accurately identify the specific virtual network functions (VNFs) that require migration and their optimal target nodes. This limitation often results in redundant migrations, inefficient resource utilization, and an increased risk of service disruption, thus hindering the balance between latency assurance and resource efficiency. To overcome these limitations, this paper proposed a graph-enhanced deep reinforcement learning–based adaptive migration optimization (DRL-GAMO) framework. By integrating the topological representation capability of graph neural networks (GNNs) with the decision-making efficiency of deep reinforcement learning (DRL), DRL-GAMO established a topology–resource–decision mapping that jointly optimized VNF selection and determination of target nodes. This pre-migration decision process effectively reduced redundant operations and directed migration behaviors toward resource-efficient strategies. The designed reward function minimized migration overhead under service-level agreement (SLA) latency constraints and penalized downtime to maintain service continuity. Simulation results demonstrated that DRL-GAMO achieved stable service latency, lower resource consumption, and shorter migration time while reducing migration volume by more than 40% compared with DRL-ADMO, thereby improving the migration success rate and validating its effectiveness in MEC environments.
Climate change poses giant, demanding situations to geotechnical systems, affecting soil behavior, slope stability, basis performance, and the resilience of coastal infrastructure through interacting thermal, hydrological, and mechanical strategies. This examination evaluates both determined and projected impacts of weather exchange drivers, along with growing worldwide temperatures, altered precipitation styles, permafrost thaw, sea-level upward push, and freeze–thaw cycles, on geotechnical structures. The evaluation makes use of the Climate Change Dataset (2000–2024) together with tests from the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Descriptive statistics, correlation evaluation, and regression modeling had been applied to quantify the relationships amongst CO$_2$ emissions, worldwide temperature anomalies, and sea-level upward push. The outcomes imply robust, superb correlations between anthropogenic CO$_2$ emissions and global temperature will increase, which can be intently associated with accelerating sea-level upward thrust. Scenario-based total projections underneath business-as-usual, moderate mitigation, and aggressive mitigation pathways display that persisted high emissions significantly intensify weather-pushed geotechnical dangers. In comparison, competitive mitigation techniques can considerably lessen the projected temperature increase and associated sea-level upward thrust. The evaluation emphasizes the need of linked thermal-hydraulic-mechanical (THM) techniques, specifically in permafrost areas, moisture-sensitive soils, and coastal regions that are undergoing erosion and subsidence. Additionally, rainfall-added landslides and infrastructural instability are exacerbated by using the growing frequency and depth of extreme precipitation sports. In order to beautify infrastructure resilience, a number of version techniques, climate-conscious geotechnical formats, ground development techniques, geosynthetic reinforcement, and wonderful monitoring systems are advised based totally on the findings. The evaluation also highlights the need of changing geotechnical layout codes to comprise multi-threat modeling techniques, lengthy-term observational statistics, and harsh weather conditions. This takes a look at provides a complete framework for evaluating weather alternative effects on geotechnical structures and permits the development of resilient and sustainable infrastructure in climate conversion via combining historical weather information, statistical analysis, and kingdom-of-affairs-based simulations.
Increased agricultural production could improve household income but often generates adverse environmental impacts, including soil degradation, rising temperatures, and drought, thereby contributing to climate change. This study aims to optimize income and carbon emissions in the trade of rice, corn, and cattle commodities in the Indonesia–Timor Leste border region and to assess the performance of integrated sustainable trade among farmers, traders, and processing industries. A Multi-Objective Linear Programming (MOLP) model and Partial Least Squares Structural Equation Modeling (PLS-SEM) were employed for analysis. The findings indicated that increased trade activities could improve economic outcomes while maintaining emissions within manageable limits. Farmer income is projected to increase by IDR 5.779 billion per production season, with improved cost efficiency at approximately IDR 64,000 per acre and maximum emissions of 356,561 tons CO₂e. Traders’ income is expected to increase by IDR 8.526 billion, with maximum emissions of 2,443.241 tons CO₂e and average transport costs of IDR 4,600 per kilometer. Carbon emissions at the farm level primarily stem from inefficient use of fertilizer and land burning, while emissions at the trader level are driven by transport capacity and travel distance. Although processing industries have established direct relationships with farmers, most farmers remain dependent on traders for market access. Strengthening the capacity of processing industries in the border region is therefore considered essential for maximizing farmers’ income.
With the rapid expansion of high-speed railway networks and the continuous growth in urban travel demand, the efficiency of first/last-mile connections at transport hubs has become a critical factor constraining the performance of integrated transportation systems. Demand-responsive customized bus services dynamically match passenger demand with available capacity, providing a feasible solution for improving travel flexibility. However, in practical applications, the rational design of customized bus networks remains challenging due to heterogeneous spatial demand distributions, vehicle capacity limitations, and various operational constraints. This study proposes an integrated methodological framework for customized bus network design that combines three key components: stop identification, route optimization, and simulation-based validation. First, a hybrid clustering approach that integrates density-based clustering with centroid partitioning is employed to extract potential stop locations from passenger origin–destination data. A capacity-constrained mechanism is further introduced to regulate clustering results, ensuring that stop sizes are compatible with vehicle carrying capacity. Based on the identified stops, the network design problem is formulated as a vehicle routing problem with time window constraints, where operational cost, passenger travel time cost, and environmental impact are jointly considered as optimization objectives. A genetic algorithm is adopted to solve the model. A case study involving a feeder service between a high-speed rail station and the urban core business district is conducted, and the proposed framework is validated through simulation using the AnyLogic platform. The results demonstrate that the proposed method improves vehicle utilization and route efficiency while maintaining service quality and system stability. This research provides a practical technical pathway and decision support for the intelligent design and operation of demand-responsive customized bus services.
The physical quality of seeds is a critical determinant of sorting efficiency and crop productivity, yet conventional assessment approaches are often labor-intensive, invasive, and time-consuming. To address these limitations, computer vision-based methods have been increasingly adopted; however, most existing techniques rely primarily on reflected visible light, thereby capturing only surface-level features and limiting the detection of internal defects. In this study, a low-cost imaging system integrating both reflection and transmission of visible light was developed to enhance the characterization of maize seed translucency. By enabling simultaneous acquisition of information from the two principal faces of white maize seeds, a more comprehensive representation of both external morphology and internal structural variations was achieved. A comparative analysis was conducted between the conventional reflection-based method and the proposed imaging approach, with correlation coefficients between seed faces determined as 0.62 and 0.84, respectively, indicating a substantial improvement in feature consistency and information richness. A dedicated dataset was subsequently constructed using both imaging techniques and employed to train a YOLOv5s-based detection model over 200 epochs. The classification performance demonstrated a marked enhancement, with the proposed method achieving an accuracy of 93.07%, compared to 81.5% obtained using the conventional approach. Furthermore, real-time detection capability was validated through the implementation of the optimized imaging system, in which improved inference stability and robustness were achieved under practical operating conditions. The results indicate that the integration of transmission with reflection imaging provides a cost-effective and reliable solution for non-destructive seed quality assessment, offering significant potential for scalable deployment in agricultural sorting systems.
The threat posed by volcanic eruptions necessitates ongoing monitoring to assess their status. Mount Semeru is one of the active volcanoes located on the island of Java. Observations are made using remote sensing, utilizing data from the Copernicus satellite Sentinel-1 Single Look Complex (SLC) to track changes in Differential Interferometric Synthetic Aperture Radar (DInSAR) deformation, and Sentinel-3 satellite sea and land surface temperature radiometer (SLTR) to observe ground surface temperature variations due to the eruption of Mount Semeru that occurred in 2022, before, during, and after the event. The DInSAR deformation recorded before the eruption ranged from -0.025 cm to -0.054 cm on the scale bar, while the land surface temperature (LST) before the eruption was at a minimum of 18.6 ℃ and a maximum of 27.8 ℃. during the eruption, DInSAR deformation changes showed inflation, with values reaching from 0.015 cm to 0.3 cm on the scale bar, and the LST also rose, peaking at 36.3 ℃. after the eruption, DInSAR deformation changes indicated deflation, with measurements between 0.049 cm and 0.099 cm on the scale bar, and the temperature trend also fell, with the highest temperature observed being 33.6 ℃.
Road traffic accidents (RTAs) are a complex crisis created by the combination of infrastructure, drivers, and varying traffic demand factors. While locating clusters of hotspots has been of prime importance in public safety, a research gap still exists in understanding the spatiotemporal evolution of accident severity in administrative hubs. This study fills this gap by focusing on the severity of RTAs in Missouri between 2020 and 2023. In a three-phased methodology, this research assesses sustained efficiency by leveraging a Geographic Information System (GIS)-based framework, involving systematic data integration, calculation of an Accident Severity Index (ASI), and sophisticated spatiotemporal statistics. For the assurance of statistical significance in the detection of clusters, the Getis-Ord Gi$^*$ (G$_i^*$) was used for the localized detection of both hot and cold spots. The methodology outcomes depicted a precipitous decline in the number of accidents in April 2020, which was regarded as a direct consequence of the coronavirus impact. Besides, adults accounted for most fatalities (59%), while speeding was a contributing factor with 29%. Some variations in the occurrence of RTAs were identified during substantial seasons by indicating an optimum persistent occurrence throughout the fall months. Besides, over the main metropolitan areas, robust clustering of RTA density was observed, such as St. Louis and Jackson counties, whereas rural areas exhibited lower densities. The G$_i^*$ identified persistent, high-confidence severity hot spots, indicating progressively clustered, temporally consistent, and persistent patterns of RTA severity in Missouri. The revealed outcomes reflected a granular, evidence-based foundation for urban planners and law-enforcement authorities to implement targeted safety interventions and optimise emergency response allocation.
Adaptive multi-scale representation learning has become a fundamental component of modern image processing systems. However, existing fusion strategies often treat features extracted from different scales equally, resulting in suboptimal performance under uncertain conditions such as noise, blur, and low contrast. To address this limitation, this paper proposes an uncertainty-aware deep feature fusion framework for adaptive multi-scale image processing. The proposed framework decomposes input images into multiple scales using wavelet-based or Laplacian pyramid representations to capture complementary spatial-frequency information. Discriminative features are extracted at each scale using lightweight Convolutional Neural Networks (CNNs) or Vision Transformer (ViT) encoders. To estimate feature reliability, Bayesian deep learning with Monte Carlo (MC) dropout is employed to model uncertainty at the feature level. A principled uncertainty-aware fusion mechanism is then introduced to dynamically combine multi-scale features according to their estimated reliability. As a result, reliable features contribute more significantly to the fused representation, while uncertain features are suppressed. The fused representation is subsequently utilized in task-specific heads for image restoration, classification, and segmentation. Extensive experiments conducted under multiple degradation conditions demonstrate that the proposed framework consistently outperforms traditional fusion and attention-based methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Fréchet Inception Distance (FID). The results further confirm the robustness and generalization capability of the proposed uncertainty-aware multi-scale fusion strategy in adverse imaging environments.
This study examined the mediating role of policy in the relationship between improvement in construction waste management and effectiveness of construction and demolition waste (C&DW) management in Malang City, Indonesia. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with data from 48 construction companies, this research investigated how policy frameworks translated improvement initiatives into sustainable waste management outcomes. Improvement in construction waste management was measured by four phases (planning, design, construction, and operation), policy through four instruments (regulation, incentive, reward-sanction, and standard), and C&DW management through triple bottom line dimensions (economic, social, and environmental). Results revealed that improvement in construction waste management significantly influenced policy formulation (β = 0.761, p < .001, f² = 1.374), and that policy substantially affected the effectiveness of C&DW management (β = 0.692, p < .001, f² = 0.950). However, the direct effect of improvement in C&DW management was not significant (β = 0.240, p = .249), indicating full mediation through policy (β = 0.526, p < .001, υ = 0.077). The model explained 78.8% variance in C&DW management with strong predictive relevance (Q² = 0.711). These findings demonstrated that improvement efforts should be channeled through robust policy frameworks to achieve systemic waste management transformation, thus highlighting the critical role of integrated policy instruments in translating operational improvements into sustainable outcomes in developing urban contexts.
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