Search Results (9,688)

In this paper, we propose a load-aware, load-balancing procedure for fifth-generation (5G) New Radio-Unlicensed (NR-U) networks in order to address performance degradation and resource inefficiencies caused by load imbalance. Load imbalances frequently occur in NR-U networks due to factors such as the dynamic spectrum, user mobility, and varying traffic demand. To tackle these challenges, a load-aware, load-balancing procedure utilizing game theoretic reinforcement learning (GT-RL) is introduced. For load awareness, an extended System Information Block (SIB) is incorporated within the framework of 5G wireless networks. The load-balancing problem is addressed as a game theoretic cost-minimization task combining conditional offloading with reinforcement learning traffic-steering to dynamically distribute loads. Reinforcement learning applies a game theoretic policy to move users from overloaded cells to less congested cells that best serve their needs. Analytically, the proposed method is proven to spread the network load toward equilibrium. The proposed method is validated through simulations that show the effectiveness of its load balancing. The proposed method achieved better performance than previous work by attaining lower load variances while achieving higher throughput and greater quality of service satisfaction. Especially under high-load dynamics, the proposed method achieved an 8% gain in UE satisfaction with QoS and a 7.61% gain in network throughput compared to existing RL-based approach, whereas compared to the non-AI approaches, UE QoS satisfaction and the network throughput were enhanced by more than 15%. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

The present study investigated the heat extraction behavior of the horizontal well closed-loop geothermal systems under multi-factor influences. Particularly, the numerical model was established based on the geological condition of the geothermal field in Xiong’an New Area, and the XGBoost-SHAP (eXtreme Gradient Boosting and SHapley Additive exPlanations) algorithm was employed for multivariable analysis. The results indicated that the produced water temperature and thermal power of a 3000 m-long horizontal well were 2.61 and 4.23 times higher than those of the vertical well, respectively, demonstrating tantalizing heat extraction potential. The horizontal section length (SHAP values of 8.13 and 165.18) was the primary factor influencing production temperature and thermal power, followed by the injection rate (SHAP values of 1.96 and 64.35), while injection temperature (SHAP values of 1.27 and 21.42), geothermal gradient (SHAP values of 0.95 and 19.97), and rock heat conductivity (SHAP values of 0.334 and 17.054) had relatively limited effects. The optimal horizontal section length was 2375 m. Under this condition, the produced water temperature can be maintained higher than 40 °C, thereby meeting the heating demand. These findings provide important insights and guidance for the application of horizontal wells in hydrothermal reservoirs. Full article

The growing demand for sustainable and efficient synthetic methodologies has brought nanocatalysis to the forefront of modern organic chemistry, particularly in the construction of heterocyclic compounds through multicomponent reactions (MCRs). Among various nanocatalysts, calcium oxide nanoparticles (CaO NPs) have gained significant attention because of their strong basicity, thermal stability, low toxicity, and cost-effectiveness. This review provides a comprehensive account of the recent strategies using CaO NPs as heterogeneous catalysts for the green synthesis of nitrogen- and oxygen-containing heterocycles through MCRs. Key reactions such as Biginelli, Hantzsch, and pyran annulations are discussed in detail, with emphasis on atom economy, reaction conditions, product yields, and catalyst reusability. In many instances, CaO NPs have enabled solvent-free or aqueous protocols with high efficiency and reduced reaction times, often under mild conditions. Mechanistic aspects are analyzed to highlight the catalytic role of surface basic sites in facilitating condensation and cyclization steps. The performance of CaO NPs is also compared with other oxide nanocatalysts, showcasing their benefits from green metrics evaluation like E-factor and turnover frequency. Despite significant progress, challenges remain in areas such as asymmetric catalysis, industrial scalability, and catalytic stability under continuous use. To address these gaps, future directions involving doped CaO nanomaterials, hybrid composites, and mechanochemical approaches are proposed. This review aims to provide a focused and critical perspective on CaO NP-catalyzed MCRs, offering insights that may guide further innovations in sustainable heterocyclic synthesis. Full article

Companies are increasingly turning to additive manufacturing as the demand for one-off 3D-printed metal parts rises. The differences in available additive manufacturing technologies necessitate considering both cost and externalities to select the most suitable alternative. This study compares some of the most prevalent metal additive manufacturing technologies through a shop floor-level operational analysis. A steel robotic gripper is considered as a case study, based on which of the complex, interconnected operational factors that influence costs over time are analyzed. The developed cost model facilitates the estimation of costs, identification of cost drivers, and analysis of the impact of various operations management decisions on overall costs. We found that cost performance across Powder-Bed Fusion (PBF), Wire Arc Additive Manufacturing (WAAM), and CNC machining is determined by part design, quantity, and machine utilization. Although producing parts with complex internal features favors additive manufacturing, CNC outperforms in terms of economy of scale. While PBF offers excellent design freedom and parallel production, it incurs high fixed costs per build in under-utilized situations. A rough but fast method, such as Directed-Energy Deposition (DED)-based additive manufacturing, is believed to be more cost-efficient for large, simple shapes, but is not suitable when fine details are required. Laser-based DED approaches address this limitation of WAAM. Full article

Aim: The study developed and validated AppER, an mHealth tool for informal caregivers of children with Duchenne Muscular Dystrophy, and examined differences between app users and non-users. Methods: Four phases were followed: (1) focus groups with experts and caregivers to identify care-related domains; (2) prototype development and validity testing (CVR, I-CVI, I-FVI) using the MARS scale; (3) implementation of the final app; and (4) psychosocial profiling of 88 caregivers (42 users and 46 non-users) measuring quality of life, dependency, somatic symptoms, and coping strategies. Results: AppER showed high content and face validity, surpassing reference thresholds. In the psychosocial analysis, users reported lower perceived quality of life than non-users, despite no significant differences in dependency, somatic symptoms, or coping strategies. Conclusions: Employment patterns differed: more users were dedicated to household tasks, while more non-users were self-employed, suggesting economic factors may affect app uptake and quality of life perceptions. Findings indicate AppER is a valid, well-rated support tool, and that caregivers with lower perceived quality of life may be more inclined to adopt digital health solutions, potentially to address complex care demands. Designing targeted digital interventions may be particularly valuable for those in less favorable socioeconomic contexts. Because of the small sample and between-group imbalances, results are exploratory and warrant confirmation in larger, balanced samples. Full article

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

With the rise in traffic congestion, time has become an increasingly critical factor for electric vehicle (EV) users, leading to a surge in demand for fast and convenient charging services at locations of their choosing. Mobile Charging Stations (MCSs) have emerged as a new and practical solution to meet this growing need. However, the limited energy capacity of MCSs combined with the increasing volume of charging requests underscores the necessity for intelligent and efficient management. This study introduces a comprehensive mathematical framework aimed at optimizing both the deployment of MCSs and the scheduling of their battery recharging using battery swapping technology, while considering grid constraints, using the Deep Q-Network (DQN) algorithm. The proposed model is applied to real-world data from Chattanooga to evaluate its performance under practical conditions. The key goals of the proposed approach are to maximize the profit from fulfilling private EV charging requests, optimize the utilization of MCS battery packages, manage MCS scheduling without causing stress on the power grid, and manage recharging operations efficiently by incorporating photovoltaic (PV) sources at battery charging stations. Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

The dynamic and time-sensitive nature of online food delivery, along with real-world factors like sudden changes in order volumes and the availability of couriers, distinguishes it from traditional vehicle routing scenarios. Apart from the many studies in the literature that handle this problem from specific angles, our solution proposes a new approach that provides real-time routing with the awareness of the expectations of multiple stakeholders in the ecosystem. For this purpose, we develop a Mixed Integer Programming (MIP) model that minimizes unmet demand and workforce requirements simultaneously to meet platform and courier expectations while maintaining the timeliness of the operation to meet restaurant and customer expectations. Since the model requires more time to provide good results for even small-size problems, we develop a multi-step algorithmic approach supported by strategies that hold or dissolve a part of the solutions to create opportunities for better results. A framework for agent-based simulation was created to implement the strategies and the algorithmic steps, accurately mimicking the operations and movements of couriers. The effectiveness of this solution was evaluated through experiments based on a real-world case study. The results indicate that our solution can generate high-quality results in a short time across various configurations, which are defined by different demand and supply patterns and varying problem sizes. Full article

Electrocoagulation (EC) is a sustainable strategy for wastewater treatment, but the role of hydrodynamics and impeller design remains underexplored. This study assessed the impacts of electrode type (Al, Fe), impeller type (SBT, PBT), treatment time, and the inclusion of zeolite (ECZ) on the efficacy of compost wastewater treatment. The results obtained were also compared with those obtained in the EC treatment of the same wastewater in a reactor equipped with a folding paddle impeller. Key performance indicators included a decrease in chemical oxygen demand (COD), residual turbidity, electrode mass loss, energy consumption, pH, temperature, and settling behaviour. Al electrodes achieved higher COD removal (80–92%) but consumed more energy, while Fe electrodes showed slightly higher electrode mass loss. Zeolite increased residual turbidity but improved the settling behaviour during longer treatments. Fe electrodes led to larger pH shifts, whereas Al electrodes caused higher temperature increases. Compared with the folding paddle impeller, SBT and PBT promoted more favourable pH evolution, slightly higher COD removal, and lower residual turbidity. These advantages could be attributed to enhanced turbulence, mass transfer, and solid–liquid interactions, which enhance coagulant formation and dispersion. L8 Taguchi optimisation identified the addition of zeolite as the main factor influencing COD reduction, while treatment time was key for minimising electrode consumption. The findings demonstrate that impeller selection, combined with process optimisation, contributes to the mechanical process intensification of EC, improving treatment efficiency, electrode durability, and cost-effectiveness. Full article

The demand for sesame oil is increasing due to its nutritious and medicinal qualities and industrial applications such as biodiesel production. Mechanical oil extraction is commonly used although yield is lower. Roasting conditions could improve oil yield. The present study investigated heating conditions (temperature: 40, 50, and 60 °C and time: 15, 30, and 45 min) on oil extraction parameters of yellowish and blackish sesame varieties under a screw pressing operation based on a factorial design involving twenty-six experimental runs. The determined amounts of moisture content of yellowish and blackish sesame samples were 3.49 ± 0.19% w.b. and 6.69 ± 0.07% w.b. In that order, the oil contents of the samples were 38.73 ± 2.61% and 45.31 ± 6.51%. The overall optimal factor levels for explaining the calculated parameters (weight loss, seedcake, sediments in the oil, extraction loss, extracted crude oil, oil yield, and oil expression efficiency) were the heating temperature of 50 °C and time of 22.5 min for yellowish sesame, whereas those of blackish sesame were 60 °C and 15 min. The determined regression models with the significant terms predicted the crude oil, oil yield, and oil expression efficiency of yellowish sesame with the amounts of 25.496 g, 25.806%, and 66.631% in comparison with blackish sesame with the amounts of 20.449 g, 22.215%, and 49.029%. Yellowish sesame produced higher oil output than blackish sesame under the heating conditions. Similarities of absorption peaks were observed which can be used to assess adulteration and oil quality parameters. Full article

Urban growth models often prioritize environmental and accessibility factors while underestimating behavioral and functional dynamics. This study develops a POI-enhanced Cellular Automata (CA) framework to simulate urban expansion by incorporating three semantic indicators derived from Point-of-Interest (POI) data—density (PD), diversity (PDI), and functional centrality (FC). Taking Yan’an, China, as a case, the model integrates these indicators with terrain and infrastructure variables via logistic regression to estimate land-use transition probabilities. To ensure robustness, spatial block cross-validation was adopted to reduce spatial autocorrelation bias. Results show that the POI-based model outperforms the baseline in both Kappa and Figure of Merit metrics. High-density and mixed-function POI zones correspond with compact infill growth, while high-centrality zones predict decentralized expansion beyond administrative cores. These findings highlight how functional semantics sharpen spatial prediction and uncover latent behavioral demand. Policy implications include using POI-informed maps for adaptive zoning, ecological buffer protection, and growth hotspot management. The study contributes a transferable workflow for embedding behavioral logic into spatial simulation. However, limitations remain: the model relies on static POI data, omits vertical (3D) development, and lacks direct comparison with alternative models like Random Forest or SVM. Future research could explore dynamic POI trajectories, integrate 3D building forms, or adopt agent-based modeling for richer institutional representation. Overall, the approach enhances both the accuracy and interpretability of urban growth modeling, providing a flexible tool for planning in functionally evolving and ecologically constrained cities. Full article

This study examines the key factors influencing the quality of distance learning in higher education during the COVID-19 pandemic, a period when online learning became the dominant mode of education. Using a descriptive method and a 26-item questionnaire, data were collected from a representative sample of 360 students in Vojvodina, Serbia. The factors analyzed include computer literacy and technology access (Ph1), students’ ability to balance life obligations with study demands (Ph2), and their motivation for distance learning (Ph3). The results show that 89% of students had adequate IT access, 47% were able to reconcile study and personal obligations, and 70% reported strong motivation. Correlation analysis confirmed a statistically significant positive relationship between all three factors and students’ perceptions of well-organized distance learning, thus supporting the main research hypothesis. Beyond these findings, this study interprets digital literacy as adaptability, time management as resilience, and motivation as value orientation and future thinking—core dimensions of sustainability competences outlined in the European GreenComp framework. Distance learning is therefore positioned not only as an emergency response but also as a transformative pedagogy that integrates brain (knowledge), hands (skills), heart (values), and spirit (purpose), contributing to sustainable and resilient higher education. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article