Search Results (2,131)

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article

Industrial synthetic and natural fibers play an indispensable role in modern manufacturing, aerospace, automotive, and textile engineering. However, the enhanced mechanical performance of advanced industrial fibers has introduced significant challenges in cutting processes, since brittle, high-tensile, and viscoelastic fibers exhibit totally different fracture behaviors from conventional solid materials. At present, the complex motion coupling mechanisms between fibers and cutting tools under free-form conditions are insufficient; there is no unified framework for understanding the mechanisms of fiber cutting; it is difficult to effectively link the microscopic fracture physics of different fiber types with their macroscopic cutting properties. Furthermore, research into the dynamic interaction between the cutting tool and the fiber, cross-scale cutting characteristics, and tool wear mechanisms has not been sufficiently systematic, and non-contact cutting methods have not yet been the subject of systematic study. Through a systematic review, this review identified three primary categories of difficult-to-cut industrial fibers and summarized the distinctions in their fundamental material properties. The static, kinematic, and dynamic characteristics of fiber cutting under both free and fixed forms were discussed. The fracture mechanisms of fibers under diverse loading scenarios were also systematically revealed. Furthermore, this review summarizes the effects of cutting tool wear characteristics, geometric parameters, and material types on cutting performance. Finally, non-contact methods for cutting fiber were listed. Based on the above analysis, three critical directions for future research were proposed to bridge the existing knowledge gaps in the literature. This review of the interdisciplinary interactions among mechanics, materials science, and textile engineering provides a theoretical foundation and research directions for achieving high efficiency and a long tool life during cutting industrial fibers. Full article

Prefabricated steel structures have been increasingly adopted in modern construction due to their high efficiency, sustainability, and industrialized production. However, their construction quality and efficiency are often compromised by accumulated geometric deviations during fabrication, transportation, assembly, and welding, while traditional construction control and welding processes remain highly dependent on manual measurements and empirical operations. To address these challenges, this study proposes a digital construction framework for prefabricated steel structures, integrating high-precision three-dimensional (3D) laser scanning, Building Information Modeling (BIM), and intelligent welding technologies. First, high-precision 3D laser scanning is employed to capture the as-built geometric information of prefabricated steel components, generating dense point cloud data for construction-stage deviation detection and quantitative comparison with BIM-based design models. Based on deviation analysis, a digital construction control strategy is established to support real-time feedback, error compensation, and assembly adjustment. An engineering case study involving a complex prefabricated steel structure is conducted to validate the proposed framework. The results demonstrate that the integrated digital construction and intelligent welding approach significantly improves assembly accuracy, weld positioning precision, and construction efficiency, while reducing manual intervention and error accumulation. Overall, this study contributes to the body of knowledge by proposing a unified closed-loop digital construction paradigm that integrates geometric perception, deviation-driven decision-making, and intelligent welding execution, thereby bridging the gap between construction control and robotic fabrication in prefabricated steel structures. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

The rapid expansion of mobile government (m-Government) platforms in Saudi Arabia has generated large volumes of user feedback, creating an opportunity for systematic, data-driven evaluation of public digital services. This study conducts a large-scale sentiment analysis of Arabic user reviews collected from five major Saudi m-Government applications, Absher Business, Tawakkalna, Sehhaty, Nusuk, and Najiz. A dataset comprising 84,000 reviews was constructed and classified into positive and negative sentiment categories. Five Arabic transformer-based baseline models, AraBERT, ArabicBERT, CAMeLBERT, SaudiBERT, and MARBERT, were evaluated under a unified experimental framework. Among these, SaudiBERT and MARBERT achieved the strongest performance, with MARBERT obtaining an accuracy of 91.2 percent, an F1-score of 0.858, and an AUC of 0.942. Furthermore, parameter-efficient fine-tuning using QLoRA on MARBERT preserved comparable performance (F1 = 0.854) while substantially reducing computational requirements. These findings demonstrate the feasibility of scalable sentiment analysis for evaluating and improving m-Government services. Full article

This paper evaluates a Sustainable Energy Efficiency Business Model (SEEBM) for small and medium sized enterprises (SMEs) in the European industrial sector. The sustainability-oriented model, developed by the authors, combines Energy Performance Contracting (EPC), Energy Supply Contracting (ESC), and Energy Saving Insurance (ESI) within a unified framework to support industrial decarbonisation. The study identifies key performance indicators and translates them into a System Dynamics model using a Design-Based Research approach. The model is built from secondary data drawn from 45 SME case studies in the European SMEmPower project and is validated through extreme condition testing and behavioural sensitivity analysis. Results indicate that the integrated model significantly enhances financial performance, reducing the average payback period from average 36 months to 10 months. Sensitivity analysis highlights the influence of contract duration, energy saving rates, and energy prices on both payback and emissions reduction outcomes. This research introduces a novel dynamic framework integrating EPC, ESC, and ESI, enabling time-based evaluation of investment viability and environmental impact. It offers a replicable decision support tool for policymakers and market actors seeking scalable, low risk pathways to SME decarbonisation. Overall, the model provides practical insights for improving investment decisions while accelerating the transition toward sustainable industrial systems across Europe. Full article

Software testing is essential for software engineering practices, as it ensures that the final software product is reliable and satisfies all requirements before delivery. However, manually designing black-box testing test cases is time-consuming, inconsistent, and difficult to maintain in accordance with changing specifications. Therefore, this paper presents BlackBoxTestGen, an automatic framework that unifies three specification-driven black-box testing techniques, including rule-based Equivalence Class Partitioning (ECP), syntax, and state transition testing. The framework utilises a redesigned XML structure for test case generation to be shared among a data dictionary, decision tree, and state machine, used by each testing technique. The degree of testing coverage is accumulatively calculated during the test case generation process. The beneficial value of our proposed framework was demonstrated with the development of a web-based prototype tool. We rigorously evaluated its performance in terms of accuracy, computational efficiency, and scalability through a multidimensional approach. This included assessment by professional experts, algorithmic stress testing via parameter scaling, and application to close-to-realistic case studies. The results indicate that BlackBoxTestGen provides a robust integration of testing techniques. By automating the generation of compact and reproducible test cases, the framework substantially reduces manual effort and minimises drift between techniques. Full article

The high accuracy achieved by deep learning techniques has made them indispensable in computer vision applications. However, their substantial memory demands and high computational complexity limit their deployment in resource-constrained environments. To address this challenge, this study introduces a Feature Enhancement Module (FEM) as part of a unified hybrid compression framework that combines mixed-precision quantization and structured pruning to improve model efficiency. Experimental results on the Tiny ImageNet dataset using ResNet50 and MobileNetV3 architectures demonstrate the strong adaptability and scalability of the proposed approach. Compared with state-of-the-art compression methods, the proposed FEM-based framework achieves up to 6% improvement in Top-1 accuracy, while reducing memory usage by 32.26% and improving inference speed by 66%. Furthermore, the ablation study demonstrates that incorporating the FEM module leads to up to 24% improvement over the baseline model, highlighting its effectiveness. The results further show that FEM effectively preserves inter-channel feature representation stability even under aggressive compression, making it well suited for real-time processing and practical Artificial Intelligence (AI) applications. By maintaining semantic richness while significantly reducing computational cost, the proposed method bridges the gap between high-performance deep models and lightweight, deployable solutions. Overall, the FEM-based hybrid compression framework establishes a scalable and architecture-independent foundation for sustainable deep learning in resource-limited environments. Full article

The sixth-generation (6G) mobile networks are envisioned to deliver seamless three-dimensional(3D) coverage from ground to sky and vice versa. In parallel, aerial corridors are emerging to elevate ground-based transportation into the air, enabling smart air mobility for unmanned aerial vehicles (UAVs). The convergence of this intelligent transportation system (ITS) with 6G introduces new challenges: how to ensure reliable, efficient connectivity within aerial corridors, and how these corridors can serve as foundational sky infrastructure to advance the 6G ecosystem. This paper presents a comprehensive survey that systematically presents aerial corridors as integrated 6G sky infrastructure, unifying corridor geometry, network architecture, channel modeling, and key enabling technologies within a single framework. It conceptualizes the aerial corridor as a tube-shaped, multi-lane, bidirectional structure to manage drone-based roles, including user equipment (UE), base stations (BS), and communication relays. To support this vision, key enablers such as air-to-ground channel modeling and integrated sensing and communication (ISAC) are investigated. The proposed infrastructure aligns with the IMT-2030 vision, supporting machine-type communication, ubiquitous connectivity, and immersive services in regulated aerial space. Full article

The widespread deployment of unmanned aerial vehicles (UAVs) in civil and commercial airspace has raised significant safety concerns, driving the demand for reliable and real-time Anti-UAV visual detection systems. However, existing deep learning-based detectors face substantial challenges in complex low-altitude environments, including drastic scale variations, severe background clutter, and weak feature representation of small UAV targets. Moreover, handcrafted Transformer-based architectures often lack adaptability across diverse scenarios and struggle to balance detection accuracy with computational efficiency. To address these limitations, this paper proposes AutoUAVFormer, a super-resolution guided neural architecture search framework for Anti-UAV detection. In contrast to conventional manually designed approaches, AutoUAVFormer leverages joint optimization of a Transformer-based detection objective and a super-resolution reconstruction objective to automatically identify a task-specific optimal network architecture for detecting UAV targets. Specifically, a unified search space is formulated by jointly embedding Transformer hyperparameters and Feature Pyramid Network (FPN) structures, facilitating end-to-end co-optimization of multi-scale feature fusion and global context modeling. To efficiently locate architectures that balance accuracy and computational cost, a three-stage pipeline, combining supernetwork training with evolutionary search, is employed. Additionally, we design a super-resolution auxiliary branch that operates only during training to enhance the model’s ability to learn fine-grained textures and sharpen edge representations of small targets, without introducing any inference overhead. Extensive experiments on three challenging Anti-UAV detection benchmarks, namely DetFly, DUT Anti-UAV, and UAV Swarm, confirm the superiority of AutoUAVFormer over current state-of-the-art methods, with mAP@0.5 scores reaching 98.6%, 95.5%, and 89.9% on the respective datasets while sustaining real-time inference speed. These results demonstrate that AutoUAVFormer achieves strong generalization and maintains robust Anti-UAV detection performance under challenging low-altitude conditions. Full article

Durian (Durio zibethinus Murray) is Thailand’s most economically significant fruit export, yet foliar diseases pose a major threat to productivity and crop quality. Early-stage symptoms of several durian leaf diseases are visually similar, making reliable diagnosis difficult for farmers and even trained agronomists. This study aims to develop and evaluate an automated deep learning-based system for durian leaf disease classification under realistic field conditions. A dataset of 6119 leaf images representing six classes—Leaf_Healthy, Leaf_Colletotrichum, Leaf_Algal, Leaf_Phomopsis, Leaf_Blight, and Leaf_Rhizoctonia—was compiled from public datasets and field-collected samples. Six convolutional neural network (CNN) architectures—ConvNeXt, ResNet, DenseNet201, InceptionV3, EfficientNet-B3, and MobileNetV3—were benchmarked using a unified transfer-learning training protocol. Class imbalance was addressed using weighted cross-entropy loss, and performance was evaluated on a stratified held-out test set using accuracy, precision, recall, and F1-score metrics. The results show that ConvNeXt achieved the highest performance with 98.00% accuracy and a weighted F1-score of 0.98, followed by ResNet (96.82%) and DenseNet201 (96.09%), while efficiency-oriented models plateaued near 91%. Confusion matrix analysis revealed consistent misclassification among visually similar disease categories—Leaf_Algal, Leaf_Blight, and Leaf_Phomopsis—indicating biological similarity in lesion appearance rather than model limitations. The best-performing model was deployed as a publicly accessible web application using Gradio, enabling real-time disease diagnosis with an average inference time of approximately 0.54 s per image. Unlike prior studies, this work combines large-scale architecture benchmarking, class imbalance mitigation, and real-world deployment within a single unified framework. These findings demonstrate that modern CNN architectures can provide highly accurate and scalable disease detection tools, supporting precision agriculture by enabling early diagnosis, reducing inappropriate pesticide use, and improving decision-making for durian farmers. Full article

Uncertainty in vessel arrival times can substantially reduce the efficiency of berth planning in port operations. To address this issue, this study proposes a unified, data-driven, predict-then-optimize framework that explicitly links vessel arrival time (VAT) prediction with downstream continuous berth allocation optimization. In the prediction stage, heterogeneous maritime data, including port call records, AIS trajectories, and vessel physical characteristics, are integrated to construct VAT prediction models. In the optimization stage, the predicted VAT is embedded into a continuous berth allocation problem (BAP) model to support berth scheduling decisions. To better reflect real operations, a two-stage evaluation framework is further developed, in which berth plans generated from estimated arrival times (ETAs) or predicted VATs are re-evaluated under realized actual arrival times while preserving the original temporal and spatial service order. Experimental results show that the proposed framework improves VAT prediction accuracy substantially, reducing the MAE and RMSE from 4.795 h and 7.255 h for the vessel-reported ETAs to 2.844 h and 4.934 h, respectively. More importantly, the predicted-VAT-based BAP consistently outperforms the ETA-based benchmark, yielding an overall 35.96% reduction in objective value across tested scenarios. These findings demonstrate that improved VAT prediction can be effectively translated into meaningful operational gains in berth allocation. Full article

Surface anomalies such as dust accumulation and bird droppings on photovoltaic (PV) panels can significantly reduce their energy production and lead to inefficient maintenance decisions. This paper proposes a vision-based deep learning framework for the automatic detection of PV panel surface conditions and validates the detected anomalies using real inverter-level energy production data. Unlike conventional studies focusing solely on detection performance, the proposed approach introduces a unified and physically interpretable framework that directly links image-based anomaly detection with inverter-level energy performance and decision-oriented PV maintenance. An EfficientNetB3-based model is trained using a two-stage transfer learning strategy on a publicly available Kaggle dataset and evaluated using standard classification metrics. The trained model is then deployed and validated at a 1 MW solar power plant located at Karaman, Türkiye. Classification results obtained from field images are systematically linked with inverter-associated hourly energy production measurements. Following panel cleaning and natural rainfall, an approximately 12.5% increase in inverter-level hourly energy production is observed for the analyzed PV group (120 panels, ~270 Wp), corresponding to an increase from 23.2 to 26.1 kWh. In addition, the study introduces an energy–water–sustainability-aware cleaning decision framework tailored for arid and semi-arid regions where water scarcity and deep groundwater extraction present critical constraints. The framework defines a quantitative decision rule in which panel cleaning is performed only when the expected recoverable energy exceeds the energy cost of water extraction and cleaning. Overall, the proposed approach enables accurate surface anomaly detection while supporting sustainability-aware, resource-efficient and data-driven maintenance decisions for PV power plant operation. Full article

Cross-chain analytics increasingly demand continuous joins across ledgers with asynchronous state evolution. Existing solutions, however, typically assume static snapshots or neglect temporal alignment, yielding semantically inconsistent results when epochs drift. This paper introduces VeriFed, a system for temporally consistent continuous cross-chain joins. We formalize the problem of snapshot-aligned continuous joins, design a Unified Adapter Layer (UAL) to align finalized snapshots across heterogeneous protocols, and develop incremental verification that composes per-chain proofs into a global summary via the Epoch Attestation Mesh (EAM) and the Delta-Linked Proof Forest (DLPF). To sustain high-throughput execution, VeriFed further adopts an incremental multi-objective optimizer that balances latency and monetary cost. Experiments on Ethereum transaction data with a simulated wide-area network (WAN) demonstrate that VeriFed achieves sub-second per-epoch latency (approx. 38 ms) and reduces verification overhead by orders of magnitude compared to state-of-the-art baselines, while effectively detecting tampering with zero false positives. These results confirm consistent efficiency and verifiability under continuous updates. Full article