Search Results (22,530)

The increasing dependence of financial supply chains on digital infrastructures has made it more necessary to design secure, resilient, and reliable networks than ever before. This research presents a self-healing framework based on blockchain and digital twins for multi-objective fuzzy optimization of financial supply chains under uncertainty. The proposed model, focusing on minimizing financial cost, cyber risk, and recovery time while simultaneously maximizing the level of trust and resilience, enables intelligent decision-making in the face of cyber threats. By combining real-time monitoring, secure transaction validation, fuzzy risk assessment, and automated recovery, the framework identifies the role of each component in maintaining the financial and operational stability of the network. The results showed that the complete model achieved an overall performance score of 0.944 in the component elimination study and increased the level of trust and resilience to 0.95 and 0.96, respectively. The cyber risk index was also maintained at 0.118, indicating the framework’s ability to control threats and maintain network stability. The findings show that the convergence of blockchain, digital twin, fuzzy logic, and self-healing mechanism can provide an effective basis for the development of smart, secure, and resilient financial supply chains. Full article

Active distribution networks (ADNs) require battery energy storage system (BESS) scheduling strategies that reduce operating costs while preserving electrical feasibility and battery lifetime. This paper proposes a parallel adapted JAYA-based methodology for the day-ahead coordinated active and reactive power dispatch of BESS units in this type of grid. The novelty of this research lies in four key contributions: (i) the coordinated optimization of active and reactive power from BESS converters, exploiting their full capabilities for both energy management and voltage support; (ii) the integration of battery degradation costs within the optimization framework, preventing short-term economic strategies that accelerate aging; (iii) the implementation of a parallel adapted JAYA algorithm (AJAYA) with stagnation control and population reactivation mechanisms to enhance solution quality and convergence; and (iv) a comprehensive assessment under both deterministic and uncertainty-based operating conditions, providing a realistic validation of the proposed approach. Our model minimizes conventional generation, DER operation and maintenance, and BESS degradation costs while subject to power balance, distributed energy resource limits, voltage and current constraints, converter capacity, and state of charge (SoC) requirements. Each solution is encoded as BESS active/reactive power setpoints and evaluated through a multi-period AC power flow based on the successive approximations method, including SoC verification and a penalized fitness function. The methodology was validated in modified 33- and 69-node ADNs under deterministic and uncertainty scenarios (based on the conditions observed in Colombia), and it was benchmarked against the population-based genetic algorithm (PGA), the multiverse optimizer (MVO), the salp swarm algorithm (SALPS), the grey wolf optimizer (GWO), and the vortex search algorithm (VSA). According to the results, AJAYA outperformed the comparison methods, providing the best economic performance and exhibiting a robust behavior, with standard deviations below 0.06% and processing times below 0.05 h within a 24-h scheduling horizon. These findings demonstrate that the proposed framework constitutes an AC-feasible and degradation-aware academic contribution and a practical decision-support tool for operators and BESS owners, enabling a cost-effective and reliable BESS scheduling that preserves battery lifetime while improving network operation. Therefore, this research addresses the critical need for advanced energy management strategies that balance short-term economic benefits, technical feasibility, and long-term asset sustainability in modern distribution networks. Full article

Small Language Models (SLMs) enable efficient deployment, but their limited parameter count constrains factual knowledge, particularly in low-resource languages like Kazakh. Integrating live web search can address this limitation, though its effectiveness is difficult to measure due to sparse in-language web indices and answer leakage during benchmarking. In this study, we systematically compare zero-shot parametric generation, in-language retrieval, and cross-lingual (translate-then-retrieve) web search using three 4B-parameter SLMs in both reasoning and non-reasoning modes. To evaluate factuality without search-engine leakage, we introduce machine-translated Kazakh versions of the FreshQA and DefAn benchmarks, and use GPQA as a Google-proof adversarial control. We also assess robustness across three prompt complexities, from simple JSON constraints to adversarial warnings that instruct the model to treat potentially unreliable context with caution. Finally, we propose a training-free, self-aware router that uses majority voting over repeated self-verification decisions to determine when to answer parametrically, when to search the web, and when to escalate to a more capable cloud model. Our results show that cross-lingual retrieval substantially outperforms in-language search on global factuality tasks, nearly doubling accuracy on FreshQA, while direct in-language search remains preferable for localized cultural queries. The choice of retrieval language depends on the task and does not always favor English. Additionally, cross-lingual retrieval is not consistently superior, because the best option depends on where relevant information is indexed. Pareto analysis indicates that cross-lingual search is on or near the optimal accuracy–latency frontier, adding minimal overhead compared to direct search. The router identifies which query types warrant retrieval, and as a system it tracks or exceeds always-search accuracy while issuing fewer searches and approaching the always-cloud ceiling at a fraction of its cost; per-query discrimination within a task family is weaker, which we quantify explicitly. Overall, this work offers a framework for optimizing and accurately measuring cross-lingual RAG pipelines in low-resource settings. Full article

Understanding river channel evolution is essential for effective management, as lateral erosion shapes riverbeds, floodplains, and riparian habitats. Despite advancements in remote sensing, translating these technologies into operational tools for institutional monitoring remains a challenge. This study introduces a semi-automated framework designed to bridge this gap by quantifying lateral mobility and bank retreat rates in high-energy, gravel-bed rivers. The methodology utilizes a Random Forest classifier applied to Copernicus Sentinel-2 time series (2016–2024) across five rivers in Piedmont (Italy). By detecting pixel-level class shifts between water, vegetation, and sediment, the procedure provides a proxy for lateral mobility. Validated against 120 km of manual delineations, the model achieved high spatial accuracy for both bank retreat rates and eroded bank lengths measurements (Mean Absolute Deviation of 2.64 m/yr and 4.34%, respectively). Integrating discharge data and the hydraulic infrastructure cadaster of the Sesia River, the proposed framework effectively detects reaches prone to significant geomorphic changes, isolates their underlying drivers, simulates 10-year evolutionary trajectories, and demonstrates strong predictive capabilities regarding future channel–infrastructure interactions. This study marks a shift from sporadic, labor-intensive assessments to a dynamic, systematic monitoring framework. Leveraging free satellite data, it provides competent authorities with an objective, cost-effective tool to prioritize interventions, supporting flood risk reduction and river restoration. Full article

Sodium-cooled fast reactors (SFRs) are being developed with various fuel cycle strategies, including once-through and recycling options. Initial U.S. deployments are expected to use compact SFR cores fueled with high-assay low-enriched uranium (HALEU). As SFR technology advances, higher fuel burnups and lower enrichments are anticipated. This study provides an economic assessment of utilizing HALEU in SFRs with a once-through fuel cycle (OTC) compared to recycling used nuclear fuel (UNF) across a range of advanced fuel and reactor designs. As an alternative to current once-through options (FC #1), three recycling alternatives are evaluated: (FC #2) recovered uranium (RU) downblended for pressurized water reactor (PWR) fuel, (FC #3) RU re-enriched for SFR fuel, and (FC #4) RU co-recycled with transuranics (RU/TRU) for SFR fuel. Results indicate that recycling RU from SFR UNF in both fresh PWR and SFR fuel can be economically advantageous compared to the OTC approach, provided the U-235 content remains sufficiently high—a condition met by current and near-term SFR designs. Increased burnup and in situ plutonium production in SFRs reduce overall OTC costs and natural uranium requirements. However, the economic viability of recycling SFR RU depends strongly on the specific UNF composition and the recycling pathway chosen. Recycling both RU and TRU consistently offers the lowest fuel cycle costs across all scenarios, even as fuel and reactor designs advance. These findings provide a framework for identifying when recycling offers economic benefits over direct disposal, informing future fuel cycle decisions for advanced reactors. Full article

The growing share of variable renewable energy sources increases the need for operational flexibility in power systems. In regions with cascaded hydropower systems, upgrading existing plants may be a more practical short-term planning option than developing new hydropower facilities. This study employed a multi-criteria decision-making (MCDM) framework based on the VIKOR method to screen and prioritize existing hydropower plants for potential infrastructure upgrading and capacity development in the Skellefte River, located in Northern Sweden. According to this prefeasibility study, six hydropower stations with installed capacities above 50 MW along that river were evaluated using six technical criteria: installed capacity, hydraulic head, efficiency, generation cost, average turbine discharge, and normal annual production; their weights were derived using the Shannon entropy method to minimize subjectivity. The ranking suggests Gallejaur, Kvistforsen, and Bastusel as the most favorable alternatives, mainly due to their strong performance in annual production and hydraulic head. Vargfors, Krångfors, and Selsforsen rank lower because of head and/or production constraints. The proposed pre-feasibility hydropower ranking workflow provides a transparent and reproducible preliminary technical screening tool. More detailed studies, including hydraulic cascade operation, environmental permitting and grid constraints, are required before practical implementation and feasibility assessment studies for future investigations. Full article

Background/Objectives: Cardiovascular toxicity and pre-existing cardiovascular disease can affect cancer-treatment tolerance, functional recovery, and survivorship. This narrative review aimed to summarize current evidence and propose an author-derived risk-adaptive clinical framework for adult cardio-oncology rehabilitation (CORE), organized around exercise prescription, multimodal monitoring, and implementation. Methods: PubMed/MEDLINE and the Web of Science Core Collection were searched from database inception through 27 July 2026. Guidelines, systematic reviews, randomized and non-randomized clinical studies, feasibility studies, and selected mechanistic sources were prioritized according to their relevance to the three review domains. Evidence was classified as direct clinical, guidance/synthesis, feasibility/implementation, or mechanistic/conceptual; no PRISMA protocol, formal risk-of-bias assessment, or meta-analysis was undertaken. Results: Clinical trials support improvements in cardiorespiratory fitness, selected cardiovascular risk factors, and functional outcomes in some settings, but effects on cancer therapy-related cardiac dysfunction, cardiovascular events, and mortality remain uncertain. Direct evidence is dominated by breast-cancer cohorts and women, although mixed-cancer, lymphoma, and lung-cancer studies broaden the functional evidence base. An evidence-informed approach is to individualize aerobic and resistance exercise to treatment context, symptoms, functional capacity, and clinical stability, with reassessment linked to actionable findings; the proposed pathway remains conceptual and non-validated. Artificial intelligence, digital twins, omics, and monitoring-guided exercise-dose adjustment remain investigational. Conclusions: CORE is best regarded as exercise-centered care linked to guideline-based risk assessment and clinically indicated reassessment. Prospective studies should test therapy-specific timing, risk-stratified delivery, monitoring-guided dose adjustment, clinical endpoints, equity, and cost-effectiveness. Full article

In the capacitated arc routing problem (CARP), a fleet of capacitated vehicles based at a depot must cover the streets of a network where the demand is located at the lowest possible total cost. Waste collection, street sweeping, winter gritting, and mail delivery are among its best-known applications. This work introduces the CARP with vehicle dependence (CARP-VD), an extension in which the cost of servicing an edge, and that of traversing it without service, are specific to each vehicle type and formulates it as a mixed-integer linear program. A two-stage matheuristic is proposed: the first stage distributes the required edges among the vehicles without exceeding their capacities, and the second builds the route of each vehicle. A bound is derived that limits the optimality loss of this decomposition by its own deadheading cost. Both approaches are evaluated on 47 benchmark instances adapted from the literature under a common one-hour budget, and their robustness is assessed over six scenarios that vary the parameters of the adaptation. The matheuristic returns good-quality solutions in a fraction of the time on the smaller instances, and on those in which almost every edge requires service it improves the best solutions found by a commercial solver applied to the complete model by up to 44%. Full article

Battery energy storage systems can reduce the operating cost of active distribution networks while supporting voltage control through their power electronic converters. This paper develops an application-specific multi-operator Differential Evolution (DE) framework for the coordinated active and reactive power scheduling of distributed battery energy storage systems in radial distribution networks with photovoltaic generation. The optimization model minimizes the daily operating cost associated with conventional energy supply, photovoltaic and storage operation and maintenance, and battery degradation. Candidate schedules encode hourly active and reactive power references for three storage converters, producing a 144 dimensional decision vector for a 24 h horizon. Each candidate is repaired to satisfy active power, state of charge, terminal energy, and converter apparent power limits before being evaluated through an alternating current power flow based on matrix successive approximations. The search framework generates three competing trial schedules per target individual by combining established best-guided, random, and current-to-random DE mutation families with a discrete parameter pool, a common feasibility-repair operator, and greedy selection after AC network evaluation. The method is tested on modified 33-node and 69-node active distribution networks and compared with AJAYA, genetic algorithm, multiverse optimizer, and particle swarm optimization. In the deterministic 33-node case, Differential Evolution obtains the lowest best cost, USD 6846.206, and the largest best cost reduction, 2.1838 percent. The scenario study performs separate deterministic optimizations for pre-generated operating realizations and is therefore interpreted as a scenario-conditioned sensitivity assessment rather than as stochastic or robust optimization of one here-and-now schedule. In this assessment, DE achieves the largest average savings: 2.3487 percent in the 33-node network and 2.9314 percent in the 69-node network. Voltage magnitudes, branch loading, converter ratings, and cyclic state of charge constraints are satisfied in all evaluated cases. The results identify the proposed framework as a competitive day-ahead solver within the evaluated cases, while no claim of global optimality or universal superiority over alternative optimizers is made. Full article

Background/Objectives: Anti-double-stranded DNA antibodies (anti-dsDNA) are a specific biomarker for systemic lupus erythematosus (SLE). We aimed to analytically compare the in-house Farr fluorescence immunoassay (Farr-FIA), developed based on classical Farr-RIA, for the detection of anti-dsDNA with commercially available assays, including a chemiluminescence assay (CLIA) and the Crithidia luciliae immunofluorescence test (CLIFT). Additionally, we evaluated analytical agreement for diagnostic combinations of these methods. Methods: We analyzed 182 samples from patients at the Department of Rheumatology, UMC Ljubljana, using Farr-FIA, CLIFT (Immuno Concepts—CLIFT 1 and Inova Diagnostics—CLIFT 2), and the QUANTA Flash dsDNA CLIA on the BIO-FLASH analyzer (Inova Diagnostics). Agreement between methods and diagnostic combinations was assessed using Cohen’s kappa. Results: Overall agreement between methods using manufacturer determined cut-offs ranged from 78.6% to 91.8% (κ = 0.582–0.836). The in-house Farr-FIA showed 78.6–86.8% comparability with commercial assays (κ = 0.582–0.730). Analytical agreement between combinations of two methods ranged from κ = 0.788 to 1.000, indicating moderate to almost perfect agreement. Our routine combination of CLIFT 1 followed by Farr-FIA showed high agreement (κ = 0.788–0.938) with other two-step approaches. Regardless of initial screening (CLIFT 1, CLIFT 2, or CLIA), applying Farr-FIA as a confirmatory method yielded highly consistent final classifications (97.3–100% agreement). However, the screening methods differed in the proportion of samples requiring confirmatory method (e.g., CLIFT 2 identified more positives requiring Farr-FIA), consequently affecting workflow and costs, although the final diagnostic results remain the same. Conclusions: Farr-FIA demonstrated moderate to substantial analytical agreement with established commercial assays (CLIA and CLIFT). Several diagnostic combinations demonstrated high analytical agreement; however, the choice of initial screening method substantially influences the number of samples requiring confirmatory testing and, therefore, affects laboratory workload. Full article

The global plastic pollution crisis demands a fundamental re-evaluation of materials systems beyond incremental improvements to fossil fuel-based polymers. Bioplastics, polymers derived from renewable biological feedstocks, biodegradable under defined conditions, or both, offer a chemically diverse and rapidly evolving platform for transitioning toward circular materials economies in which the value of carbon, energy, and material is retained across multiple use cycles. This review provides a comprehensive and critically organized account of the bioplastics field, spanning three generations of feedstock development from food crops through lignocellulosic residues to algae and waste streams; primary production pathways including microbial fermentation, ring-opening polymerization, and biosynthesis; forming processes from extrusion and injection molding to additive manufacturing; and the mechanical, thermal, and barrier properties that determine application fitness. Particular emphasis is placed on life cycle assessment, which reveals that bioplastics’ climate benefits are conditional on feedstock choice, land-use management, energy source at manufacturing, and end-of-life pathway, and that burden-shifting from greenhouse gas emissions to land use, water consumption, and eutrophication is a systematic risk requiring integrated LCA evaluation rather than single-metric optimization. The review further examines end-of-life recycling, composting, and biodegradation pathways; market applications across packaging, agriculture, automotive, biomedical, and electronics sectors; and the growing role of artificial intelligence and machine learning in accelerating materials design, process optimization, and lifecycle data management. Critical barriers to scale, such as cost premiums of 20–75% over conventional plastics, inadequate composting infrastructure, recycling stream contamination, regulatory fragmentation, and consumer labeling confusion, are systematically analyzed alongside mitigation strategies. The review concludes with a forward-looking discussion of emerging feedstocks, smart and functional bioplastics, and the policy and infrastructure investments required to translate the environmental promise of bio-based polymers into realized circular economy impact. Full article

The growing demand for scalable and cost-effective digital learning environments has increased interest in lightweight cloud-based learning management solutions that can support instructional delivery without the complexity and infrastructure requirements of conventional Learning Management Systems (LMSs). While enterprise LMS platforms provide extensive functionality, their deployment and maintenance often require substantial technical, financial, and administrative resources. This study proposes a lightweight cloud-based LMS architecture using low-code web technologies as an alternative framework for educational content management, learner engagement, resource distribution, and instructional support. The proposed architecture integrates four functional system layers: course management, performance management, content delivery, and productivity support. These components are designed to operate within a cloud-hosted environment that leverages web-based content management, embedded digital resources, collaborative productivity tools, and centralized storage services. The architecture emphasizes accessibility, modularity, low deployment overhead, and cross-platform compatibility, enabling rapid implementation in resource-constrained educational settings. To evaluate the feasibility of the proposed architecture, a large-scale deployment was conducted involving 1765 end users interacting with the platform within an educational environment. System functionality was assessed through feature-level evaluation across the core LMS components and supported by user capability indicators related to digital engagement and platform utilization. Analytical results demonstrated strong functional performance across all architectural modules, with course management and content delivery components exhibiting the highest operational effectiveness. Findings further indicated that the cloud-based architecture successfully supported essential LMS functions through integrated web services and low-code platform technologies. The study contributes a replicable systems architecture and deployment framework for lightweight learning management environments. The proposed model offers a practical foundation for the development of cloud-based educational platforms that support scalable content delivery, learner interaction, instructional management, and future integration with learning analytics, adaptive learning engines, and intelligent educational support systems. The framework provides an engineering-oriented approach for designing accessible and sustainable digital learning infrastructures using low-code technologies. Full article

Cost is one of the decisive factors when selecting a fibre type for structural strengthening. This study therefore tested the hypothesis that a low-cost fibre can still provide a substantial improvement in the mechanical performance of strengthened timber beams. Four-point bending tests were carried out on 25 pine beams, comprising an unstrengthened reference series and four series strengthened with glass fibre reinforced polymer (GFRP) sheets, each series consisting of five specimens. The beams had nominal dimensions of 80 mm × 80 mm × 1600 mm. The reinforcement was bonded exclusively to the external surfaces, with a focus on the tension zone. Two variables were examined: the number of sheet layers and the extent of coverage of the timber surface. The reinforcement ratio ranged from 0.38% to 1.15%. The response of the beams was assessed in terms of load-bearing capacity, stiffness, ductility, and failure mode. Bonding three layers of sheet to the bottom face of the beams increased the load-bearing capacity by up to 58.20%. The effect on stiffness was less pronounced, with a maximum increase of 20%, which is attributable to the relatively low elastic modulus of the sheets. However, the ductility of the beams increased significantly (up to 126.14%, based on energy considerations), a result that can be directly attributed to the composite’s high adhesion and high elongation at rupture. The transformed cross-section method and finite element simulations were used to predict the behaviour of the strengthened elements, and both showed good agreement with the test results within the elastic range. It is concluded that GFRP sheets are a rational strengthening solution, although satisfactory effectiveness was obtained only at higher reinforcement ratios; at least two layers are recommended for the configurations tested. The effect of the strengthening configuration on the load-bearing capacity, on the deflection at maximum load and on the ductility was statistically significant, whereas its effect on the bending stiffness was not. Full article

Energy-intensive foundries require methods to explore trade-offs among delivery performance, production horizon, electricity use, and cost when industrial data are incomplete. This paper presents an offline, process-level digital twin case study for the melting and casting area of a Danish cast-iron foundry. A multi-agent simulation represents production orders, enterprise resource planning and manufacturing execution system functions, induction furnaces, holding furnaces, crane-based transfer of molten metal, vertical moulding lines, the operating calendar, and electricity cost accounting for the induction furnaces. The model is assessed through boundary definition, assumption registration, implementation checks, material flow plausibility, a diagnostic comparison of furnace temperature, controlled scenario experiments, and local sensitivity analysis. These activities support internal consistency and bounded interpretation but do not constitute independent operational validation of the full production system. In the simulated 200-order monthly case, First-Come-First-Served and Earliest Deadline First complete the same 288,620 pieces and 5482.00 t. Earliest Deadline First increases the simulated on-time completion rate from 87.5% to 100%, while makespan, model-estimated electricity use by induction furnaces, and model-estimated electricity cost increase by 7.52%, 0.58%, and 3.42%, respectively. The case indicates that deadline-oriented sequencing may improve delivery performance but lead to a longer production horizon and higher energy use and cost within the defined model boundary. The contribution is an auditable foundry-specific modelling workflow that links heterogeneous data conditions to modelling choices, supporting evidence, and interpretation limits. The model is therefore intended for preliminary offline scenario exploration rather than validated operational decision support. Full article

Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units. Full article