Search Results (1,196)

Wind power, photovoltaic (PV), hydropower, and energy storage, along with other multi-type clean energy sources, transmitted via islanded voltage source converter based high voltage direct current (VSC-HVDC) systems, will become an important form of delivery for renewable energy bases. However, due to the volatility and uncertainty of renewable energy, its high-proportion integration significantly exacerbates system frequency fluctuations and voltage violation risks, posing severe challenges to the stable operation of the system. To strike a balance between maximizing clean energy integration and maintaining the stability of the islanded system, this paper presents a capacity optimization approach for multiple types of clean energy within an islanded VSC-HVDC transmission system. First, typical wind power and PV output scenarios are obtained via Monte Carlo simulation, and a virtual slack bus is introduced to establish a power flow calculation model for the islanded VSC-HVDC transmission system. Second, the active power is regulated through fast VSC-HVDC support and droop control mechanisms, while a quadratic programming model for voltage is established based on the relationship between reactive power and voltage, aiming to drive the virtual slack bus power to zero, thereby improving system frequency and voltage stability. Finally, a genetic algorithm (GA) is employed to achieve optimal capacity configuration for maximizing renewable energy integration, and the corresponding optimal energy storage capacity is determined accordingly. Simulation results demonstrate that, while satisfying operational constraints, the proposed method identifies the maximum installable capacities of wind power and PV while simultaneously reducing the required energy storage capacity. Full article

Background: Overweight and obesity adversely affect respiratory mechanics and ventilatory capacity. This study evaluated the incremental effects of combined Spot Marching Exercise and BreatheMAX-assisted deep breathing (CSMEB) versus Spot Marching Exercise alone (SME) on pulmonary function in young adults with overweight/obesity. Methods: Thirty-eight participants (aged 18–25 years, BMI 25.0–39.9 kg/m²) were randomized into SME (n = 19) or CSMEB (n = 19) groups, with 36 completing the study (18 per group) after two withdrew before treatment. Both performed 30 min of SME 5 days/week for 8 weeks; CSMEB additionally performed daily device-assisted deep breathing (10 sets of 10 breaths). The primary outcome was maximal voluntary ventilation (MVV). Secondary outcomes included spirometry and body composition parameters. Results: At Week 8, CSMEB achieved significantly greater improvements in MVV compared to SME (adjusted mean difference = 6.31 L/min; 95% CI: 0.08–12.55; p = 0.047). Peak expiratory flow rate (PEFR) also significantly favored CSMEB (adjusted mean difference = 0.71 L/s; 95% CI: 0.02–1.39; p = 0.044). No significant between-group differences were found for FVC, FEV₁, FEV₁/FVC, muscle mass, or body fat percentage (p > 0.05). No adverse events occurred. Conclusions: Adding BreatheMAX-assisted deep breathing to a home-based SME program yields targeted enhancements in MVV and PEFR in young adults with overweight or obesity. Full article

Estuarine and river-influenced coastal ecosystems are recognized as important sinks and channels for transfer of heavy metals into the marine environment. Continuous intake of metal pollutants could create chronic exposure situations, perhaps leading to molecular and cellular damage to resident biota, even if environmental concentrations are within regulatory limits. Thus, the incorporation of molecular and genotoxicity biomarkers into environmental monitoring programs has received growing interest, as changes in gene expression are among the earliest detectable responses to pollutant stress and may precede genotoxic effects, including DNA damage, at higher or prolonged levels of contaminant exposure. The present study aimed to determine the levels of heavy metals in the coastal zone where the Deliçay River flows into the Gulf of Iskenderun in the extreme northeastern Mediterranean Sea, Türkiye, and to investigate the genotoxic effects in the euryhaline ray-finned fish golden grey mullet (Chelon auratus). In this study, seasonal water samples (n = 3 per site per season) and C. auratus specimens (n = 10 per site per season; total n = 80) were collected from a reference site and the Deliçay estuary. Water samples were analyzed for metals (cadmium (Cd), chromium (Cr), iron (Fe), lead (Pb), and zinc (Zn)) and fish samples were analyzed with the micronucleus (MN) test for determining nuclear abnormalities and the comet test for DNA damage levels. The concentrations of Fe, Zn, and Pb in seawater exceeded the Criterion Continuous Concentration (CCC) thresholds during the summer and autumn seasons, as well as in terms of annual mean values, indicating a potential chronic ecological risk to marine organisms. From the results of the micronucleus test performed in the present study, the highest MN frequencies (10.16 ± 0.15%) and other erythrocytic nuclear anomalies [kidney-shaped (10.36 ± 0.32%), binucleated (14.20 ± 0.10%), notched (14.63 ± 0.20%), lobed (15.43 ± 0.11%), and budded (15.33 ± 0.15%)] were found along the studied coastal zone in summer season. Results of the comet test, supporting the micronucleus test results, showed the highest percentages of DNA damage determined in all seasons in the gill and liver tissues of fish sampled in the studied coastal zone. This study is the first to evaluate the effects of heavy metal-induced genotoxic stress on ecological integrity in this coastal zone using a biomarker-based approach, and the results underscore the need for comprehensive environmental monitoring and pollution reduction strategies to protect ecosystem health. Full article

This paper investigates the energy-efficient hybrid flow shop scheduling problem considering deteriorating maintenance and transportation times (EHFSP-DMT), which is an NP-hard combinatorial optimization problem in complex manufacturing systems. To formulate this problem, a mixed-integer programming mathematical model is established. A computational intelligence approach, named the adaptive feedback multi-start variable neighborhood search (AFMS-VNS) algorithm, is proposed to simultaneously minimize the makespan and total energy consumption. In AFMS-VNS, a decoding strategy integrating conflict detection is designed to satisfy the mathematical constraints of equipment maintenance. A reinforcement learning (Q-learning) mechanism evaluates and adaptively selects search operators to enhance search efficiency. A search strategy based on the feedback of population distribution status is proposed to balance the optimization directions between the two conflicting objectives. An iterated greedy reconstruction strategy, guided by an elite external archive, is adopted to replace stagnant individuals. Experiments on 44 instances and Wilcoxon signed-rank tests show that AFMS-VNS outperforms five comparison algorithms in terms of inverted generational distance, hypervolume, and set coverage when solving the EHFSP-DMT. Full article

This paper studies a risk-averse stochastic unit commitment framework for an energy aggregator, operating a portfolio of conventional generators, renewable units, and battery energy storage in a network-constrained environment. Renewable generation and demand uncertainty are represented through a scenario-based extensive-form mixed-integer linear program. To avoid exposure to rare but high cost events, the model incorporates conditional value-at-risk as part of the objective function. The approach captures key market interactions, including day-ahead commitments, imbalance penalties, and power exchange with a neighboring network, while respecting generator constraints, storage dynamics, line flow limits, and bus voltage security. A comprehensive parametric study is conducted to quantify the influence of two risk parameters: the conditional value-at-risk confidence level $\alpha$ and the risk-aversion weight $\lambda$. Using a 300-scenario test set on a modified IEEE 9-bus system, the results show that risk-neutral scheduling exposes the aggregator to larger operational costs in extreme scenarios. Minor levels of risk aversion (0.1–0.5) reduce CVaR and tighten the distribution of costs. Increasing $\lambda$ further yields diminishing returns, while higher $\alpha$ values focus risk mitigation on the most severe outcomes. The results demonstrate how CVaR-based stochastic scheduling can support aggregator decision-making by quantifying downside risk under renewable uncertainty. Full article

Transportation networks are subject to multiple sources of uncertainty, ranging from stochastic fluctuations in demand and travel times to epistemic indeterminacy in infrastructure condition, disruption risk, and user behavior. A diverse body of mathematical frameworks has emerged in response, including stochastic programming and probabilistic reliability analysis, fuzzy and possibilistic approaches, Liu’s uncertainty theory and uncertain programming, and robust or distributionally robust optimization. This article delivers a comprehensive, mathematically oriented synthesis of these paradigms for transportation networks, with emphasis on network-level structures—paths, flows, spanning trees, and network design problems—and on reliability notions including connectivity, travel-time, capacity, and max-type reliability. A central theme is that modelling choices about uncertainty representation and reliability indices are inseparable from questions of stability and sensitivity: how robust are optimal or near-optimal configurations when parameters vary within plausible ranges? Building on deterministic post-optimal analysis, this paper reviews tolerance-based stability concepts for uncertain most reliable paths, maximum reliable transmission paths, and uncertain minimum spanning trees under Liu-type uncertainty and demonstrates how inverse-distribution mappings yield exact deterministic equivalents and belief-based robustness margins. A dedicated comparative framework is developed, summarizing the data requirements, core advantages, typical limitations, and suitable engineering scenarios of each uncertainty paradigm to guide model selection in practice. The discussion extends to practical applications in post-disaster planning, infrastructure investment prioritization, and supply chain network design and identifies open research directions including network-wide travel-time reliability under belief-based uncertainty, unified stability frameworks across paradigms, and the integration of machine learning for uncertainty distribution elicitation. The emphasis throughout is on conceptual structure, modelling assumptions, and interpretability of reliability and stability indices, thereby positioning uncertain transportation networks as a rich interface between applied mathematics, operations research, and infrastructure planning. Full article

Micro-credentials are reshaping higher education credentialing, yet existing models mostly link their components through one-way causality, while systems thinking pedagogy has developed largely around STEM and sustainability rather than higher education policy itself. This conceptual paper reframes the micro-credential ecosystem as a complex adaptive system governed by feedback loops, addressing both gaps at once. Following theory synthesis and model-building approaches and Sterman’s system dynamics modeling steps, a four-stage process integrates system dynamics, institutional theory, signaling theory, and adult learning theories. The study yields four testable propositions and one balancing loop, formalized in a single integrated causal loop diagram with defined polarities and delays: the bidirectional need–program link (P1), certification–design feedback (P2), demand–standardization network effect (P3), an ecosystem-level reinforcing loop (P4), and credential inflation as a balancing loop (B1). The coupling of the reinforcing and balancing loops frames credential inflation as a transferable sub-form of the Overshoot and Collapse archetype. The core loop is operationalized as a stock-flow structure, a three-level moderator architecture links the model to variable-based designs, and the framework maps onto SDGs 4, 8, and 10. Analysis of Meadows’s leverage points and a step-by-step design guide translate the model into policy and curriculum practice and into a teaching case for systems thinking. Full article

Low seawater temperatures constrain the operation of open rack vaporizers (ORVs) and intermediate fluid vaporizers (IFVs), while also increasing pumping-related emissions at LNG terminals. This study establishes a carbon-oriented framework for an expanded ORV–IFV regasification system sharing a fixed-speed seawater pump network and evaluates thermal discharge from an adjacent power plant as a supplementary heat source. Using measured LNG composition, we developed an Aspen HYSYS model based on the Peng–Robinson equation of state and steady-state energy balances, which was validated against field data. Electricity-related CO₂ emissions from seawater pumps and auxiliaries were quantified using the regional grid emission factor, while pump scheduling was formulated as a mixed-integer nonlinear programming (MINLP) problem. Model predictions differed from measurements by approximately 2%. Lower seawater temperatures increased emissions and restricted maximum regasification capacity to 80% and 57% of the design value at 3–4 °C and 2–3 °C, respectively. For LNG throughputs of 300, 500, and 700 t/h, CO₂ reduction increased with warm-seawater flow and inlet temperature; maximum reductions reached approximately 50–55% under 3–7 °C ambient seawater conditions and 40% under 6–20 °C conditions, with a 95% confidence interval of ±3.9 percentage points. Monthly discharge data indicated reductions of approximately 20% in winter and 45% in summer. Integrating power-plant waste heat with load-dependent pump scheduling can improve the carbon performance of LNG regasification. Full article

Following extreme disasters, hybrid wind farms comprising grid-following (GFL) and grid-forming (GFM) turbines can serve as black-start resources for power system restoration. However, wind power uncertainty, wake effects, and collection-grid restoration constraints complicate the startup sequence planning of GFL turbines within hybrid wind farms. To address these challenges, this paper proposes a multi-objective mixed-integer linear programming (MILP) model that jointly considers electrical impedance paths, wind uncertainty, and spatial wake effects. Information Gap Decision Theory (IGDT) is incorporated into active power support constraints to account for wind uncertainty through a robust adjustment of available generation capacity, while the Dijkstra algorithm is employed to convert collection-cable parameters into impedance-based cost factors for identifying minimum-impedance restoration paths and mitigating transient overvoltage risks. In addition, dynamic wake losses under non-uniform turbine layouts are quantified to capture the influence of startup sequences on local flow fields, and the resulting nonlinear terms are reformulated using the big-M linearization technique. Case studies considering different wind directions, time-varying wind speed conditions, and cable-impedance sensitivities demonstrate the effectiveness of the proposed framework. The results show that the proposed strategy provides a favorable balance between impedance-cost minimization, wake-effect mitigation, and robustness enhancement, while maintaining reliable restoration performance under diverse operating conditions. Full article

This paper develops a two-period analytical model of trade-in insurance and rebate design in a product upgrade and recovery system with successive product generations. In standard trade-in programs, customers face uncertainty about future rebate eligibility because rebates depend on the condition of the used product at the upgrade stage. We study an optional insurance mechanism through which customers prepay a fixed fee and receive a guaranteed future trade-in rebate. Customers are financially risk-neutral, but insurance may generate a first-period non-monetary psychological benefit: once future rebate eligibility is guaranteed, customers can use the product during the first-period ownership stage with less anxiety about an unexpected failure eliminating their future trade-in opportunity. Under the baseline specification in which this psychological benefit increases with product valuation, trade-in insurance generates valuation-based customer segmentation among insured upgraders, uninsured trade-in customers, product keepers, delayed buyers, and non-buyers. This insured–uninsured single-crossing result extends to general increasing psychological-benefit functions, including concave forms. The model further shows that the insurance price and trade-in rebate should be coordinated because both instruments affect customer upgrade behavior, product sales, rebate obligations, return flows, and recovery value. When product innovation is sufficiently high, customers no longer keep functional used products, and the optimal insurance price and trade-in rebate become independent of innovation and durability. Compared with a standard trade-in program, optional trade-in insurance weakly improves manufacturer profit, while the economic magnitude of the gain depends on market and operational conditions. Full article

Maintaining genetic diversity is essential for the long-term conservation of migratory freshwater fishes facing increasing anthropogenic pressures. Prochilodus magdalenae (bocachico), one of Colombia’s most economically and ecologically important freshwater fish species, supports artisanal fisheries, regional food security, and the livelihoods of local communities but has experienced population declines associated with overfishing, habitat degradation, and hydrological alteration. This study evaluated the genetic diversity and population structure of wild and hatchery-associated P. magdalenae from northern Colombia using sequence-based microsatellite genotyping (SSR-GBS). Sixty individuals (48 wild adults from three localities in the Department of Sucre and 12 hatchery-associated fingerlings from Cereté) were genotyped at seven polymorphic microsatellite loci. Wild populations retained high genetic diversity and exhibited weak but significant genetic differentiation (FST = 0.017, p = 0.003), with only 2% of the molecular variation distributed among populations and high estimated gene flow (Nm = 14.899). Hatchery-associated fingerlings consistently exhibited lower allelic richness while maintaining relatively high heterozygosity. Rarefied allelic richness did not differ significantly among populations (Kruskal–Wallis, p = 0.400); however, hatchery-associated fish consistently showed lower values, highlighting the value of allelic richness as a complementary indicator for evaluating hatchery-produced stocks. These findings provide a genetic baseline for the conservation of P. magdalenae and support incorporating routine genetic monitoring into supportive breeding and restocking programs. Full article

Fairness is critical in delay-sensitive group applications—multiplayer online games, live collaborative editing, and distributed interactive simulations—where every participant should receive each message within a bounded delay and with minimal timing differences between recipients. We study fairness-aware multicast routing under two parameters: the maximum end-to-end delay ($\Delta$) and the inter-destination delay variation ($\delta$). Although several heuristics address this NP-hard problem and exact integer linear programs (ILPs) exist for the minimum-cost multi-constrained case, exact methods that directly optimize inter-destination delay variation under bounded delay remain underexplored. We present flow-based ILP formulations that treat $\Delta$ and $\delta$ as either objectives or constraints. Their feasible solutions are partial spanning hierarchies, a class that contains partial spanning trees as a special case; consequently a hierarchy optimum is, by construction, at least as good as the best tree-constrained solution. On proven-optimal instances, hierarchy optima reduce inter-destination delay variation considerably relative to the best tree-constrained solution. A sensitivity analysis, a real-topology study on the Abilene backbone, and an exact-ILP scalability study quantify and corroborate these gains. Full article

Ensuring continuous load supply under N-1 contingencies poses a significant challenge for active distribution networks. Traditional fault recovery relies on mechanical tie switches for network reconfiguration; however, such rigid interconnections often fail due to severe terminal voltage drops during long-distance load transfers, leading to forced load shedding. To address this, this paper proposes an optimal planning framework for Electric Energy Routers (EERs) to secure load supply, aiming to maximize load preservation within investment budget constraints. First, a bi-level robust optimization model is constructed: the upper level determines the optimal EER configuration to minimize load shedding in the worst-case N-1 scenarios, while the lower level evaluates the optimal restoration strategies via mixed-integer second-order cone programming. To handle the inherent complexity of discrete–continuous variable coupling and the computational burden of the bi-level structure, a novel topological manifold evolution algorithm based on Wasserstein geometric flow is developed. By embedding the electrical topology into a sensitivity-driven Riemannian metric field, the algorithm effectively enhances global optimization capabilities. Case studies on the IEEE 33-node system demonstrate that the integration of EERs facilitates a transition from rigid to flexible interconnection, circumventing power transfer bottlenecks caused by low voltage through controllable power flow. Compared with baseline schemes, the optimal EER scheme reduces load shedding in the worst-case scenario by 60.3%, providing a robust and economical solution for high-resilience distribution network planning. Full article

Low-voltage violations in rural distribution transformer areas (RDTAs) are a persistent and widespread problem in China, affecting over 5% of such areas and compromising the power supply quality for millions of rural end-users. These violations are primarily caused by long supply distances, undersized conductors, dispersed load distributions, and low power factors at terminal feeders, resulting in degraded equipment performance, increased line losses, and hindered integration of renewable distributed generation. The key to mitigating such violations lies in the rapid and coordinated regulation of primary equipment. Considering the actual equipment configurations in rural distribution transformer areas, a low-voltage mitigation method based on power-mileage aggregation is proposed. First, a generative adversarial network is employed to preprocess the collected electrical data, and device ranging is achieved through power-line characteristic pulses, enabling the spatiotemporal distribution analysis of low-voltage phenomena. Second, based on the line lengths from power sources—including distributed generators, soft open points, energy storage systems, and distribution transformers—to the smart meters, a power-mileage aggregation metric is developed to quantify the impact of power-source output variations on low-voltage meters. Subsequently, with the objective of minimizing the power mileage for low-voltage mitigation, the Newton iteration method is applied to determine the optimal output powers of energy storage systems and soft open points. The effectiveness of the proposed method is validated through case studies. Comparative analyses with optimal power flow, second-order cone programming, and interval robust control demonstrate that the proposed approach achieves superior low-voltage mitigation performance while maintaining higher economic efficiency. Full article

Smart Factory programs increasingly connect shop-floor, quality, asset and planning data, but integrated data infrastructures do not necessarily align operational decisions. This paper reviews how smart manufacturing literature explains the transition from industrial data integration to closed-loop operations synchronization and value capture in high-throughput manufacturing contexts. Using the Systematic Search Flow method, 1949 records were screened and reduced to a final portfolio of 73 studies. The papers were coded by thematic cluster, dominant technology, research method, primary theme, value-stream coverage and operations-synchronization relevance. The coding shows that roadmaps, interoperability architectures, analytics applications and digital-twin models dominate the portfolio. Explicit operations-synchronization mechanisms are addressed in 16 of the 73 studies, mainly through planning-execution coupling and digital-twin-based decision support. Coverage across value streams is uneven, with stronger evidence for Strategy, Make and Plan than for Quality and Assets. Based on this evidence map, the paper proposes a Data-Driven Operations Synchronization Stack that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture. Full article