Search Results (1,329)

Traffic reallocation is usually framed as moving flights toward apparent spare airport capacity, yet the same move can redirect demand through different runways and shared corridors. We tested whether the local capacity response to a fixed reallocation remains invariant to this resource-chain composition. Using a discrete-event model of the Beijing Capital, Beijing Daxing and Tianjin Binhai airports, we crossed seven airport allocations with five prespecified composition levels. At a model-success threshold of 0.90, increasing Beijing Capital’s share by five percentage points produced a finite-search response of $[-145,-115]$ flights under the lower-pressure composition but $[10,25]$ under the higher-pressure composition. The propagated interaction interval was $[125,170]$ flights. The reversal was consistent across three independent seed families, and 95% paired-bootstrap percentile intervals excluded zero for both outer compositions. None of 100 prespecified pressure-label controls met the certain-tail criterion, while seven control intervals overlapped the observed band. These are reference-tail proportions, not a randomization p-value. Runway and corridor relief each restored all seven failing operating-state boundary cases. These results show that reallocation acts on a coupled airport–resource system whose response depends on the complete chains carried by demand. These findings are model-conditional finite-search results under expert-bounded perturbations, not an official capacity determination or a calibrated estimate of operational reliability. Full article

Electromobility is central to urban decarbonisation, but its charging infrastructure must be sized under substantial uncertainty about user behaviour that varies across stations, time of day and user type. This study couples a hierarchical Bayesian model with an agent-based, discrete-event simulation of a charging network. It is fitted by Markov chain Monte Carlo to the public ACN-Data dataset (13,694 sessions across 52 stations; 16,468 user requests), with partial pooling across stations. Posterior parameters drive a 24 h simulation of 500 vehicles across nine configurations and 30 to 180 slots. Service success rises from 19% to 81% and mean waiting falls from 110 to 62 min; long workplace dwell times limit turnover, so capacity rather than energy binds. TOPSIS with a paired bootstrap selects 160 slots under balanced weighting, but that optimum holds for only a tenth of the weight simplex, and the recommendation spans 140–180 slots. Spreading the arrival peak at fixed hardware raises service from 67% to 83%, matching a 29% expansion. Spatially explicit assignment costs three percentage points when stations are evenly sited, and six when clustered. The framework makes the cost of over-provisioning explicit and preference-conditional rather than naming a single optimum, giving a reproducible basis for sustainable capacity planning. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

Hemopericardium is a rare but serious complication of radiofrequency catheter ablation (RFCA) for atrial fibrillation (AF). While prompt pericardial drainage is effective, the long-term clinical course and the role of adjunctive anti-inflammatory therapy remain unclear. In this prospective single-center cohort study, we included patients who developed hemopericardium requiring pericardiocentesis during RFCA for AF. All patients underwent immediate percutaneous drainage. Colchicine (0.6 mg twice daily for 14 days) was prescribed at the operator’s discretion. The primary outcome was freedom from AF or atrial tachycardia (AT) at 12 months after a 3-month blanking period. Among 2133 patients undergoing RFCA, 75 (3.5%) developed hemopericardium. Of these, 21 received colchicine and 54 received usual care. During a median follow-up of 367 days, freedom from AF/AT did not differ between groups (76.2% vs. 74.1%, log-rank p = 0.93). Residual pericardial effusion was infrequent and resolved in all patients by 3 months. Although colchicine was not associated with a reduction in atrial arrhythmia recurrence, patients receiving colchicine had lower CRP levels at 3 months than those receiving usual care (p = 0.021). However, treatment-limiting adverse events occurred in 6 of 21 patients (28.6%) receiving colchicine. No cases of constrictive pericarditis were observed. In patients with procedural hemopericardium during AF ablation, prompt pericardial drainage was associated with favorable clinical outcomes and absence of long-term pericardial sequelae. Adjunctive colchicine therapy was not associated with a significant reduction in arrhythmia recurrence; however, given the small sample size and limited number of events, a clinically meaningful treatment effect cannot be excluded. Full article

Quantifying the impacts of climate change and extreme climatic events on crop yields is essential for safeguarding global food security. The rapid growth of data availability and advances in computational capacity have established machine learning (ML) as a critical tool for unraveling the complex, nonlinear relationships between climatic factors and agricultural productivity. This systematic review synthesizes evidence from 137 peer-reviewed studies published between 2015 and 2025 that applied ML models to assess the effects of both long-term climate trends and discrete extreme events on crop yields worldwide. Bibliometric and thematic analyses reveal a rapidly evolving field, with over 85% of studies published since 2020, and a strong concentration on staple cereals—wheat, maize, and rice—in major agricultural regions including China, the United States, and India. Random Forest (RF) was the most commonly used algorithm; ensemble and deep-learning models achieved high predictive accuracy within well-resourced study contexts. Temperature and precipitation extremes emerged as the most frequently examined stressors, with distinct methodological patterns: studies focusing on climate change trends predominantly employed RF and LSTM models, whereas those investigating extreme events increasingly adopted hybrid approaches that integrate ML with process-based crop models. This review highlights the transformative potential of ML while identifying persistent challenges, such as geographical imbalances in research coverage, the need for enhanced interpretability in extreme event attribution, and the critical importance of modeling compound extremes. Future research should prioritize the development of explainable, causally informed, and transferable ML frameworks to support equitable climate adaptation strategies in global agriculture. Full article

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support. Full article

Escalating energy costs and peak power demand charges pose significant challenges to the manufacturing sector. In response, industries are increasingly adopting on-site renewable energy sources and Battery Energy Storage Systems (BESSs). However, maximizing their economic benefit requires sophisticated control strategies that integrate energy management with production operations. This paper proposes and evaluates an integrated and adaptive rule-based coordination framework for BESS and machine-level switch-off policies in a production environment. Using discrete-event simulation, we model a four-machine manufacturing flow line powered by the grid and an on-site solar PV plant. We compare six distinct control policies, ranging from a benchmark case without storage to progressively more integrated context-aware strategies that incorporate price-aware BESS charging, dynamic peak-shaving, and adaptive machine switch-offs. The results demonstrate that integrated policies yield substantial economic benefits. The most advanced policy dynamically coordinates BESS dispatch with machine-level switch-off decisions based on electricity prices, production conditions, and energy availability, achieving the largest reduction in total energy costs and peak grid demand among the evaluated policies. This study quantifies the synergistic effects of combining supply-side (BESS) and demand-side (switch-off) strategies, providing a framework for developing resilient and cost-effective energy management systems in modern manufacturing. Full article

Testbed configurations and interfacing methods for real-time digital simulation of cyber-physical power systems (CPPS) remain complex and fragmented across the literature, limiting the clarity with which researchers can compare tools, reproduce testbeds, and select suitable platforms for communication-aware control and cyber security studies. This paper addresses this issue by first comparing the latest works with particular focus on the method of interfacing and configuration architecture, and second, by showcasing two representative testbeds, used for analysing the impact of cyber events on distribution system and microgrid operation. The first testbed interfaces HYPERSIM (OPAL-RT Technologies) with MATLAB (version R2025a) using Modbus over TCP/IP for data exchange. This testbed configuration is capable of capturing the impacts of communication delays on Volt-VAR control in a modified IEEE 13-node test feeder. The second testbed uses two real-time power simulators, OPAL-RT and Typhoon HIL, both interfaced with the discrete-event simulator (DES) EXata Network Modelling (by Keysight Technologies) through modular Python-based scripts. This interfacing effectively enables the simulation of cyber attacks in microgrids. These testbeds show how recent advances in real-time simulation are enabling more practical cross-domain analysis of communication effects, control performance, and cyber vulnerabilities, while informing the design of more capable and future-ready CPPS simulation environments. Full article

To comply with stringent emission regulations, the deployment of hybridized powertrains is continuously expanding. However, architectures such as plug-in and parallel hybrids intrinsically reduce the overall runtime of the internal combustion engine (ICE). Because the battery state-of-charge (SOC) dictates intermittent engine activation, this operational strategy inevitably induces frequent cold-start events. This study investigates the thermal dynamics of commercial catalyst geometries (300–1200 cpsi, 2–8 mil) via 1D numerical simulations under real-world driving conditions. Without active heating, high-thermal-mass substrates unexpectedly outperform ultra-thin-wall variants by buffering against convective quenching during prolonged idling. However, integrating start–stop functionality halts cold exhaust flow, elevating mean temperatures and marginalizing geometric disparities. Evaluating electrically heated catalysts (EHCs) reveals that discrete preheating is highly inefficient due to rapid heat dissipation. Conversely, continuous closed-loop heating coupled with start–stop functionality sustains operational temperatures for over 90% of the cycle. Under continuous heating, substrate geometry ceases to dictate thermal performance; instead, it governs electrical efficiency. Low-thermal-mass monoliths minimize cumulative energy demand to 213 kJ (versus 277 kJ for high-mass variants), incurring a negligible CO₂ penalty. Consequently, future hybrid architectures must integrate lightweight EHCs to ensure sustainable emission control. Full article

To simulate the fracture behavior of members during structural collapse, this paper proposes a member fracture simulation algorithm that integrates the plastic hinge model with a ductile fracture damage model within the member discrete element method (MDEM) framework. The coupling is achieved by computing stresses at the four most unfavorable edge points of the contact section and using the minimum fracture strain as the section-level failure criterion. The algorithm is validated against a cantilever beam fracture example, yielding results in good agreement with reference data under two yield stress conditions. The fracture algorithm is then embedded as a self-contained module into an adaptively coupled DEM/FEM algorithm and applied to simulate the shaking table collapse test of a single-layer spherical reticulated shell. The simulation predicts structural collapse at a peak ground acceleration (PGA) of 2268 gal—consistent with the experimental value—with 126 members fractured at collapse onset, and reproduces the observed fracture sequence in which first-ring diagonal members near the supports fail progressively from the bottom upward. The proposed framework provides a computationally robust and practically deployable tool for collapse analysis of large-span reticulated structures, with direct implications for progressive-collapse prevention in seismic design and post-event structural forensic investigation of collapse mechanisms. Full article

Lower respiratory tract infections caused by the respiratory syncytial virus (RSV) pose a major global public health challenge. The use of next-generation sequencing technologies enables detailed monitoring of viral genetic variability, which is crucial for evaluating the efficacy of immunoprophylactic measures. In this study, whole-genome sequencing of RSV was performed on 106 samples collected in the Russian Federation between September 2021 and April 2025. Three NGS platforms were employed: Illumina MiSeq, Oxford Nanopore Technologies MinION, and Qitan Tech QNome-3841. Using discrete phylogeographic methods, we estimated a minimum of 45 introduction events into Russia for RSV-A and 39 for RSV-B among the genomes included in the analysis. Most events were represented by a single Russian genome. These results indicate recurrent introductions of RSV into Russia from abroad. Given the limited genomic sampling available, most of these introductions were not associated with detectable transmission within the country. Full article

Carbon efficiency, measuring effective output per unit of carbon emissions, is vital for managing low-carbon workshops and advancing sustainable manufacturing. However, production processes often face concurrent discrete events (e.g., equipment failures, parameter adjustments) and numerous emission factors with complex relationships, making it hard to identify dominant factors and event impact degrees, thus lacking direction for operation and maintenance decisions. This paper proposes a deductive monitoring model to analyze carbon efficiency changes under event concurrency. First, for traceability, a multi-resolution enhanced carbon efficiency information transfer network is proposed. It classifies emission factors into time-driven and event-driven accounting, and under the Parallel Discrete Event System Specification framework, adopts a multi-resolution approach with high- and low-resolution models for hierarchical aggregation from equipment to workshop, establishing a traceability path to specific factors. Second, for unclear impact degrees, a dynamic monitoring model for concurrent events is designed. A state-driven dynamic carbon efficiency accounting method automatically settles upon equipment state switching, and a rule-driven priority deduction strategy enables independent accounting of each event’s impact degrees in a determined order. A case study on a machine tool spindle production workshop validates the proposed model. Under baseline conditions, the relative accounting errors for 8 h cumulative carbon emissions and effective output are approximately 1.05% and 0.89%, respectively. In concurrent event scenarios, the model achieves deterministic trajectory reproducibility across 30 independent deduction runs and enables independent impact-degree decomposition, whereas traditional discrete event simulation exhibits trajectory ambiguity. Furthermore, testing under 42 multi-parameter perturbation combinations demonstrates traceability path integrity and accurate root-cause localization, delivering a transparent and reliable quantitative basis for low-carbon maintenance decisions. Full article

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events. Full article

Tropical cyclones reduce port capacity and leave heterogeneous congestion. Yet empirical measurements of port resilience are rarely connected to operational strategy evaluation on the same observed event baseline. This study develops a dual-layer framework that measures event-level operational resilience under prevailing practice and estimates modelled marginal improvements from three operational levers. Aggregate baseline measures calibrate multi-server queues for 28 ports without event-period tuning, and discrete-event simulation replays 178 port-event trajectories. The calibrated representation places heterogeneous ports on a common queueing scale. Measured states are benchmarked against a dynamic four-hour business-as-usual (BAU) baseline that retains normal temporal variation. During exposure, median service-capacity loss reaches 0.627, and sustained operational-capacity restoration is confirmed after a median 44 h. Service-focused recovery (SES), coordinated recovery (CRS), and two-stage proactive–reactive response (TPRS) are compared after matching costs within each event. CRS gives the largest mean reduction in cumulative queue burden under the base-case cost coefficients and ranks first in 113 of 128 cost-sensitivity scenarios. SES leads when capacity coordination becomes sufficiently expensive, while TPRS becomes more competitive under prolonged, high-loss exposure. The framework supports strategy assessment according to event state, operational feasibility, and implementation cost. Full article

Vehicle-to-grid (V2G) integration in commercial building microgrids (CBMGs) offers a promising path for grid support, economic arbitrage, and resilience enhancement. However, practical implementation is hindered by the optimization–execution gap, where high-level aggregated commands fail to match low-level physical charger capacities and individual battery boundaries, as well as by the lack of sociotechnical coupling under extreme weather events, where vehicle owner range anxiety dominates. To address these challenges, a closed-loop multi-timescale energy management system for V2G-enabled CBMGs under exogenous meteorological conditions is proposed. The framework features an integrated four-layer cyber–physical control architecture connecting macroscopic day-ahead scheduling, receding-horizon model predictive control (MPC), discrete real-time parking slot allocation with hardware safety boundary constraints, and equipment-level power flow execution. To handle extreme events, an exogenous meteorological stress index is formulated to quantify ambient structural hazards and temperature deviations, which are then mapped to owner range anxiety and loss-aversion behaviors using prospect theory. Rather than relying on heuristic rule-switching, the optimizer executes a smooth and continuous transition from normal economic peak-shaving to active pre-disaster energy reservation and load demand survival. The cyber–physical system is validated using high-fidelity simulations under typical summer and winter blizzard scenarios. The results demonstrate that the proposed hierarchical architecture successfully eliminates optimization–execution mismatches and guarantees zero load shedding. Furthermore, sensitivity analyses establish the optimal system configuration with the critical defense tolerance of 0.6 and the baseline anxiety ratio of 4, which successfully resolves the trade-off between premature defensive actions and insufficient energy reserves while considering human behavioral uncertainty. Full article