Search Results (544)

This study presents a DSDEVS-based method to accelerate simulation execution for AI training in USV (Unmanned Surface vehicle) naval combat scenarios. The proposed approach introduces an event filtering technique that selectively suppresses low-importance sensing events based on the distance to enemy targets. By dynamically adjusting structural couplings and modifying sensing frequency through domain-specific thresholds, the method reduces execution time while maintaining a balance between speed and fidelity. Two key parameters—Event Filtering Distance (EFD) and Sensor Acceleration Time Advance (SATA)—enable conditional event filtering and time advance adjustments within the sensor model. Experimental results demonstrate a 3.03 improvement in runtime, highlighting the effectiveness of the method and the trade-off between simulation speedup and fidelity. Full article

Earthquakes are sudden and destructive natural events caused by tectonic movements in the Earth’s crust. Although they cannot be predicted with certainty, rapid and reliable detection is essential to reduce loss of life and property. This study aims to automatically distinguish earthquake and noise signals from real seismic data by analyzing time-frequency features. Signals were scaled using z-score normalization, and extracted with Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT), and combined EMD+DWT methods. Feature selection methods such as Lasso, ReliefF, and Student’s t-test were applied to identify the most discriminative features. Classification was performed with Ensemble Bagged Trees, Decision Trees, Random Forest, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). The highest performance was achieved using the RF classifier with the Lasso-based EMD+DWT feature set, reaching 100% accuracy, specificity, and sensitivity. Overall, DWT and EMD+DWT features yielded higher performance than EMD alone. While k-NN and SVM were less effective, tree-based methods achieved superior results. Moreover, Lasso and ReliefF outperformed Student’s t-test. These findings show that time-frequency-based features are crucial for separating earthquake signals from noise and provide a basis for improving real-time detection. The study contributes to the academic literature and holds significant potential for integration into early warning and earthquake monitoring systems. Full article

Demand-Driven Material Requirements Planning (DDMRP) has been proposed as a solution for managing uncertainty and variability in supply chains by combining decoupling, buffer management and demand-driven planning principles. A key element of DDMRP is its inventory replenishment model, which relies on dynamically adjusted inventory buffers rather than fixed stock levels. However, parameterization of these buffers often involves subjective choices, raising concerns about consistency and performance. This paper assesses the DDMRP replenishment model through discrete-event simulation of a multi-echelon, capacity-constrained production system. Two alternative formulations of the safety stock term in the red zone are compared: the original factor-based approach and a revised formula that incorporates measurable variability coefficients. While both safety stock formulations yield similar numerical results, the revised formula enhances transparency and reduces subjectivity. Assessing the impact of introducing a buffer for components in addition to a finished goods buffer further shows that the components buffer can reduce finished goods inventory requirements while maintaining service levels. These findings contribute to a better understanding of the DDMRP replenishment model, offering practical insights for parameter selection and supply chain design. Full article

As global copper demand continues to grow, China, being the largest copper consumer, faces increasingly complex challenges in ensuring the security of its supply chain. However, a substantive gap remains: prevailing assessments rely on static index systems and discrete scenario analyses that seldom model uncertainty-driven, continuous-time strategic interactions, leaving the conditions for self-enforcing cooperation and the attendant policy trade-offs insufficiently identified. This study models the interaction between Chinese copper importers and foreign suppliers as a continuous-time stochastic differential game, with feedback Nash equilibria derived from a Hamilton–Jacobi–Bellman system. The supply security utility is specified as a diffusion process perturbed by Brownian shocks, while regulatory intensity and profit-sharing are treated as structural parameters shaping its drift and volatility—thereby delineating the parameter region for self-enforcing cooperation and clarifying how sudden disturbances reconfigure equilibrium security. The research findings reveal the following: (i) the mean and variance of supply security utility progressively strengthen over time under the influence of both parties’ maintenance efforts, while stochastic disturbances causing actual fluctuations remain controllable within the contract period; (ii) spontaneous cooperation can be achieved under scenarios featuring strong regulation of domestic importers, weak regulation of foreign suppliers, and a profit distribution ratio slightly favoring foreign suppliers, thereby reducing regulatory costs; this asymmetry is beneficial because stricter oversight of domestic importers curbs the primary deviation risk, lighter oversight of foreign suppliers avoids cross-border enforcement frictions, and a modest supplier-favored profit-sharing ratio sustains participation—together expanding the self-enforcing cooperation set; (iii) sudden events exert only short-term impacts on supply security with controllable long-term effects; however, an excessively stringent regulatory environment can paradoxically reduce long-term supply security. Security effort levels demonstrate positive correlation with supply security, while regulatory intensity must be maintained within a moderate range to balance incentives and constraints. Full article

Various types of anomalies can arise in cyber–physical production systems, caused by either faulty devices or incorrect processes. Anomalies within individual devices can often be detected by applying machine learning techniques to the respective produced multivariate time series. While this data typically shows temporal and spatial changes and can therefore be efficiently utilized by models, detecting anomalies within the process is often more challenging, as process data usually only consists of events, binary signals, or changes in unique process states. Due to the low variance of data, existing anomaly detection methods struggle to detect anomalies effectively and accurately. To address this challenge, in this paper, we propose a novel concept for process anomaly detection based on conditional discrete-time dynamic graphs. Through the conditional connections of the graph, essential characteristics can be generated and utilized to effectively train machine learning models to detect anomalies in the process data. Identified anomalies can be related to the current graph, facilitating transparent and explainable detections. By evaluating the concept against process data from an industrial unit and achieving an F1-Score of 0.96 and 1 for the realized repetitive processes, the accuracy and effectiveness of the concept can be demonstrated. Full article

Forecasting real-world time series in domains with strong event sensitivity and regional variability poses unique challenges, as predictive models must account for sudden disruptions, heterogeneous contextual factors, and structural differences across locations. In tackling these challenges, we draw on the concept of symmetry that refers to the balance and invariance patterns across temporal, multimodal, and structural dimensions, which help reveal consistent relationships and recurring patterns within complex systems. This study is based on two multimodal datasets covering 12 tourist regions and more than 3 years of records, ensuring robustness and practical relevance of the results. In many applications, such as monitoring economic indicators, assessing operational performance, or predicting demand patterns, short-term fluctuations are often triggered by discrete events, policy changes, or external incidents, which conventional statistical and deep learning approaches struggle to model effectively. To address these limitations, we propose an event-aware multimodal time-series forecasting framework with graph-based regional transfer built upon an enhanced PatchTST backbone. The framework unifies multimodal feature extraction, event-sensitive temporal reasoning, and graph-based structural adaptation. Unlike Informer, Autoformer, FEDformer, or PatchTST, our model explicitly addresses naive multimodal fusion, event-agnostic modeling, and weak cross-regional transfer by introducing an event-aware Multimodal Encoder, a Temporal Event Reasoner, and a Multiscale Graph Module. Experiments on diverse multi-region multimodal datasets demonstrate that our method achieves substantial improvements over eight state-of-the-art baselines in forecasting accuracy, event response modeling, and transfer efficiency. Specifically, our model achieves a 15.06% improvement in the event recovery index, a 15.1% reduction in MAE, and a 19.7% decrease in event response error compared to PatchTST, highlighting its empirical impact on tourism event economics forecasting. Full article

This study introduces an approximate solution for the M/G/1 queueing model in scenarios where the service time distribution follows a pure mixture distribution. The derivation of the proposed approximation leverages the analytical tractability of the variance for certain mixture distributions. By incorporating this variance into the Pollaczek–Khinchine equation, an approximate closed-form expression for the M/G/1 queue is obtained. The formulation is extended to service-time distributions composed of two or more components, specifically Gamma, Gaussian, and Beta mixtures. To assess the accuracy of the proposed approach, a discrete-event simulation of an M/G/1 system was conducted using random variates generated from these mixture distributions. The comparative analysis reveals that the approximation yields results in close agreement with simulation outputs, with particularly high accuracy observed for Gaussian mixture cases. Full article

Accidental opioid overdose and Sudden Unexpected Death in Epilepsy (SUDEP) represent major forms of preventable mortality, often involving sudden-onset catastrophic events that could be survivable with rapid detection and intervention. The current physiological monitoring technologies are potentially applicable, but face challenges, including complex setups, poor patient compliance, high costs, and uncertainty about community-based use. Paradoxically, simple clinical observation in supervised injection facilities has proven highly effective, suggesting observable changes in central body motion may be sufficient to detect life-threatening events. We describe a novel wearable biosensor for continuous central body motion monitoring, offering a potential early warning system for life-threatening events. The biosensor incorporates a low-power, triaxial MEMS accelerometer within a discreet, chest-worn device, enabling long-term monitoring with minimal user burden. Two system architectures are described: stored data for retrospective analysis/research, and an in-development system for real-time overdose detection and response. Early user research highlights the importance of accuracy, discretion, and trust for adoption among people who use opioids. The initial clinical data collection, including the OD-SEEN study, demonstrates feasibility for capturing motion data during real-world opioid use. This technology represents a promising advancement in non-invasive monitoring, with potential to improve the outcomes for at-risk populations with multiple health conditions. Full article

This paper investigates the data-driven event-triggering control of discrete time-delay systems. When there is enough data available, the system parameters can be determined by identified methods, and the model-based controller design can be implemented. However, with little data, this method does not result in an accurate system. The data-driven control method is introduced to address this issue. This paper classifies discrete-time systems with time delays into those with known delays and those with unknown delays. Controllers for systems with known delays and unknown delays are designed based on limited data, and stability is ensured by constructing improved Lyapunov functions. Two analyses are introduced: For the known delay condition, the lifting model method is presented to raise order and change the time-delay system to a delay-free system. Further, the stabilization criterion is presented. For the unknown time-delay system, according to the basic data-driven assumption, the data-driven stabilization criterion is presented. Also, the introduction of a dynamic event-triggering scheme and the discussion in this paper on how its parameters can be chosen can save more computational resources. Based on the two methods, the Lyapunov function is constructed separately, and the controller is derived through Linear Matrix Inequality. Finally, a discrete time-delay system is used as an example to show the effectiveness of these two methods. In addition, the dynamic event-triggering scheme proposed in this paper is compared with other articles to show that the parameter selection method proposed in this paper has better performance. Full article

Background: Antimicrobial drugs are used to treat bacterial diseases in livestock production systems, including bovine respiratory disease (BRD) in feedlot cattle. It is recommended that therapeutic antimicrobial use (AMU) in food animals be informed by diagnostic tests to limit the emergence of antimicrobial resistance (AMR) and preserve the effectiveness of available drugs. Recent evidence demonstrates preliminary support for the pen as a prospective target for AMR testing-based interventions in higher-risk cattle. Methods: A previously reported agent-based model (ABM) was modified and then used in this study to investigate the potential for different pen-level sampling and laboratory testing-informed BRD treatment strategies to favorably impact selected antimicrobial stewardship and management outcomes in the western Canadian context. The incorporation of sample testing to guide treatment choice was hypothesized to reduce BRD relapses, subsequent AMU treatments and resultant AMR in sentinel pathogen Mannheimia haemolytica. The ABM was extended to include a discrete event simulation (DES) workflow that models the testing process, including the time at sample collection (0 or 13 days on feed) and the type of AMR diagnostic test (antimicrobial susceptibility testing or long-read metagenomic sequencing). Candidate testing scenarios were simulated for both a test-only control and testing-informed treatment (TI) setting (n = 52 total experiments). Key model outputs were generated for both the pen and feedlot levels and extracted to data repositories. Results: There was no effect of the TI strategy on the stewardship or economic outcomes of interest under baseline ecological and treatment conditions. Changes in the type and number of uses by antimicrobial class were observed when baseline AMR in M. haemolytica was assumed to be higher at feedlot arrival, but there was no corresponding impact on subsequent resistance or morbidity measures. The impacts of sample timing and diagnostic test accuracy on AMR test positivity and other outputs were subsequently explored with a theoretical “extreme” BRD treatment protocol that maximized selection pressure for AMR. Conclusions: The successful implementation of a pen-level sampling and diagnostic strategy would be critically dependent on many interrelated factors, including the BRD treatment protocol, the prevalences of resistance to the treatment classes, the accuracy of available AMR diagnostic tests, and the selected “treatment change” thresholds. This study demonstrates how the hybrid ABM-DES model can be used for future experimentation with interventions proposed to limit AMR risk in the context of BRD management. Full article

Logistics and warehouse operations experience an increasing pressure to adopt sustainable practices. The logistics industry generates substantial material waste, with cardboard being the primary packaging material. Adopting Circular Economy (CE) principles to control this waste is important for enhancing sustainability. However, there is a lack of studies on transforming warehouses into more sustainable operations. This paper studies the ability to transform the linear supply chain of a distribution warehouse into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A structured framework is presented to outline the project’s methodology and illustrate the steps taken to apply the concept of CE. The paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate various scenarios. This study contributes by showing how discrete-event simulation combined with VSM and 6R principles can provide operational insights under data-constrained conditions. Bridging the gap between theory and practice. Multiple operational scenarios were modelled and run, including peak and off-peak demand periods, as well as a sensitivity analysis for recycling durations. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the most feasible solution. Results indicate that introducing recycling activities created some bottlenecks in the system and reduced its efficiency. Furthermore, suggestions for future improvements are presented, ensuring that on-site actions are grounded in a simulation that reflects reality. Full article

The National Research Council Canada (NRC) is actively engaged in the development of an advanced autonomy system for the Bell 412 helicopter. This system’s capabilities extend to the execution of complex missions, such as arctic resupply missions. In an arctic resupply mission, the helicopter autonomously delivers supplies to a remote arctic base. During the mission it performs tasks such as takeoff, navigation, obstacle avoidance, and precise landing at its destination, all while minimizing the need for pilot intervention. The complexity of this autonomy system necessitates the inclusion of a high-level supervisory controller. This controller plays a critical role in monitoring mission progress, interacting with system components, and efficiently allocating resources. Conventionally, supervisory controllers are embedded within monolithic programs, lacking transparent state flows. This causes system modification and testing to be a significant challenge. In our research, we present an innovative approach and methodology to develop supervisory controllers for autonomous aircraft on the example of the NRC Bell 412. Using the Discrete Event System Specification (DEVS) formalism and the Cadmium simulation engine, we effectively address the challenges above. We discuss the entire development process for a state-based, event-driven supervisory controller for autonomous rotorcraft using the NRC’s Bell-412 autonomy system as a comprehensive case study. This process includes modeling, implementation, verification, validation, testing, and deployment. It incorporates a simulation phase, in which the supervisor integrates with components within a Digital Twin of the Bell 412, and a real-time operations phase, where the supervisor becomes an integral part of the actual Bell 412 helicopter. Our method outlines the smooth transition between these phases, ensuring a seamless and efficient process. Full article

In geo-distributed deployments, sensor data are collected under the coordination of smart terminals and relayed on-chain via decentralized oracles. A motivating scenario involves healthcare networks where regional hospitals submit aggregated medical data to blockchain systems while maintaining strict information security—often designating one gateway per region for external communication. Long geographical distances between smart terminals stress traditional consensus with excessive network overhead and limited efficiency. To address this, we propose a layered BFT consensus method, 2-layer EaZy BFT (2EZBFT). The system forms multiple independent groups of smart terminals and builds a two-layer consensus architecture—“intra-group synchronization, inter-group consensus”—to complete cross-group data aggregation and final on-chain consensus. This layered design reduces intra-group communication complexity by lowering the number of nodes per group and reduces cross-group interactions via leader-side aggregation, thereby lowering overall network overhead. Compared with other BFT algorithms, the proposed scheme improves the efficiency of data collection and on-chain reporting while ensuring consensus security and consistency. Experiments show improvements in metrics such as network overhead and consensus latency. In a discrete-event simulation with an asymmetric WAN latency matrix and geo-partitioned groups, 2EZBFT achieves up to $45\%$ higher throughput than flat BFT algorithms such as PBFT and HotStuff under high load. It provides a practical path for efficient data interaction in decentralized oracles and offers guidance for improving the performance of blockchain–real-world data exchange. Full article

The increasing penetration of renewable energy presents substantial challenges to frequency stability, particularly in low-inertia microgrids. This study introduces an agent-based microgrid model that integrates generators, loads, an energy storage system (ESS), and renewable sources, mathematically formalized through the discrete-event system specification (DEVS) to ensure both structural clarity and extensibility. To dynamically simulate power system behavior, the model incorporates multiple control strategies—including ESS scheduling, automatic generation control (AGC), predictive AGC, and grid-forming (GFM) inverter control—each posed as an mathematically defined control problem. Simulations on the IEEE 13-bus system demonstrates that the coordinated operation of ESS, GFM, and the proposed strategies markedly enhances frequency stability, reducing frequency peaks by 1.14, 1.14, and 0.72 Hz, and shortening the average recovery time by 9.05, 0.15, and 2.58 min, respectively. Collectively, the model provides a systematic representation of grid behavior and frequency regulation mechanisms under high renewable penetration, and establishes a rigorous mathematical framework for advancing microgrid research. Full article

This article investigates the zonotope-based state estimation for boost converter system with Markov jump process. DC-DC boost converters are pivotal in modern power electronics, enabling renewable energy integration, electric vehicle charging, and microgrid operations by elevating low input voltages from sources like photovoltaics to stable high outputs. However, their nonlinear dynamics and sensitivity to uncertainties/disturbances degrade control precision, driving research into robust state estimation. To address these challenges, the boost converter is modeled as a Markov jump system to characterize stochastic switching, with time delays, disturbances, and noises integrated for a generalized discrete-time model. An adaptive event-triggered mechanism is adopted to administrate the data transmission to conserve communication resources. A zonotopic set-membership estimation design is proposed, which involves designing an observer for the augmented system to ensure $H_{\infty }$ performance and developing an algorithm to construct zonotopes that enclose all system states. Finally, numerical simulations are performed to verify the effectiveness of the proposed approach. Full article