Search Results (16,830)

The mechanical behavior of aqueduct structures exhibits highly complex characteristics during prestress tensioning, making it difficult for the traditional double-control method to accurately predict and real-time control the key stresses. To improve the construction safety of prestressed tensioning and the prediction accuracy of structural prestress responses, this study develops a rapid structural mechanical property prediction method based on machine learning. Taking prestressed aqueducts as the research object, a system of “finite element simulation—sample generation—machine learning prediction” is established. Firstly, multiple groups of tensioning parameter combinations are designed via Latin hypercube sampling, and the stress responses are obtained through finite element analysis to form a high-quality training sample library. Subsequently, critical structural features are extracted based on mesh reconstruction, and stress prediction models are established using the K-Nearest Neighbors (KNN) and Random Forest algorithms respectively; the prediction performance of both models is compared and validated against finite element simulation results. Furthermore, the prediction outputs of the optimal machine learning model were used to analyze the stress distribution and potential stress concentration issues of the structure during the tensioning process. The comparative analysis results indicate that the Random Forest model performs best in terms of stress prediction accuracy and stability, and its prediction results are highly consistent with those of the finite element method. Compared with traditional finite element condition analysis, the machine learning model can complete multi-condition stress prediction in a shorter time. Leveraging its high-efficiency prediction capability, local high-stress areas of the structure in the tensioning construction scheme can be identified, thereby providing effective optimization schemes to improve the stress distribution. The mechanical response prediction method for the prestress tensioning process of aqueducts, with machine learning as the core, constructed in this paper realizes the rapid and reliable prediction of key stresses throughout the entire prestress tensioning process. This method can be applied to assist in optimizing tensioning construction schemes and construction monitoring, providing a practical technical solution for safety control of aqueduct structures during the prestress construction stage. Full article

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-Time Responsive (CTR) scheme through a two-step selection procedure: it first identifies the phase with the highest cumulative travel time (CTT) and then selects the compatible phase combination with the greatest group CTT, providing an explicit improvement over the single-combination evaluation of the original CTR that allows for a more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion-contribution step function, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. Under a 100% CAV penetration setting, SUMO-based simulations across moderate-to-heavy traffic conditions (vehicle inter-arrival times of 5 to 9 s) show that CATS reduces the mean end-to-end travel time by up to 23.72% and improves the throughput by up to 93.19% over three baselines (fixed-time navigation with enhanced signal control, congestion-aware navigation with original signal control, and fixed-time navigation with original signal control), confirming that the co-design of navigation and signal control produces complementary benefits. Full article

To address the poor adaptability and rigid initiation modes of the loitering munition fuze in complex environments and the inadequacy of single fuzzy control against strong interference, this paper proposes a dual-loop adaptive reconfiguration control method. The architecture integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm with fuzzy logic. The inner loop uses TD3 to dynamically optimize fuzzy scaling factors based on real-time interference and state deviations. Concurrently, the outer loop utilizes a Fuze Readiness Index (FRI) and a finite state machine to manage real-time multi-modal mission switching (e.g., proximity, delay, and airburst) and reverse safety-state conversions. Co-simulations under non-stationary composite interference show that the proposed method reduces the burst height RMSE by 82.4% and 61.6% compared with the fixed-threshold and standard fuzzy baselines under the considered non-stationary composite interference setting, respectively. The false alarm rate (FAR) is reduced to 0.15%, and the reconfiguration response time under sudden interference is shortened to 12 ms. Even under extreme conditions, such as 400 ms sensor signal loss, the relative error remains within 5%. These simulation results demonstrate the potential of the proposed architecture to improve precision, responsiveness, and robustness under dynamic interference conditions and show good robustness to intermittent observation loss within the simulated operating envelope. Full article

The increasing digitalization of energy transmission and distribution infrastructures has made industrial control systems (ICS), and especially IEC 61850-based communication structures, critical. IEC 61850 performs protection and control functions in substations in real time via GOOSE and MMS protocols. The fast and low-latency operation of these protocols is essential; however, their open structure leaves systems vulnerable to cyberattacks. Traditional signature-based solutions are insufficient for detecting such anomalies, and models capable of learning both time and state relationships are needed. This study develops a time-aware probabilistic NFA model to detect anomalous behavior in IEC 61850 traffic. The model analyzes GOOSE and MMS message sequences with both state transitions and time differences (Δt). Thus, not only the message sequence but also the timing variations between events are learned. The probability of each transition is dynamically updated, and deviations from normal behavior are marked as “anomalies”. The dataset used in this study was created based on normal and attack scenarios conducted in the Sakarya University Critical Infrastructure National Testbed Center Energy Laboratory (Center Energy). The experimental results obtained in the study show that the model detects time-based, structural, and behavioral anomalies with high accuracy. With a dual-model configuration, results of 91.7% accuracy, 88.9% precision, 100% recall, and a 94.1% F1-score were achieved; particularly in time-based attack scenarios, the model performance reached an accuracy level of up to 93%. Full article

Large infrastructure projects frequently experience schedule slippage and cost escalation; however, time–cost planning still relies on expert-assigned activity parameters that fail to reflect the variability induced by construction modes, resource supply, and on-site conditions. This study focuses on activity-level multi-mode time–cost trade-off planning and its dynamic correction during project execution. The proposed methodology is intended for project-level short-term operational scheduling and rolling re-scheduling within a finite project execution horizon, rather than long-term strategic or portfolio-level scheduling. A predict–optimize–update framework is proposed, where light gradient boosting machine (LightGBM) is employed to predict the duration and direct cost of activity–mode pairs using unified features extracted from BIM/IFC records, schedule-resource ledgers, and cost-settlement data, covering engineering quantities, mode and resource decisions, and contextual factors. These predicted parameters are then fed into a time-indexed bi-objective mixed-integer linear program (MILP), which minimizes both project makespan and total cost (including indirect cost) to generate an interpretable Pareto frontier via a weighted-sum approach. Meanwhile, real-time monitoring updates refresh the predictors and re-solve the remaining project network to ensure dynamic adaptability. Validated on a desensitized proprietary enterprise multi-source dataset comprising 25 completed infrastructure projects and 5258 activity–mode samples, the proposed method achieves a mean absolute error (MAE) of 2.7 days and a coefficient of determination (R²) of 0.89 for duration prediction, as well as an MAE of 7.4 × 10⁴ CNY and an R² of 0.91 for direct-cost prediction. The generated Pareto set exhibits a diminishing return trend: as the project duration is relaxed from 101 to 146 days, the total cost decreases from 45.10 to 40.27 million CNY. A weather-triggered update case demonstrates that the completion forecast is revised from 133 to 128 days, with the total cost reduced from 53.05 to 52.75 million CNY. This framework enables explainable schedule–cost co-control, thereby effectively aiding decision-making for the planning and control of large infrastructure projects. Full article

Three-dimensional integrated circuit (3D IC) packaging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as thermal hot spots, warpage-induced stresses, and interconnect aging demands monitoring and control capabilities that surpass traditional offline metrology. Although Digital Twin (DT) technology provides a principled route to real-time reliability management, the existing literature remains fragmented and frequently blurs the distinction between static multi-physics simulation workflows and truly dynamic, closed-loop twins. This critical review addresses these deficiencies through three main contributions. First, we clarify the Digital Twin hierarchy to resolve terminological ambiguity between digital models, shadows, and twins. Second, we synthesize three foundational enabling technologies. We examine physics-based modeling, emphasizing the shift from finite-element analysis (FEA) to real-time surrogates. We analyze data-driven paradigms, highlighting virtual metrology (VM) for inferring latent metrics. Finally, we explore in situ sensing, which serves as the “nervous system” coupling the physical stack to its virtual counterpart. Third, beyond a descriptive survey, we outline a possible hybrid DT architecture that leverages physics-informed machine learning (e.g., PINNs) to help reconcile data scarcity with latency constraints. Finally, we outline a standards-aligned roadmap incorporating IEEE 1451 and UCIe protocols to support the transition from passive digital shadows toward more adaptive and fully coupled Digital Twin frameworks for 3D IC manufacturing and field operation. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article

Long-term time series forecasting (LTSF) is critical for modern power systems, energy management, and grid planning. Yet virtually all existing forecasting models employ stationary activation functions that apply identical nonlinear mappings regardless of temporal context—a fundamental mismatch with real-world load data, which exhibits strongly regime-dependent dynamics such as summer demand peaks, winter heating patterns, and overnight low-load periods. We address this gap by proposing TC-KAN (Time-Conditioned Kolmogorov–Arnold Network), the first forecasting architecture to augment KAN activation functions with position-aware coefficient parameterisation. The core innovation replaces the static polynomial coefficients in standard KAN activations with position-conditioned coefficients produced by a lightweight positional-embedding MLP, providing additional learnable capacity beyond standard KAN while adding negligible parameter overhead. TC-KAN further integrates a dual-pathway processing block—combining depthwise convolution for local temporal pattern extraction with the time-conditioned KAN layer for enhanced nonlinear transformation—within a channel-independent framework with Reversible Instance Normalisation. Experiments were conducted on four standard ETT benchmark datasets and the high-dimensional Weather dataset. TC-KAN achieves superior or competitive accuracy in most configurations while requiring merely 51K parameters—approximately 40% of DLinear and ∼100× fewer than iTransformer. On ETTh2, TC-KAN reduces the mean squared error by up to 61.4% over DLinear, and matches the current state-of-the-art iTransformer on ETTm2 at a fraction of the computational cost. This extreme parameter reduction circumvents the steep memory bottlenecks endemic to massive Transformer models, positioning TC-KAN as a highly practical architecture tailored precisely for resource-constrained edge deployments—such as on-device load forecasting inside smart grid sensors and industrial IoT controllers. Full article

This study presents a capacitive sensor with a zirconium oxide (ZrO₂) coating for real-time measurement of component concentration in liquid media. The ZrO₂ layer was formed on stainless steel electrodes by magnetron sputtering, and its structural, morphological, and chemical properties were characterized using SEM, EDS, FTIR, and XRD. It was found that increasing coating thickness results in more continuous and highly crystalline layers, while reducing the influence of the substrate on surface properties. The performance of the capacitive sensor was evaluated by analysing the dependence of capacitance on frequency and NaCl concentration. The results show that the thickness of the ZrO₂ layer has a significant influence on sensor sensitivity and measurement stability. A thinner layer (~2 µm) provides higher sensitivity but is more affected by parasitic effects, while thicker layers improve measurement stability at the expense of reduced sensitivity. An optimal trade-off between sensitivity and stability is achieved at a ZrO₂ layer thickness of approximately 4 µm, ensuring sufficient sensitivity and good measurement repeatability. The results indicate that ZrO₂-modified capacitive sensors are a promising technology for monitoring liquid quality, particularly in environmental protection and industrial process control. Full article

The increasing complexity of Cyber-Physical Energy Systems, driven by the high penetration of power electronics, advanced control, and digitalization, demands scalable, flexible real-time simulation platforms beyond traditional laboratory-based solutions. This paper investigates the feasibility of deploying open-source real-time power system simulation frameworks on cloud-based infrastructures while meeting real-time computational constraints. An open-source architecture based on DPsim and the VILLAS framework is implemented and evaluated across five computing environments using open-source tools: bare-metal, non-cloud virtual machines, private cloud Kubernetes clusters, public cloud virtual machines, and public cloud Kubernetes clusters. Each environment is carefully configured and tuned using real-time operating systems, CPU isolation, and affinity mechanisms to improve deterministic behavior. Performance and scalability are assessed through a benchmark based on replicated IEEE 9-bus systems, progressively increasing system size, and measuring simulation-timestep execution time. The results show that cloud and cloud-like infrastructures can support soft and, under controlled conditions, firm real-time simulation tasks, although achievable system scale decreases as additional abstraction layers are introduced. The study identifies practical performance limits for each infrastructure and discusses their suitability for different real-time simulation and co-simulation applications. These findings demonstrate that cloud-based real-time simulation can complement traditional digital real-time simulators, enabling scalable and cost-effective CPES experimentation. Full article

Background/Objectives: Skin-to-skin contact (SSC) between mother and infant is known to promote breastfeeding initiation and early bonding. However, evidence regarding the sustained effects of daily SSC during the postpartum period on breastfeeding outcomes and maternal mental health remains limited. This study aimed to evaluate the effects of structured daily SSC on breastfeeding outcomes, lactation status, and maternal postnatal mental health in a real-world clinical setting. Methods: A quasi-experimental design was used to compare mothers who performed daily SSC (SSC group) with those receiving care as usual (control group). Data were collected on postpartum Day 1, Day 3, Week 2, and Week 4. Primary outcomes included exclusive breastfeeding duration, continued breastfeeding duration, and lactation status. Multiple linear regression analyses adjusted for baseline breastfeeding intention and maternal age. Results: A total of 50 mother–infant dyads were included (SSC: n = 40; control: n = 10). The SSC group was associated with longer exclusive and continued breastfeeding durations and better lactation status (p < 0.05). Depressive symptoms did not differ significantly between groups, although both groups showed decreasing trends over time. After adjustment, daily SSC remained significantly associated with longer exclusive breastfeeding duration (adjusted β = 9.18 days, p = 0.034) and continued breastfeeding duration (adjusted β = 10.57 days, p = 0.001). Conclusions: Daily SSC is a simple and feasible intervention that may be associated with improved breastfeeding outcomes and lactation performance. Incorporating structured SSC into routine postpartum care may support breastfeeding sustainability and maternal recovery. Full article

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control. Full article

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

Autonomous traversal flight in unknown 3D environments remains challenging due to mapping bottlenecks and computational latency. Inspired by pigeons navigating cluttered forests through instantaneous visual perception rather than constructing global metric maps, this paper presents a pigeon-inspired depth-reasoning-driven decision framework for agile quadrotor traversal in unmapped spaces without explicit map construction. To ensure feasibility, we leverage a robust state estimation backbone enhanced by deep-learning-based feature matching, providing stable pose feedback under aggressive maneuvers. The core contribution is a pigeon-inspired depth-reasoning framework that translates raw sensory depth data into a hybrid optimization framework, integrating both hard safety constraints and soft geometric smoothness constraints, directly emulating the three avian mechanisms: gap selection via instantaneous depth gradients, path selection that minimizes posture changes, and a safety field driven by the looming effect. By bypassing time-consuming mapping and spatial discretization processes, the framework significantly reduces perception-to-control latency. Finally, validated via simulations and real-world experiments on a resource-constrained quadrotor platform, our map-less approach achieves superior decision frequencies and comparable safety margins to those of state-of-the-art map-based planners. This framework offers a practical, high-frequency solution for autonomous flight where computational resources and environmental knowledge are strictly limited. Full article