Search Results (9,568)

With the continuous growth of highway traffic volume and the increasing proportion of heavy vehicles, vehicle–bridge collisions have emerged as a significant accidental hazard threatening the safe operation of bridge infrastructure. Systematic investigation of the collision resistance of critical bridge components is therefore essential for the development of rational anti-collision design strategies and reliable risk assessment methods. Focusing on the representative disaster scenario of high-speed heavy vehicles impacting concrete bridge piers, this study first develops a finite element model of an RPC beam and validates its reliability through impact experiments. The validated modeling approach is then extended to bridge piers, where a high-fidelity finite element model established using ANSYS/LS-DYNA 2020 is employed to simulate the vehicle–pier collision process and to systematically investigate collision force characteristics, bridge damage evolution, and collision response behavior. The results show that the established reactive powder concrete (RPC) beam model, validated through drop hammer impact tests, reliably captures the impact-induced damage and dynamic response of concrete members. During heavy-vehicle impacts, the vehicle head and cargo compartment successively interact with the pier, generating two distinct collision force peaks, with the peak force induced by the cargo compartment being approximately 38.2% higher than that caused by the vehicle head. Severe damage is mainly concentrated within the impact region, characterized by punching shear failure on the impact face, tensile damage on the rear face, and shear failure near the pier top. The collision-induced structural response is dominated by horizontal displacement, which remains below 10 mm during the vehicle head impact but exceeds 260 mm under the cargo compartment impact. Significant displacements are also observed in the cap beam, with maximum horizontal and vertical values of 24 mm and 19 mm, respectively. These findings provide valuable insights into the impact behavior and failure mechanisms of concrete bridge piers, offering a sound theoretical basis and technical support for anti-vehicle collision design, collision-resistant structural optimization, bridge damage assessment, and the refinement of relevant design specifications. Full article

The deployment of autonomous robots in construction remains constrained by the complexity and variability of real-world environments. Conventional programming and single-agent approaches lack the adaptability required for dynamic multi-robot operating conditions, underscoring the need for cooperative, learning-based systems. This paper presents an ROS-based modular framework that integrates Multi-Agent Reinforcement Learning (MARL) into a generic 2D simulation and execution pipeline for cooperative mobile robots in construction-oriented digital environments to enable adaptive task allocation and coordinated execution without predefined datasets or manual scheduling. The framework adopts a centralized-training, decentralized-execution (CTDE) scheme based on Multi-Agent Proximal Policy Optimization (MAPPO) and decomposes the system into interchangeable modules for environment modelling, task representation, robot interfaces, and learning, allowing different layouts, task sets, and robot models to be instantiated without redesigning the core architecture. Validation through an ROS-based 2D simulation and real-world experiments using TurtleBot3 robots demonstrated effective task scheduling, adaptive navigation, and cooperative behavior under uncertainty. In simulation, the learned MAPPO policy is benchmarked against non-learning baselines for multi-robot task allocation, and in real-robot experiments, the same policy is evaluated to quantify and discuss the performance gap between simulated and physical execution. Rather than presenting a complete construction-site deployment, this first study focuses on proposing and validating a reusable MARL–ROS framework and digital testbed for multi-robot task allocation in construction-oriented digital environments. The results show that the framework supports effective cooperative task scheduling, adaptive navigation, and logic-consistent behavior, while highlighting practical issues that arise in sim-to-real transfer. Overall, the framework provides a reusable digital foundation and benchmark for studying adaptive and cooperative multi-robot systems in construction-related planning and management contexts. Full article

Background: Chemotherapy is a cornerstone of cancer treatment; however, resistance to first-line chemotherapeutic agents remains a major challenge. ROS1, one of fifty-eight receptor tyrosine kinases, has been implicated in various cancer subtypes, including glioblastoma, non-small-cell lung cancer, and cholangiocarcinoma. Notably, the Gly2032Arg mutation in the ROS1 protein has been linked to resistance against the kinase inhibitor crizotinib. Objectives: Given the challenge, we conducted a comprehensive in silico study to identify new drug candidates. Methods: The study starts with modeling the Gly2032Arg-mutated ROS1 protein, followed by structure-based screening of the PubChem database. Results: Out of 1760 molecules screened, we selected the top 4 molecules (PubChem CID: 67463531, 72544946, 139431449, and 139431487) with structural features similar to crizotinib, a high docking score, and drug likeness. To further validate the effectiveness of the identified compounds, we assessed their binding affinity using the Molecular Mechanics with Generalized Born Surface Area (MM-GBSA) scoring method. To underpin the behavior and stability of protein–ligand complexes, 500 ns molecular dynamics (MD) simulations were conducted, and parameters including RMSD, RMSF, and H-bond dynamics were studied and compared. Density functional theory (DFT) at the B3LYP/6-31G* level was performed to elucidate molecular features of the identified compounds. Conclusions: Overall, this study sheds light on a new series of compounds effective against mutated targets, thereby offering a new horizon in this area. Full article

Industrial Control Systems (ICSs) are critical infrastructure for maintaining social and economic stability, but they face increasing security threats that require robust anomaly detection mechanisms. Anomaly detection in ICS, based on sensor data, is essential for identifying abnormal behaviors caused by factors such as equipment failures, cyber-attacks, and operational mistakes. However, industrial time series data are often multimodal, noisy, and exhibit both short-term fluctuations and long-term dependencies, making them difficult to model effectively. Additionally, ICS data often contain high-frequency noise and complex periodic patterns, which traditional methods and standalone models, such as Long Short-Term Memory (LSTM), fail to capture effectively. To address these challenges, we propose a novel anomaly detection framework that leverages Gated Recurrent Units for short-term dynamics and PatchTST for long-term dependencies. The GRU module extracts dynamic short-term features, while PatchTST models long-term dependencies by segmenting the feature sequence processed by GRU into overlapping patches. Additionally, we innovatively introduce Frequency-Enhanced Channel Attention Module to capture frequency domain features, mitigating high-frequency noise and enhancing the model’s ability to detect long-term trends and periodic patterns. Experimental results on the SWaT and WADI datasets show that the proposed method achieves strong anomaly detection performance, attaining F1 scores of 0.929 and 0.865, respectively, which are superior to those of representative existing methods, demonstrating the effectiveness of the proposed design for robust anomaly detection in complex ICS environments. Full article

Gaming Disorder (GD) is becoming more widely acknowledged as a behavioral addiction characterized by impaired control and functional impairment. While resting-state impairments are well understood, the neurophysiological dynamics during active gameplay remain underexplored. This study identified task-based occipital EEG biomarkers of GD and assessed their diagnostic utility. Occipital EEG (O1/O2) data from 30 participants (15 with GD, 15 controls) collected during active mobile gaming were used in this study. Spectral, temporal, and nonlinear complexity features were extracted. Feature relevance was ranked using Random Forest, and classification performance was evaluated using Leave-One-Subject-Out (LOSO) cross-validation to ensure subject-independent generalization across five models (Random Forest, KNN, SVM, Decision Tree, ANN). The GD group exhibited paradoxical “spectral slowing” during gameplay, characterized by increased Delta/Theta power and decreased Beta activity relative to controls. Beta variability was identified as a key biomarker, reflecting altered attentional stability, while elevated Alpha power suggested potential neural habituation or sensory gating. The Decision Tree classifier emerged as the most robust model, achieving a classification accuracy of 80.0%. Results suggest distinct neurophysiological patterns in GD, where increased low-frequency power may reflect automatized processing or “Neural Efficiency” despite active task engagement. These findings highlight the potential of occipital biomarkers as accessible and objective screening metrics for Gaming Disorder. Full article

This study investigated a 460 MPa marine engineering steel’s microstructure and low-cycle fatigue (LCF) behavior along the thickness direction. The results showed that the low-cycle fatigue life was reduced from 9681, 4395, 2107, 1020, 829 to 7222, 1832, 1015, 630, 242 with the specimen taken from the surface to the middle of steel plate, increasing grain size and decreasing the content of high-angle grain boundaries (HAGBs). All specimens showed notable cyclic hardening and softening. This was related to the dislocation movement, interaction, accumulation, annihilation, and dynamic recovery during fatigue tests. Furthermore, the crack propagation paths in the fatigue specimens were also observed and discussed. Finally, the Basquin and Coffin–Manson relationships were used to suggest a prediction model for the LCF life at strain amplitudes ranging from 0.4% to 1.2%, and the anticipated outcomes agreed well with the test results. Full article

Accurately assessing fracture complexity and parameter evolution after fracturing is crucial for optimizing stimulation effectiveness in tight gas reservoirs. In such reservoirs, volume fractures often interact with natural fractures, resulting in pressure-dependent changes in fracture compliance and effective fracture area during closure. Based on shut-in pressure analysis, percolation mechanics, and material balance theory, this study develops diagnostic models for naturally fractured, dynamically fractured, and multi-level closure fracture systems, together with corresponding G-function and double-logarithmic interpretations. The proposed framework characterizes fracture-closure behavior through identifiable closure stages, explicitly ordered closure-pressure intervals, and pressure-dependent evolution of fracture compliance and effective fracture area. Sensitivity analyses are conducted to evaluate the influence of key parameters on diagnostic curve responses. A field application using shut-in pressure data from a tight gas well demonstrates that variations in dominant fracture parameters produce distinct concavity or hump features in G-function superimposed pressure-derivative curves. These results indicate that the proposed method provides a structured quantitative diagnostic interpretation of shut-in pressure responses, enabling systematic identification of staged fracture-closure behavior without relying on fitting-based accuracy metrics. Full article

The behavior of shield tunnel lining structures is known to be influenced by segmental joints. Most studies conducted in this area use simplified models, which may not properly simulate the behavior of the segmental joints. This study utilizes a full-reinforced concrete segment model to rigorously investigate the seismic behavior of joints in a segmental tunnel lining, explicitly accounting for segment–segment contact, interaction, and joint bolts. Specifically, a comprehensive full dynamic analysis of a two-dimensional (2D) lining–soil model, incorporating nonlinear constitutive models for both concrete (CDPM) and soil (Mohr–Coulomb), was conducted to investigate the effects of joint bolt type, seismic intensity, and vertical excitation component on the seismic response. The lining–soil model was excited using three ground motions. The results indicate that the joint rotation is significantly influenced by the amplitude and frequency content of ground motions, which has implications for the watertightness of the gasketed joint. In particular, including the vertical component of the excitations was found to increase the diametral deformation by at least 150% and tended to increase other structural responses. Moreover, the bolt tension increased significantly by over 400% with only a 150% increase in seismic intensity, highlighting the strong nonlinear sensitivity. However, due to the inherent constraints of the 2D plane-strain assumption, the influence of the bolt type remains inconclusive. Full article

Lithium-ion batteries are widely applied in transportation, communication, and other fields. Nevertheless, during prolonged cycling operation, internal electrochemical reactions inevitably lead to the degradation of the state-of-health (SOH). To ensure the reliability and safety of lithium-ion batteries, accurate SOH estimation is of critical importance. Nevertheless, under practical operating conditions, obtaining fully recorded charge–discharge data is often impractical. Motivated by the practical charging behaviors of lithium-ion batteries, this paper proposes a practical SOH estimation method based on incremental capacity analysis, dynamic time warping (DTW), and gradient-boosting regression trees (GBRTs). Three health indicators—interval incremental capacity features, local capacity–voltage curve similarity, and segmented voltage curve similarity—are extracted. The proposed method requires only 0.13 V and 0.07 V voltage windows on the Oxford and CALCE datasets. The effectiveness of the proposed model is verified across both public datasets and laboratory test data. Experimental results demonstrate RMSE values of approximately 2.5% and 2.0%, respectively. Compared with mainstream SOH estimation algorithms, the proposed approach delivers comparable accuracy while achieving training time reductions of up to 57.6% and 91.9% relative to GPR and SVM, making it suitable for real-time battery management systems. Full article

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency. Full article

Brain electrical activity, as recorded through electroencephalography (EEG), displays scale-free temporal fluctuations indicative of fractal behavior and complex dynamics. This study explores the use of the Higuchi Fractal Dimension (HFD) as a proxy of two complementary aspects of EEG temporal organization: local signal irregularity, interpreted within a Kolmogorov-type framework, and persistence related to temporal structure, associated with statistical complexity. The latter can be used to evidence persistence in the EEG signal, serving as an alternative to previously used approaches for estimating the Hurst exponent. Thirty-eight healthy participants underwent resting-state EEG recordings in open- and closed-eyes conditions. HFD was computed for the original signals to assess Kolmogorov complexity and for the signals’ cumulative envelopes to evaluate statistical complexity and, consequently, persistence. The results confirmed that HFD values align with theoretical expectations: higher for random noise in the Kolmogorov model (~2) and lower in the statistical model (~1.5). EEG data showed condition-dependent and topographically specific variations in HFD, with parieto-occipital regions exhibiting greater complexity and persistence. The HFD values in the statistical model fall within the 1–1.5 range, indicating long-term correlation. These findings support HFD as a reliable tool for assessing both the local roughness and global temporal structure of brain activity, with implications for physiological modeling and clinical applications. Full article

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

This article proposes an interpretable adaptive control model for dynamically regulating task difficulty in Artificial intelligence (AI)-augmented reading-comprehension learning systems. The model adjusts, on the fly, the level of task complexity associated with reading comprehension and post-text analytical tasks based on learner performance, with the objective of maintaining an optimal difficulty level. Grounded in adaptive control theory and learning theory, the proposed algorithm updates task difficulty according to the deviation between observed learner performance and a predefined target mastery rate, modulated by an adaptivity coefficient. A simulation study involving heterogeneous learner profiles demonstrates stable convergence behavior and a strong positive correlation between task difficulty and learning performance (r = 0.78). The results indicate that the model achieves a balanced trade-off between learner engagement and cognitive load while maintaining low computational complexity, making it suitable for real-time integration into intelligent learning environments. The proposed approach contributes to AI-supported education by offering a transparent, control-theoretic alternative to heuristic difficulty adjustment mechanisms commonly used in e-learning systems. Full article

The growing penetration of power electronics and nonlinear loads in industrial electrical networks has increased the dynamic complexity of these systems, exceeding the analysis capabilities of traditional approaches based on quasi-stationary models. In this context, this paper presents a methodology for the dynamic characterization of an industrial electrical network based on high-resolution synchrophasor measurements obtained using a microPMU. The proposed approach is based on the identification of a linear dynamic model in state space using subspace techniques based on real data recorded during a short-duration transient event. The results show that the identified model is capable of adequately capturing local underdamped dynamics and reproducing the temporal response observed in the measurements. This evidences the presence of dynamic modes associated with the interaction between the network and power electronics-based devices. Similarly, the stability analysis of the identified model demonstrates its consistency and robust gains in temporal variations within the analysis window. Overall, the results confirm that the combination of microPMU and data-based modeling techniques is an effective tool for improving dynamic observability and understanding the transient behavior of industrial power grids, complementing classical analysis and simulation methods. Full article

Understanding the shallow rupture mechanisms on coseismic faults and assessing the influence of fault area propagation is essential for disaster prevention. Since 2000, Hualien and nearby areas in eastern Taiwan have experienced frequent earthquakes, making it a good area to study the evolution of fault rupture. This study proposes a two-dimensional dynamic discrete element model to simulate the shallow rupture behavior of the Milun Fault. Results indicate that the rupture process proceeds through multiple evolutionary stages, with fractures propagating upward from depth but failing to fully break through to the surface, resulting instead in surface cracking without complete rupture. The second deviatoric stress invariant serves as an effective indicator of stress accumulation and release during rupture progression. For the preferred model, the modeled vertical uplift near the fault reached 0.6 m, consistent with field observations reporting a maximum coseismic uplift of approximately 0.585 m along the Milun Fault. Given the scarcity of near-fault observational constraints, the simulation represents a physically plausible scenario rather than a unique reconstruction. The integration of stress evolution, crack propagation, and near-field displacement provides new insight into the mechanical processes governing shallow thrust fault rupture and can be applied to similar fault systems exhibiting near-surface deformation. Full article