Search Results (1,917)

Symmetry plays a fundamental role in the evolution of mining-induced stress fields and the deformation behavior of roadway surrounding rock. To improve control of roadway deformation under strong mining-induced disturbance, this study takes the 12 Upper 301 face at Buertai Coal Mine and investigates the deformation mechanism and corresponding control methods. Based on an analysis of in situ monitoring data, the key stratum responsible for energy accumulation in the overlying strata was identified. Based on the inherent symmetry of the longwall mining layout, a symmetric predictive model of overburden key-stratum abutment pressure is established, which reveals the spatially symmetric distribution characteristics of the mining-induced stress field. The accuracy of the theoretical model was further verified through a large-scale geomechanical similarity model test, which reproduced the fracture trajectory and stress evolution law of the overburden key strata. To mitigate strong mining pressure, a targeted hydraulic fracturing control technique aimed at specific overburden horizons was proposed and verified through field testing and application. Field monitoring results indicate that roof-to-floor convergence peaked at 235 mm, and rib convergence peaked at 115 mm. Compared with sections without hydraulic fracturing control, the surrounding rock deformation was reduced by 62.3% and 69.7%, respectively, demonstrating a significant pressure relief effect. This approach effectively ensured the roadway stability and enabled safe mining operations. Full article

Introduction: Sleep is an essential dimension of good physical and mental health. Lesbian, gay, and bisexual (LGB) women experience inequities in sleep duration and disturbance compared to heterosexual women. Psychosocial and behavioral characteristics are important to sleep in the general population; they may advance our understanding about sleep inequities among LGB women and provide key information for developing promising interventions. Methods: Data for this project were provided by the Women’s Health Initiative (WHI). The sample size for this project was 1436: 884 LG women and 552 bisexual women. Outcome variables were sleep duration and disturbance. The authors sought to clarify the associations, including the strength, between psychosocial factors and sleep outcomes among LGB women. Associations between psychosocial characteristics, health behaviors, and sleep outcomes were tested using multivariable, hierarchical, nested, linear regression models, stratified by sexual orientation. Results: Social strain, social function, optimism, and negative emotional expressiveness were significantly associated with sleep outcomes for LGB women. Health behaviors were not consistently or strongly associated with sleep outcomes for LGB women. Conclusions: The findings point to the importance of social strain, social function, negative emotional expressiveness, and optimism in LGB women’s sleep. It is possible that LGB women’s sleep could be improved with evidence-based interventions that use our findings. Full article

Deep Reinforcement Learning (DRL) policies often exhibit fragility in unseen environments, limiting their deployment in safety-critical applications. While Robust Markov Decision Processes (R-MDPs) enhance control performance by optimizing against worst-case disturbances, the resulting conservative behaviors are difficult to interpret using standard Explainable RL (XRL) methods, which typically ignore adversarial disturbances. To bridge this gap, this paper proposes RAISE (Robust and Adversarially Informed Safe Explanations), a novel framework designed for the Noisy Action Robust MDP (NR-MDP) setting. We first introduce the Decomposed Reward NR-MDP (DRNR-MDP) and the DRNR-Deep Deterministic Policy Gradient (DRNR-DDPG) algorithm to learn robust policies and a vector-valued value function. RAISE utilizes this vectorized value function to generate contrastive explanations (“Why action $\mathbf{a}$ instead of $\mathbf{b}$?”), explicitly highlighting the reward components such as safety or energy efficiency prioritized under worst-case attacks. Experiments on a continuous Cliffworld benchmark and the MuJoCo Hopper task demonstrate that the proposed method preserves robust performance under dynamics variations and produces meaningful, component-level explanations that align with intuitive safety and performance trade-offs. Ablation results further show that ignoring worst-case disturbances can substantially alter or invalidate explanations, underscoring the importance of adversarial awareness for reliable interpretability in robust RL. Full article

Background: Schizophrenia is strongly associated with severe oral health deterioration, driven by cognitive deficits, behavioral dysfunction, and medication-related biological changes. Objective: To examine how neurocognitive dysfunction in schizophrenia, particularly cognitive deficits, is associated with poorer oral hygiene control, motivation, and self-regulation, contributes to oral health decline by disrupting everyday oral hygiene behaviors and dental care engagement, and to discuss the implications of this framework for interdisciplinary prevention strategies. Methods: This manuscript follows a narrative review design aimed at conceptually integrating evidence on neurocognitive mechanisms underlying oral health decline in schizophrenia. To identify relevant literature, a targeted search of PubMed/MEDLINE, Scopus, and Web of Science was conducted, covering publications from 2000 to 2025. The search strategy was used to support thematic exploration and conceptual synthesis, rather than to perform a systematic study selection or quantitative evidence aggregation. This narrative review summarizes findings from 90 peer-reviewed studies selected from the available literature. Results: Executive dysfunction, attentional deficits, and low motivation impair routine oral hygiene and delay dental care-seeking. Antipsychotic-induced xerostomia, metabolic disturbances, oxidative stress, immune dysregulation, and oral microbiome dysbiosis accelerate periodontal breakdown and caries progression. These interacting processes generate a self-reinforcing cycle of inflammation, tissue destruction, and treatment avoidance. Epidemiological data show markedly elevated DMFT/DMFS indices and up to a three-fold higher risk of edentulism compared with the general population. Emerging evidence suggests that integrated psychiatric–dental care models may be associated with improvements in oral health and care engagement, although current findings are largely preliminary and based on small or heterogeneous study populations, including related neurocognitive disorders. Conclusions: Unlike existing epidemiological syntheses, this review highlights oral health deterioration in schizophrenia as a functionally mediated consequence of neurocognitive impairment, underscoring the need for preventive approaches aligned with patients’ cognitive and motivational capacities. Full article

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control strategies, the PLL with grid following, VSG with grid shaping, VSG+BESS, VSG+STATCOM, and VSG+BESS+STATCOM, implemented within a coherent simulation framework based on Python. Unlike previous works that analyze these methods in isolation, this study provides a comprehensive quantitative comparison of their dynamic characteristics, including frequency root mean square deviation, maximum deviation, and composite resilience index (RI). To extend the analysis beyond static conditions, a multi-generator (multi-PMSG) scenario with heterogeneous inertia constants and variable load profiles is introduced. This dynamic model allows the evaluation of natural inertia diversity and the effects of inter-generator coupling compared to the synthetic inertia emulation provided by VSG-based control. The combined VSG+BESS+STATCOM configuration achieves the highest synthetic resilience, improving frequency and voltage stability by up to 15%, while the multi-PMSG system demonstrates comparable or even higher RI values due to its inherent mechanical inertia and decentralized response behavior. In addition, a cyber-physical scenario is included to evaluate the effect of communication delays and false data injection (FDI) on VSG frequency control. The results show that a communication delay of 50 ms reduces RI by approximately 0.2%, confirming that even minor cyber disturbances can affect synchronization and transient recovery. However, hybrid control architectures with local energy buffering (BESS) show superior resilience under such conditions. The main technical contribution of this work is the establishment of an integrated analytical and simulation framework that enables the joint assessment of synthetic, natural, and cyber-physical resilience in converter-dominated smart grids. This framework provides a unified basis for the analysis of dynamic stability, hybrid control interaction, and the impact of cyber uncertainty, thereby supporting the design of low-inertia, resilient, and secure next-generation power systems. Full article

The coordination of switched fractional-order nonlinear heterogeneous multi-agent systems (FONHMASs) with cooperative and antagonistic interactions presents significant challenges due to the complex coupling of switched fractional-order dynamics. Crucially, existing control methods typically rely on integer-order assumptions and precise system modeling, which are inadequate for capturing the inherent non-local memory behaviors of fractional dynamics. Furthermore, they generally assume fixed agent dynamics, and cannot be applied to switched FONHMASs where the continuity of agents’ dynamics is violated at switching instants. Considering the constraints of precise modeling difficulties and limited task time for switched FONHMASs in practice, a distributed $D^{\alpha }$-type iterative learning control (ILC) protocol is proposed to achieve bipartite consensus in the presence of cooperative and antagonistic interactions. Also, without relying on repetitive initial conditions, based on a presented initial state learning mechanism and $D^{\alpha }$-type ILC protocol, the bipartite consensus error convergence property with each iteration is achieved. Additionally, in consideration of external disturbances, the robustness of the iterative bipartite consensus controller for the switched FONHMASs is analyzed. Simulation results confirm that the switched FONHMASs achieve the convergence and robustness of the bipartite consensus errors along the iteration direction. In addition, the proposed $D^{\alpha }$-type ILC protocol achieves a maximum root-mean-square-error (MRMSE) of 0.0168 in time domain, significantly outperforming the integer-order ILC (MRMSE = 0.3601) and fractional-order PID control (MRMSE = 0.7550), confirming its superiority. Full article

Maintaining stable pressure in the oxidation–compressor section of purified terephthalic acid (PTA) plants is essential for ensuring efficient and reliable operation. Conventional single-loop proportional integral derivative (PID) controllers frequently perform inadequately because of the large pressure drop between the compressor discharge and reactor inlet, which should ideally remain at approximately 1.2 kg/cm² above the reactor pressure setpoint but can reach up to 2.8 kg/cm² due to downstream vapor-phase disturbances. Through this study, we aimed to address this issue by developing a robust cascade pressure control strategy to improve pressure stability and reduce energy losses. Dynamic process models were constructed using system identification techniques to represent real plant behavior, and the best-performing models—identified based on minimum root mean square error (RMSE)—were determined using the Wade method for pressure indicating controller PIC-101, the Lilja method for PIC-102, and the Smith method for pressure differential indicating controller PDIC-101. The proposed cascade configuration was tuned using the Lopez ISE method and evaluated under representative disturbance scenarios. The results showed that the cascade controller significantly improved pressure control, enhanced disturbance rejection, and lowered the risk of reactor shutdowns compared with the conventional proportional-integral PI-based approach. Overall, this study demonstrated that model-driven cascade control can enhance robustness, operational reliability, and energy efficiency in large-scale PTA oxidation processes. Full article

Temporary ground anchors are widely used to provide anchorage for winches, tensioners, and guy wires during power transmission construction. In mountainous terrain, the drilling efficiency is limited, and conventional cement-grouted rock anchors are typically abandoned after use, causing resource waste and local environmental disturbances. This study proposes a plate–umbrella composite recyclable rock anchor in which a hinged umbrella head can unfold and retract within an end-plate sleeve to mobilize slab-bearing resistance under pull-out. A composite grouting scheme (epoxy mortar plus hot-melt adhesive) combined with resistive heating enables component recovery after service. Field pull-out/recovery trials and ABAQUS simulations were conducted to evaluate load–displacement behavior, recovery feasibility, and key influencing factors (embedment length and drilling/tension angle combinations). Compared with a conventional end-plate anchor of the same short embedment length (1 m), the proposed anchor achieved a markedly higher ultimate capacity and smaller displacement. Angle mismatch between the drilling and tension directions caused substantial capacity loss, highlighting the need for alignment control in practice. Parametric simulations further indicate stable performance across representative weathered granite conditions. The proposed system provides a promising approach for efficient and reusable temporary anchorage in mountainous transmission projects. Full article

Accurately describing driver response mechanisms is fundamental to microscopic traffic modeling. Traditional car-following models typically assume a fixed reaction time, implying a temporal symmetry where drivers exhibit identical response characteristics during acceleration and deceleration. To address this limitation, this paper proposes a Delay Adaptive Car-following Model that incorporates an asymmetric dynamic delay function to capture the symmetry breaking in driving behavior. Calibrated using empirical trajectory data from the Next Generation Simulation program, the proposed model demonstrates superior accuracy over the conventional Full Velocity Difference Model by effectively reproducing the realistic phenomenon of sluggish acceleration and agile deceleration. Linear stability analysis and numerical simulations reveal that, unlike fixed symmetric delays which often induce instability, the asymmetric dynamic delay acts as a self-adaptive damper. This mechanism suppresses the amplification of disturbances and prevents the formation of stop-and-go waves. The results confirm that incorporating temporal symmetry breaking into delay mechanisms significantly enhances the robustness of traffic flow against oscillations. Full article

Background/Objectives: Most patients with alcohol use disorder (AUD) suffer from mild cognitive decline, which does not meet the diagnostic criteria of the severe form of alcohol-related cognitive impairment (ARCI). ARCI is associated with executive abnormalities in addictive behaviors and therefore influences relapse and daily functioning. Abnormalities in speech production reflect cognitive disturbances. The aim of this study was to examine the temporal speech parameters (TSPs) in ARCI. Methods: The TSPs were measured with the S-GAP Test^® on 34 AUD patients with intact cognitive functions and 31 age- and gender-matched control participants. Results: Ten out of fifteen parameters of TSPs were significantly different between the AUD and healthy groups. Speech tempo and the total pause duration rate have significant classification potential. Conclusions: Our exploratory study revealed that filled pause-related temporal parameters appear to be particularly altered in ARCI and indicated that marked TSP alterations could serve as early indicators of cognitive deficits. Full article

With the large-scale integration of renewable energy and power electronic devices, power quality disturbances exhibit strong nonlinearity and complex dynamic behavior. Traditional methods are limited by insufficient feature extraction and cumbersome classification, often failing to meet practical accuracy and robustness requirements. To address this issue, this paper proposes a multi-level ensemble method for power quality disturbance identification. A time–frequency dual-branch feature extraction module was designed, combining residual networks and bidirectional temporal convolutional networks to capture both local discriminative features and long-range temporal dependencies in the time and frequency domains. A cross-attention mechanism was further employed to fuse the time–frequency features, enabling adaptive focus on the most critical information for disturbance classification. The fused features were fed into fully connected layers and a Softmax classifier for multi-class identification. Experimental results demonstrated superior accuracy, robustness, and generalization capability compared with existing methods, validating the effectiveness of the proposed model. Full article

To reveal the real deformation behavior and control mechanism of surrounding rock in hard rock drifts under deep high-stress conditions, a systematic study was conducted involving engineering geological investigation, in situ monitoring of surrounding rock microstrain, and numerical simulation, taking the −1465 m deep main drift of Shaling Gold Mine as the engineering background. Joint and fissure characteristics of the surrounding rock were acquired via the traverse method, and dominant joint sets were identified to evaluate rock mass integrity, providing a geological basis for deformation analysis. On this premise, vibrating wire microstrain sensors were employed to continuously monitor the time-dependent deformation of surrounding rock at different depths in the drift roof and two sidewalls. The strain evolution law of deep hard rock surrounding rock under the combined action of excavation disturbance and high ground stress was systematically analyzed. The results demonstrate that the surrounding rock is dominated by compressive strain in the early stage after excavation, which gradually transforms into tensile strain over time, exhibiting distinct time-dependent deformation characteristics. The deformation magnitude of the surrounding rock decreases significantly with increasing distance from the drift exposure surface, and the overall deformation amplitude of the roof is greater than that of the two sidewalls. Integrating the monitoring results with the surrounding rock structural characteristics, a combined support scheme of “resin rock bolt + wire mesh + shotcrete” was proposed, and its control effect was verified using RS2 numerical simulation. The simulation results indicate that this support system can effectively constrain the near-surface surrounding rock deformation, reduce the degree of stress concentration, and significantly improve drift stability. The research findings provide engineering references for understanding the surrounding rock deformation and optimizing support parameters of deep hard rock drifts in metal mines. Full article

Depleted oil and gas reservoirs, owing to their large storage capacity and well-established infrastructure, are attractive sites for storing green energy carriers such as natural gas, hydrogen, and compressed air. During injection–production cycling in underground gas storage (UGS), variations in effective stress can cause repeated stress disturbances in the reservoir and surrounding rock, which may trigger borehole sand production. In this study, laboratory sand-production simulation tests were conducted to evaluate the effects of cyclic-loading stage, upper stress limit, and cycling frequency on borehole damage and sand-production behavior. The results show that sand production is stage-dependent. During the rapid-hardening and stable stages, the borehole remains largely intact and sand production is negligible. Once the failure and collapse stages are reached, borehole integrity deteriorates and sand production increases sharply, with fine particles becoming dominant. Cumulative sand production increases with the upper stress limit. Increasing the upper limit from 80% to 95% leads to a 2.53-fold increase in produced sand mass, together with a higher fine-sand fraction and a shift in the particle-size distribution (PSD) toward smaller sizes. The cycling frequency also plays an important role. When the frequency decreases, cumulative sand production increases and becomes 53.1% higher than the baseline at 0.001 Hz. Meanwhile, the median particle size (D50) decreases, indicating stronger particle breakage under low-frequency cycling. These findings provide guidance for designing injection–production schemes for UGS and for selecting appropriate sand-control completion strategies. Full article

The rapid rise of inverter-based resources (IBRs) such as solar, wind, and battery energy storage is transforming power grids and creating new challenges for protection. Unlike synchronous generators, many IBRs are interfaced through grid-following (GFL) inverters that operate as controlled current sources and rely on an external voltage reference, resulting in fault responses that are current-limited and controller-shaped. These characteristics reduce fault current magnitude and can undermine conventional protection schemes. In contrast, emerging grid-forming (GFM) inverters behave as voltage sources that establish local voltage and frequency, offering improved disturbance support but still transitioning to current-limited operation under severe faults. This review summarizes GFL versus GFM operating principles and deployments, compares their behavior under balanced and unbalanced faults, and evaluates protection impacts using a protection-relevant taxonomy supported by illustrative electromagnetic transient (EMT) case studies. Key challenges, including underreach/overreach of impedance-based elements, reduced overcurrent sensitivity, and directional misoperation, are identified. Mitigation options are discussed, spanning adaptive/supervised relaying, communication-assisted and differential protection, and inverter-side fault current shaping and GFM integration. The implications of IEEE 1547-2018 and IEEE 2800-2022 are reviewed to clarify ride-through and support requirements that constrain protection design in high-IBR systems. Full article

Urban microgrids are evolving into socially coupled energy systems in which prosumer decisions are shaped by both market incentives and peer influence. Conventional optimization approaches overlook this behavioral interdependence and offer limited adaptability under environmental disturbances. This study develops a behaviorally embedded multi-agent optimization framework that integrates social influence propagation with physical power network coordination. Each prosumer’s decision process incorporates economic, comfort, and behavioral components, while a community operator enforces system-wide feasibility. The resulting bilevel structure is formulated as an equilibrium problem with equilibrium constraints (EPEC) and solved using an iterative hierarchical algorithm. A modified 33-bus urban microgrid with 40 socially connected agents is assessed under stochastic wildfire ignition and propagation scenarios to evaluate resilience under hazard-driven uncertainty. Incorporating behavioral responses increases welfare by 11.8%, reduces cost variance by 9.1%, and improves voltage stability by 23% compared with conventional models. Under wildfire stress, socially cohesive agents converge more rapidly and maintain more stable dispatch patterns. The findings highlight the critical role of social topology in shaping both equilibrium behavior and resilience. The framework provides a foundation for socially responsive and hazard-adaptive optimization in next-generation human-centric energy systems. Full article