Search Results (3,219)

Local recovery decisions made by individual supply chain members may improve node-level performance. Such improvements do not necessarily enhance system-level resilience. This study examines how information generated by a digital supply chain twin can be translated into coordinated recovery decisions. The study assesses whether such coordination can mitigate the mismatch between local recovery and system-level resilience. A digital twin-driven multi-agent simulation–optimization framework is developed to integrate state synchronization, causal forecasting, cross-node coordination, and rolling-horizon feedback within a common physical execution environment. Seven recovery strategies are evaluated through a structured capability comparison, functional ablation, information-quality sensitivity analysis, network-structure robustness tests, and paired statistical inference. The results show that the transition from decentralized local decision-making to a system-level coordinated optimization architecture produces the largest resilience improvement among the architecture transitions examined. Local prediction alone provides only limited gains. Rolling-horizon prediction and feedback provide conditional incremental value, primarily through improved intertemporal cost control rather than uniform improvements across all resilience indicators. Information delays and prediction errors weaken coordination effectiveness, while greater effective utilization of the latest available operational information generally improves recovery outcomes. Network structure further shapes the value of coordination: sparse networks constrain its effectiveness through insufficient alternatives, whereas high redundancy reduces its marginal value, with moderately redundant networks providing the clearest scope for coordination gains. This study conceptualizes the digital supply chain twin as an information-to-coordination mechanism. The mechanism links local recovery decisions with system-level resilience. The study clarifies the mechanisms and boundary conditions under which digital twin-driven coordination contributes to supply chain recovery. Full article

Water environment management in industrial parks within river basins faces dual pressures from advancing industrialization and the protection of basin ecological environments, necessitating a shift from traditional management models toward more efficient, intelligent, and risk-coordinated approaches. Industrial parks in the Yellow River Basin contribute over 60% of the region’s economic output, yet they commonly suffer from low water use efficiency, low wastewater recycling rates, and high water environment risks, creating a structural contradiction between economic growth and ecological protection. To address this challenge, this study systematically reviews domestic and international practices, identifies development trends, and constructs a theoretical model for industrial park water environment management based on the “Three Waters and One Risk” (water use, pollution control, recycling, and risk prevention) framework, driven by organizational and technical management with smart empowerment as an integrative link. This forms a closed-loop feedback mechanism aimed at the synergistic goals of water conservation, pollution reduction, carbon reduction, and risk mitigation. Unlike existing studies that tend to focus on a single dimension, the model adopts a dual “park-basin” perspective. Furthermore, in contrast to purely theoretical models or single-park case studies, this study conducts an empirical analysis of the Baotou Industrial Park in the Yellow River Basin. It identifies key problems in management systems, policy instruments, and technical pathways, and proposes integrated, context-specific solutions covering organizational optimization, policy improvement, and technical systems for water conservation, treatment, and reuse. The model’s validity is examined through field data validation and comparative validation against the park’s existing management practices, confirming its accuracy in capturing actual conditions and its practical applicability. In conclusion, this study provides both theoretical references and practical guidance for enhancing integrated water environment governance in industrial parks within river basins, with the Baotou case offering a transferable framework for similar parks across the Yellow River Basin. Future research should further validate and refine the model through comparative studies across multiple parks and diverse policy contexts. Full article

Intelligent control of the ladle furnace (LF) process and its endpoint is essential for product quality and stable continuous casting. Existing studies mainly address endpoint prediction or operating-parameter recommendation. Prediction models rarely provide multivariable operating schemes directly, whereas recommendation models often suffer from insufficient coordination among modules and complex commissioning. This study proposes a collaborative LF operating-parameter optimization method combining endpoint prediction with case-guided genetic–tabu search. Given the initial heat state and target endpoint temperature, the method treats electric energy input, power-on duration, and key material additions as decision variables, evaluates each candidate using temperature and composition prediction models, and coordinates the variables through a unified objective. A dynamic weighting mechanism coupling generational annealing with feasible-population temperature-error feedback balances endpoint quality against resource input. Case-based reasoning guides population initialization, while a real-coded genetic algorithm and tabu search strengthen global exploration and local exploitation. For 400 independent historical heats, the method obtained a recommendation satisfying all model constraints for every heat. Relative to the corresponding historical operations, the mean recommended quantities of lime, slag agent, aluminum granules, high-carbon ferromanganese, electric energy input, and power-on duration were reduced by 6.98%, 12.79%, 9.34%, 8.85%, 7.89%, and 8.95%, respectively. Case-guided initialization improved first-generation solution quality and early convergence, whereas tabu search enhanced mid-to-late local refinement. The method converts existing endpoint-prediction capability into coordinated multivariable operating recommendations. Full article

Background/Objectives: Recovery from schizophrenia-spectrum disorders is increasingly recognized as achievable, yet no synthesis has simultaneously examined long-term outcome rates, intervention effectiveness, cognitive predictors, social determinants, personal-recovery trajectories, and mediating mechanisms within a unified framework. This systematic review aimed to address these six domains and to propose an integrative theoretical model of functional recovery, applying meta-analysis where the evidence permitted and structured narrative synthesis elsewhere. Methods: Seven databases (PubMed/MEDLINE, PsycINFO, Embase, Cochrane CENTRAL, Web of Science, Scopus, CINAHL) were searched through December 2025 following PRISMA 2020 and MOOSE guidance. We extracted eligible records (longitudinal designs, ≥6 months, adults with schizophrenia-spectrum disorders reporting functional outcomes) into two structured databases, then consolidated and de-duplicated them, yielding 467 unique papers. After topical screening and a duplicate audit, we retained 368 studies (1980–2025) and grouped them into six thematic clusters. A pre-specified rule pooled only clusters with at least five independent primary studies. Proportions were pooled with the Freeman–Tukey transformation and associations with Fisher’s z under DerSimonian–Laird random-effects models; certainty was appraised with GRADE. Results: Three pools met the threshold. Long-term functional recovery was 35.4% (95% CI 22.6–49.4; k = 11) and symptomatic remission 39.6% (95% CI 29.3–50.5; k = 7), both with very high heterogeneity (I² = 92–98%) and low certainty. The pooled association between psychological mediators and functional outcome was weak and imprecise (r = 0.23; 95% CI −0.09–0.51; k = 7; very low certainty), reflecting facilitators (positive mental health, social support, self-efficacy, hope) and barriers (internalized stigma, substance-use comorbidity, longer duration of untreated psychosis, persistent negative symptoms) acting in opposite directions. The questions concerning specific interventions, cognitive prediction, rehabilitation, and personal recovery were synthesized narratively: structured psychosocial and combined interventions and integrated rehabilitation were associated with small-to-medium functional gains; cognition, particularly social cognition and motivation, predicted later functioning; and personal recovery followed a course partly independent of clinical status. Conclusions: Recovery and remission are attainable for a substantial minority but remain heterogeneous and modestly certain. The proposed Multi-Pathway Dynamic Recovery Model organizes the evidence into five testable principles—an ordered recovery hierarchy, social-cognitive mediation of the cognition–function link, an early-phase intervention window, social-ecological embedding, and self-reinforcing feedback loops, offered as a hypothesis-generating framework rather than a validated structure. Services should prioritize comprehensive early intervention, target social cognition and stigma, and address structural determinants to optimize long-term recovery. Full article

The fast development of Internet of Things (IoT) gadgets has brought both a new level of connectivity and automation, at the same time making the networks more vulnerable to more sophisticated cyber attacks. Existing intrusion detection systems have a number of fundamental limitations, such as extreme class imbalance in network traffic data, inadequate feature interaction modeling, inability to train on dynamic attack patterns, heavy reliance on fully labeled data, weak evaluation capabilities, and limited interpretability. To deal with these issues, this research employs a data balancing mechanism with an autoencoder to reduce class imbalance by learning meaningful latent representations of the minority attack classes. A Parallel Hybrid single-step BiLSTM-FCNN (PH-BiLSTM-FCNN) model is then proposed as a single network to learn contextual dependencies and nonlinear discriminative features simultaneously along parallel learning pathways. To further enhance adaptability and convergence stability, a Q-learning Optimized PH-BiLSTM-FCNN (Q-PH-BiLSTM-FCNN) is introduced, enabling dynamic optimization of training behavior based on feedback-driven interactions. In addition, an Entropy-based Active Learning on PH-BiLSTM-FCNN (EAL-PH-BiLSTM-FCNN) is developed to significantly reduce labeling requirements by selectively querying the most informative samples. Furthermore, a blockchain layer with smart contracts is embedded consistently in all of the proposed models to ensure tamper-resistant logging, transparent validation, and reliable recording of training and evaluation results. From an algorithm design perspective, the proposed framework is structured as a set of verifiable procedures for PH-BiLSTM-FCNN training, Q-learning optimization, and entropy-based active sample selection. Its algorithmic behavior and robustness are evaluated through execution-time analysis, 10-fold cross-validation, and permutation feature importance, ensuring both predictive reliability and interpretability. Experimental results demonstrate that the proposed PH-BiLSTM-FCNN, Q-PH-BiLSTM-FCNN, and EAL-PH-BiLSTM-FCNN models consistently outperform state-of-the-art FCNN, Bi-LSTM, GRU, LSTM, LR models, achieving improvements of 4.80%, 13.52%, and 6.85% in accuracy, 4.81%, 13.53%, 6.86% in recall, and 5.18%, 18.44%, and 8.52% in precision recall-area under the curve, respectively. These results affirm that the proposed framework provides improved detection performance, statistical reliability, and interpretability while addressing practical deployment limitations in IoT security settings. Full article

Extrusion-based additive manufacturing has emerged as a transformative route for 3D-printed continuous-fiber-reinforced polymer composites (3DP-CFRPCs), offering unprecedented opportunities to engineer lightweight structures with programmable mechanical and multifunctional properties. However, existing studies largely treat constituent materials, printing processes, structural design, and functional integration as isolated research topics, limiting the development of robust design principles for intelligent composite systems. This review proposes an architecture-centric smart architecture framework that extends conventional material–process–structure–property relationships by explicitly incorporating reinforcement-path architecture, interface/defect evolution, multifunctional states, and cyber–physical feedback as coupled design and state variables. Within this framework, we critically synthesize the coupled roles of matrix rheology, fiber impregnation, interfacial bonding, and fiber trajectory engineering in determining printability, defect evolution, and mechanical performance. Recent advances in co-extrusion technologies, multi-axis printing, topology optimization, and programmable fiber placement are further discussed as enabling strategies for architected composite design. Particular emphasis is placed on defect-controlled reliability, multifunctional integration, and emerging AI-enabled digital twins that facilitate closed-loop process optimization and intelligent manufacturing. Finally, the remaining challenges regarding scalability, repeatability, and intelligent composite architectures are outlined to accelerate the transition from laboratory-scale demonstrations to industrial deployment. Full article

Operator fatigue, stress, delayed response, and abnormal operation may compromise the safety of unmanned intelligent system operation in port-security scenarios. This study proposes a bio-inspired engineering architecture for physiological–behavioral fusion risk assessment of operators. The architecture is motivated by homeostatic regulation, coordinated physiological–behavioral responses, selective attention, and feedback concepts; it does not aim to reproduce a biological or neural mechanism. It combines operator-specific baseline calibration, physiological and behavioral feature representation, cross-modal coupling representation, modality-level adaptive weighting, and temporal risk-state classification to identify normal, fatigue, stress, and abnormal-operation states. Physiological information was acquired using a non-invasive smart-cushion sensing system and supplementary electrodermal-activity monitoring, while behavioral information was derived from operation-platform and task-event logs. The proposed method was evaluated under representative port-operation tasks using a participant-level data split and repeated computational training-and-evaluation runs. These repeated runs were used to characterize stochastic optimization stability and were not interpreted as independent participant experiments. The results indicate that the proposed method achieved improved overall classification performance, warning-related reliability, and repeated-run stability compared with single-modal, conventional machine-learning, direct-concatenation, and temporal-fusion baselines. The findings provide pilot evidence that physiological–behavioral fusion can support operational human-factor risk-state classification under the examined port-operation conditions. Larger cross-operator and cross-scenario studies are required to establish broader generalizability. Full article

Camera-equipped Unmanned Aerial Vehicle (UAV) planning couples vehicle motion, camera pose, and scene-dependent sensing utility. The relevant literature is distributed across aerial reconstruction, inspection, target tracking, active perception, cinematography, and coverage planning, with substantial differences in vehicle models, camera mechanisms, visibility assumptions, and evaluation practice. A common vehicle–camera formulation is used here to compare two physical camera-realization mechanisms—independent gimbal actuation and body-coupled orientation—while treating viewpoint-first camera-pose planning as a separate representation that may defer physical realization. Mixed-integer formulations are first examined in detail because coverage, visibility, sequencing, assignment, and discrete camera modes introduce a logical structure; a representative time-expanded MILP is then provided for joint motion–view selection. Evolutionary methods are reviewed from early genetic and differential-evolution UAV planners through information-driven, constrained multiobjective, and hybrid formulations; and cooperative, surrogate-assisted, and transferability-aware methods from adjacent problem classes are then examined as possible extensions. In the frozen coded corpus, 13 direct studies use a physically independently actuated camera/gimbal, but none uses an evolutionary method as the primary optimizer for joint vehicle–gimbal motion; direct evolutionary joint vehicle–gimbal optimization therefore remains sparse. Simulation-to-reality transfer is analyzed as mismatch in dynamics, tracking, gimbal response, calibration, image formation, scene geometry, perception, and timing, with corresponding discussion of randomization, adaptive models, HIL evaluation, robust optimization, and transferability-aware search. A structured reproducibility audit through 31 August 2026 freezes the evidence base at 124 coded sources (62 direct studies, 48 adjacent precedents, and 14 proposed-transfer sources) and confirms persistent fragmentation in scenes, sensors, metrics, and computational budgets. The remaining technical questions concern mechanism-aware camera realization, scalable visibility, multi-view sensing utility under feedback, solver decomposition for mixed discrete–continuous problems, and transfer-sensitive evaluation. By synthesizing these methodological differences, this review identifies persistent research gaps and formulates recommendations for algorithm design, hybrid optimization, benchmarking, and sim-to-real validation. Full article

To enhance the fuel economy and operational adaptability of hybrid tractors under variable plowing loads, this paper proposes a slip-ratio-aware equivalent consumption minimization strategy (ECMS) calibrated via dynamic programming (DP). A resistance–slip ratio prediction model was first identified using plowing resistance and slip ratio data collected from soil-bin tests. The predicted slip ratio was integrated into the demand power model to quantify slip-induced traction losses. Offline DP was subsequently applied to generate globally optimized power split trajectories and establish a baseline equivalence-factor map indexed by plowing resistance level and battery state of charge (SOC). For real-time operation, the equivalence factor is dynamically adjusted via SOC feedback and normalized slip ratio deviation, enabling coordinated power distribution among the engine, MG1, and MG2. Powertrain bench tests were conducted by reproducing variable plowing loads using a dynamometer. The equivalent plowing resistance was calculated from measured load torque, and the corresponding slip ratio was estimated using the identified prediction model. Compared with A-ECMS, the proposed strategy reduced equivalent fuel consumption by 14.02% in simulation and 7.33% in bench tests. The proportion of engine operation in the high-efficiency region increased from 61% to 80% in simulation and from 65% to 77% in the bench test, while the corresponding proportion for the electric motors increased from 87% to 92% and from 88% to 90%, respectively. The SOC deviation decreased from 3.03% to 2.26% in simulation and from 3.07% to 2.43% in the bench test. These results demonstrate that the proposed strategy improves fuel economy, SOC regulation, and component operating efficiency under variable plowing loads. Full article

Bezier curve-based optimization (BCO) provides a population-based search framework that generates candidate solutions along linear, quadratic, and cubic Bezier paths defined by control points. This paper presents IBCO, an improved BCO configuration that integrates three established controls: dimension- and bound-gated diversity feedback, reflective boundary repair, and stagnation-triggered differential or elite-guided perturbation. The contribution is an auditable integration and activation design, not a claim that these operators are individually new. Existing records comprise 30 runs on 26 classical instances, 29 evaluated CEC2017 functions (F1 and F3–F30; F2 excluded), 24 CEC2022 instances, 5 constrained engineering problems, and 26 high-dimensional classical instances. The CEC2017, CEC2022, and engineering profiles used fixed per-run realized evaluation counts within each documented profile; this does not imply identical candidate-evaluation sequences within every run. Classical and high-dimensional IBCO runs used 9030–9144 evaluations, versus 9030 for Original BCO; therefore, those profiles do not establish fixed-evaluation superiority. A retrospective, problem-blocked reanalysis of existing runs rejected the omnibus null of equal treatments in all seven comparison and ablation profiles at $\alpha =0.05$ (largest $p=0.0220$), but IBCO’s advantage over Original BCO did not exceed profile-level Nemenyi critical differences. Among 24 classical rank-1 results, only 4 were unique first places. Ablation records identify reflective repair as the most stable component but do not establish synergy among the complete configuration combining diversity feedback, reflective boundary repair, and stagnation-triggered perturbation. IBCO is therefore interpreted as a competitive, incremental BCO configuration relative to the implemented historical baselines, not as a scale-invariant, evaluation-efficient, or universally dominant optimizer. Full article

High-fidelity continuum models of electrostatically actuated MEMS accurately capture distributed electromechanical interactions but are computationally expensive for repeated simulation, optimization, and real-time applications. This work develops a physics-informed reduced-order modeling framework based on Lie symmetry classification for a nonlinear electrostatic MEMS microplate governed by a fourth-order integro-partial differential equation. The continuum model is recast as an extended canonical system separating local differential operators from nonlocal stretching and capacitive contributions. Lie group classification of the complete boundary value problem shows that the electrostatic singularity, constitutive coefficients, fixed geometry, and clamped boundary conditions suppress nontrivial continuous spatial symmetries in the generic bounded problem, while time translation survives only in the autonomous subclass. The discrete reflection invariances of the centered rectangular device are treated separately to identify invariant functional subspaces for Galerkin projection. A symmetry-preserving reduced-order model is then constructed in the even–even subspace, retaining bending, geometric stretching, pre-stress, capacitive feedback, dielectric inhomogeneity, and fringing field effects. Numerical verification against the high-fidelity continuum model shows close agreement with the FOM for static and transient responses while preserving reflection symmetry and remaining robust under parameter variations. For the nominal configuration, the monomodal Lie-ROM predicts a pull-in voltage of $127.73\mathrm{V}$ versus $128.21\mathrm{V}$ for the FOM, corresponding to an absolute relative error of $0.37\%$ and a signed error of $-0.37\%$. The monomodal formulation substantially reduces computational cost and consistently outperforms a classical lumped-parameter approximation, providing an interpretable and efficient basis for parametric analysis, design optimization, control-oriented modeling, and future digital twin applications. Full article

This paper presents an inverse-design method for disturbance observers of single-input single-output (SISO) plants of relative degree one, based on closed-loop transfer function matching. Given a designer-specified target closed-loop transfer function, the proposed framework directly derives the corresponding observer feedback transfer function for single-input single-output plants of relative degree one. Thus, the observer structure, parameters, and discrete-time realization are obtained systematically, avoiding heuristic structure selection and complex iterative gain tuning. The proposed inverse-design method is applied to back-EMF estimation for a surface-mounted permanent-magnet synchronous motor (SPMSM) to validate its effectiveness. Motivated by the need to extract rotor position and speed information from the back-EMF for sensorless control, the target closed-loop transfer function is chosen as a second-order band-pass filter to preserve phase information while attenuating harmonic distortion. The resulting back-EMF observer achieves near-zero phase delay at the operating frequency and provides improved harmonic attenuation compared with a gain-optimized PI-type back-EMF observer. Simulation results under injected 5th-, 7th-, and 11th-order harmonics and ±30% motor parameter variations confirm robust harmonic rejection, stable magnitude response, and near-zero phase delay at the selected center frequency, with 44%, 52%, and 70% reductions in the corresponding harmonic components compared with the gain-optimized PI-type back-EMF observer. Experimental results further show near-zero phase delay at different operating speeds and angle estimation error bounded within ±5°. These results validate the proposed closed-loop transfer function matching approach for practical SPMSM back-EMF estimation. Full article

Modern supply chains face abrupt supply disruptions, under which the stable lead time assumption behind conventional inventory control no longer holds. Most replenishment models, including existing deep reinforcement learning (DRL) formulations, act as feedback controllers. A disruption is treated as an unobservable event, and correction begins only after deliveries are already delayed. In practice, however, disruptions are often preceded by observable precursor signals. This paper develops a risk sensing DRL framework that incorporates such signals into the state of a proximal policy optimization (PPO) agent, introducing feedforward control into the replenishment decision. The signal quality (detection rate) and the predictive horizon are treated as explicit design parameters. In a two-echelon system with graded disruptions, the risk sensing policy achieves substantial cost savings against both a per-environment calibrated (s, Q) policy and an identically trained reactive DRL policy, even with an imperfect signal. The required horizon is short, as horizons longer than needed bring no further gain. In contrast, the savings depend critically on the signal quality, as a meaningful gain over the calibrated benchmark requires a signal that detects well over half of upcoming disruptions. The advantage is greatest where severe disruptions are infrequent. The learned policy operates a state-dependent reorder point that stays low in calm conditions and rises with the severity and proximity of a predicted threat. A transparent rule driven by the same signal captures about 83% of the gain, indicating that most of the value comes from the information itself rather than from the learning method. These results quantify the value of advance supply information in inventory control and show how it supports the transition from reactive response to proactive mitigation. Full article

This paper develops a finite-horizon stochastic optimal harvesting model that links constrained Hamilton-Jacobi-Bellman (HJB) control with a nonlinear Feynman-Kac/BSDE representation. Harvesting effort is bounded, yielding a projected feedback policy with lower-bound, interior, and upper-saturation regimes. Under appropriate regularity conditions, the HJB and BSDE formulations characterize the same value function and optimal feedback through the Markovian relation $Z_s=\sigma X_s{J}_X^{\ast }(s,X_s)$. Numerically, the HJB equation is solved using a monotone implicit upwind Bellman scheme with policy iteration, while the associated BSDE is approximated independently by Monte Carlo conditional-expectation regression, permitting an ex post assessment of numerical consistency. The framework is illustrated using annual capture fisheries production data for the United States, Japan, China, and Indonesia. Country-specific drift and multiplicative volatility are estimated from normalized state-relative increments. The empirical state is interpreted as a normalized capture-production index rather than a biological stock, providing a data-informed illustration of constrained harvesting under stochastic dynamics and uncertainty. Full article

Power-grid inspection requires both high detection reliability and low-latency deployment, yet existing solutions often face a fundamental trade-off between lightweight edge models with limited semantic capability and large multimodal models with high inference cost. To address this issue, we propose GridScope, a cloud-edge collaborative object detection framework for power-grid inspection. GridScope integrates an SLA-aware confidence routing to adaptively coordinate local inference, cloud verification, and dual-channel response according to task priority and detection confidence. To improve communication efficiency, we further design a split-VLM collaborative inference pipeline, in which compact visual features are extracted at the edge and only feature-level representations are transmitted to the cloud for semantic verification. In addition, cloud-side refined predictions are used to periodically optimize the edge detector, forming a continual feedback loop that improves local detection quality over time. Experiments on wildfire and foreign-object intrusion benchmarks demonstrate that GridScope consistently achieves a more favorable accuracy-efficiency trade-off than both lightweight detectors and full VLM baselines. Full article