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The development of fractional-order control has been derived from the mathematical generalization of classical calculus and has become an important tool in the modeling and control of dynamical systems with memory and hereditary effects. In spite of the rapid development of this area of control theory and applications, the overall scientific development, structure, and engineering relevance of fractional-order control remain insufficiently understood. In this paper, we address this problem by combining large-scale bibliometric analysis with representative controller performance studies. A total of 6482 publications indexed in the Web of Science database during the period 2010–2026 are analyzed. The bibliometric results indicate that fractional-order control is an increasingly connected global research field with strong roots in fractional calculus, advanced control theory, and growing interdisciplinary links with applied mathematics, automation, and computer science. To further illustrate controller level behavior, representative simulations are performed on a fractional-order time-delay process and an uncertain nonlinear system. For the fractional-order time-delay process, a well-tuned PID controller is compared with a realizable FOPID controller implemented through Oustaloup recursive approximation. The results show that the FOPID controller improves several performance measures, including overshoot, settling time, control energy, total variation, and sensitivity peak, while the comparison is interpreted as a performance trade-off rather than universal superiority. For the uncertain nonlinear system, fractional-order sliding mode control produces smoother control action and substantially reduces chattering. By combining bibliometric mapping with representative performance evaluation, this paper provides a comprehensive overview of fractional-order control as a globally active and practically relevant discipline in control engineering. Full article

Against the background of global green transition and industrial chain restructuring, the new energy vehicle (NEV) industry chain faces systemic challenges, including high resource dependence, technological constraints, and geopolitical risks. It is therefore necessary to build a sustainable resilience framework that reflects security, controllability, green development, and long-term transformation. Drawing on the resource-based view, dynamic capability theory, institutional theory, and national innovation system theory, this study constructs an integrated indicator framework based on four-dimensional capabilities and a three-level structure. The framework includes four dimensions, namely resistance, adaptive recovery, autonomous controllability, and sustainable innovation, and three structural levels, namely the node, chain, and network levels. A total of 23 secondary indicators are developed. Using the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL–ISM) method and scoring data from 15 industry experts, this study systematically examines the influence relationships and hierarchical structural relationships among the indicators. The results show that sustainable resilience in the NEV industry chain is not shaped by a single capability, but by the structural coordination among basic protection, adaptive recovery, autonomous controllability, and sustainable innovation. Autonomous controllability occupies a core linkage position in the framework, while network-level indicators provide important foundational support across different dimensions. This study further suggests that resilience improvement should move beyond short-term emergency response and place greater emphasis on long-term capability building, including supply security, coordinated recovery, technological autonomy, and green innovation governance. The findings provide theoretical insights and practical references for strengthening the security, controllability, and sustainability of the NEV industry chain. Full article

Pancreatic ductal adenocarcinoma is highly malignant with poor prognosis. Its dense tumor microenvironment severely limits the efficacy of conventional chemotherapy and causes severe side-effects. Herein, we adopt the established Schiff-base crosslinked thermoresponsive injectable self-healing poly(2-(2-methoxyethoxy)ethyl methacrylate-co-oligo(ethylene glycol) methyl ether methacrylate-co-aldehyde 2-hydroxyethyl methacrylate)/carboxymethyl chitosan (APMOH/CMCS) hydrogel as the delivery scaffold. By regulating monomer composition, the volume phase transition temperature (T_VPT) of the hydrogel was tuned to around 43 °C to match the therapeutic temperature requirement. Subsequently, copper–metal organic framework (Cu-MOF) nanoparticles co-loaded with 2,2′-azobis(2-methylimidazoline) dihydrochloride (AIPH) and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) cationic radicals (ABTS·⁺) (denoted as AB@Cu-MOF) were uniformly incorporated into the hydrogel network. Under near-infrared (NIR) irradiation, ABTS·⁺ acts as a photothermal agent to generate hyperthermia for tumor ablation; the elevated temperature further activates AIPH to produce alkyl radicals, which can oxidize inactivated ABTS back to ABTS·⁺ and construct a sustainable photothermal therapy–thermodynamic therapy (PTT-TDT) circulation. Meanwhile, Cu-MOF can consume intracellular glutathione (GSH) to protect active components from deactivation and initiate chemodynamic therapy (CDT) via Fenton-like reactions to produce toxic reactive oxygen species. Benefiting from the thermoresponsive characteristic, the hydrogel undergoes volume shrinkage upon heating, achieving NIR-triggered on-demand drug release with a cumulative release rate of 81.1%. In vitro and in vivo experiments verified that this integrated platform realizes remarkable triple synergistic efficacy of PTT, TDT, and CDT. The tumor volume of the treatment group was merely 13.3% of the control group, and the system also exhibited excellent biocompatibility. Collectively, it offers a feasible and promising intelligent platform for precise local treatment of pancreatic cancer. Full article

Voltage and reactive power control in active distribution networks is a safety-critical and highly dynamic problem, where traditional optimization methods often struggle to balance efficiency and robustness under complex operating conditions. Recently, large language models (LLMs) have shown promise in sequential decision-making tasks, but their direct application to power system control remains limited by the lack of physical grounding and safety guarantees. In this paper, we propose SafeVolt, a closed-loop LLM-based framework that integrates multi-candidate action generation, simulator-in-the-loop evaluation, and a fine-tuned expert judge for safety-aware decision making. In addition, a high-level rule distillation mechanism that converts successful control experiences into reusable operational axioms is introduced to enable iterative self-improvement. Experiments on a standard distribution network scenario demonstrate that the proposed method outperforms representative baselines, achieving substantial improvements in average reward, voltage violation rate, reactive power loss, and system stability. In particular, voltage violations and extreme events are substantially reduced, indicating enhanced operational safety. These results suggest that combining LLM reasoning with physical simulation and structured feedback provides a promising direction for reliable and adaptive power system control. Full article

With the increasing complexity of distribution networks, higher demands have been placed on systems for efficient power conversion. The solid-state transformer (SST), which integrates power electronic converters with a high-frequency transformer (HFT), has become a major research focus in end-user power supply applications. This paper first compares the technical advantages of SSTs over conventional transformers and systematically explains their operating principles. It then reviews the development trajectory of SSTs in terms of topological evolution, prototype-based engineering validation, and the application of emerging materials. Next, it classifies and summarizes the current mainstream topologies and identifies core devices and key control technologies, including SiC devices and advanced soft magnetic materials. Finally, it introduces representative SST applications in data centers, smart grids, and charging stations and summarizes and discusses future research directions and challenges. This paper clarifies the technological evolution and existing bottlenecks of SSTs, provides a useful reference for the high-quality and highly flexible operation of distribution networks, and offers clear guidance and directions for the subsequent engineering deployment of SSTs. Full article

Direct methanol fuel cells (DMFCs) have attracted considerable attention recently for various applications ranging from portable ones to transportation. The efficiency of DMFCs depends on the kinetics of anodic and cathodic electrocatalysts. Due to sluggish anodic methanol oxidation reaction (MOR), DMFCs require an effective and bifunctional catalyst for promoting efficient MOR. The state-of-the-art MOR catalysts, such as Pt/C and Pt-Ru/C, have been shown to exhibit reasonable MOR activity; however, the insufficient mass activity and poor stability of carbon-supported catalysts have been a major limitation, requiring an alternative, efficient, electrocatalyst that exhibits high mass and specific activities. In addition, electrocatalysts without any carbon support (self-supported electrocatalysts) further mitigate their poor stability and therefore enhance their durability. In this regard, metal aerogel catalysts, which are entirely composed of metallic networks, recently attained special interest due to their specific advantages over conventional carbon supports, such as high catalyst utilization and improved electronic conductivity and stability. In this review, we systematically reviewed various metal aerogel catalysts developed for MOR since their first discovery in 2009. The metal aerogel demonstrated superior MOR performance relative to carbon-supported commercial catalysts, with enhancements ranging from 2-fold to 22-fold of mass activity. We also statistically compared the mass activity of metal aerogels with traditional carbon-supported, non-carbon-supported, and advanced shape-controlled catalysts and found that metal aerogels exhibited high mass activities compared to other catalyst systems. Therefore, we clearly establish that metal aerogel catalysts possess great potential as efficient MOR catalysts in DMFCs. In addition, we have provided several future research directions and strategies for further development of metal aerogel-integrated DMFC devices. Full article

Ultra-low-phosphorus high-purity iron (ULP-HPFe) is essential for advanced electromagnetic, aerospace, and defense systems, yet stabilizing basic-oxygen-furnace (BOF) dephosphorization remains challenging. To address this instability, we present an intelligent partition-and-prediction framework (iDePP) that first auto-classifies 5102 industrial data records into medium-phosphorus (iDePP-MP), low-phosphorus (iDePP-LP), and ultra-low-phosphorus (iDePP-ULP) subsets, and dedicated ensemble prediction models are then developed for each subset based on representative machine learning algorithms, including random forest (RF), extreme gradient boosting (XGBoost), and neural networks (NNs). Compared with a single global predictor, iDePP reduces the mean absolute error from 0.0018% to 0.0011%, 0.0007%, and 0.0004% for the three classes, respectively, and increases the iDePP-ULP hit rate (HR) to 82.7% within ±6 ppm. Shapley additive explanations (SHAP) and quantitative feature coupling analysis reveal two critical mechanisms governing extreme dephosphorization: limestone-induced thermal penalties and furnace-age effects. Guided by these insights, three consecutive 200-ton BOF industrial trials preliminarily verified the practical feasibility of producing ULP-HPFe, with model plant deviations of approximately 4 ppm, 1 ppm, and 1.5 ppm, respectively. Notably, this work demonstrates the value of automatic domain partitioning combined with subset-specific ensemble learning for complex BOF control, highlighting the potential applicability of iDePP to other data-sparse industrial processes. Full article

High penetrations of distributed energy resources make active distribution networks strongly time-varying, nonlinear, and spatially coupled, which limits the online applicability of centralized voltage/reactive-power optimization. This paper proposes a data–physics fusion dynamic partitioning method for fast power coordination. A physics-based rolling partition baseline is first developed by integrating node operating behavior, voltage/reactive sensitivity, electrical distance, and feeder topology, providing an interpretable and efficient partitioning scheme for normal operating conditions. For high-volatility and strongly coupled scenarios, a heterogeneous dynamic graph and a heterogeneous spatio-temporal graph attention network are introduced to learn control-oriented latent node embeddings. Physical regularization, boundary-coupling penalties, and temporal smoothing constraints are further embedded into soft clustering to reduce cross-partition coupling and partition fluctuation. Tests on the IEEE 33-bus, IEEE 123-bus, and practical Feeder Z systems show that the dynamic partition closely approximates global OPF results, achieving normalized costs of 1.00017 and 1.00099 on the two IEEE systems with 74.3% and 83.2% time reductions. It further reduces the Feeder Z fixed-partition cost gap by 88.0%, while HST-GAT lowers boundary P/Q exchanges by 1.55%/6.57% under volatile conditions. Full article

As modern electrical grids increasingly incorporate renewable generation—specifically from wind and solar–thermal installations—they face heightened volatility and operational complexities, which severely complicate load frequency regulation. While fractional-order proportional-integral-derivative (FOPID) controllers are commonly employed for this purpose, their conventional formulations rely on fixed fractional parameters that cannot adapt to fluctuating network conditions. To address this limitation, the present study develops an adaptive FOPID (AFOPID) control architecture capable of real-time adjustment of fractional orders, thereby enhancing regulatory effectiveness. The Coot Optimization Algorithm (COA) is utilized to optimally determine the operational parameters of all controllers under investigation. The proposed strategy is validated on a simulated hybrid power system comprising wind generation, solar–thermal units, and physical nonlinearities including governor dead band and generation rate constraints. A comparative analysis is conducted across four distinct operating scenarios, benchmarking the COA-tuned AFOPID against conventional PI, PID, and standard FOPID controllers. Quantitative results demonstrate that the proposed COA-AFOPID configuration achieves superior performance, with improvements in settling time up to 46.06% and reductions in ITAE index up to 89.89% compared to traditional methods. These findings confirm the enhanced stability and robustness of the proposed approach for frequency regulation in sustainable energy networks. Full article

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions. Full article

With the widespread integration of renewable energy, power flow in the system has become extremely complex and variable. This not only exacerbates the operational safety issues of distribution networks but also intensifies the three-phase unbalance situation. The traditional volt/var control (VVC) model is facing significant challenges such as high-dimensional nonlinearity and low efficiency. To address these problems, this paper proposes a volt/var control for three-phase unbalanced distribution network based on trust region safe reinforcement learning. Firstly, a model is constructed based on the three-phase linear power flow matrix. Then it is transformed into a Markov Decision Process (MDP) to overcome the computational burden. Secondly, a trust region construction method based on the Clip mechanism is introduced to ensure stable policy gradient updates and computational efficiency. Further, the Lagrange multiplier is introduced in the trust region optimization to convert the node voltage safety boundary into a cost function, establishing a distribution network safety reinforcement learning (SDRL) model, which limits the output of dangerous action. Finally, through case studies, it is verified that the proposed method can effectively mitigate three-phase unbalance, enhance online decision-making efficiency, and strictly guarantee the safe operation of distribution networks, demonstrating significant feasibility and superiority. Full article

Background: Network medicine has reshaped how systemic comorbidities are quantified, but the internal comorbidity structure of oral diseases remains undescribed at four-character ICD-10 granularity. Methods: A total of 2,863,671 outpatient visit records from 583,614 patients (2011–2025) were analyzed. Using ICD-10 four-character codes (75 disease nodes), comorbidity networks were constructed for five age strata, with edges selected by relative risk (RR) > 1.5 and Bonferroni-corrected Fisher’s exact tests. Patient-level longitudinal sequences were mined for progression trajectories, and quasi-causal analyses—Cox regression, negative outcome controls, and Baron–Kenny mediation—were used to evaluate pathway directionality and specificity. Results: The all-age network contained 75 nodes and 167 edges (modularity = 0.53), forming eight communities. Network complexity peaked at 18–29 years and declined with age. Dental caries emerged as the strongest hub in the 60+ stratum (degree = 9). Cox regression adjusted for age, sex, and healthcare utilization confirmed pathway directionality (pulpitis → tooth defect: hazard ratio (HR) = 2.65; caries → pulpitis: HR = 2.25), and negative outcome controls confirmed biological specificity. Mediation analysis showed that pulpitis completely mediated the caries → tooth defect association (proportion mediated ≈ 100%; 95% confidence interval (CI), 90–128%). An oral mucosal immune cluster (burning mouth syndrome, lichen planus, candidiasis, and xerostomia) emerged as a clinically actionable community. Conclusions: Oral diseases form biologically coherent, age-evolving comorbidity communities, and pulpitis is the critical mediating intervention point in the caries-to-tooth-defect cascade. The framework provides a reusable network-medicine substrate for age- and sex-specific risk-stratified oral disease management. Full article

To address the energy efficiency optimization problem in power wireless private networks caused by fixed node positions, strong coupling between relay selection and power allocation, and strict quality of service (QoS) constraints, an intelligent reflecting surface (IRS)-assisted hybrid multi-hop relay network model is proposed. An IRS is deployed on the surface of an obstacle located between the source node and the first-hop relay to specifically enhance the first-hop link. By integrating path planning and cooperative power control, a joint optimization problem is formulated to maximize the system energy efficiency. To tackle the coupling issues in resource allocation, a joint optimization algorithm based on the block coordinate descent framework is developed, where the original problem is decomposed into three subproblems: relay selection, power allocation, and IRS phase shift configuration. These subproblems are solved using a greedy strategy, the Dinkelbach method, and a closed-form phase alignment solution, respectively. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes in terms of system energy efficiency, reliability, and latency, making it suitable for power communication scenarios with extremely stringent QoS requirements. Full article

This paper investigates the fault-tolerant constrained control problem of a nonlinear quarter-car active suspension system subject to road disturbances, body-state constraints, and mixed actuator faults. When mixed actuator faults, state constraints, unknown nonlinear suspension dynamics, and convergence-time requirements coexist, it remains challenging to simultaneously guarantee fault-tolerant compensation, constraint preservation, and implementable control laws. To address these challenges, a neural-network control method based on an adaptive prescribed-time filter (APF) is proposed. A logarithmic state transformation is introduced to convert the body-displacement and velocity constraints into boundedness problems of transformed variables, and the sprung-mass subsystem is represented in a strict-feedback form. The unknown nonlinearities induced by suspension dynamics, road disturbances, and additive actuator faults are approximated online by radial basis function neural networks. Meanwhile, the APF is employed to avoid repeated differentiation of virtual control laws in backstepping and to achieve practical prescribed-time stability. Lyapunov analysis proves that all closed-loop signals are bounded, the body-state constraints are preserved, and sufficient conditions are obtained for the boundedness of the unsprung-mass dynamics, as well as the safety of suspension travel and tire dynamic load. Simulation results under sinusoidal road excitation and smooth-transition actuator faults show that, compared with PID control, passive suspension, and sliding mode control, the proposed method reduces the body-displacement RMSE by 77.39%, 91.83%, and 73.12%, respectively, and the RMS body acceleration by 70.34%, 87.73%, and 50.22%, respectively, while maintaining suspension travel and tire dynamic load within their safety bounds. Full article

With the gradual improvement of residential electricity reliability, the lower design strength of the distribution network makes it more prone to large-scale power outages in resisting natural disasters. Among them, the cold load start-up effect will significantly prolong the recovery time and affect reliability indicators. Based on this, this article proposes a reliability evaluation method for urban distribution networks based on a lightning search algorithm, which is used for optimal recovery planning and reliability calculation of urban power systems with highly concentrated load under constant temperature control. Firstly, a delay index model is used to establish a time-sharing power demand calculation model for cold load start-up events, and an optimal recovery model with the goal of minimizing recovery time and its corresponding constraints are proposed. Then, the cold load start-up event is incorporated into the Monte Carlo simulation platform for reliability assessment, and the lightning search algorithm is used to develop the optimal recovery plan. The recovery time and sequence are determined based on the duration of the power outage and the electricity demand at the time of recovery. Finally, the test distribution system was used to verify that the optimal recovery plan considering cold load start events does not violate the constraint conditions, and the stability and convergence robustness of the lightning search algorithm are stronger than the current mainstream algorithms. It can effectively improve the reliability of the distribution grid when considering cold load start events. Full article