Search Results (3,100)

An integrated control strategy is proposed for direct-drive wave energy conversion (DDWEC) systems to address displacement safety constraints and improve the robustness of sensorless position estimation. Under strong wave excitation, buoy displacement may exceed its stroke limit due to conventional amplitude control, leading to mechanical risks. To mitigate this, a displacement-constrained damping regulation law is introduced, incorporating a displacement-dependent correction factor that retains optimal damping within a safe region and increases additional damping smoothly as the displacement approaches its limit. For sensorless operation, a dual-time-scale adaptive amplitude modulation strategy is developed, based on high-frequency square-wave voltage injection. By decoupling the fast position-estimation loop from the slow injection-amplitude adjustment, the demodulated high-frequency current remains within an optimal band, ensuring a high signal-to-noise ratio (SNR) under disturbances and parameter variations. Simulation results show that displacement boundary violations are eliminated, with a 25.7% reduction in peak displacement and only a 7.65% reduction in average captured power. The injection amplitude is adaptively regulated to maintain the demodulated current within the measurement band, enhancing position-estimation stability and accuracy. A fail-safe boundary for extreme sea states (H_s ≈ 2.2 m) is also identified, ensuring robust operation under varying conditions. Full article

Quantum fidelity can serve as an effective diagnostic tool for detecting topological phase transitions. We investigate quantum critical behavior and topological phase transitions in a one-dimensional non-Hermitian Su–Schrieffer–Heeger lattice with periodic driving. The interplay between periodic driving and non-Hermiticity opens gaps at both zero and $\pi$ quasienergies and gives rise to stable topological zero and $\pi$ modes under open boundary conditions. To characterize the critical properties of the transition, we construct the fidelity susceptibility based on Floquet eigenstates and systematically compare two definitions: the self-normal fidelity and biorthogonal fidelity. In contrast to the self-normal fidelity susceptibility, the biorthogonal fidelity susceptibility exhibits a clear power-law scaling with system size and converges more reliably to the analytically expected critical points in the thermodynamic limit. Our results demonstrate that the biorthogonal fidelity susceptibility provides a robust and accurate approach to identifying Floquet non-Hermitian topological phase transitions and their quantum critical properties. Full article

Gears are one of the most important machine elements widely used to transmit motion and power in various machines. The gear tooth stiffness has a significant impact on the load distribution, vibration characteristics, and overall efficiency of gear systems. Therefore, accurate analysis of tooth stiffness is crucial for optimizing gear performance and ensuring reliable operation. In this study, the effects of geometric parameters on single tooth stiffness (STS) and time-varying mesh stiffness (TVMS) of involute spur gears are investigated numerically. The gear design parameters, such as drive side pressure angle (DSPA) (20°, 25°, 30°), addendum (1–1.5 × module), and dedendum (1.25–1.7 × module), are varied. Gear configurations with both low contact ratio (LCR) and high contact ratio (HCR) are evaluated. Parametric models are first developed using MATLAB, and then 3D CAD models are created in CATIA for static structural analysis in ANSYS Workbench. The results indicate that increasing the pressure angle enhances stiffness in the tooth root region, whereas the effect is less significant near the tooth tip. Increasing the addendum length generally reduces stiffness. In some cases, a rise in contact ratio results in up to a 25% increase in mesh stiffness. These findings demonstrate that single tooth and mesh stiffness can be optimized through precise control of gear geometry. Ultimately, the study provides valuable insights for improving gear performance and durability through informed design choices. Full article

Chronic obstructive pulmonary disease, interstitial lung disease, and post-lung trans-plantation are often accompanied by skeletal muscle dysfunction that worsens the quality of life. Such physiological changes are driven by physical inactivity, systemic inflammation, oxidative stress, anabolic and hormonal resistance, and medication effects. Structural changes include impaired capillarization, fiber-type shifts (slow-to-fast in limb muscle and fast-to-slow in respiratory muscles), mitochondrial dysfunction, reduced oxidative capacity, and early lactate accumulation. Electromyography and dynamometry, both isokinetic and isometric, quantify neuromuscular drive through measuring strength, power, and endurance and are associated with functional outcomes (6-min walk, sit-to-stand, stair climbing tests). Pulmonary rehabilitation (PR) improves neuromuscular efficiency, dyspnea, exercise tolerance, and quality of life by combining resistance, endurance, and eccentric training. The effects of PR generally plateau at three months, emphasizing the need for maintenance and the personalization of rehabilitation plans. While nutritional optimization is important, supplements have shown little benefit. Future priorities include defining EMG/dynamometry thresholds to allow standardized routine testing for comparable benchmarks and more precise PR protocols. Future research targeting mitochondrial remodeling, inflammatory signaling, and anabolic resistance offer potential pathways for preventing and reversing muscle wasting. Full article

The paper proposes an adaptive architecture of an electromechanical drive with internally controlled energy exchange, implemented through the integration of an inertial flywheel and a controlled clutch into the structure of a planetary transmission. A multi-mass dynamic and energy model of the system is developed, and the power balance is verified. Based on the energy formulation, adaptive energy and predictive energy control strategies are implemented. The results of numerical simulation confirm that the use of the internal energy exchange loop increases system stability, reduces peak motor torque by 30–40%, decreases maximum output speed deviations by 35–45% under step load conditions, and reduces the root-mean-square tracking error by 20–30% compared with reactive energy-based control, demonstrating improved tracking performance and reduced actuator load compared to the classical drive architecture. Full article

Accurately forecasting the risk matrix and constructing a well-controlled portfolio based on these forecasts is the core objective of effective asset allocation. This paper takes the Chinese stock market as the research object, employing multiple machine learning algorithms to systematically compare the predictive performance of the Financial Stress (FS) indicator and the Economic Policy Uncertainty (EPU) index in sectoral risk management. The forecast results are subsequently applied to portfolio construction and optimization. The findings indicate that, in terms of predictive dimensions, EPU demonstrates strong performance in short-term forecasts, but its explanatory power decays rapidly as the forecasting horizon extends. In contrast, the FS factor achieves forecasting accuracy that is significantly superior to both the EPU factor and traditional price series across all time horizons, exhibiting robust long-memory characteristics and cross-period stability. At the portfolio application level, the minimum variance strategy constructed based on FS forecasts effectively reduces out-of-sample portfolio variance, achieving superior risk control performance compared to strategies based on EPU factor forecasts. This result reveals the differentiated mechanisms of the two factor types: EPU acts as a driving force for short-term risk structure reshaping, while financial stress serves as the core variable driving the evolution of long-term risk structures. Machine learning methods provide an effective technical pathway for capturing these complex nonlinear relationships. The research conclusions offer new empirical evidence for investors to optimize asset allocation decisions and for regulatory authorities to improve risk monitoring systems. Full article

Sliding contact wear at the pantograph–catenary interface directly impacts the current collection performance and power supply reliability of electrified railways. Addressing the challenges in multi-environmental wear studies—namely, fragmented modeling chains, inconsistent parameter calibrations, and prohibitive computational costs that hinder horizontal comparisons—this study develops an equivalent parameterized modeling framework tailored for engineering assessment. The framework encapsulates environmental effects as equivalent load increments and interface coefficient corrections, facilitating efficient multi-scenario parameter scanning within a 3D contact model. Findings reveal that environmental factors drive wear through a distinct “pressure-wear” nonlinear decoupling mechanism. In sandy environments, abrasive-mediated micro-cutting dominates, leading to a monotonic surge in wear depth as sand concentration increases, despite a buffered contact pressure response. In icing conditions, the synergy of low-temperature brittleness and geometric impact renders hotspot wear highly sensitive to temperature fluctuations. For salt spray conditions, the environmental impact is represented via equivalent corrections to the interfacial parameters; within this equivalent framework, the results suggest that salt spray intensity has a more pronounced effect on wear accumulation than humidity alone. This work reveals the divergence of dominant damage pathways across environments, offering a quantitative basis for the differentiated maintenance and remaining life estimation of pantograph–catenary systems in extreme climates. Full article

Compared with rule-based and optimization energy management strategies, online optimal energy management control strategies for a dual-mode power-split hybrid electric vehicles (PSHEVs) are able to achieve better fuel economy and real-time performance. Global online optimization of a finite time domain energy management strategy based on the hybrid model predictive control (HMPC) algorithm is proposed in this study. To reduce the computing time, a linearized predictive model is built; because dual-mode PSHEVs can be considered hybrid systems that include continuous and discrete states, the hybrid states can be expressed uniformly. Therefore, a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory, and an HMPC energy management strategy is proposed based on the MLD predictive model. To solve the optimal control problem online to obtain the optimal control sequence, the optimal control problem is converted into a mixed-integer linear programming (MILP) problem. The HMPC-based energy management strategy is compared with dynamic programming (DP)-based and rule-based energy management strategies over two different driving cycles. Simulation results indicate that the HMPC-based EMS achieves 80.60% and 83.79% of the fuel economy performance obtained by the DP-based EMS. In comparison, the rule-based EMS only achieves 66.46% and 70.51% of the DP-based control performance. Therefore, the HMPC-based energy management strategy is favorable for real-time control while effectively improving fuel economy. Full article

Beyond supporting ultra-high-capacity data transmission, metropolitan and access networks are expected to enable real-time infrastructure monitoring, driving the emergence of integrated sensing and communication (ISAC). Distributed acoustic sensing (DAS) has proven to be well-suited to urban sensing application requirements, yet its seamless integration into ISAC remains challenging—conventional high-peak-power sensing pulses in DAS induce nonlinear crosstalk in communication channels. DAS inherently suffers from interference fading due to single-frequency laser sources, which limits sensitivity. Here, we propose an ISAC architecture based on an electro-optic (EO) comb and a 7-core fiber, achieving nonlinearity-suppressed self-homodyne transmission and fading-suppressed DAS. Unmodulated comb lines and sensing pulses are polarization-multiplexed into orthogonal polarization states within the central core to minimize nonlinear crosstalk while delivering local oscillators (LOs) for wavelength division multiplexing (WDM) coherent transmission within six outer cores—achieving 10.56 Tbit/s capacity. In addition to supporting WDM transmission, the EO comb’s wavelength diversity is also exploited to enhance DAS performance. Specifically, a dual-pulse probe loaded onto four comb lines yields a 6 dB signal-to-noise ratio gain and a 64% reduction in fading occurrences, achieving a sensitivity of 1.72 $p\epsilon /\sqrt{\mathrm{Hz}}$ with 8 m spatial resolution. Moreover, our system supports simultaneous multi-wavelength backscatter detection in sensing and simplified digital signal processing in self-homodyne communication, reducing receiver complexity and cost. Our work presents a scalable, energy-efficient ISAC framework that unifies high-capacity communication with high-sensitivity sensing, providing a blueprint for future intelligent optical networks. Full article

Acute respiratory distress syndrome (ARDS) has traditionally been managed with population-based, protocolized mechanical ventilation strategies designed to limit ventilator-induced lung injury. While these approaches have improved outcomes, they fail to account for the pronounced biological, mechanical, radiological, and temporal heterogeneity that characterizes ARDS. Accumulating evidence shows that patients differ markedly in functional lung size, recruitability, chest wall mechanics, inflammatory burden, and tolerance to ventilatory stress, making uniform ventilatory targets physiologically imprecise and, at times, harmful. This narrative review examines the evolution from conventional lung-protective ventilation toward a precision-based paradigm that aligns ventilatory support with individual patient physiology. We conceptualize ARDS not as a static syndrome but as a dynamic spectrum, viewing the injured lung as a heterogeneous mechanical system susceptible to regionally amplified stress and strain. Within this framework, we discuss key principles underlying precision ventilation, including functional lung size (the “baby lung”), driving pressure, mechanical power, patient–ventilator interaction, spontaneous breathing-associated injury, and the time-dependent evolution of lung mechanics. We synthesize current evidence supporting mechanical, biological, and radiological subphenotyping as complementary strategies to individualize ventilatory management, while critically appraising their current limitations. This review also evaluates bedside tools that may operationalize precision ventilation in clinical practice, including esophageal pressure monitoring, lung ultrasound, and electrical impedance tomography, and examines the role of artificial intelligence as a clinician-directed decision-support aid rather than a prescriptive substitute for physiological reasoning. Implications for clinical trial design, ethical considerations, and future directions toward predictive and adaptive ventilation strategies are also addressed. Precision mechanical ventilation represents a shift from rigid thresholds toward proportional, physiology-guided intervention across the disease trajectory. By integrating evolving lung mechanics, ventilatory load, and patient effort over time, this approach provides a coherent framework for safer and more effective mechanical ventilation in ARDS while preserving the core principles of lung protection. Full article

The year 2020 marked the eve of the explosive growth in China’s BEV market, which may lead to substantial carbon emission implications. This study quantifies the full life-cycle carbon emissions of battery electric vehicles (BEVs) across China’s 31 provinces using a multi-regional input-output-based life-cycle assessment (MRIO-based LCA) model, covering four phases: manufacturing, driving, battery replacement, and scrapping. Moreover, the coupling coordination degree (CCD) model was employed to evaluate the coordination degree between provincial BEV deployment and a green electric system. Results show that the total carbon emissions amount to 48.95 million tons, with manufacturing contributing 58.4% and driving for 33.4%. Hebei (5.72 million tons) and Shandong (4.16 million tons) account for the largest shares, driven by embodied emissions from heavy industry and coal-intensive power systems. Interprovincial embodied carbon flows reveal a dominant north-to-south transfer pattern. Furthermore, coupling coordination between BEV deployment and a green electric system is generally medium (0.5 < CCD ≤ 0.7), with Guangdong (CCD = 0.73) standing out as an exemplary case, demonstrating an effective equilibrium between BEV industry expansion and the integration of renewable energy. These findings highlight that in provinces with rapidly growing BEV industries, such as Guangdong, policies promoting low-carbon supply chains and accelerating green electricity infrastructure development are crucial to reducing emissions. Full article

How digital technology can effectively drive ecological collaborative governance in trans-administrative river basins is a core prerequisite for achieving intelligent ecological governance of river basins. Based on 402 micro survey responses collected from water conservancy, environmental protection and other relevant departments in the middle reaches of the Yangtze River Basin, this study identifies the characteristics of digital technology from three dimensions: tool, power and capacity. By integrating factor analysis and the mediating effect model, it empirically examines the impact of digital technology on inter-provincial ecological collaboration in river basins and its underlying mechanism. The results show that: (1) Digital technology exerts a significantly positive driving effect on inter-provincial ecological collaboration in the middle reaches of the Yangtze River Basin, and this conclusion remains robust after conducting robustness tests including the re-measurement of digital technology and the exclusion of interference from smart water conservancy pilot projects. (2) Mechanism analysis reveals that central government support and public participation play partial mediating roles in the relationship between digital technology and inter-provincial ecological collaboration, and both variables exert a masking mediating effect on the sustainability of inter-provincial ecological collaboration. These findings provide micro-evidence-based policy implications for optimizing the digital collaborative governance system of river basins. Full article

Accurate fault diagnosis of gear transmission systems is crucial for ensuring mechanical reliability and preventing catastrophic failures. However, existing research predominantly focuses on single-gear crack faults, often overlooking the complex coupling effects when cracks occur simultaneously on meshing gears in practical engineering scenarios. To address this research gap, a multi-degree-of-freedom dynamic model incorporating time-varying mesh stiffness under normal, single-crack, and coupled-crack conditions is established. Experimental validation is conducted based on an FZG closed test rig for power flow. The results indicate that the mesh stiffness under coupled-crack conditions is generally lower than that under single-crack conditions. In the time-domain vibration response, the periodic impact amplitudes induced by coupled cracks are significantly amplified, with the impact period jointly influenced by the rotational speeds of both the driving and driven gears. According to frequency-domain analysis, coupled cracks result in a notable increase in harmonic peaks of the mesh frequency, enhanced sideband amplitudes, and a modulation period that is between the rotational frequencies of the driving and driven gears. The simulation results from the dynamic model show high consistency with the experimental signals in terms of time-frequency characteristic trends and time-domain indicators such as the crest factor, thereby validating the effectiveness of the dynamic model. This study elucidates the unique influence mechanism of coupled cracks on the dynamic behavior of gear systems and can provide theoretical guidance for the accurate diagnosis and condition assessment of multi-tooth faults in subsequent research. Full article

Quasi-Static Electromagnetic Forming (QSEF) technology utilizes stable magnetic fields generated by long-pulse flat-top currents to achieve non-contact, high-precision forming of large-scale integral aerospace components. To meet the immense energy demands of large-scale component forming, the drive system requires instantaneous power output capabilities at the Gigawatt level. Consequently, the precise regulation of ultra-high flat-top current waveforms becomes a critical challenge for ensuring forming quality. However, traditional meta-heuristic methods, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), exhibit limited adaptability and robustness when addressing strong geometric nonlinearities induced by workpiece deformation and the performance degradation of pulsed power modules. To address engineering challenges such as capacitor degradation, inductance drift, and module failures, this paper proposes a Staged Deep Reinforcement Learning (Staged-DQN) adaptive current control framework. This framework decouples the discharge scheduling into “heuristic rapid rise” and “DQN fine compensation” stages, adaptively optimizing triggering timing to suppress plateau oscillations and compensate for energy deficits caused by faults. Simulation results demonstrate that under typical high-energy operating conditions, the proposed method achieves superior tracking accuracy compared to traditional PSO in fault-free scenarios. In extreme scenarios involving 25 faulty modules, the Mean Absolute Percentage Error (MAPE) is maintained between 1.13% and 1.80%, significantly lower than the 2.65–3.52% of the baseline DQN. This study validates the effectiveness of the proposed method in enhancing waveform quality and system fault tolerance, offering a reliable intelligent control solution for large-scale electromagnetic manufacturing equipment. Full article

This study advances a hierarchical bilevel optimization paradigm to rigorously characterize the intertwined processes of strategic bidding and regulatory market participation in electricity systems increasingly dominated by renewable resources. At the upper tier, a central regulatory authority orchestrates participation rules, renewable integration mandates, and incentive mechanisms with the overarching aim of maximizing system-wide social welfare while driving decarbonization and reliability objectives. At the subordinate level, profit-maximizing generation firms—each managing heterogeneous renewable portfolios—pursue strategic bidding under deep uncertainty, conceptualized as a multi-agent game governed by imperfect and asymmetric information. The interaction between these tiers is formalized as a bilevel Stackelberg game that encapsulates price-responsive demand, intertemporal reserve adequacy, and policy-driven incentive structures. To ensure both computational tractability and robustness against strategic indeterminacy, the lower-level equilibrium is reformulated into a mathematical program with equilibrium constraints (MPEC), enabling a hybrid solution procedure that combines penalty-based regularization with exact decomposition algorithms. The framework’s efficacy is validated through a stylized multi-zone case study featuring diverse renewable assets and strategic participants, revealing how policy signals, capacity ceilings, and market power asymmetries reshape efficiency frontiers and bidding equilibria. A set of high-resolution post-processing visualizations is further employed to illustrate the dynamic evolution of marginal prices, equilibrium trajectories, and regulatory impacts under uncertainty. Full article