Search Results (1,840)

This study addresses the inherently low damping and vibration susceptibility of carbon fiber reinforced polymer (CFRP) laminated cantilever plates by developing a comprehensive dynamic modeling and active vibration control framework. An electromechanical coupling model incorporating macro-fiber composite (MFC) actuators and sensors is established using the first-order shear deformation theory (FSDT) and the assumed mode method, with virtual springs introduced to account for non-ideal clamped boundary conditions. A reduced-order state-space model is then derived through model reduction, and a linear quadratic Gaussian (LQG) controller is designed for optimal state estimation and feedback control. The theoretical model is systematically validated via convergence analysis, ANSYS finite element simulations, and LMS impact hammer testing. The results demonstrate that, with the relative errors of the first four natural frequencies controlled within 2%, the theoretical mode shapes are highly consistent with those obtained from ANSYS simulations. An active vibration control experimental platform is established, and the effectiveness of the control strategy is verified under dual-spectrum harmonic and impact excitations. The results show that the designed LQG controller can effectively suppress multi-modal vibrations, substantially attenuating the response amplitudes of dominant modes and significantly accelerating the transient vibration convergence. This study addresses the challenge of precisely characterizing actual non-ideal clamped boundary conditions. Through model order reduction and closed-loop LQG control experiments, it provides a comprehensive set of theoretical methodologies, numerical solution strategies, and engineering-oriented experimental schemes for the electromechanical coupling dynamic modeling and optimal vibration suppression of CFRP thin-walled composite structures. Full article

Electromechanical actuators (EMAs) are increasingly used in aerospace servo actuation because of their compact structure, high power density, and convenient integration with electric flight-control systems. However, load-side aerodynamic torque, friction, parameter perturbations, and unmodeled transmission effects enter the EMA dynamics through a channel different from the motor-current input, which leads to a mismatched disturbance rejection problem. This paper develops a feedback-linearization-assisted observer-based interconnection and damping assignment passivity-based control (IDA-PBC) method for EMA trajectory tracking. A fourth-order input–output feedback-linearized normal-coordinate model is first derived, through which the original load-side mismatched disturbance is transformed into a matched term acting on the highest-order channel. An extended state observer is then constructed to estimate the transformed disturbance. Based on the observer output, a desired Hamiltonian function is generated from a Lyapunov equation, and the interconnection and damping matrices are explicitly assigned so that the closed-loop tracking-error dynamics admit a dissipative port-Hamiltonian representation. A composite Lyapunov analysis proves closed-loop exponential stability under the assumption of slowly varying disturbance. The resulting framework combines the disturbance-channel-reshaping capability of feedback linearization with the energy-shaping interpretation of IDA-PBC, providing a systematic controller design for high-precision EMA servo systems subject to load-side disturbances. Full article

Background: The transition from nursing education to professional practice requires nurse interns to consolidate clinical competence, develop professional identity, and establish future expectations within their work environments. Aim: This study examined the associations among perceived work environment, professional identity, and dispositional hope among nurse interns and assessed whether perceived work environment and professional identity remained independently associated with dispositional hope after adjustment for demographic and internship-related characteristics. Methods: An analytical cross-sectional correlational study was conducted among 1450 nurse interns at Benha University Hospitals, Egypt, using a total population sampling approach. Data were collected using the Perceived Work Environment Questionnaire, Professional Identity Scale for Nursing Students, and Adult Hope Scale. Descriptive statistics, Pearson’s correlation, and multiple linear regression were performed. Results: The mean scores were 138.42 ± 18.92 for perceived work environment, 57.84 ± 8.96 for professional identity, and 47.20 ± 5.68 for dispositional hope. Perceived work environment showed significant positive correlations with professional identity (r = 0.58, p < 0.001) and dispositional hope (r = 0.52, p < 0.001), while professional identity was also positively correlated with dispositional hope (r = 0.49, p < 0.001). In the multiple linear regression analysis, perceived work environment (β = 0.359, p < 0.001) and professional identity (β = 0.282, p < 0.001) were both positively associated with dispositional hope after adjustment for the included demographic and internship-related covariates. The overall regression model was statistically significant—F (10, 1439) = 70.054, p < 0.001—and explained 32.7% of the variance in dispositional hope (R² = 0.327; adjusted R² = 0.323). Conclusions: Perceived work environment and professional identity were independently associated with dispositional hope among nurse interns. These findings underscore the relevance of structured supervision, constructive feedback, mentoring, and professional identity development within nursing education and internship programs. Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

Additively manufactured components require machining of functional surfaces to meet geometric requirements. Due to low stiffness and non-nominal as-built geometry, they are susceptible to milling-induced shape deviations. This paper presents a geometric–numerical milling process simulation for predicting shape errors in compliant metallic laser powder bed fusion components. The method combines real-geometry-based technological numerical control simulation, quasi-static force prediction, finite element-based structural response simulation, and surface reconstruction between roughing and finishing to enable multistage operation. The approach is validated for linear and non-linear toolpaths with varying immersion angles and compliance conditions. The results show reproduced force profiles, while magnitude deviations highlight the relevance of deformation-dependent engagement feedback in high-compliance regions. An analytical back-calculation based on the effective engagement cross-section reveals that accounting for deflection-induced engagement reduction reduces force deviations. During roughing, maximum shape errors for linear and non-linear toolpaths are overestimated by 4–5%, and critical high-error regions are identified. The reconstructed intermediate geometry after roughing is essential for finishing, since neglecting geometry feedback underestimates finishing forces. With geometry feedback, the maximum finishing shape error is predicted as 0.090 mm, while the measured value is 0.086 mm. The simulation captures dominant quasi-static shape-error regimes and supports process-chain-oriented prediction in additive–subtractive manufacturing. Full article

Human–robot collaborative assembly line balancing and scheduling constitutes an NP-hard combinatorial optimization problem involving the simultaneous optimization of task assignment, resource allocation, processing mode selection, station-level scheduling, and ergonomic constraints. This study proposes a Q-learning-based hyper-heuristic genetic algorithm (QLHH-GA) to solve the cost-oriented ergonomic mixed-model human–robot collaborative assembly line balancing and scheduling problem. The proposed approach integrates bio-inspired evolutionary mechanisms of population variation and selection with adaptive, Q-learning-guided low-level heuristic selection. The Q-learning layer uses performance feedback to adapt the search strategy to different solution states while maintaining solution feasibility. A mixed-integer linear programming (MILP) model is also developed to minimize the total operating cost, including station opening, labor, robot operation, and energy consumption costs, while enforcing station-level energy expenditure (EE) limits. Computational experiments conducted using benchmark instances of varying sizes and a literature-based industrial case study demonstrate that QLHH-GA produces solutions comparable to those obtained by the MILP model on small-scale instances and maintains strong solution quality on larger instances, for which exact optimization becomes computationally prohibitive. These findings demonstrate the scalability and effectiveness of reinforcement-learning-guided hyper-heuristic search for designing cost-efficient and ergonomically constrained human–robot collaborative assembly lines. Full article

This paper proposes a method to analyze motion errors in a five-degree-of-freedom hydrostatic turntable with internal feedback under eccentric load. The motion error models of thrust and journal bearings are derived separately, revealing the mechanism of the influence of manufacturing errors of thrust plate and shaft on motion errors. The results demonstrate that the hydrostatic oil film exhibits an error averaging effect. When the amplitude of the mating surface error reaches 15 μm, the corresponding linear deviation of the turntable remains below 0.3 μm, indicating that the oil film can effectively suppress the transmission of manufacturing errors. However, the pressure oil film cannot completely balance the errors on the film binding surface, especially when the amplitude of the binding surface error is larger, resulting in a weaker ability of the oil film to balance. Full article

Background: Executive functions (EFs) are cognitive and emotional abilities that underpin goal-directed behavior, decision-making, and problem-solving. EFs can be categorized into two broad types, Hot EFs and Cool EFs, distinguished by their relation to emotional processing. Cool EFs are associated with cognitive control, while Hot EFs involve emotional regulation and decision-making in emotionally charged contexts. EFs contribute to self-regulation, including the ability to adjust behavior in response to feedback. Methods: The aim of this study is to investigate the relationship between Hot (decision-making under uncertainty) and Cool (cognitive flexibility) EFs in middle childhood. To assess these dimensions of EFs, we used two tasks, the Iowa Gambling Task (IGT) and the Wisconsin Card Sorting Test (WCST), completed by children (N = 90) aged 6–12 years. Results: Traditional WCST indices of cognitive flexibility were not associated with IGT performance. Using hierarchical linear regression models, the most significant overlap concerns the negative relationship between post-loss adjustment in the WCST (post-loss reaction time—plRT) and learning across the IGT (learning index—LI). Children who responded faster after negative feedback (plRT ↓) showed greater improvement in advantageous choice over the course of the task (LI ↑). Conclusions: The findings suggest that decision-making in the IGT is more closely linked to dynamic, process-based aspects of self-regulation than to static measures of cognitive flexibility. It can be concluded that the relationship between WCST and IGT during middle childhood is best described as weak, process-specific, and developmentally modulated, indicating that Cool and Hot EFs are partially distinct yet interconnected systems. Full article

When a robot performs a grinding operation, the steady-state force/position tracking accuracy of its end-effector is critical to achieving high grinding quality. To solve this problem, a fuzzy adaptive impedance method is incorporated into the robot’s compliant control framework. Firstly, the robot dynamics model is established based on the Newton–Euler method. To describe the robot dynamics more comprehensively, a linear friction compensation model is also introduced. Secondly, a dynamic feedforward trajectory-tracking controller is proposed based on the dynamic model, and its stability is verified using a Lyapunov function. The impedance parameters are adjusted in real time according to the feedback contact force and its rate of change, thereby enabling dynamic equilibrium between the end contact force and end position. This allows the robot end-effector to exhibit compliance during external environmental interactions. Finally, a control platform of a force/position compliance controller was constructed, and two grinding conditions of plane and arc were designed to validate the effectiveness of force/position compliance control based on impedance control. Compared with the fixed impedance approach, the proposed method reduces overshoot by 11.6% (plane) and 12.45% (arc), improves surface roughness from Ra 0.042 μm to Ra 0.021 μm, and achieves faster force tracking with fewer oscillations. Full article

Terrestrial ecosystems play a critical role in achieving carbon neutrality, yet the carbon sequestration potential (CSP) of China’s terrestrial ecosystems remains incompletely characterized, particularly regarding the roles of future climate scenarios and land–atmosphere moisture feedbacks. Here we quantify China’s CSP using a process-based model (LPJ-GUESS) that explicitly simulates vegetation dynamics, combined with potential natural vegetation (PNV) benchmarks and moisture recycling. We estimate China’s historical (1993–2022) actual terrestrial (AT) carbon stock at 91.7 PgC, with a PNV ceiling of 132.9 PgC, yielding a CSP of 41.2 PgC. Under future scenarios (2071–2100), CSP follows a non-linear trajectory. Relative to the historical baseline, it declines slightly under SSP119, peaks under SSP2-4.5, and remains elevated but slightly reduced under SSP5-8.5. High-CSP areas are concentrated along the Hu Line and the belts of the Tianshan, Kunlun, and Qilian mountains, while scattered areas in southern China exhibit negative CSP due to intensive human interventions. Moisture recycling consistently enhances CSP across all periods and scenarios. Historically, it raised national CSP by approximately 5 PgC, and this enhancement gradually weakens to 4 PgC under SSP119 and further declines to around 2 PgC under SSP2-4.5/SSP5-8.5—revealing that high emissions erode this positive feedback. Our findings identify a critical window for maximizing restoration gains under moderate forcing (SSP2-4.5) and demonstrate that low-to-moderate emission pathways not only preserve direct carbon benefits but also safeguard indirect moisture recycling amplification. Regionally tailored restoration strategies accounting for moisture recycling are essential for achieving China’s 2060 carbon neutrality goals. Full article

A magnetic-levitation direct-drive oil-free scroll compressor (MLDD-OFSC) eliminates the anti-rotation mechanism to achieve oil-free operation. Still, its large-stroke planar motion introduces strong sensor–DOF coupling and air-gap-dependent stiffness variation that degrade fixed-gain PID performance. This paper proposes a control strategy integrating acceleration feedback linearization, gain-scheduled PID, and coordinate decoupling. An inverse electromagnetic force model is derived to compensate for the nonlinear force–air-gap relationship, linearizing the suspension dynamics. A phase-adaptive gain scheduling law is developed, where gains vary with trajectory phase via a cosine-based mapping. A homogeneous transformation matrix decouples raw sensor signals into independent X, Y, and yaw DOFs. Frequency-domain analysis at three air-gap positions confirms closed-loop stability. Simulations show that the proposed acceleration-linearized gain-scheduled PID (AL_GS_PID) outperforms traditional PID and fixed-gain AL_PID in tracking accuracy. Experiments demonstrate progressive improvement across four configurations—decentralized PID, decoupled PID, fixed-gain AL_PID, and AL_GS_PID—with the full scheme reducing peak errors to 0.043 mm in X and 0.04 mm in Y, corresponding to 74.7% and 33.3% reductions over decentralized PID. These results demonstrate that the proposed strategy effectively addresses coupling and stiffness variation in large-stroke maglev systems under no-load and light-load conditions. Full article

Addressing nonlinear fluctuations in inbound logistics demand in the intelligent automotive industry, this paper proposes a SARIMA-LSTM-Attention forecasting model incorporating Real-Time Production Progress Feedback (RPF) features. By introducing production progress deviation into a hybrid framework that combines SARIMA-based linear forecasting with attention-enhanced LSTM residual correction, the proposed model effectively captures both linear trends and nonlinear demand disturbances. The model was evaluated using 995 daily observations collected from the inbound logistics system of a large new energy vehicle manufacturer from January 2023 to July 2025, with data from January 2023 to December 2024 used for training and validation and January to July 2025 reserved for testing. Compared with six representative benchmark models, the proposed model achieved the best overall performance, reducing sMAPE from 43.23% to 33.13% relative to the SARIMA baseline, representing an absolute reduction of 10.10 percentage points and a relative improvement of 23.36%. These results demonstrate the effectiveness of integrating real-time production feedback for demand forecasting and provide practical support for lean inventory management and logistics decision-making in automotive supply chains. Full article

This paper presents a novel approach for protecting transformers during an $E_3$ HEMP insult as well as the associated technology-agnostic voltage, power, energy storage, and bandwidth requirements of various control laws. The mitigation is performed by placing a controlled voltage supply in series with the primary winding of a transformer. The controlled voltage supply is subjected to four control laws: an integral controller (capacitor), a linear quadratic regulator (LQR), a nonlinear energy storage optimal feedforward control law, and a Hamiltonian feedback control law. The research gap addressed is that most $E_3$ mitigation discussions emphasize neutral-side blocking, whereas transmission-level (high-side) assets may offer a lower-upgrade pathway in some grids and require different actuator sizing and control structure. The results show that the Hamiltonian feedback control law performs the same as the energy storage optimized control law and requires the same specifications. Both of these control laws require less than 30 kV of control effort, 0 W of power, 0 kWh of energy storage, and 16 Hz of bandwidth. This suggests that the Hamiltonian feedback control law is an energy storage optimized feedback control law. These specifications should be considered bounds to the requirements, as power and energy storage requirements will change, depending on the efficiency of the actuator chosen to implement the control laws. These two controllers outperform the blocking capacitor and LQR solutions, despite having significantly less stringent specifications. Full article

A high-stability actively mode-locked fiber laser (AMLFL) is proposed and experimentally demonstrated. This AMLFL is based on a distributed feedback laser diode (DFB-LD) injection locking with Fabry-Perot (F-P) etalon frequency stabilization. In this system, a wavelength modulation method is employed to generate the feedback signal for frequency stabilization. The stabilization mechanism utilizes the linear response characteristic of the first-order derivative of the F-P etalon transmission peak. This achieves wavelength stabilization of the DFB-LD. Subsequently, the stabilized light source is injected into the ring cavity of the AMLFL. The proposed system does not require modification to the existing AMLFL cavity. It also features a simple structure and low implementation cost. Experimental results show that, with frequency stabilization, the wavelength drift of a selected spectral line is reduced to within the 10 pm resolution of the OSA. Meanwhile, the standard deviations of the 5 GHz spectral component power fluctuation and the average output optical pulse power are 0.01 dB and 0.01 dB, respectively. Full article

Day-ahead dispatch of integrated energy systems (IESs) is commonly solved by mixed-integer linear programming when objectives, constraints, and forecasts are fully specified. In practice, however, operators often express temporary preferences in natural language, and day-ahead forecasts inevitably deviate from realized operation. These two conditions make a fixed optimization interface difficult to use without additional modeling effort. This paper proposes DARC (Decompose-Act-Repair-Critique), a constraint-diagnostic LLM agent framework for day-ahead dispatch of campus-level microgrid-type IES with under-specified operating requirements. DARC combines three language modules, namely a Decomposer for temporal structure, a Resolver for numerical schedule generation, and a Critic for root-cause diagnosis, with deterministic repair, constraint checking, and metric evaluation. In all main experiments, the Decomposer is instantiated by its deterministic rule-based variant for reproducibility, so the reported results reflect a loop with two LLM modules (Resolver and Critic). The Projector repairs candidate schedules where possible, while the Checker supplies mathematical facts that ground the Critic’s feedback. DARC does not model forecast uncertainty through sets or scenarios; robustness to forecast deviation is pursued operationally, through margin-aware repair and checker-grounded revision, and is assessed empirically. Experiments on a campus-level microgrid IES testbed show that DARC can incorporate natural-language preferences, improve realized feasibility under forecast noise relative to both a point-forecast MILP and an interval-robust MILP baseline in terms of strictly feasible configurations, at higher operating cost, and produce diagnostic feedback that is robustly more useful than an ungrounded LLM diagnoser under judge models from three families, and at least as useful as checker facts alone, with its advantage concentrated in actionable revision guidance. DARC is therefore positioned not as a replacement for MILP, robust formulations, or model predictive control on fully specified problems, but as a complementary interface for operational settings where preferences and forecasts are not completely formalized. Full article