Search Results (1,297)

This paper presents a hybrid reinforcement learning framework for trajectory tracking control of a 5-degree-of-freedom (DOF) Mitsubishi RV-2AJ robotic arm by integrating model-free deep reinforcement learning (DRL) algorithms with classical control strategies. A novel hybrid PID + TD3 agent is proposed, combining a Proportional–Integral–Derivative (PID) controller with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and is compared against standalone TD3 and PID controllers. In this architecture, the PID controller provides baseline stability and deterministic disturbance rejection, while the TD3 agent learns residual corrections to enhance tracking accuracy, robustness, and control smoothness. The robotic system is modeled in MATLAB/Simulink with Simscape Multibody, and the agents are trained using a reward function inspired by artificial potential fields, promoting energy-efficient and precise motion. Extensive simulations are performed under internal disturbances (e.g., joint friction variations, payload changes) and external disturbances (e.g., unexpected forces, environmental interactions). Results demonstrate that the hybrid PID + TD3 approach outperforms both standalone TD3 and PID controllers in convergence speed, tracking precision, and disturbance rejection. This study highlights the effectiveness of combining reinforcement learning with classical control for intelligent, robust, and resilient robotic manipulation in uncertain environments. Full article

Hydrothermal conditions are a key indicator influencing the evolution of aquatic ecosystems, closely affecting the physical, chemical, and biological properties of water bodies. The construction of cascaded dams on the main stem of the Yangtze River has altered the natural water temperature regime, impacting the hydrothermal status of the water. Utilizing multi-source remote sensing data from Google Earth Engine to invert river surface water temperatures, a parameter-optimized CNN-LSTM-Attention hybrid interpretable water temperature prediction model was constructed. The model demonstrated credible accuracy. Based on the inversion results, the study revealed the spatiotemporal evolution characteristics of water temperature in the main stem of the Yangtze River before and after cascaded dam construction in the lower Jinsha River region and the Three Gorges Reservoir area. The results found that after the construction of the Three Gorges Dam, the annual average water temperature increased significantly by 0.813 °C. The “cold water stagnation effect” induced by cascaded development caused the water temperature amplitude to increase from 8.96 °C to 10.6 °C. Furthermore, the regulating effect of tributary confluence exhibited significant differences. For instance, colder tributaries like the Yalong River reduced the main stem water temperature, while warmer tributaries like the Jialing River, conversely, increased the main stem temperature. The construction of cascaded dams led to distinct variation characteristics in the areas downstream of the dams within the reservoir regions, where tributary inflows caused corresponding changes in the main stem water temperature. This study elucidates the long-term spatiotemporal variation characteristics of water temperature in the main stem of the Yangtze River. The model prediction results can assist in constructing an early warning indicator system for water temperature changes, providing reliable data support for promoting water environment sustainability and ecological civilization construction in the river basin. Full article

The accurate motion of roller-bearing-supported rings is critically influenced by shape and positional tolerances, which are often underestimated in conventional modeling approaches. The aim of this study is to develop and validate a multibody dynamic framework capable of quantifying the impact of roundness and positional errors on the motion accuracy of roller-bearing-supported rings. Shape errors are modeled using Fourier series and incorporated into a high-fidelity multibody simulation environment. Experimental validation using laser triangulation reveals a maximum runout error of 72.9 μm, compared to a numerically predicted value of 88.6 μm, resulting in a quantified numerical overestimation of 21.5%. Parametric studies investigated the effects of harmonic order, error amplitude, and combined error scenarios on key performance metrics, including trajectory runout and initial offset displacement. Results reveal that the trajectory errors range between 0.29 mm and 0.63 mm for shape error orders and can escalate to 2.84 mm for high amplitude errors, demonstrating the critical role of error order and amplitude. Furthermore, combined simulations show that bearing position errors exert a more pronounced effect on radial accuracy than shape deviations alone. The proposed approach enables precision design evaluation and tolerance optimization in high-accuracy applications, including robotics, aerospace mechanisms, and optical alignment systems. Full article

Under the background of “dual-carbon”, the development of energy internet is an inevitable trend for China’s low-carbon energy transition. This paper proposes a hydrogen-coupled electrothermal integrated energy system (HCEH-IES) operation mode and optimizes the source-side structure of the system from the level of carbon trading policy combined with low-carbon technology, taps the carbon reduction potential, and improves the renewable energy consumption rate and system decarbonization level; in addition, for the operation optimization problem of this electric–gas–heat integrated energy system, a flexible energy system based on electric–gas–heat is proposed. Furthermore, to address the operation optimization problem of the HCEH-IES, a deep reinforcement learning method based on Soft Actor–Critic (SAC) is proposed. This method can adaptively learn control strategies through interactions between the intelligent agent and the energy system, enabling continuous action control of the multi-energy flow system while solving the uncertainties associated with source-load fluctuations from wind power, photovoltaics, and multi-energy loads. Finally, historical data are used to train the intelligent body and compare the scheduling strategies obtained by SAC and DDPG algorithms. The results show that the SAC-based algorithm has better economics, is close to the CPLEX day-ahead optimal scheduling method, and is more suitable for solving the dynamic optimal scheduling problem of integrated energy systems in real scenarios. Full article

This study focuses on the nonlinear departure dynamics of spacecraft from the Near Rectilinear Halo Orbit (NRHO) to the outer regions of Selenocentric Space. By carefully selecting the combination of orbital parameters and the order of the evaluation process, it becomes possible to precisely identify the divergence moment and to reliably classify the subsequent dynamical space. An empirical divergence detection algorithm is proposed by integrating multiple parameters derived from multi-body dynamical models, including gravitational potentials and related quantities. In an applied analysis using this method, it is found that the majority of perturbed trajectories diverge into the outer Earth–Moon Vicinity, while transfers into the inner Earth–Moon Vicinity are relatively limited. Furthermore, transfers to Heliocentric Space are found to be dependent not on the magnitude of the initial perturbation but on the geometric configuration of the Sun, Earth, and Moon during the transfer phase. The investigation of the Sun’s initial phase reveals a rotationally symmetric structure in the perturbation distribution within the Sun–Earth–Moon system, as well as localized conditions under which the destination space varies significantly depending on the initial state. Identifying the divergence moment allows for comparative evaluation of the spacecraft’s nonlinear dynamical state, providing valuable insights for the development of safe and efficient transfer strategies from selenocentric orbits, including those originating from the NRHO. Full article

The whale shark Rhincodon typus is the largest known extant omnivorous fish species, reaching up to 17 m in length. Because of its slow growth and late maturity, R. typus is particularly vulnerable to human activities and is listed as endangered on the IUCN Red List. Understanding its biological characteristics, such as growth rate, is essential for their conservation. Non-invasive methods, including stereo-image measurements, have been used to measure the body length of the species over the years, which aggregates in coastal areas during specific life stages. This method enables us to estimate fish length by recording the target using a stereo camera, which commonly consists of two cameras. However, measurement errors increase in the setup as the target moves away from the camera. Therefore, we conducted a multi-stereo video shoot of a free-swimming whale shark in an aquarium tank and compared the performance of stereo cameras using two, three, and four cameras. The setups with three and four cameras outperformed the traditional two-camera stereo setup in terms of precision and accuracy, suggesting that a multi-stereo camera system can effectively estimate the body length of large animals such as whale sharks from a considerable distance. Full article

With the rapid development of transportation infrastructure, bridges increasingly face prominent issues of dynamic response and fatigue damage induced by vehicle–bridge interaction (VBI). To effectively suppress the coupled vibrations and enhance both vehicle ride comfort and bridge service life, this paper proposes an active inerter-spring-damper (ISD) suspension system based on Particle Swarm Optimization (PSO) algorithm and Linear Quadratic Regulator (LQR) control. By establishing a VBI model considering general boundary conditions and employing the modal superposition method to solve the system response, an LQR controller is designed for multi-objective optimization targeting the vehicle body acceleration, suspension dynamic travel, and tire dynamic load. To further improve control performance, the PSO algorithm is utilized to globally optimize the LQR weighting matrices. Numerical simulation results demonstrate that, compared to passive suspension and unoptimized LQR active suspension, the PSO-LQR control strategy significantly reduces vertical body acceleration and tire dynamic load, while also improving the convergence and stability of the suspension dynamic travel. This research provides a new insight into the control method for VBI systems, possessing both theoretical and practical engineering application value. Full article

As the development of electric tractors progresses, battery systems have become a key component, accounting for a significant portion of the vehicle’s total weight. With rollover accidents remaining a leading cause of fatal injuries in agricultural machinery, the stability of electric tractors is drawing increasing attention. In particular, battery placement may critically affect the overall mass distribution and rollover behavior, highlighting the need for safety-focused design optimization. This study evaluates the static rollover stability of a 55 kW electric tractor by analyzing the effect of battery mounting position and comparing it with a conventional tractor. Three tractor models were considered: an electric tractor with a front-mounted battery, one with a center-mounted battery, and a conventional tractor. Multibody dynamic simulations were conducted using RecurDyn, and a total of 24 orientations, at 15° intervals, were simulated to determine the tipping angles in all directions. The results revealed that battery placement had a significant impact on rollover stability. The front-mounted battery type exhibited up to 30% higher tipping angles than the conventional tractor in the forward pitch direction near 90°, indicating improved stability. In contrast, the center-mounted battery type showed a tipping angle distribution generally similar to that of the conventional tractor, with smaller variations across directions. These findings demonstrate the influence of mass distribution on rollover safety and provide valuable insight for structural design of electric tractors. Full article

Ecosystems are nonlinear systems that can shift between multiple stable states. Ecosystem service bundles (ESBs) integrate the supply and trade-offs of multiple services, yet the conditions for achieving high-supply and balanced states remain unclear from a nonlinear, threshold-based perspective. In this study, six representative ecosystem services in Fujian Province were quantified, and ESBs were identified using a Self-Organizing Map (SOM). By integrating the Multiclass Explainable Boosting Machine (MC-EBM) with the API interpretable algorithm, we propose a framework for exploring ESB driving mechanisms from a nonlinear, threshold-based perspective, addressing two key questions: (1) Which factors dominate ESB formation? (2) What thresholds of these factors promote high-supply, balanced ESBs? Results show that (i) the proportion of water bodies, distance to construction land, annual solar radiation, annual precipitation, population density, and GDP density are the primary driving factors; (ii) higher proportions of water bodies enhance and balance multiple services, whereas intensified human activities significantly reduce supply levels, and ESBs are highly sensitive to climatic variables; (iii) at the 1 km × 1 km grid scale, optimal threshold ranges of the dominant factors substantially increase the likelihood of forming high-supply, balanced ESBs. The MC-EBM effectively reveals ESB formation mechanisms, significantly outperforming multinomial logistic regression in predictive accuracy and demonstrating strong generalizability. The proposed approach provides methodological guidance for multi-service coordination across regions and scales. Corresponding land management strategies are also proposed, which deepen understanding of ESB formation and offer practical references for enhancing ecosystem service supply and reducing trade-offs. Full article

As high-speed railway systems continue to develop toward intelligent operation, axle box bearings integrated with sensors have become key components for real-time condition monitoring. However, introducing sensor-embedded slots disrupts the structural continuity and thermal conduction paths of traditional bearing rings. This results in localized stress concentrations and thermal distortion, which compromise the bearing’s overall performance and service life. This study focuses on a double-row tapered roller bearing used in axle boxes and develops a multi-physics finite element model incorporating the effects of sensor-embedded grooves, based on Hertzian contact theory and the Palmgren frictional heat model. Both contact load verification and thermo-mechanical coupling analysis were performed to evaluate the influence of two key design parameters—groove depth and arc length—on equivalent stress, temperature distribution, and thermo-mechanical coupling deformation. The results show that the embedded slot structure significantly alters the local thermodynamic response. Especially when the slot depth reaches a certain value, both stress and deformation due to thermo-mechanical effects exhibit obvious nonlinear escalation. During the design process, the length and depth of the arc-shaped embedded slot, among other parameters, should be strictly controlled. The study of the stress and temperature characteristics under the thermos-mechanical coupling effect of the axle box bearing is of crucial importance for the design of the intelligent bearing body structure and safety assessment. Full article

This study presents the design, development, and experimental validation of a novel cable-driven shoulder exosuit (CDSE) for upper limb rehabilitation and assistance. Unlike existing exoskeletons, which are often bulky, limited in degrees of freedom (DOFs), or impractical for home use, the proposed DSE offers a lightweight (≈2 kg), portable, and wearable solution capable of supporting three shoulder movements: abduction, flexion, and horizontal adduction. The system employs a bioinspired tendon-driven mechanism using Bowden cables, transferring actuation forces from a backpack to the arm, thereby reducing user load and improving comfort. Mathematical models and inverse kinematics were derived to determine cable length variations for targeted motions, while control strategies were implemented using a PID-based approach in MATLAB Simscape-Multibody simulations. The prototype was fabricated in three iterations using PLA, aluminum, and carbon fiber—culminating in a durable and ergonomic final version. Experimental evaluations on a healthy subject demonstrated high accuracy in position tracking (<5% error) and torque profiles consistent with simulation outcomes, validating system robustness. The CDSE successfully supported loads up to 4 kg during rehabilitation tasks, highlighting its potential for clinical and at-home applications. This research contributes to advancing wearable robotics by addressing portability, biomechanical alignment, and multi-DOF functionality in upper limb exosuits. Full article

As stroke is becoming more frequent nowadays, cutting edge rehabilitation approaches are required to recover upper limb functionalities and to support patients during daily activities. Recently, focus has moved to robotic rehabilitation; however, therapeutic devices are still highly expensive, making rehabilitation not easily affordable. Moreover, devices are not easily accepted by patients, who can refuse to use them due to not feeling comfortable. The presented work proposes the exploitation of a virtual prototype of the human–robot ecosystem for the study and analysis of patient–robot interactions, enabling their simulation-based investigation in multiple scenarios. For the accomplishment of this task, the Dynamics of Multi-physical Systems platform, previously presented by the authors, is further developed to enable the integration of biomechanical models of the human body with mechatronics models of robotic devices for motion assistance, as well as with PID-based control strategies. The work begins with (1) a description of the background; hence, the current state of the art and purpose of the study; (2) the platform is then presented and the system is formalized, first from a general side and then (3) in the application-specific scenario. (4) The use case is described, presenting a controlled gym weightlifting exercise supported by an exoskeleton and the results are analyzed in a final paragraph (5). Full article

A spacecraft is a typical rigid–flexible–thermal coupled multibody system, and the study of such rigid–flexible–thermal coupled systems has important engineering significance. The dissipation effect of material damping has a significant impact on the response of multibody system dynamics. Owing to the increasing multitude of computational dimensions, computational efficiency has remained a significant bottleneck hindering their practical applications in engineering. However, due to the fact that the stiffness matrix is a highly nonlinear function of generalized coordinates, traditional methods of modal truncation are difficult to apply directly. In this study, the absolute nodal coordinate formulation (ANCF) is used to uniformly describe the modeling of rigid–flexible–thermal coupled multibody systems with large-scale motion and deformation. The constant tangent stiffness matrix and damping matrix can be obtained by locally linearizing the dynamic equation and heat transfer equations, which are based on the Taylor expansion. The dynamic and heat transfer equations obtained by reducing the order of complex modes are transformed into a unified first-order equation, which is solved simultaneously. The orthogonal complement matrix of the constraint equation is proposed to eliminate the nonlinear constraints. A strategy based on energy preservation was proposed to update the reduced-order basis vectors, which improved the calculation accuracy and efficiency. Finally, a systematic method for rigid–flexible–thermal coupled viscoelastic multibody systems via modal truncation with complex global modes is developed. Full article

Functional bowel disorders (FBDs) are chronic gastrointestinal conditions characterized by recurrent symptoms associated with gut microbiota dysbiosis. Although accumulating evidence suggests that probiotics can improve symptoms in patients with FBD, the underlying mechanisms remain to be fully elucidated. In this randomized, double-blind, placebo-controlled clinical trial, 38 adults meeting the Rome IV diagnostic criteria of functional constipation (FC) and functional diarrhea (FD) received either a multi-strain probiotic complex or placebo for 8 weeks. Clinical outcomes were evaluated using the Irritable Bowel Syndrome Severity Scoring System (IBS-SSS), bowel habits questionnaire, and IBS Quality of Life (IBS-QoL) instrument. Fecal samples were collected at baseline and at week 8 for gut microbiota profiling via 16S rRNA gene sequencing and metabolomic analysis using gas chromatography–mass spectrometry. Probiotic supplementation significantly reduced the severity of abdominal bloating and its interference with quality of life, and improved the body image domain of the IBS-QoL. Beta diversity analysis showed significant temporal shifts in the probiotic group, while 16S rRNA sequencing revealed an increased relative abundance of Faecalibacterium prausnitzii and Blautia stercoris. Fecal metabolomic analysis further indicated elevated levels of metabolites implicated in the gut–brain axis. Multi-strain probiotic supplementation alleviated gastrointestinal symptoms and improved aspects of psychosocial well-being in adults with FBDs, potentially through modulation of the human gut microbiome. Full article

Flexible and wearable sensors have emerged as transformative technologies in the field of real-time health monitoring, offering non-invasive, continuous, and personalized healthcare solutions. These devices are designed to conform intimately to the human body, enabling seamless detection of vital physiological and biochemical signals under dynamic conditions. Recent advancements in material science and device engineering have led to the development of sensors with enhanced sensitivity, biocompatibility, and wearability, addressing the growing demand for preventive healthcare and remote patient monitoring. This review provides a comprehensive overview of the progress in flexible wearable sensors, including novel materials, sensor designs, and system integration strategies. It begins by surveying the latest advances in substrate and functional materials and hybrid structures that enable mechanical flexibility, skin conformability, and high sensitivity. The review then examines various sensor mechanisms and their implementation in monitoring vital signs, physical activity, and chronic diseases. Real-world applications are explored in depth, covering scenarios from cardiovascular and respiratory monitoring to motion tracking and rehabilitation support. Despite the significant strides made, challenges related to material robustness, sensor accuracy, and multi-modal integration remain, and this review discusses these challenges alongside potential future directions for enhancing the functionality and adoption of flexible wearable sensor systems. Full article