Search Results (7,684)

Clock steering, the core time-frequency technology for high-precision single-satellite time reference generation in global navigation satellite systems, can effectively combine the excellent short-term stability of Oven-Controlled Crystal Oscillators (OCXOs)—whose top performance indicators have already surpassed many space-borne atomic clocks in recent years—with the superior long-term stability of atomic clocks, to obtain time signals with optimal full-range stability. This paper proposes a novel clock steering scheme that integrates an adaptive variational Bayesian Kalman filter and a Proportional-Integral-Derivative (PID) automatic controller: the filter constructs a separable variational approximation for the joint posterior distribution of clock states and measurement noise parameters, to achieve real-time adaptive estimation of noise at each timestamp, while the PID controller performs closed-loop fine adjustment on the output frequency. Comparative simulations with the classic Linear Quadratic Gaussian (LQG) control scheme verify that the proposed method can generate steered time signals with better stability performance in both short-term and long-term dimensions. This work further investigates the influence of measurement noise at different intensity levels on clock steering performance and conducts corresponding mechanism analysis supported by quantitative data. The proposed scheme and conclusions can provide a reliable reference for selecting appropriate clock steering strategies under different noise conditions. Full article

With the large-scale integration of Electric Vehicles (EVs) into distribution systems, the spatiotemporal uncertainty of charging loads and the interplay between user charging behavior and network operational constraints present new challenges to the safe and economical operation of the power system. To address the insufficient coordination among flexibility characterization, distributed optimization, and user-side responses, this paper proposes a closed-loop collaborative dispatch strategy. The strategy integrates flexibility aggregation, endogenous dynamic pricing, and user charging-station selection behavior. Firstly, a three-tier collaborative architecture comprising the Distribution System Operator (DSO), Electric Vehicle Aggregators (EVAs), and EV users is established, with rolling updates implemented using Model Predictive Control (MPC). A flexible aggregation model is developed based on set operations of vehicle-level constraints, dynamically calculating power boundaries and energy feasibility domains. Furthermore, a distributed coordinated optimization model between the DSO and multiple EVAs is established and solved via the Alternating Direction Method of Multipliers (ADMM) under privacy-preserving conditions. By analyzing the correlation between ADMM dual variables and the marginal value of network constraints, a Distribution Locational Marginal Pricing (DLMP) -inspired dynamic price signal—endogenous to the optimization—is constructed to guide spatial reallocation of charging loads. Joint simulations based on an IEEE 33-node distribution network and the Sioux Falls transport network demonstrate that the proposed strategy reduces 24 h network losses from 9.05 MWh (uncoordinated) to 8.41 MWh, lowers user total costs from 22,200 yuan to 7100 yuan, and eliminates voltage limit violations (duration reduced from 1.50 h to 0), while exhibiting good distributed solution performance and closed-loop control capability. Full article

Hydrogel-and-nanomaterial-integrated wearable biosensor networks are promising platforms for real-time biomedical monitoring because they combine soft biointerfaces, sensitive signal transduction, and wireless data connectivity. Hydrogels provide tissue-like softness, hydration, adhesion, permeability, and biocompatibility, whereas nanomaterials such as graphene, carbon nanotubes, MXenes, metallic nanoparticles, and conductive polymers enhance conductivity, electrochemical activity, optical responsiveness, mechanical durability, and signal amplification. This review summarizes recent advances in hydrogel-and-nanomaterial-integrated wearable biosensors, ranging from soft material interfaces and stand-alone sensing devices to wireless wearable nodes, IoT-connected platforms, and emerging closed-loop sensor–actuator systems. Because these platforms differ substantially in their level of integration and validation, this review distinguishes enabling material and device concepts from fully connected or closed-loop systems. The distinctive contribution of this review is a materials-to-systems, evidence-graded framework that links hydrogel and nanomaterial interface design with sensing mechanisms, wearable sensor-node integration, wireless and IoT connectivity, and closed-loop actuation while distinguishing device-level proof of concept from clinically validated performance. We discuss functional hydrogel design, nanomaterial-based conductive networks, hybrid hydrogel–nanomaterial structures, and key requirements for skin compatibility, adhesion, stretchability, and long-term stability. Major sensing mechanisms and biomedical targets are reviewed, including electrochemical and optical biosensing, mechanical and physiological signal sensing, and sweat biomarker monitoring. We further highlight system-level integration strategies involving wearable sensor nodes, wireless communication, smartphone and cloud connectivity, data processing, power management, security, and reliability. Representative biomedical applications are summarized, including sweat-based metabolic monitoring, smart wound monitoring, hydrogel-based wound dressings, cardiovascular and respiratory monitoring, and motion sensing. Finally, current technical and translational challenges are discussed with emphasis on the distinction between analytical sensing performance, physiological correlation, and clinical validation. Disease-management and closed-loop healthcare applications are discussed as emerging directions that require appropriate human studies, reference-method comparison, agreement analysis, long-term monitoring, and safety validation before clinical implementation. Full article

Introduction: International shipping faces the dual imperative of decarbonization and the maintenance of safety and service reliability. However, evidence concerning the conditions under which artificial intelligence (AI) generates verifiable carbon benefits remains fragmented. Methods: This review examines 479 English-language articles and reviews retrieved from the Web of Science Core Collection and Scopus and published from 2016 to 23 August 2026 through bibliometric analysis, science mapping, auxiliary document-level thematic coding, and full-text synthesis of six representative reviews and one perspective. A post hoc domain-validation sensitivity analysis used two independently specified deterministic rule sets to test whether broad search terms altered the main conclusions. Results: Publication output accelerated markedly after 2022, with 277 papers (57.83%) published during 2022–2025 and a further 137 records already indexed in the partial year 2026. The two screening rules agreed on 96.87% of records (Cohen’s kappa = 0.753). A conservative sensitivity subset of 437 records, obtained through a strict rule-based title-abstract screen and removal of one retracted and one withdrawn record, reproduced the principal temporal, source-journal, and leading-keyword patterns. Machine learning remained the most frequent keyword, while recent studies increasingly addressed deep learning, ship energy efficiency, port operations, federated learning, and energy management. Discussion: Based on these findings, the review advances an evidence-informed AI-to-Carbon Value Chain (AICV) conceptual synthesis comprising data observability, model credibility, decision executability, system coordination, and carbon verification. This synthesis is interpretive rather than a validated causal framework. Future research should prioritize carbon-ready benchmarks, calibrated physics-informed and causal models, human-in-the-loop field evaluation, network-level coordination, and auditable well-to-wake assessment. Review registration and appraisal: This review was not registered, and no formal protocol was prepared. Because no effect-size synthesis was undertaken, formal study-level risk-of-bias, reporting-bias, and certainty assessments were not applied. Funding: The review received no external funding. Full article

A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions. Full article

This review provides a comprehensive analysis of Ylang-Ylang (Cananga odorata) essential oil (YYEO), describing its botany, historical evolution, and global commercial significance. It systematically provides information on traditional extraction techniques such as hydrodistillation, steam distillation, and solvent extraction alongside innovative green technologies, including microwave-assisted distillation (MAD), supercritical fluid extraction (SFE), and ultrasound-assisted extraction (UAE). Conventional distillation methods are compared with greener technologies. The reviewed studies indicate that microwave-assisted processing can reduce YYEO extraction time from approximately 19 h for conventional hydrodistillation to about 40 min while improving the retention of light oxygenated compounds. In particular, light oxygenated compounds have been reported at approximately 81.23% in solvent-free microwave extracts, compared with 69.94% for hydrodistillation and 57.98% for steam distillation. It has also been reported that YYEO contains more than 50 volatile secondary metabolites, with linalool representing about 28% of the oxygenated fraction, while sesquiterpene-rich hydrocarbons can account for up to 63% of the essential oil. The reviewed studies further demonstrate insecticidal, antimicrobial, antioxidant, anti-inflammatory, and neurobiological activities, supporting the potential use of YYEO in sustainable protective materials and health-related applications. Finally, emerging frontiers in protective smart textiles, living fabrics, and sustainable closed-loop manufacturing paradigms are discussed to outline future directions for bio-based material science. Full article

Commercial truck path tracking is strongly affected by large mass and yaw moment of inertia, steering-rate constraints, and signal delays, whereas optimization-based predictive controllers can impose high online-computational costs. This study proposes a real-time path-tracking method based on Constraint-Handling Trajectory Prediction (CHTP). A dynamic model predicts the vehicle’s future pose, and a Stanley-based tracking law computes the desired steering angle from the predicted state. A constraint-handling module then explicitly limits the steering angle and its rate of change. The proposed CHTP method requires no online optimization. The method was evaluated through MATLAB/Simulink-TruckSim co-simulations and hardware-in-the-loop (HIL) tests. Under the low-speed unladen condition, CHTP reduced the maximum absolute-displacement error by 74.60% compared with the conventional Stanley controller. Under the high-speed unladen, low-speed heavy-load, and high-speed heavy-load conditions, CHTP completed the lane-change maneuver with bounded tracking errors, whereas the conventional Stanley controller failed to maintain convergent tracking. Across the four basic path-tracking conditions, the maximum absolute displacement and heading errors of CHTP did not exceed 0.3930 m and 0.1133 rad, respectively. Although nonlinear model predictive control (NMPC) generally achieved higher tracking accuracy, CHTP reduced the mean solution time by 94.17–95.96% relative to NMPC, with a maximum solution time of 1.1416 ms. Additional robustness tests showed that CHTP maintained bounded tracking errors and stable lateral dynamic responses under positioning errors, low road adhesion, and random response delays, while preserving its real-time computational performance. In the HIL test with a total loop delay of approximately 0.22 s, extending the prediction time from 0.20 s to 0.42 s limited the maximum displacement and heading errors to 0.2290 m and 0.1046 rad, respectively, with a maximum solution time of only 0.9895 ms. Additional prediction-time tests showed a trend consistent with the simulation results, further supporting the proposed delay-compensation mechanism. These results demonstrate that CHTP provides a favorable balance among tracking accuracy, robustness, and real-time performance. Full article

This work presents a finite-time adaptive fuzzy control approach for a category of multi-input multi-output nonlinear systems in the presence of input saturation and external disturbances. A hyperbolic function is adopted to convert the unconstrained control command into a bounded signal so that the actuator constraints are strictly respected. Unknown nonlinearities are approximated by fuzzy logic systems, whereas external disturbances are attenuated by adaptive mechanisms together with tanh-based robust terms. In addition, dynamic surface control is incorporated into the backstepping framework, where first-order filters are employed to avoid the computational burden associated with the repeated differentiation of virtual control laws. On this basis, a Lyapunov-based design is carried out to derive the finite-time adaptive control protocol. It is shown that every signal in the resulting closed-loop system is bounded, the closed-loop system is semi-globally practically finite-time stable, and the state variables converge to a small neighborhood of the desired states within a finite settling time, and the control inputs never exceed the prescribed bounds. Simulation results obtained using a representative rigid spacecraft as an example confirm the feasibility and disturbance-rejection capability of the developed method. Full article

Embedding an evolutionary algorithm (EA) into control structures offers an effective solution for specific control problems. Often, it predicts the best control values using a process model (PM). The primary limitation is its high computational time. Our work addresses optimal control problems (OCPs) with a final cost, using receding horizon control (RHC) with an EA as a predictor. This work is a continuation of a previous article, in which the EA predictor was replaced with a multilinear regression-based predictor. Our objective is to propose a predictor based on regression neural networks (RNNs) that emulates the behavior of the (EA, PM) couple. A number of closed-loop simulations using the existing EA controller produce sequences of optimal control values and corresponding state values, which are stored in a data structure. Datasets for each sampling period are derived from these data and are used to train RNN objects employing a unique RNN model. The model, which is an “optimizable” RNN plus the list of hyperparameters preset before optimization, is determined after a thorough analysis of possible candidates using a MATLAB R2025b application. The presented method of constructing an RNN predictor is the main contribution. Algorithms for (a) constructing the sequence of RNN objects and (b) simulating the closed loop are also proposed. A case study illustrates our method. The RNN predictor successfully emulated the (EA, PM) couple: (a) the control-loop dynamics were nearly identical; (b) the performance indices were essentially the same; and (c) the execution time of the controller significantly decreased from 38 to 0.054 s, demonstrating that RHC can be applied more broadly. Full article

Generalized category discovery (GCD) aims to classify known categories while discovering novel ones in unlabeled data, yet existing methods lack mechanisms to correct boundary-ambiguous samples that receive noisy pseudo-labels, as they primarily rely on visual feature learning without external semantic guidance. Vision-language models (VLMs) offer a natural source of cross-modal semantic correction. However, applying VLM-guided contrastive signals directly within the GCD training loop proves counterproductive because the locally-oriented InfoNCE loss conflicts geometrically with the globally oriented K-means objective in the shared backbone space. We identify the root cause as a dual resource allocation problem: the VLM-derived signal must be allocated to the correct feature subspace to avoid geometric conflict with K-means clustering (space allocation), and the limited VLM inference budget must be allocated to the correct samples to maximize discriminative return (budget allocation). These two decisions are coupled; failure on either renders the other ineffective. To resolve this, we propose DualGLEAN, a framework that addresses the dual allocation challenge through two coupled mechanisms: decoupled contrastive alignment (DCA), which routes the VLM-guided neighbor contrastive loss to a dedicated projector space while preserving the backbone space for global clustering, and compound uncertainty querying (CUQ), a three-stage filtering metric that jointly evaluates predictive entropy, boundary proximity, and local label inconsistency to direct VLM queries exclusively to truly boundary-critical samples. Extensive experiments on the AID and RSSDIVCS datasets demonstrate that DualGLEAN achieves strong performance, improves four diverse GCD baselines as a plug-in module, generalizes across seven VLM backbones, introduces zero additional trainable parameters to the base GCD network, and incurs a total VLM API cost of only CNY 2.45 per full training run on the AID dataset under the default search-scope configuration, with the cost scaling linearly with the query budget. Full article

Aiming at the problems of large speed synchronization error and prominent speed overshoot existing in conventional PID control algorithms widely adopted for diesel-engine parallel-unit speed-governing systems, this paper proposes a speed control algorithm based on Model Predictive Control (MPC) for dual-diesel-engine parallel operation. A quasi-steady-state method is employed to establish the coupled state-space model for dual-engine parallel operation. Leveraging the prediction-optimization and multi-constraint regulation characteristics of MPC, the fuel-injection outputs of the two diesel engines are regulated respectively by two independent SISO MPC controllers, which share the identical speed reference and are coordinated at the logic level through the Stateflow engagement/disengagement state machine to achieve speed synchronization. Comparative simulations under different operating conditions are carried out on the Matlab/Simulink platform. The simulation results show that the proposed MPC algorithm can effectively suppress speed fluctuations between the two diesel engines under the tested operating conditions compared with the traditional PID control. Furthermore, a hardware-in-the-loop test platform is constructed for validation in the embedded environment. The test results reproduce all operating conditions of offline simulation, achieving zero speed overshoot and restricting the dual-engine synchronization deviation within ±3 rpm, which are consistent with the offline simulation results. The real-time computational capability and engineering feasibility of the proposed algorithm are therefore verified. The proposed method is applicable to stable speed-governing scenarios of marine dual-diesel-engine parallel-unit sets. Full article

Owing to unknown hydrodynamic characteristics, unmeasurable external disturbances and uncertain control coefficients, conventional control strategies that rely on precise mechanistic models often suffer from notable performance degradation. To address this problem, a novel data-driven Nussbaum-RBF Sliding Mode Control (NRSMC) strategy is proposed for USV heading control. In the proposed framework, a Radial Basis Function (RBF) neural network is employed as a data-driven approximator to learn unknown nonlinear system dynamics online, avoiding the dependence on accurate prior mathematical models. Based on the online-learned dynamic information, a sliding mode control (SMC) mechanism is developed to enhance robustness against approximation errors and external disturbances, and a boundary layer technique is introduced to alleviate the chattering phenomenon. Furthermore, a Nussbaum function is incorporated to address the unknown control coefficients problem, ensuring system stability without requiring prior knowledge of the control coefficients. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory. Comparative simulation results demonstrate that the proposed NRSMC strategy achieves superior tracking accuracy and robustness compared with the Nussbaum-RBF Adaptive Backstepping Control (NRABC) method. Moreover, field experiments conducted further validate the effectiveness and adaptability of the proposed data-driven control approach. Full article

Background: Major spine surgery carries an inherent risk of hemodynamic instability due to prone positioning, significant blood loss, and the strict necessity to maintain adequate spinal cord perfusion. Hemodynamic management mainly relies on a reactive approach, treating hypotension only after it occurs, which increases the risk of postoperative complications such as acute kidney injury and ischemic events. This narrative review evaluates the clinical impact, current evidence, and future perspectives of integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into perioperative hemodynamic care. Methods: A comprehensive literature search was conducted through PubMed, EMBASE, and the Cochrane Library, spanning from inception to January 2026. The search strategy employed combinations of Medical Subject Headings terms and keywords related to “Artificial Intelligence,” “Machine Learning,” “Hypotension Prediction Index,” “hemodynamic monitoring,” and “major spine surgery.” Studies were selected based on their relevance to predictive hemodynamic algorithms, goal-directed fluid therapy (GDFT), and automated closed-loop systems within the perioperative setting of complex spinal interventions. Results: Five studies show that AI/ML tools can improve hemodynamic management in spine surgery: an hypotension prediction index (HPI)-guided algorithm reduced intraoperative hypotension during prone spinal fusion; a machine learning model accurately predicted massive blood loss in metastatic spinal disease; an AutoML framework linked intraoperative hypertension to worse neurological recovery after spinal cord injury (SCI); a case report showed HPI-guided goal-directed therapy enabled safe, transfusion-free major spine surgery; and topological network analysis identified a narrow optimal mean arterial pressure (MAP) range for neurological recovery after SCI. Collectively, these preliminary findings suggest a potential role for AI/ML in reducing hemodynamic instability and enabling more individualized perioperative management in spine surgery. Rather than converging on a single verdict, these five studies fall into three distinct evidentiary categories when appraised using a structured model-validation (V1–V4) and clinical-translation (T0–T4) framework applied within each category: a real-time monitoring technology (HPI) with a substantial extra-spinal evidence base but a limited spine-specific replication record; a single, externally validated but clinically unproven preoperative prediction model; and two retrospective, hypothesis-generating discovery frameworks that remain exploratory irrespective of surgical domain. Conclusions: The evidence identified does not support a single, unified statement about “AI/ML in spine surgery.” Instead, it points to three distinct situations that warrant separate research priorities: consolidating spine-specific replication of an otherwise mature monitoring technology (HPI); externally confirming the clinical utility, rather than only the discriminative accuracy, of a single preoperative prediction model; and prospectively testing the retrospectively derived targets generated by discovery-oriented analytic frameworks. Considered together, these findings should inform hypothesis-driven research design rather than a single implementation-readiness judgment. Full article

Soft and compliant rehabilitation devices may provide improved anatomical adaptability, reduced distal mass, and more flexible human–robot interaction compared with conventional rigid exoskeletons. This scoping review examines elbow rehabilitation technologies across three interconnected domains: (1) the broader landscape of soft and compliant actuation, (2) the structural, material, and operating architectures of pneumatic soft actuators, and (3) the sensing, intention-detection, and closed-loop control methods used in pneumatic systems. The review included 109 peer-reviewed reports. Four main actuation families were identified: pneumatic soft actuators, motor-driven cable and tendon systems, series-elastic or variable-stiffness actuators, and shape-memory-alloy-based devices. Pneumatic architectures were primarily organized around linear artificial muscles and chamber-based bending or rotary actuators, complemented by rigid–soft integrated, cable-transmitted, modular, antagonistic, self-sensing, and variable-stiffness configurations. Feedback most relied on pressure, kinematic, force, and electromyographic signals. Control architectures combined position, force, torque, impedance, and pressure regulation, while nonlinearities and uncertainties were addressed using model-based, adaptive, sliding-mode, fuzzy, neural, and hybrid methods. Full article

With an increase in the High-Voltage Direct Current (HVDC) infeed, the strength of the receiving-end AC grid decreases, leading to transient voltage instability. Voltage source converter (VSC)-HVDC stations have a large unit capacity and high controllability, offering great potential for voltage support of the receiving-end grid. A grid-following/grid-forming (GFL–GFM) hybrid control can improve the oscillation stability of VSC stations under both strong and weak grid conditions; however, most relevant studies have focused on oscillation stability, while little attention has been paid to transient voltage regulation performance. Moreover, a quantitative analysis method for the transient active- and reactive-power characteristics of the hybrid control is lacking. This paper proposes a series-connected GFL/GFM hybrid control strategy along with a quantitative dynamic power analysis method. By establishing the closed-loop transfer function model, the steady-state power control performance and transient reactive-power response of the proposed control are quantitatively analyzed. Electro-Magnetic Transient (EMT) simulation results verify that, compared with the existing hybrid synchronization-type control, the proposed series-connected scheme exhibits superior performance in mitigating transient low-voltage and overvoltage issues, with the minimum voltage dip improved from 0.3 p. u. to 0.8 p. u. and the maximum overvoltage after fault clearance decreasing from 1.38 p. u. to 1 p. u. Full article