Search Results (219)

Waterborne polyurethane (WPU) and waterborne poly(urethane-urea) (WPUU) dispersions allow safer and more sustainable manufacturing of rechargeable batteries via water-based processing, while offering tunable adhesion and segmented-domain mechanics. Beyond conventional roles as binders and coatings, WPU/WPUU chemistries also support separator/interlayer and polymer-electrolyte designs for lithium-ion and lithium metal systems, where interfacial integrity, stress accommodation, and ion transport must be balanced. Here, we review WPU/WPUU fundamentals (building blocks, dispersion stabilization, morphology, and film formation) and review prior studies through a battery-centric structure–processing–property lens. We point out key performance-limiting trade-offs—adhesion versus electrolyte uptake and ionic conductivity versus storage modulus—and relate them to practical formulation variables, including soft-/hard-segment selection, ionic center/counterion design, molecular weight/topology control, and crosslinking strategies. Applications are reviewed for (i) electrode binders (graphite/Si; cathodes such as LFP and NMC), (ii) separator coatings and functional interlayers, and (iii) gel/solid polymer electrolytes and hybrid composites, with a focus on practical design guidelines for navigating these trade-offs. Future advancements in WPU/WPUU chemistries will depend on developing stable, low-impedance interlayers, enhancing electrochemical behavior, and establishing application-specific design guidelines to optimize performance in lithium metal batteries (LMB). Full article

This study presents the design, optimization, and experimental validation of a dual-band dielectric monopole antenna. The proposed antenna structure consists of three concentric cylindrical dielectric layers, each with independently tunable permittivities and radii. This configuration allows the effective control of electromagnetic performance over distinct frequency bands. To determine the optimal geometric and material parameters, the bio-inspired Honey Bee Mating Optimization (HBMO) algorithm is employed. The optimization process simultaneously maximizes antenna gain and minimizes reflection coefficient in the X and Ku bands. A cost function incorporating both gain and impedance matching criteria is formulated to achieve well-balanced solutions. The final antenna prototype was fabricated using a fused deposition modeling (FDM)-based 3D printer, where the dielectric properties of each layer are adjusted through variable infill rates. Simulated and measured results confirm stable dual-band operation with reflection coefficients below −10 dB, while the maximum in-band realized gains reach approximately 6.6 dBi in the X-band and 7.1 dBi in the Ku-band. These findings demonstrate the effectiveness of the proposed optimization approach and validate the feasibility of using 3D-printed dielectric-loaded structures as an efficient solution for high-frequency and space-constrained communication systems. Full article

Objective: This study evaluated whether higher adherence to the Mediterranean Diet (MD), specifically through increased legume consumption, is associated with improved functional, autonomic, and performance parameters in adolescents and young adult competitive swimmers. Methods: Thirty-nine swimmers (mean age 19.7  ±  2.3 years; 22 men, 17 women) monitored over a five-month period under standardized training conditions. Based on baseline dietary assessment, participants were allocated into three groups according to habitual legume intake: Control group (<1 serving/week, no dietary modification), 3Legumes group (~2 servings/week, increased to 3/week), and 6Legumes group (~3–4 servings/week, increased to 6/week). Functional evaluation encompassed bioelectrical impedance parameters (phase angle, extracellular and intracellular water, ECW/ICW ratio), heart rate variability (HRV), cardiac coherence, and critical swimming speed test (CSS) results. Results: After 5 months, the 6Legumes group showed an increase in phase angle (Δ  =  +0.34  ±  0.35°, p =  0.004), a reduction in extracellular water (Δ  =  −1.77  ±  0.93%, p <  0.001), and an increase in intracellular water (Δ  =  +1.77  ±  0.93%, p <  0.001), resulting in a lower ECW/ICW ratio (Δ  =  −0.051  ±  0.028, p <  0.001). HRV (Δ  =  +6.92  ±  5.02, p =  0.0003) and cardiac coherence (Δ  =  +0.40  ±  0.35, p =  0.0015) also demonstrated statistically significant improvements, whereas CSS exhibited a positive trend (Δ  =  +0.011  ±  0.019 m/s, p =  0.067) without reaching statistical significance. Between-group comparisons confirmed significant differences in phase angle and water-distribution parameters (all p <  0.01). Conclusions: In this cohort of adolescents and young adult competitive swimmers, increased legume consumption within a Mediterranean dietary framework was associated with beneficial adaptations in cellular hydration status, autonomic regulation, and functional performance. Full article

Accurate prognosis of the remaining useful life (RUL) for lithium-ion batteries is critical for mitigating range anxiety and ensuring the operational safety of electric vehicles. However, existing data-driven methods often struggle to maintain robustness when transferring from controlled laboratory conditions to complex, sensor-limited, real-world environments. To bridge this gap, this study presents U-H-Mamba, a novel uncertainty-aware hierarchical framework trained on a massive hybrid repository comprising over 146,000 charge–discharge cycles from both laboratory benchmarks and operational electric vehicle datasets. The proposed architecture employs a two-level design to decouple degradation dynamics, where a Multi-scale Temporal Convolutional Network functions as the base encoder to extract fine-grained electrochemical fingerprints, including derived virtual impedance proxies, from high-frequency intra-cycle measurements. Subsequently, an enhanced Pressure-Aware Multi-Head Mamba decoder models the long-range inter-cycle degradation trajectories with linear computational complexity. To guarantee reliability in safety-critical applications, a hybrid uncertainty quantification mechanism integrating Monte Carlo Dropout with Inductive Conformal Prediction is implemented to generate calibrated confidence intervals. Extensive empirical evaluations demonstrate the framework’s superior performance, achieving a RMSE of 3.2 cycles on the NASA dataset and 5.4 cycles on the highly variable NDANEV dataset, thereby outperforming state-of-the-art baselines by 20–40%. Furthermore, SHAP-based interpretability analysis confirms that the model correctly identifies physics-informed pressure dynamics as critical degradation drivers, validating its zero-shot generalization capabilities. With high accuracy and linear scalability, the U-H-Mamba model offers a viable and physically interpretable solution for cloud-based prognostics in large-scale electric vehicle fleets. Full article

Hydraulic manipulators are vital for heavy-duty applications such as rescue robotics due to their high power density, yet these scenarios increasingly demand safe and compliant physical interaction. Impedance control is a key enabling technology for such capabilities. However, a significant challenge arises when implementing impedance control on Independent Metering Systems (IMS), which are widely adopted for their energy efficiency. The inherent multi-mode operation of IMS relies on discrete switching logic. Crucially, when mode switching occurs during physical interaction with the environment, the unpredictable external forces can trigger frequent and abrupt switching between operating modes (e.g., resistive and overrunning), leading to severe chattering. This phenomenon not only undermines the smooth interaction that impedance control aims to achieve but also jeopardizes overall system stability. To address this critical issue, this paper proposes a seamless control framework based on a Takagi–Sugeno (T-S) fuzzy model. Two premise variables based on the physical characteristics of the system are innovatively designed to make the rule division highly consistent with the dynamic nature of the system. Asymmetric membership functions are introduced to handle direction-dependent switching, with orthogonal functions ensuring logical exclusivity between extension and retraction, and smooth complementary functions enabling seamless transitions between resistance and overrunning modes. Experimental validation on a small hydraulic manipulator validates the effectiveness of the proposed method. The controller eliminates switching-induced instability and smooths velocity transitions, even under dynamic external force disturbances. This work provides a crucial solution for high-performance, stable hydraulic interaction control, paving the way for the application of hydraulic robots in complex and dynamic environments. Full article

Off-road vehicle control designs often neglect the complex tire–soil interactions inherent to soft terrain. This paper proposes a Variable Impedance Control (VIC) strategy integrated with a high-fidelity terramechanics model. First, a real-time sinkage estimation algorithm is derived using experimentally identified Bekker parameters and the quasi-rigid wheel assumption to capture the nonlinear feedback between soil deformation and vehicle dynamics. Building on this, the VIC strategy adaptively regulates virtual stiffness, damping, and inertia parameters based on real-time suspension states. Comparative simulations on an ISO Class-C soft soil profile demonstrate that this framework effectively balances ride comfort and safety constraints. Specifically, the VIC strategy reduces the root-mean-square of vertical body acceleration by 46.9% compared to the passive baseline, significantly outperforming the Linear Quadratic Regulator (LQR). Furthermore, it achieves a 48.6% reduction in average power relative to LQR while maintaining suspension deflection strictly within the safe range. Moreover, unlike LQR, the VIC strategy improves tire deflection performance, ensuring superior ground adhesion. These results validate the method’s robustness and energy efficiency for off-road applications. Full article

Crop root development, and in turn crop growth, is strongly influenced by soil strength and the mechanical impedance of compacted layers, which restrict root elongation and exploration. Because the depth and thickness of compacted layers vary across a field, their identification is essential for site-specific tillage and sustainable root-zone management. A sensing approach that can support future real-time identification of compacted layers after soil-specific calibration, which would enable variable-depth tillage, reducing mechanical impedance and improving energy-use efficiency while maintaining crop yields. This study aimed to develop and evaluate prediction models that can support future real-time identification of compacted soil layers using soil electrical conductivity (EC) and moisture content as non-destructive indicators. A sandy clay soil (48.6% sand, 29.3% clay, 22.1% silt) was tested in a soil-bin laboratory under controlled conditions at three moisture levels (13, 18, and 22% db.) and six depth layers (C1–C6, 0–30 cm) identified from the penetration-resistance profile to measure penetration resistance, shear resistance, and EC. Penetration and shear resistance increased toward the most resistant depth layer and decreased with increasing moisture content, whereas EC generally increased with both depth layer and moisture content. Linear regression models relating penetration resistance ($R^2=0.893$) and shear resistance ($R^2=0.782$) to EC and moisture content were developed and evaluated. Field validation in a paddy field of similar texture showed that predicted penetration resistance differed from measured values by 3–6% across the three compaction treatments evaluated. Root length density and root volume decreased with increasing machine-induced compaction, confirming the agronomic relevance of the modeled patterns and supporting the suitability of the proposed indicators. Together, these results demonstrate that EC and moisture content can potentially be used as non-destructive proxies for compacted-layer identification and provide a calibration basis for future on-the-go sensing systems to support site-specific, variable-depth tillage in agricultural fields. Full article

When offshore wind power is connected to a grid via Modular multilevel converter-based High Voltage Direct Current (MMC-HVDC), the sending-end alternating current (AC) system is susceptible to asymmetrical faults. These faults lead to overcurrent surges, voltage drops, and second harmonic circulating currents, which seriously threaten the safe operation of the system. To quickly suppress fault current surges, achieve precise control of system variables, and improve fault ride-through capability, this study proposes a collaborative control strategy. This strategy integrates generalized virtual impedance current limiting, positive- and negative-sequence collaborative feedforward control, and model-predictive control-based suppression of arm energy and circulating currents. The positive- and negative-sequence components of the voltage and current are quickly separated by extending and decoupling the decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL). A generalized virtual impedance with low positive-sequence impedance and high negative-sequence impedance was designed to achieve rapid current limiting. Simultaneously, negative-sequence current feedforward compensation and positive-sequence voltage adaptive support are introduced to suppress dynamic fluctuations. Finally, an arm energy and circulating current prediction model based on model predictive control (MPC) is established, and the second harmonic circulating currents are precisely suppressed through rolling optimization. Simulation results based on PSCAD/EMTDC show that the proposed control strategy can effectively suppress the negative-sequence current, significantly improve voltage stability, and greatly reduce the peak fault current. It significantly enhances the fault ride-through capability and operational reliability of offshore wind power MMC-HVDC-connected systems and holds significant potential for engineering applications. Full article

The reliability of the Medium-Voltage Direct-Current (MVDC) power supply system is crucial for train operation, as it powers control, communication, and other critical onboard systems. Accurately locating insulation faults within this system can significantly reduce troubleshooting difficulty and prevent major operational losses. This study addresses a key challenge in applying Time-Domain Reflectometry (TDR) for fault location in single-core cables of IT systems: the incident-end impedance mismatch caused by the variable characteristic impedance of such cables, which fluctuates with installation distance from a ground plane. First, the mechanism through which this mismatch attenuates the primary fault reflection and generates secondary reflections is theoretically modeled. A resistive-capacitive (RC) coupling network is then designed to achieve bidirectional impedance matching between the test equipment and the cable under test while maintaining essential DC isolation. Simulation and experimental results demonstrate that the proposed network effectively mitigates the mismatch issue. In experiments, it increased the proportion of the primary reflected wave entering the receiver by over 30 percentage points and suppressed the secondary reflection by approximately 80%. These improvements enhance waveform clarity and signal strength, directly leading to more accurate fault location. The proposed solution, validated in a railway context, also holds significant potential for improving insulation fault diagnosis in analogous high-voltage cable applications, such as electric vehicle powertrains. Full article

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments. Full article

The development of implantable neural interfaces is essential for enabling bidirectional communication between the nervous system and prosthetic devices, yet their evaluation still relies primarily on in vivo models which are costly, variable, and ethically constrained. Here, we report a bio-inspired artificial nerve simulator engineered as a reproducible ex vivo platform for pre-implantation testing of plug-type electrodes. The simulator is fabricated from a conductive hydrogel composite based on reduced graphene oxide (rGO), polyaniline (PANI), agarose, sucrose, and sodium chloride, with embedded conductive channels that replicate the fascicular organization and conductivity of peripheral nerves. The resulting construct exhibits impedance values of ~2.4–2.9 kΩ between electrode needles at 1 kHz, closely matching in vivo measurements (~2 kΩ) obtained in Sus scrofa domesticus nerve tissue. Its structural and electrical fidelity enables systematic evaluation of electrode–nerve contact properties, signal transmission, and insertion behavior under controlled conditions, while reducing reliance on animal experiments. This bio-inspired simulator offers a scalable and physiologically relevant testbed that bridges materials engineering and translational neuroprosthetics, accelerating the development of next-generation implantable neural interfaces. Full article

Background: This scoping review systematically maps and synthesises contemporary literature on the biomechanics of active below-knee prosthetic devices, focusing on gait kinematics, kinetics, energy expenditure, and muscle activation. It further evaluates design advancements, including powered ankle–foot prostheses and variable impedance systems, that seek to emulate physiological ankle function and enhance mobility outcomes for transtibial amputees. Methods: This review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted on ScienceDirect, PubMed and IEEE Xplore for studies published between 2013 and 2023. Search terms were structured according to the Population, Intervention, Comparator, and Outcome (PICO) framework. From 971 identified articles, 27 peer-reviewed studies were found to meet the inclusion criteria between January 2013 and December 2023. Data were extracted on biomechanical parameters, prosthetic design characteristics, and participant demographics to identify prevailing trends and research gaps. This scoping review was registered with Research Registry under the following registration number: reviewregistry 2055. Results: The reviewed studies demonstrate that active below-knee prosthetic systems substantially improve gait symmetry and ankle joint range of motion compared with passive devices. However, compensatory trunk and pelvic movements persist, indicating that full restoration of natural gait mechanics remains incomplete. Metabolic efficiency varied considerably across studies, influenced by device design, control strategies, and user adaptation. Notably, the literature exhibits a pronounced gender imbalance, with only 10.7% female participants, and a reliance on controlled laboratory conditions, limiting ecological validity. Conclusions: Active prosthetic technologies represent a significant advancement in lower-limb rehabilitation. Nevertheless, complete biomechanical normalisation has yet to be achieved. Future research should focus on long-term, real-world evaluations using larger, more diverse cohorts and adaptive technologies such as variable impedance actuators and multi-level control systems to reduce asymmetrical loading and optimise gait efficiency. Full article

This study evaluates the morphological performance of the CardioBAN wearable electrocardiogram (ECG) device by comparing its beat-level waveform accuracy against a clinically certified reference system (GE Vivid E9). A cycle-by-cycle Dynamic Time Warping (DTW) analysis was employed to assess beat-level waveform similarity between both devices in 17 healthy participants under controlled conditions. Each cardiac cycle from CardioBAN was aligned to its reference counterpart, enabling a fine-grained comparison of waveform shape. The resulting DTW distances (mean 0.493 ± 0.166) demonstrated overall high morphological agreement, with lower values occurring in recordings with stable beat morphology and higher values primarily reflecting normal variability related to minor motion artifacts or electrode–skin impedance fluctuations. A complementary Bland–Altman analysis of point-wise amplitude differences after DTW alignment showed minimal bias (0.079) and narrow limits of agreement (−0.897–1.055), confirming strong amplitude concordance between systems. These findings indicate that the CardioBAN wearable reliably reproduces key ECG morphological features under controlled, short-term recording conditions. Further studies encompassing ambulatory environments and clinical populations are needed to evaluate its suitability for real-world and pathological scenarios. Full article

Background: Rare diseases (RDs) and neurodevelopmental disorders (NDDs) remain under-researched due to their low prevalence, leaving significant gaps in diagnostic strategies. Beyond next-generation sequencing, epigenetic profiling and particularly episignatures have emerged as a promising complementary diagnostic tool and for reclassifying variants of uncertain significance (VUS). However, clinical implementation remains limited, hindered by non-standardized methodologies and restricted data sharing that impede the development of sufficiently large datasets for robust episignature development. Methods: We conducted a systematic literature review following PRISMA 2020 guidelines to identify all studies reporting episignatures published between 2014 and 2025. The review summarizes methodological approaches used for episignature detection and implementation, as well as reports of epimutations. Results: A total of 108 studies met the inclusion criteria. All but three employed Illumina methylation arrays, mostly 450 K and EPIC versions for patient sample analysis. Three main methodological phases were identified: data quality control, episignature detection, and classification model training. Despite methodological variability across these stages, most studies demonstrated high predictive capabilities, often relying on methodologies developed by a small number of leading groups. Conclusions: Epigenetic screening has significant potential to improve diagnostic yield in RDs and NDDs. Continued methodological refinement and collaborative standardization efforts will be crucial for its successful integration into clinical practice. Nevertheless, key challenges persist, including the need for secure and ethical data-sharing frameworks, external validation, and methodological standardization. Full article

Crystallization is a fundamental unit operation in chemical, pharmaceutical, and energy industries, where strict control of crystal size distribution (CSD) is essential for ensuring product quality and process efficiency. However, the nonlinear dynamics of crystallization and the absence of explicit functional relationships between process variables make effective control a significant challenge. This study proposes a hybrid approach that integrates process tomography with deep reinforcement learning (RL) for adaptive crystallization control. A dedicated hybrid tomographic system, combining Electrical Impedance Tomography (EIT) and Ultrasound Tomography (UST), was developed to provide complementary real-time spatial information, while a ResNet neural network enabled accurate image reconstruction. These data were used as input to a reinforcement learning agent operating in a Simulink-based simulation environment, where temperature was selected as the primary controlled variable. To evaluate the applicability of RL in this context, four representative algorithms: Actor–Critic, Asynchronous Advantage Actor–Critic, Proximal Policy Optimization (PPO), and Trust Region Policy Optimization, were implemented and compared. The results demonstrate that PPO achieved the most stable and effective performance, ensuring improved control of CSD and improved control proxies consistent with potential energy savings. The findings confirm that hybrid tomographic imaging combined with RL-based control provides a promising pathway toward sustainable, intelligent crystallization processes with enhanced product quality and energy efficiency. Full article