Search Results (205)

Aiming at the engineering problems of composite roll, pitch, and yaw pose errors of the roadheader body induced by floor undulation and geological variation, as well as insufficient anchoring accuracy caused by the incapability of traditional 1–3-degree-of-freedom (DOF) drilling–anchoring arms in dynamic error compensation during coal mine roadway excavation and bolting, this paper proposes an inverse kinematics solving method for a 5-DOF drilling–anchoring arm based on an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the modified Denavit–Hartenberg (MDH) approach is adopted to establish a full-link kinematic model incorporating body pose errors, where the drill rod length, mounting offset, and world coordinate transformation are fully considered. Secondly, an Actor–Critic dual-network architecture tailored for drilling and anchoring tasks is constructed with an 11-dimensional state space and a 5-dimensional action space. The coupling optimization between body pose errors and joint adjustments is realized by designing a hierarchical gradient reward function, a dynamic noise decay exploration strategy, and an optimal state restart mechanism. Finally, 1000 episodes of training and verification are carried out on a Python 3.10 simulation platform. The simulation results reveal that the average end-effector position error of the improved algorithm reaches 0.71 mm, and the deflection angle toward the roof is less than 1°, which outperforms the specified industrial standard. The proposed method realizes real-time compensation for dynamic body pose errors and provides crucial technical support for intelligent excavation and anchoring in underground coal mines. Full article

Accessibility evaluation in built environments increasingly requires performance-based methods that capture dynamic user–environment interaction rather than static compliance with dimensional standards. Existing computational approaches, including BIM-based rule checking and path-finding analysis, are effective for geometric verification but cannot quantify the physical demands imposed on users during task execution. To date, no existing framework combines task-driven motion generation with quantitative biomechanical analysis specifically for accessibility evaluation in built environments. This paper presents a modular simulation framework that integrates procedural kinematic planning with inverse dynamics analysis to enable performance-based accessibility assessment. The system generates deterministic motion trajectories using a gait-based abstraction layer and inverse kinematics, then computes net joint forces and torques via a stabilized Newton–Euler recursive algorithm. External contact forces, including ground reactions during locomotion, are modeled through a dedicated force management subsystem. The framework is implemented in Unity and evaluated in three representative scenarios: level walking, stair ascent, and stair descent. Computed knee joint forces fall within magnitude and temporal ranges reported in the biomechanical literature (peak forces of 2.5–4.0 body weights for stairs and 1.5–2.0 body weights for level walking), indicating biomechanical plausibility rather than subject-specific experimental validation. The framework offers a reproducible, extensible foundation for evidence-based accessibility design, allowing designers to detect and quantify biomechanically demanding interactions in architectural environments prior to construction, and supporting a shift from prescriptive compliance toward dynamic, human-centered evaluation. Full article

Inverse kinematics (IK) is fundamental to robot motion planning. Classical analytical solvers require complete Denavit–Hartenberg (DH) parameters that are often proprietary or degraded by mechanical wear, and numerical solvers based on damped least squares (DLS) are sensitive to initialization, particularly near singularities. We propose XGNN, an explainable graph neural network positioned as a model-free, interpretable warm-start initializer for downstream numerical IK refinement rather than as a standalone replacement for analytical solvers. Each IK query is encoded as a 12-node graph in which six pose nodes and six joint nodes are connected through bipartite pose-to-joint attention edges and chain edges along the kinematic structure. GATv2 message passing aggregates information at each joint node; two ablation-validated design contributions (a learnable node-type embedding and an angle-aware composite loss) enable training to convergence. Evaluated on 300,000 trajectory-style samples generated from the ABB IRB 2400 kinematic model, XGNN achieves ${3.66}^{\circ }$ joint mean absolute error (MAE), comparable to a multilayer perceptron baseline (${3.09}^{\circ }$) and a bidirectional LSTM (${3.14}^{\circ }$) under identical training. The standalone joint accuracy of all learned models is too coarse for direct industrial use, but XGNN provides the strongest warm start for DLS refinement: the convergence rate improves from $98.4\%$ to $100\%$, mean iterations drop from 14.6 to 3.2, and wall-clock time per pose drops $5.0\times$ on the IRB 2400. The benefit transfers cross-platform to the Universal Robots UR5 collaborative manipulator (convergence rate $82.2\%$ to $100\%$, $10.0\times$ speedup) and survives DH parameter perturbation of up to $\pm 10\%$, simulating calibration drift or mechanical wear. The GATv2 attention coefficients additionally provide an interpretability signal at zero inference cost. XGNN therefore complements analytical and numerical IK methods as an interpretable, calibration-robust warm start when DH parameters are unavailable, proprietary, or degraded. Full article

Background: The Mercer Universal Prosthesis (MUP), designed with a default “neutral” (vertical) socket alignment, was developed to simplify transtibial prosthetic fitting, reduce labor costs, and improve access to prosthetic care in low-resource settings. Methods: This present longitudinal study evaluated biomechanical and functional outcomes at baseline, 6 months, and 12 months in 20 transtibial amputees fitted with the MUP. Results: Functional outcomes, assessed using the SF-36, showed significant improvement in overall health scores at 12 months (p < 0.001), while physical function and energy/fatigue domains remained unchanged (p = 0.686 and p = 0.211, respectively). Biomechanically, sagittal kinematics, measured using inertial motion capture, revealed significant limb × time interactions for hip flexion, knee flexion, and ankle plantarflexion. At 6 months, maximum hip flexion (−7°, p = 0.008) and knee flexion (−11°, p = 0.005) of the prosthetic limb were decreased versus baseline. At 12 months, the only observed difference was increased maximum ankle plantarflexion of the intact limb (+5° vs. baseline, p = 0.016). Muscle effort, quantified via the integral of EMG throughout the gait cycle, did not differ significantly between prosthetic and intact limbs across time points. Gait symmetry index (GSI) scores for hip, knee, and ankle range of motion trended toward gradual improvement but without statistical significance (p > 0.05). Conclusions: The MUP performance was maintained over 12 months, with stable biomechanical performance and meaningful quality-of-life gains. These findings support its potential as a cost-effective solution to expand prosthetic accessibility in low- and middle-income countries. Full article

Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization. Full article

The design of dexterous robotic hands has long been constrained by empirical paradigms that struggle to balance anthropomorphic fidelity with dynamic performance. This study aims to establish a systematic methodology that bridges this gap through data-driven inverse design. We construct a quantitative association map between design variables and performance metrics using a comprehensive dataset of existing dexterous hands, then apply this map to translate explicit high-frequency dynamic targets into an optimized hardware configuration. The analysis reveals that the dominant principles for high-speed performance—tendon-driven transmission, proximal actuation, and lightweight rigid structures—closely mirror the biomechanical architecture of the human hand. Guided by this convergence, we develop the Beyond Hand, a 20-degree-of-freedom (DoF) anthropomorphic hand that preserves human-scale dimensions. Standardized frequency-response tests across all 15 joints show magnitude attenuation below 3 dB at 14 Hz and cutoff frequencies clustered around 10 Hz. In rhythm-game and Tetris-style manipulation tasks, the hand maintains over 90% accuracy at actuation frequencies up to 12 Hz. These results demonstrate that a performance-driven pathway can systematically elevate the dynamic capabilities of humanoid dexterous hands, offering a scalable framework for biomimetic robotic design. Full article

Background: Human exposure to environmental endocrine-disrupting chemicals (EDCs) rarely occurs in isolation, yet most epidemiological research has assessed chemicals individually. PFASs, toxic metals, phthalates, and VOCs are ubiquitous contaminants with well-documented reproductive toxicity. Objective: The aim of this study was to investigate the joint and individual effects of 28 EDCs spanning four chemical classes on six reproductive hormone biomarkers in a nationally representative U.S. population—using an innovative approach that simultaneously characterizes nonlinear mixture effects and chemical interactions across multiple exposure domains. Methods: This cross-sectional study used NHANES 2017–2018 data (n = 9254). Multivariable linear regression and Bayesian Kernel Machine Regression (BKMR) characterized individual and mixture associations, respectively. Missing data were handled using multiple imputations by chained equations. Survey design weights were applied in linear regression models. Results: Linear regression revealed heterogeneous associations across chemical classes and hormones. PFOA was positively associated with SHBG (β = 12.35; 95% CI: 8.33, 16.38) and LH (β = 6.91; 95% CI: 1.44, 12.38), while mercury was inversely associated with estradiol (β = −3.38; 95% CI: −5.12, −1.65). BKMR analyses identified pronounced non-monotonic dose–response relationships and emergent mixture effects not predictable from single-chemical analyses for all six hormones. Posterior inclusion probabilities identified cadmium, PFOA, MEHP, and MBzP as the most influential predictors across hormone endpoints. Conclusions: Concurrent real-world exposure to PFASs, toxic metals, phthalates, and VOCs is associated with measurable, nonlinear alterations in reproductive hormone profiles. Chemical mixture effects cannot be reliably predicted from single-pollutant analyses, underscoring the necessity of mixture-based methodologies in environmental reproductive epidemiology. Prospective studies are needed to establish causal temporality and identify critical windows of susceptibility. Full article

Background/Objectives: Halitosis in geriatric patients is multifactorial, but the joint contribution of prosthetic rehabilitation type and polypharmacy after routine dental procedures has rarely been quantified. We investigated how prosthesis type, polypharmacy, and salivary function were associated with volatile sulfur compound (VSC) burden and self-perceived halitosis in elderly dental patients. Methods: This cross-sectional study enrolled 88 patients aged ≥65 years, four weeks after completing routine dental procedures. Participants were stratified into three groups: complete denture wearers (n = 30), partial removable denture wearers (n = 28), and fixed prostheses/implants (n = 30). We measured unstimulated salivary flow rate (uSFR), tongue coating index (TCI), denture biofilm index, total VSCs (Halimeter^®), organoleptic score (0–5), and self-perceived halitosis. Polypharmacy, comorbidities, and the Geriatric Oral Health Assessment Index (GOHAI) were recorded. Analyses included one- and two-way ANOVA, Spearman correlations, theory-informed multivariable linear and logistic regression, exploratory mediation analysis, and ROC curves. Results: Forty-two participants (47.7%) reported halitosis. Mean VSC differed across groups (complete dentures 278.2 ± 38.6 ppb; partial 211.2 ± 46.3 ppb; fixed 164.4 ± 43.9 ppb; ANOVA p < 0.001). uSFR correlated inversely with VSC (ρ = −0.61, p < 0.001) and TCI correlated positively (ρ = 0.56, p < 0.001). A significant prosthesis × polypharmacy interaction was observed (F = 3.74, p = 0.029, η²_p = 0.082): polypharmacy was associated with higher VSC most clearly among partial and fixed prostheses wearers, whereas complete denture wearers showed high VSC levels regardless of polypharmacy status. Exploratory mediation findings were consistent with partial indirect association, with 45.9% of the polypharmacy–VSC association statistically explained by reduced uSFR; however, the cross-sectional design precludes causal or temporal interpretation. The full multivariable model showed apparent discrimination for self-perceived halitosis (AUC = 0.92), while the simplified four-item chairside composite model showed AUC = 0.89; neither estimate was optimism-corrected or externally validated. Conclusions: In elderly post-procedure patients, complete denture wearing, polypharmacy, and salivary hypofunction were independently and jointly associated with higher halitosis burden. Reduced salivary flow was consistent with a partial indirect statistical pathway in the polypharmacy–VSC association, supporting hydration counseling and meticulous prosthesis hygiene as low-cost geriatric interventions. Sensitivity analyses excluding implant-supported restorations, participants with MMSE scores of 24–26, and expanded mediation models including TCI and biofilm/plaque did not materially change the main inference. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article

Lake depth is a fundamental parameter for estimating lake storage, analyzing basin morphology, and understanding the evolution of plateau lakes. Compared with typical shallow lakes, Tibetan Plateau lakes are characterized by high elevation, strong radiation, pronounced inter-lake and inter-annual variability, and in some cases considerable basin depth, which limits the accuracy, stability, and generalization ability of existing bathymetric inversion methods based on single-source optical imagery. Meanwhile, although ICESat-2 can provide sparse but high-precision along-track bathymetric constraints, a unified framework suitable for plateau-lake scenarios is still lacking. To address this issue, this study proposes TabKAN, a bathymetric inversion framework for Tibetan Plateau lakes under joint constraints from hyperspectral imagery and ICESat-2 data. TabKAN constructs tabular input features from hyperspectral reflectance, water indices, imaging geometry, and environmental variables; employs TabNet for feature selection and encoding; and introduces a KAN regression head to enhance nonlinear bathymetric mapping. A joint-supervision and bias-correction mechanism is further designed to incorporate ICESat-2 samples, thereby improving model robustness across lakes and acquisition dates. To enhance the temporal coverage of training samples, multi-year sample expansion based on stereo-mapping data is introduced, and a stripe-aware self-supervised learning strategy is developed for hyperspectral image restoration and pretraining. Experiments on five Tibetan Plateau lakes, including Anglaren Co, Caiduo Chaka, Cuoe, Geren Co, and Qixiang Co, show that the proposed method outperforms benchmark methods in both overall accuracy and depth-stratified evaluation, while providing more stable recovery of basin morphology and depth gradients. These results demonstrate that combining hyperspectral information, ICESat-2 laser constraints, and stripe-aware pretraining can effectively improve the accuracy and robustness of bathymetric inversion for Tibetan Plateau lakes and provide a new technical route for storage estimation and change monitoring of cold inland lakes. Full article

Cooperative deployment of mobile agents under geometric and line-of-sight constraints gives rise to high-dimensional constrained optimization problems whose underlying physical configuration often exhibits exploitable structure. This paper develops a symmetry-driven inverse design framework that leverages two structural features of the engagement geometry—the ${\mathbb{Z}}_2\times {\mathbb{Z}}_2$ mirror symmetries of the extended target silhouette and a closed-form forward–inverse correspondence between line-of-sight-aligned burst locations and physical agent parameters—to construct low-dimensional seeds for subsequent physical parameter optimization. The framework is developed and validated on a representative naval defense instance in which a fleet of unmanned aerial vehicles (UAVs) releases spherical obscuration payloads to interrupt the line of sight between incoming mobile threats and a cylindrical extended target. Instead of searching only over the four-dimensional UAV parameter space (heading angle, speed, drop time, fuse delay), the method first specifies a desired burst location in a two-dimensional inverse space and analytically back-calculates feasible agent parameters, which are then refined by multi-start Nelder–Mead optimization in the physical parameter space. A conservative three-dimensional cylindrical line-of-sight obscuration model is developed by constructing four extreme tangent sightlines from the missile to the cylindrical target and verifying whether the spherical smoke cloud simultaneously blocks all of them. A hierarchical multi-agent task allocation framework combines a performance matrix, assignment enumeration, and joint multi-start refinement. Numerical experiments on five progressively complex sub-problems demonstrate obscuration durations of $1.362 \mathrm{s}$ (single fixed shot), $4.580 \mathrm{s}$ (optimized shot), $7.324 \mathrm{s}$ (three-shot relay), $11.140 \mathrm{s}$ (three-UAV cooperation), and $20.652 \mathrm{s}$ (full five-UAV three-missile assignment). Additional high-dimensional benchmarks, sensitivity tests, and error analyses clarify the reproducibility and limitations of the approach. Full article

Fiber dispersion causes pulse broadening and signal distortion. Existing dispersion compensation approaches depend on standardized dispersion parameters at specific wavelengths (e.g., 1550 nm), which often mismatch actual fiber dispersion, leading to residual dispersion. We develop a Sagnac ring interferometry and electro-optic modulation system, combined with machine learning, to accurately characterize the C-band dispersion curve of a G.652D fiber, and inversely design a chirped fiber Bragg grating (CFBG) for tailored compensation. However, when attempting to quantify the residual dispersion numerically, conventional differentiation methods yield physically implausible results. Monte Carlo simulations confirm this fundamental unreliability, yielding a 95% confidence interval of 319,605 ps/(nm·km). To circumvent this limitation, we propose a joint evaluation method based on refractive index flatness and group delay uniformity. Within 1545–1555 nm, both indicators fluctuate by no more than 0.015% relative to their means, confirming that residual dispersion has been effectively suppressed. This approach provides a precise, personalized compensation mechanism applicable to optical fibers with individual dispersion characteristics, offering a controllable path for adaptive dispersion compensation in high-speed communication systems. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

Lubricin, also known as proteoglycan 4 (PRG4), is a key glycoprotein involved in boundary lubrication and maintenance of joint homeostasis in the temporomandibular joint (TMJ). However, the clinical evidence regarding synovial fluid (SF) lubricin levels remains limited and fragmented. This scoping review aimed to map and synthesize the available clinical evidence on lubricin levels in patients with temporomandibular disorders (TMDs) and their relationship to clinical outcomes, particularly pain and mandibular mobility. Searches were conducted in PubMed, Scopus, ACM, BASE, Cochrane, ClinicalTrials.gov, and Google Scholar. After duplicate removal and screening, two studies were included in the final synthesis. Preliminary findings from these studies suggest that (SF) lubricin levels may be lower in more advanced TMJ pathology, particularly in degenerative disease. Earlier stages of internal derangement showed lubricin concentrations closer to those observed in healthy controls, whereas advanced internal derangement and osteoarthritic disease were potentially associated with lower levels. One study also reported an inverse correlation between lubricin concentration and pain intensity, while the other demonstrated impaired boundary lubrication in TMD groups. Overall, the available clinical evidence is very limited and insufficient to establish PRG4 as a validated biomarker but suggests a possible association between reduced SF lubricin levels and more advanced TMJ disease. Further well-designed clinical studies are required to confirm these observations and clarify their diagnostic and therapeutic relevance. Full article

Environmental condition assessment is essential for the design of floating wind turbines, particularly when determining design sea states that balance safety and economy. The environmental contour method, typically constructed through the Inverse First Order Reliability Method combined with parametric joint distributions, is widely adopted for this purpose. However, conventional models often struggle to adequately characterize complex sea states involving mixed wind and swell systems, which exhibit multimodality and irregular dependence structures. To address this limitation, this study applies the use of Gaussian mixture models (GMM) to construct environmental contours. The GMM-based approach models the joint distribution of environmental variables in a flexible and data-adaptive manner, with the number of mixture components determined by the Bayesian Information Criterion and model parameters estimated via the expectation-maximization algorithm. Compared with the conventional conditional Weibull–Lognormal model, the GMM significantly improves fitting accuracy: the RMSE decreases from approximately 0.06 to below 0.0013, and the R² increases to nearly 1.000 across all three datasets. The KS and χ² tests confirm that the GMM adequately fits the observed data at the 0.05 significance level, whereas the baseline model is rejected in several cases. For the 100-year return period, the GMM yields maximum significant wave heights of 4.19–4.55 m with associated peak periods of 18.8–20.3 s, while the baseline model gives 4.02–4.18 m and 14.3–14.6 s, respectively. These quantitative improvements demonstrate that the mixture-based contours capture the intricate characteristics of wind–swell coexisting sea conditions more accurately, leading to enhanced representativeness of extreme sea states. Consequently, the adopted method enables more refined and reliable design sea state assessments for tested datasets, contributing to the optimization of environmental parameter selection for floating wind turbines. Full article