Search Results (2,573)

Mobile crane repositioning and on-site logistics coordination constitute a highly coupled, nonlinear decision problem in constrained construction environments. Existing approaches largely decouple these tasks, limiting achievable system-level efficiency. This study introduces EVCrane, a kinematics-informed evolutionary optimization framework that simultaneously optimizes crane stopping positions, stockpile deployment, and task allocation within a unified mixed continuous–binary formulation. Unlike distance-based approximations, the proposed model propagates geometric decisions through coordinated crane motion components—including radial boom adjustment, slewing rotation, and vertical hoisting—ensuring physically consistent cycle-time estimation. A real industrial case study was used to benchmark five optimization algorithms under identical MATLAB R2026a implementations. The Genetic Algorithm (GA) achieved the lowest total crane engaged time (34.516 h), reducing operational duration by 6.45% and utilization cost by 6.32% compared with a deterministic nonlinear programming baseline. Comparative analysis reveals that recombination-based evolutionary search exhibits superior compatibility with assignment-driven non-convex landscapes, outperforming swarm-based and trajectory-based alternatives. Sensitivity analysis confirms structural robustness of optimal spatial configurations under parametric perturbations. The proposed framework advances crane planning from decoupled geometric heuristics toward integrated, physics-consistent, and computationally robust optimization, supporting intelligent and sustainable construction site management. Full article

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications. Full article

Accurate assessment of mitral valve (MV) and pulmonary vein (PV) flow velocities is important for left ventricular diastolic function testing. This study investigated the scan–rescan reproducibility of 4D Flow MRI-assessed MV and PV flow velocities in 21 healthy volunteers (25 ± 4 years). Participants underwent repeated whole-heart 3T 4D Flow MRI involving repositioning and different respiratory compensation strategies (motion-uncompensated free-breathing vs. respiratory motion-compensated navigator gating). MV parameters (net flow volume (NFV), E-wave velocity, A-wave velocity, E/A ratio, E deceleration time (DT), annular e’ velocity, E/e’ ratio) and PV parameters (NFV, S-wave velocity, D-wave velocity, S/D ratio, atrial reversal (AR) wave velocity) were derived from velocity–time curves and compared using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and Pearson’s correlation (r). Results showed significant moderate-to-strong scan–rescan agreement and correlation for most MV and PV parameters (ICC = 0.51–0.92; r = 0.51–0.92; all p < 0.05), except E DT, e’ velocity, E/e’ ratio, PV NFV, and AR velocity (ICC = −0.13–0.47; r = −0.14–0.47). Subanalysis of respiratory motion strategies showed moderate-to-strong agreement and correlation for MV and PV parameters (ICC = 0.61–0.99; r = 0.52–0.99; all p < 0.05 excluding E DT), except E DT (ICC = 0.44) and PV NFV (ICC = 0.46; r = 0.46). While intraobserver agreement was mostly moderate-to-excellent (ICC = 0.58–0.97; ICC = 0.41 for E DT), interobserver agreement was poor for E DT and PV parameters (ICC = −0.12–0.34). Overall, 4D Flow MRI shows acceptable reproducibility for selected diastolic flow parameters, particularly mitral inflow indices, but substantial variability and limited robustness for key indices currently restrict its clinical applicability. Full article

Heart rate variability (HRV) has emerged as a key biomarker for assessing autonomic nervous system activity, stress, fatigue, and emotional states. With the rapid development of wearable sensor technologies, HRV analysis has expanded from clinical environments to real-world, continuous monitoring. This review summarizes current applications of HRV metrics in wearable devices, including fitness tracking, mental stress assessment, sleep quality evaluation, and early detection of physiological or psychological disorders. Recent advances in photoplethysmography (PPG)-based HRV estimation have enabled noninvasive and user-friendly measurement, though challenges remain in accuracy under motion and variable environmental conditions. We also discuss methodological considerations, such as artifact correction, data segmentation, and the integration of HRV with other biosignals for multimodal analysis. Emerging research suggests that combining HRV with metrics such as respiration rate, skin conductance, and accelerometry can enhance robustness and interpretability in dynamic settings. Finally, future directions are proposed toward personalized health analytics, emotion-aware computing, and real-time adaptive feedback systems. This review highlights the growing potential of wearable HRV analysis as a foundation for preventive healthcare and human–machine symbiosis. Full article

Minimizing the impacts of coupling, randomness, time variation and uncertain nonlinearity to enhance real-time performance is critical for controlling complex industrial systems. This paper proposes an optimized adaptive filtering method (LAF-MTNF) for time-varying uncertain nonlinear systems with multiple-input multiple-output (MIMO) measurement noise, which integrates the multi-dimensional Taylor network (MTN) with Lyapunov stability theory (LST). Leveraging MTN’s inherent advantages—simple structure, linear parameterization, and low computational complexity—LAF-MTNF achieves efficient real-time filtering while avoiding the exponential computation burden of neural networks. The contributions of this work are threefold: (1) A novel integration of LST and MTN is proposed for MIMO filtering, in which an energy space is constructed with a unique global minimum to eliminate local optimization traps, addressing the stability deficit of traditional MTN filters using LMS/RLS algorithms. (2) Convergence performance is systematically quantified by deriving explicit expressions for the error convergence rate (regulated by a positive constant) and convergence region (a sphere centered at the origin) while modifying adaptive gain to avoid singularity, filling the gap of incomplete performance analysis in existing Lyapunov-based filters. (3) The design is disturbance-independent, relying only on input/output measurements and requiring no prior knowledge of noise statistics, thus enhancing robustness to unknown industrial disturbances. We systematically analyze the Lyapunov stability of LAF-MTNF, and simulations on a complex MIMO system verify that it outperforms existing methods in filtering precision (mean error 0.0227 vs. 0.0674 of RBFNN) and dynamic response speed, while ensuring asymptotic stability and real-time applicability. The proposed LAF-MTNF method achieves significant advantages over traditional adaptive filtering methods in filtering accuracy, convergence speed and anti-cross-coupling capability. This method has broad application prospects in high-precision industrial servo motion control, power system state monitoring and other multi-variable nonlinear industrial scenarios with complex noise environments. Full article

This paper introduces the concept of the Point of No Return as a physically grounded safety indicator for rear-end vehicle conflicts, addressing fundamental limitations of the widely used time-to-collision metric. Unlike purely kinematic approaches, the proposed formulation incorporates braking capability and reaction constraints, enabling a direct assessment of whether a collision can still be avoided. To illustrate the applicability of the concept, a vision-based framework using a single camera is developed based on dashcam data, combining YOLO-based object detection, Kalman-filter tracking, and geometric distance estimation derived from bounding-box features and camera projection models. The estimated distance is further processed to obtain relative motion, allowing a unified analysis of time to collision and the Point of No Return within the same evaluation pipeline. Experimental results on real-world driving sequences show that the Point of No Return consistently precedes critical conditions identified by time to collision and provides a more stable and physically interpretable characterization of the transition toward collision inevitability. The results also highlight the sensitivity of the proposed indicator to braking capability, while showing lower sensitivity to variations in relative speed. Overall, this study demonstrates the relevance of the Point of No Return as a complementary indicator for collision risk assessment, offering a physically meaningful basis for decision-making in driver assistance systems and improving the interpretation of critical traffic situations. The proposed approach supports sustainable urban mobility by enabling earlier and more reliable intervention strategies, contributing to improved traffic safety, smoother traffic flow, and reduced environmental impact. Full article

Mobile EEG enables investigating brain activity during real-world behaviour, but remains susceptible to motion artefacts, limiting signal interpretability and the use of advanced analytical techniques. Methods developed for removing motion-related artefacts induced by periodic activity like cycling, walking or juggling showed degraded performance with increasing movement variability and speed. To fill this gap, we developed a method based on generalized eigenvalue decomposition (GED) to identify and suppress highly variable, non-periodic—especially transient—artefacts due to very rapid, free full body movements of different types, as they occur during sports practice. By leveraging the contrast between covariance matrices of artefactual and resting-state EEG segments, this approach isolates motion-related components for removal during multichannel EEG signal reconstruction. The method was validated on two ecological datasets featuring stereotyped head and body movements and dynamic table tennis. Comparison with state-of-the-art technique showed superior performance of our method in terms of signal-to-error ratio (SER), artefact-to-residue ratio (ARR), brain spectral power preservation and computation time. Sensitivity analysis was applied to demonstrate the method’s robustness to parameter changes. These findings highlight the potential of the proposed method as a robust, generalizable approach for motion artefact suppression in mobile EEG, particularly when applied in extreme recording conditions like during active sports activity. Full article

In this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory, power, and processing capacity are limited. The system works by collecting body motion data using accelerometer sensors placed on the human body. The data is then processed using a feedforward neural network trained on preprocessed signals. The trained model is quantized so that it can run on low-power embedded hardware with small memory size. The model performs inference directly on the device. This reduces latency and avoids sending raw sensor data to the cloud. When a fall is detected, the result is sent through Bluetooth to a gateway. The gateway forwards the data to a cloud server using the MQTT protocol. The cloud stores the data and supports monitoring and analysis. The experimental results show that the quantized TinyML model achieves 98.40% accuracy with more than 80% F1-score and more than 99% recall. The deployed model uses only ∼5 KB of RAM and ∼40 KB of flash memory. The inference time is 7 ms per class. These results show that wearable sensing with quantized TinyML models and IoT communication can provide fast and reliable fall detection for real-world safety monitoring systems. Full article

Photovoltaic systems deployed on large floating platforms in nearshore waters are strongly influenced by hydroelastic effects, nonlinear shallow-water waves, and variable bathymetry. This study develops a time-domain hydroelastic framework that couples the fully nonlinear non-hydrostatic wave model NHWAVE with a Rankine-source potential-flow solver and a discrete-module Cummins formulation. The wave model provides incident pressures and kinematics over uneven seabeds, while the potential-flow solver evaluates radiation and diffraction effects and transfers the resulting hydrodynamic coefficients into the time domain. Numerical simulations are carried out for a 600 m modular floating structure under regular waves over flat and sloped bathymetries with $tan\alpha =0.0133$, wave periods of 4–6 s, and wave heights of 0.3–1.0 m. The results show that bathymetric variation intensifies shoaling-induced excitation, modifies added-mass and damping distributions, increases the spatial non-uniformity of hydroelastic motions, and amplifies bending-moment RMS responses relative to the flat-bottom case. Additional comparisons between rigid-body and hydroelastic models show clear period-dependent redistribution of motions and bending demand. These results demonstrate that both local bathymetry and structural elasticity must be considered for the reliable analysis and design of nearshore floating photovoltaic systems and other large floating structures. Full article

This study examines the 3D attributes of submesoscale eddies identified over a 12-month period within the Western Pacific Ocean. Composite parameters of cyclonic submesoscale eddies (CSMEs) occurring within and away from the Kuroshio Current system are compared and analyzed for their surface and subsurface features, as well as the seasonality of their core properties. Within the Kuroshio Current (KC) region, CSMEs are faster, stronger and deeper than in the open water (OW) region, with composite eddy depths of 97.5 m and 77.5 m, or 2.8 and 2.0 times the mixed layer depth, respectively. Prominent dipolar divergence patterns both at the surface and at depth reveal the presence of ageostrophic influence, with KC CSME cores deviating 48% and OW CSMEs deviating 40% from geostrophic balance at the surface. This imbalance drives strong vertical motion with maximum upward velocities of 19.2 m day⁻¹ at 57.7 m and 9.3 m day⁻¹ at 157.1 m within the KC and OW region CSME cores, respectively. Subsurface extrema analysis reveals structural differences in CSMEs between dynamic regions. These results provide a useful model-based estimate for subsurface CSME features which are difficult to quantify with observations. Full article

This paper presents a systematic asymptotic analysis of the least squares estimator (LSE) for the drift parameter in a fractional stochastic heat equation driven by fractional Brownian motion. Fractional Brownian motion, capable of capturing stylized features in financial markets such as long memory, has become an important modeling tool in financial econometrics and risk management. Based on continuous-time observations of the Fourier coefficients of the solution, we first establish the strong consistency and asymptotic normality of the estimator. We then construct an alternative estimator based on the LSE and analyze its asymptotic behavior. This study provides new asymptotic inference tools for stochastic systems with long-memory properties and extends the theoretical framework for parameter estimation in fractional stochastic partial differential equations. Full article

Oscillatory motions in stratified atmospheric fluid layers significantly influence weather and climate dynamics. Shallow-water equations effectively describe these motions. This study extends the shallow-water model to the time-fractional domain using the conformable fractional derivative. This derivative preserves the local differential structure while introducing tunable time scaling in the dynamics. Approximate analytical solutions were obtained using the conformable Laplace Adomian Decomposition Method (CLADM). This method combines the conformable Laplace transform with Adomian decomposition. Numerical results for fractional orders $\vartheta \in (0, 1]$ demonstrate that the fractional parameter systematically modulates the system dynamics. The solutions at $\vartheta =1$ align well with the established Elzaki Adomian Decomposition Method (EADM), Homotopy Analysis Method (HAM), Fractional Reduced Differential Transform Method (FRDTM), and reference numerical solutions (NUM). This fractional framework offers a flexible approach to modeling atmospheric fluid-layer dynamics. Full article

Background: In rehabilitation medicine, efficient gait analysis is crucial for evaluating postoperative recovery and frailty, especially given the increasing burden on clinicians due to an aging population. Objectives: This study aims to conduct preliminary validation of an automated linear walking evaluation system using 2D AI posture tracking. By evaluating the basic accuracy of the system on healthy individuals, we aim to establish a technical foundation for future introduction into clinical rehabilitation settings. Methods: In this observational study, we utilized a standard visible light camera for practical use. To evaluate accuracy, we compared 2D AI tracking against a gold-standard three-dimensional (3D) motion capture system during normal walking trials with 10 healthy participants. Specifically, we employed Dynamic Time Warping (DTW) to temporally align the asynchronous data streams from the 2D and 3D systems, ensuring precise comparison of joint angles. Results: Following the DTW-based alignment, the similarity with the 3D system was 0.806 ± 0.094 overall (Left: 0.797 ± 0.101, Right: 0.814 ± 0.086). Conclusions: In this preliminary validation, the proposed 2D AI posture tracking showed good agreement with the gold standard 3D motion capture for gait in healthy individuals. While the average systematic bias was within clinically acceptable limits, the observed limits of agreement suggest that this system is currently optimal as a foundational tool for gait screening. These results establish a technical foundation for the clinical application of this system. Full article

The installation of floating offshore wind turbines presents significant operational challenges due to coupled vessel platform dynamics and sensitivity to environmental conditions. This study proposes a response-based methodology for defining allowable operational limits and assessing operability for floating wind turbine generator (WTG) installation using the Nordic Wind concept. The approach integrates hydrodynamic modelling, time-domain simulations, and probabilistic weather-window analysis to evaluate installation feasibility under realistic offshore conditions. The methodology explicitly accounts for coupled vessel spar interactions, heading-dependent system response, and response-based failure criteria, including relative motion, gripper forces, and impact velocity. Allowable sea-state limits are derived for key installation phases and applied to multiple case studies representing different geographical locations and project scales. The results show that installation operability is governed primarily by system response rather than environmental parameters alone. Peak wave period and wave heading are identified as dominant factors, with longer wave periods leading to reduced operability due to response amplification. Across all case studies, the mating phase is found to be the most restrictive operation, controlling overall installation feasibility. Head sea conditions generally provide improved operability, while following seas lead to increased relative motion and reduced performance. The comparative analysis further demonstrates that environmental severity and project scale jointly influence installation duration. Milder environments result in higher operability, whereas harsher conditions, particularly those characterised by long-period swell, significantly reduce feasible weather windows. Larger installation campaigns increase cumulative exposure to weather downtime, even under favourable conditions. The proposed framework extends existing operability assessment methods by incorporating coupled multi-body dynamics and response-based criteria specific to floating wind installations. The results provide a quantitative basis for defining operational limits and support improved planning and decision making for offshore wind turbine installation. Full article

Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field. Full article