Search Results (1,824)

Background: Wrist-worn consumer activity trackers are widely used to promote physical activity (PA) and reduce sedentary behavior (SB). However, evidence regarding their validity for measuring PA and SB in free-living school-aged children remains limited. This study evaluated the concurrent validity and wear compliance of a wrist-worn consumer activity tracker in school-aged children under free-living conditions with protocol-defined wear requirements. Methods: A total of 102 children (mean age: 10.2 years; 44.1% girls) wore a wrist-worn device (Fitbit Ace) and a waist-worn accelerometer (Omron Active Style Pro HJA-750c, ASP-750c). Of the 1122 person-days collected over 11 days, 135 person-days meeting inclusion criteria for both devices were included (≥10 h/day wear time and an inter-device wear time difference of ≤60 min). Step count and time in SB, light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous PA (MVPA) were assessed. Correlations, mean absolute percentage error (MAPE), agreement, and wear compliance between the two devices were examined. Results: Correlations were strong for step count (r = 0.86), SB (r = 0.72), and LPA (r = 0.71); however, agreement was poor, with systematic overestimation of step count, SB, VPA, and MVPA and underestimation of LPA and MPA by the Fitbit Ace, and MAPE exceeding 20% for all PA variables. Wear compliance (≥10 h/day on ≥4 days) was higher for the Fitbit Ace (97.0%) than for the ASP-750c (62.2%). Conclusions: Although the Fitbit Ace may be useful for characterizing general patterns of LPA and SB in school-aged children, caution is warranted for accurate individual-level PA assessment. Full article

Background: Walking cadence is commonly adjusted in sport and rehabilitation, yet its effects on spatiotemporal gait parameters and regional plantar pressure distribution under controlled speed conditions remain incompletely characterized. Therefore, this study aimed to determine whether imposed cadence increases at a constant walking speed would (i) systematically reduce temporal gait parameters while preserving inter-limb symmetry and (ii) be associated with region-specific increases in forefoot plantar loading, representing the primary novel contribution of this work. Methods: Fifty-two adults walked at three imposed cadences (110, 120, 130 steps·min⁻¹) while maintaining a fixed treadmill speed of 1.39 m·s⁻¹ via auditory biofeedback. Spatiotemporal parameters were recorded with an OptoGait system, and plantar pressure distribution was measured using in-shoe pressure insoles. Normally distributed variables were analyzed using repeated-measures ANOVA, whereas plantar pressure metrics were assessed using the Friedman test, followed by Wilcoxon signed-rank post-hoc comparisons with false discovery rate (FDR) correction. Associations between temporal parameters and plantar loading metrics (peak pressure, pressure–time integral) were examined using Spearman’s rank correlation with FDR correction (α = 0.05). Results: Increasing cadence produced progressive reductions in gait cycle duration (~8–10%), contact time (~7–8%), and step time (all p < 0.01), while inter-limb symmetry indices remained below 2% across conditions. Peak plantar pressure increased significantly in several forefoot regions with increasing cadence (all p_FDR < 0.05), whereas changes in the first ray were less consistent across conditions. Regional forefoot pressure–time integral also increased modestly with higher cadence (p_FDR < 0.01). Spearman’s correlations revealed moderate negative associations between temporal gait parameters and global plantar loading metrics (ρ = −0.38 to −0.46, all p_FDR < 0.05). Conclusions: At a constant walking speed, increasing cadence systematically shortens temporal gait components and is associated with small but consistent region-specific increases in forefoot plantar loading. These findings highlight cadence as a key temporal constraint shaping plantar loading patterns during steady-state walking and support the existence of concurrent temporal–mechanical adaptations. Full article

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

To improve DC-bus voltage regulation of bidirectional DC/DC converters in photovoltaic–energy storage DC microgrids, this paper proposes an improved linear active disturbance rejection control (LADRC) strategy based on observation error reconstruction. In conventional LADRC, the linear extended state observer (LESO) is driven solely by the output tracking error, which may lead to weakened disturbance excitation after rapid error convergence and thus degraded disturbance estimation performance. To address this limitation, an observation error reconstruction mechanism is introduced, in which a reconstructed error variable incorporating higher-order estimation deviation information is used to redesign the LESO update law. This modification fundamentally enhances the disturbance-driving mechanism without excessively increasing observer bandwidth, resulting in improved mid- and high-frequency disturbance estimation capability. The proposed method is analyzed in terms of disturbance estimation characteristics, frequency-domain behavior, and closed-loop stability. Comparative simulations and hardware-in-the-loop experiments under typical load and photovoltaic power step variations within the safe operating range demonstrate that the proposed LADRC–PI significantly outperforms conventional PI and LADRC–PI control. Experimental results show that the maximum DC-bus voltage fluctuation is reduced by over 60%, and the voltage recovery time is shortened by approximately 40–50% under the tested operating conditions. Full article

Camellia oleifera, a woody oil crop unique to China, plays a crucial role in alleviating the global pressure of edible oil supply and maintaining ecological security. However, it remains challenging to accurately forecast Camellia oleifera yield in complex landscapes using only remote sensing data. The aim of this study is to develop an interpretable deep learning model, namely Shapley Additive Explanations–guided Attention–long short-term memory (SALSTM), for estimating Camellia oleifera yield by integrating an improved spectral bloom index and environmental parameters. The study area is located in Hengyang City in Hunan Province. Sentinel-2 imagery, meteorological observation from 2019 to 2023, and topographic data were collected. First, an improved spectral bloom index (ISBI) was constructed as a proxy for flowering density, while average temperature, precipitation, accumulated temperature, and wind speed were selected to represent environmental regulation variables. Second, a SALSTM model was designed to capture temporal dynamics from multi-source inputs, in which the LSTM module extracts time-dependent information and an attention mechanism assigns time-step-wise weights. Feature-level importance derived from SHAP analysis was incorporated as a guiding prior to inform attention distribution across variable dimensions, thereby enhancing model transparency. Third, model performance was evaluated using root mean square error (RMSE) and coefficient of determination (R²). The result show that the constructed SALSTM model achieved strong predictive performance in predicting Camellia oleifera yield in Hengyang City (RMSE = 0.5738 t/ha, R² = 0.7943). Feature importance analysis results reveal that ISBI weight > 0.26, followed by average temperature and precipitation from flowering to fruit stages, these features are closely associated with C. oleifera yield. Spatially, high-yield zones were mainly concentrated in the central–southern hilly regions throughout 2019–2023, In contrast, low-yield zones were predominantly distributed in the northern and western mountainous areas. Temporally, yield hotspots exhibited a gradual increasing while low-yield zones showed mild fluctuations. This framework provides an effective and transferable approach for remote sensing-based yield estimation of flowering and fruit-bearing crops in complex landscapes. Full article

Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL), are used as a crucial step in a framework of reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high-frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for fault prediction, which is based on extraction of semantic state vectors (SSVs) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state, is proposed. In this method, working cycles are detected in input data stream, and then they are converted to images, on which an autoencoder is trained. The output of an intermediate layer of an autoencoder is extracted and processed into SSVs. SSVs are then combined into a time series on which RUL is forecasted. After optimization of hyperparameters, the proposed method shows the following results: RMSE = 1.799, MAE = 1.374. These values are significantly more accurate than those obtained using existing methods: RMSE = 14.02 and MAE = 10.71. Therefore, SSV extraction is a viable technique for forecasting RUL. Full article

Manual alignment between the trocar, surgical instrument, and robot during minimally invasive surgery (MIS) can be time-consuming and error-prone, and many existing systems do not provide autonomous localization and pose estimation. This paper presents an artificial intelligence (AI)-assisted, vision-guided framework for automated localization and positioning of the ATHENA parallel surgical robot. The proposed approach combines an Intel RealSense RGB–depth (RGB-D) camera with a You Only Look Once version 11 (YOLO11) object detection model to estimate the 3D spatial coordinates of key surgical components in real time. The estimated coordinates are streamed over Transmission Control Protocol/Internet Protocol (TCP/IP) to a programmable logic controller (PLC) using Modbus/TCP, enabling closed-loop robot positioning for automated docking. Experimental validation in a controlled setup designed to replicate key intraoperative constraints demonstrated submillimeter positioning accuracy (≤0.8 mm), an average end-to-end latency of 67 ms, and a 42% reduction in setup time compared with manual alignment, while remaining robust under variable lighting. These results indicate that the proposed perception-to-control pipeline is a practical step toward reliable autonomous robotic docking in MIS workflows. Full article

Usually, gluten-free “beers” are produced by replacing cereals containing gluten with substitutes that do not contain it or, alternatively, through enzymatic, precipitation, and/or clarification steps. The research was aimed at increasing the concentration of antioxidant compounds and improving the sensory quality of gluten-free craft beers produced from gluten-containing raw materials according to a patented brewing method that represented the starting point of the research. The experiments were organized to evaluate the effects of original combinations of four brewing procedures (Strong, Light, Very Light, Ultra-Light—differing from each other by grains/water ratio, hops/water ratio, protein rest, and boiling time), three yeast strains (M21, K97, S33), and a possible dry hopping. The beer gluten contents ranged from <5 to 13.90 mg/L. The maximum total phenolic content (200 mg/L) was detected in beers produced by combining the Light procedure, inoculation with M21 strain, and dry hopping. The highest overall sensory quality scores (4.0) were assigned to the beers obtained through the Light and Ultra-Light procedures, fermented by M21 and S33 strains, and dry hopped. Dry hopping was the main factor capable of differentiating the beers, increasing antioxidant content and improving perlage, foam characteristics, the intensity of many olfactory and gustatory characteristics, and the overall sensory quality. The brewing procedure affected all the physico-chemical indices and most sensory characteristics, except for color, citrous and spicy flavors, sweetness, effervescence, and body. The use of different yeasts did not impart significant differences for most of the variables considered. Full article

Ionic polymer–metal composite (IPMC) sensors generate voltages or currents when subjected to deformation. The magnitude and time constant of the electrical response vary significantly with ambient humidity and water content. However, most conventional physical models focus solely on cation dynamics and do not consider water dynamics. In addition to cation dynamics, Zhu’s model explicitly incorporates the dynamics of water. Consequently, Zhu’s model is considered one of the most promising approaches for physical modeling of IPMC sensors. This paper presents exact analytical solutions to Zhu’s model of IPMC sensors for the first time. The derivation method transforms Zhu’s model into the frequency domain using Laplace transform-based analysis together with linear approximation, and subsequently solves it as a boundary value problem of a set of linear ordinary differential equations. The resulting solution is expressed as a transfer function. The input variable is the applied bending deformation, and the output variables include the open-circuit voltage or short-circuit current at the sensor terminals, as well as the distributions of cations, water molecules, and electric potential within the polymer. The obtained transfer functions are represented by irrational functions, which typically arise as solutions to a system of partial differential equations. Furthermore, this paper presents analytical approximations of the step response of the sensor voltage or current by approximating the obtained transfer functions. The steady-state and maximum values of the time response are derived from these analytical approximations. Additionally, the relaxation behavior of the sensor voltage is characterized by a key parameter newly derived from the analytical approximation presented in this paper. Full article

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing and analyzing classical algorithms—LMS, NLMS, RLS, and a variable step-size LMS (VSS-LMS)—their theoretical stability and mean-square error behavior are formalized under a slow-variation system model. Comprehensive simulations using drifting autoregressive (AR(2)) processes, piecewise-stationary FIR systems, and time-varying sinusoidal signals confirm the classical trade-off between performance and complexity: RLS achieves the lowest steady-state error, at a quadratic cost, whereas LMS remains computationally efficient with slower adaptation. A stabilized VSS-LMS algorithm is proposed to balance these extremes; the results show that it maintains numerical stability under abrupt parameter jumps while attaining steady-state MSEs that are comparable to RLS (approximately 3 × 10⁻²) and superior robustness to noise. These findings are validated by theoretical tracking-error bounds that are derived for bounded parameter drift. Building on this foundation, a deep-learning-driven adaptive filter is introduced, where the update rule is parameterized by a neural function, $U_{\theta }$, that generalizes the classical gradient descent. This approach offers a pathway toward adaptive filters that are capable of self-tuning and context-aware learning, aligning with emerging trends in AI-augmented system architectures and next-generation computing. Future work will focus on online learning and FPGA/ASIC implementations for real-time deployment. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system that typically affects adults aged 20–50. Its early stages can be difficult to diagnose due to the variable clinical course, although subtle impairments often appear in balance and motor control. The Timed Up and Go (TUG) test is commonly used to assess functional mobility; however, traditional evaluation based solely on total test duration may not be sensitive to early gait alterations. The use of inertial measurement units enables instrumented analysis of individual TUG subphases (iTUG). The aim of this study was determine whether iTUG parameters can help detect balance and movement difficulties indicative of early-stage MS. Methods: A total of 30 healthy people and 30 people in the early stages of MS with an expanded disability status score between 1 and 2 were included. The iTUG was performed using three Noraxon inertial sensors placed on the feet and upper spine. Results: No significant differences were observed in total iTUG duration between the MS and control groups (p = 0.888). In contrast, individuals with MS demonstrated significant differences in spatiotemporal gait parameters, trunk flexion range of motion (p = 0.003), number of steps during gait (p = 0.004), and turning velocity compared with healthy controls (p = 0.008). Conclusions: Analysis of iTUG duration is not enough to identify subtle gait and balance impairments in individuals with early-stage MS. Parameters that should be considered when performing an iTUG for the assessment of early stages of MS are spatiotemporal parameters, number of steps, and speed of rotation and subphase times. Full article

The photovoltaic field has seen significant development in recent years, with continuously expanding installation capacity and increasing grid integration. However, due to the intermittency of solar energy and meteorological variability, PV output power poses serious challenges to grid security and dispatch reliability. Traditional forecasting methods largely rely on modeling historical power and meteorological data, often neglecting the consideration of cloud movement, which constrains further improvement in prediction accuracy. To enhance prediction accuracy and model interpretability, this paper proposes a dual-branch attention-based PV power prediction model that integrates physical features from ground-based cloud images. Regarding input features, a cloud segmentation model is constructed based on the vision foundation model DINO encoder and an improved U-Net decoder to obtain cloud cover information. Based on deep feature point detection and an attention matching mechanism, cloud motion vectors are calculated to extract cloud motion speed and direction features. For feature processing, feature attention and temporal attention mechanisms are introduced, enabling the model to learn key meteorological factors and critical historical time steps. Structurally, a parallel architecture consisting of a linear branch and a nonlinear branch is adopted. A context-aware fusion module adaptively combines the prediction results from both branches, achieving collaborative modeling of linear trends and nonlinear fluctuations. Comparative experiments were conducted using two years of engineering data. Experimental results demonstrate that the proposed model outperforms the benchmarks across multiple metrics, validating the predictive advantages of the dual-branch structure that integrates physical features under complex weather conditions. Full article

The aim of this study was to analyze the effects of different competition formats on the plyometric performance of under-13 basketball players, considering the influence of maturational age and monitored through GPS devices. Thirty-seven under-13 male basketball players (age = 12.91 ± 0.57 years) from four southeast Spanish teams participated in two different tournaments. On the first day, the tournament was played according to the official Spanish Basketball Federation (FEB) rules for under-14 players. On the second day, the competition was held with modified rules (Modified Tournament), in which the basket height was lowered to 2.90 m and the three-point line was replaced by a rectangle located 4 m from the basket. Plyometric variables, such as number of impacts (total and in zones), number of horizontal impacts (total and in zones), number of steps, number of jumps (total and in zones) and g-force of jumps during takeoff and landing, were assessed using GPS monitoring. In addition, the moderating effect of maturational age on the intervention in each of the variables under study will be evaluated. The results showed that the modified tournament (MT) showed significant differences compared to the standard format (FEB) in playing time, steps, landings 5–8 G, and takeoffs >8 G during positional attacks, as well as in horizontal impact variables during counterattacks and effective playing time. Bayesian analysis provided moderate-to-strong evidence for several of these variables, and extreme evidence for playing time and impacts during effective time. Moreover, maturational age (%PAH) consistently moderated the intervention effects, particularly in impact loads and locomotor demands. These findings can provide useful insights for coaches and practitioners in youth basketball. Adjusting competition rules and considering maturational status may optimize player development by creating contexts that enhance plyometric performance while adapting to the physical and biological characteristics of young athletes. Full article

Background/Objectives: Considering that the kinematics of the temporomandibular joints (TMJs) is concomitant with head movements and that temporomandibular joint disorders (TMDs) are frequently associated with neck pain in clinics but seldom or never investigated, the aim of this study was to develop a reliable in vivo measurement protocol of the simultaneous amplitudes of the mandible and of the skull. The development of such a protocol is part of a project to build an accurate kinematic assessment tool for clinicians in the orofacial field who treat patients suffering from TMD. Methods: Mouth opening, laterotrusion and protrusion movements for three different positions of the head (neutral, slouched and military) on 12 asymptomatic voluntary subjects (5 men and 7 women, mean 33.6 yo +/− 11.1) were recorded using 20 markers palpated and taped and 14 optoelectronic cameras. The acquisition frequency was set at 150 hertz. The inter- and intra-examiner reliability of marker palpation in mm was calculated using standard deviation (SD), mean difference (MD) and standard error (SE). Amplitudes of movement according to axes defined by the International Society of Biomechanics (ISB) are given for the mandible and skull segments. The propagation of error on the amplitudes was calculated with the root mean square propagation error (RMSPE) in degrees. Repeated-measures ANOVA or Friedman tests were used to assess the influence of the position of the head on the amplitudes of the jaw. Power analysis of the sample size was estimated with Cohen’s f3 size effect test. Steady-state plots (SSPs) and normalized motion graphs between the skull and the mandible motion were performed to study the coordination of their maximum amplitude over time. Results: The protocol demonstrated good intra-examiner reliability (1.5 < MD < 5.8; 2.6 < SD < 7.8; 2.0 < SE < 3.8), good inter-examiner reproducibility (0.2 < MD < 4.0; 3.5 < SD < 4.6; 2.0 < SE < 2.5) and small error propagation (0.0 < RMSPE intra < 2.8; 0.0 < RMSPE inter < 1.0). The amplitudes of the jaw and head found during the three types of movements correspond to the values reported in the literature. Head positions did not appear to significantly influence the amplitudes of jaw movements, which could be explained by the power estimation of our sample (Type II error β = 0.692). The participation of head movements in those of the jaw, for all motions and in all positions, was demonstrated and discussed in detail. Conclusions: The accuracy, test–retest reliability, and intra-individual variability of the TMJ kinematic analysis, including head movements, was ensured. The small sample size and the absence of standardized head positions for the subjects limit the scope of the intra- and inter-group analysis results. Given the natural biological and complex coordination of jaw–head movement, the authors consider its evaluation useful in clinical intervention and would like to further develop the present protocol. The next step should be to test the feasibility of its clinical application with a larger group of asymptomatic subjects compared to patients suffering from TMD. Full article

Precise electromagnetic torque estimation for permanent magnet synchronous motors (PMSMs) is crucial for enhancing the dynamic performance and energy efficiency of electric vehicles. To address the dynamic variations in dq-axis inductance caused by magnetic cross-coupling and saturation effects during motor operation—which lead to significant torque estimation errors in traditional fixed-parameter models under variable torque and speed conditions—this paper proposes a dynamic torque estimation method that integrates online dq-axis inductance identification based on a variable-step adaptive linear neural network (ADALINE) with an extended flux observer. The online identified inductance values are embedded into the extended flux observer in real time, forming a closed-loop torque estimation system with adaptive parameter updating. Experimental results demonstrate that, under complex operating conditions with varying torque and speed, the proposed method maintains electromagnetic torque estimation errors within ±3%, with a convergence time of less than 20 ms, while achieving inductance identification accuracy also within ±3%. These results significantly outperform conventional methods that do not incorporate inductance identification. This study provides a highly adaptive and engineering-practical solution for high-precision torque control of interior permanent magnet synchronous motors (IPMSMs) in automotive applications. Full article