Search Results (888)

In the present work, a direct oil cooling strategy using a multi-nozzle configuration is proposed for the thermal management of high-power density electric machines. The stator and winding temperatures, heat transfer coefficient, injection pressure, and power consumption are investigated for different nozzle types, nozzle numbers, heights of nozzle combinations, and oil flow rates. In addition, an artificial neural network (ANN) model based on two algorithms is developed for predicting thermal performance under various operating conditions. The flat jet nozzle shows the lowest maximum winding temperature of 120.3 °C and a superior heat transfer coefficient of 3028.6 W/m²-K compared to both full cone nozzles. The power consumption for the flat jet nozzle is higher at 123.9 W compared to other nozzle types. The combination of four flat jet nozzles shows improved oil spray distribution and enhanced cooling compared to combinations of two and six flat jet nozzles. Further, the thermal performance of oil spray cooling with four flat jet nozzles improves when height and oil flow rate are increased. Oil spray cooling with the best configuration shows a winding temperature, heat transfer coefficient, and injection pressure of 98.9 °C, 3408.6 W/m²-K and 4.86 bar, respectively, at a flow rate of 20 LPM. The proposed neural network model with a Levenberg–Marquardt (LM) training variant and logarithmic–sigmoidal (Log) transfer function shows the lowest prediction error within ±2%. Full article

Infrared thermal field (ITF) prediction for meter boxes is crucial for the early warning of power system faults, yet this method faces three major challenges: data sparsity, complex geometry, and resource constraints in edge computing. Existing physics-informed neural network-graph neural network (PINN-GNN) approaches suffer from redundant physics residual calculations (over 70% of flat regions contain little information) and poor model generalization (requiring retraining for new box types), making them inefficient for deployment on edge devices. This paper proposes the PhysGraphIR framework, which employs an Adaptive Residual Sampling (ARS) mechanism to dynamically identify hotspot region nodes through a physics-aware gating network, calculating physics residuals only at critical nodes to reduce computational overhead by over 80%. In this study, a ‘hotspot region’ is explicitly defined as a localized area exhibiting significant temperature elevation relative to the background—typically concentrated around electrical connection terminals or wire entrances—which is critical for identifying potential thermal faults under sparse data conditions. Additionally, it utilizes a Physics Knowledge Distillation Graph Neural Network (Physics-KD GNN) to decouple physics learning from geometric learning, transferring universal heat conduction knowledge to specific meter box geometries through a teacher–student architecture. Experimental results demonstrate that on both synthetic and real-world meter box datasets, PhysGraphIR achieves a hotspot region mean absolute error (MAE) of 11.8 °C under 60% infrared data missing conditions, representing a 22% improvement over traditional PINN-GNN. The training speed is accelerated by 3.1 times, requiring only five infrared samples to adapt to new box types. The experiments prove that this method significantly enhances prediction accuracy and computational efficiency under sparse infrared data while maintaining physical consistency, providing a feasible solution for edge intelligence in power systems. Full article

In photovoltaic (PV) power generation, the intermittency and uncertainty caused by meteorological factors pose challenges to grid operations. Accurate PV power prediction is crucial for optimizing power dispatching and balancing supply and demand. This paper proposes a PV power prediction model based on Density Peak Clustering Algorithm (DPCA)–Crested Porcupine Optimizer (CPO)–Random Forest (RF)–Gated Recurrent Unit (GRU)–Kolmogorov–Arnold Network (KAN). First, the DPCA is used to accurately classify weather conditions according to meteorological data such as solar radiation, temperature, and humidity. Then, the CPO algorithm is established to optimize the factor screening characteristic variables of the RF. Subsequently, a hybrid GRU model with a KAN layer is introduced for short-term PV power prediction. The Shapley Additive Explanation (SHAP) method values evaluating feature importance and the impact of causal features. Compared with other contrast models, the DPCA-CPO-RF-KAN-GRU model demonstrates better error reduction capabilities under three weather types, with an average fitting accuracy R² reaching 97%. SHAP analysis indicates that the combined average SHAP value of total solar radiation and direct solar radiation contributes more than 70%. Finally, the Kernel Density Estimation (KDE) is utilized to verify that the KAN-GRU model has high robustness in interval prediction, providing strong technical support for ensuring the stability of the power grid and precise decision-making in the electricity market. 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

Mobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for a coupled base–arm dynamics computed torque controller (CTC) for trajectory tracking of a differential-drive mobile manipulator, which considers the dynamics of the fixed base manipulator and the mobile base in a coupled way and compares its performance with that of a Proportional Derivative (PD) controller. Both controllers are tuned using Particle Swarm Optimization (PSO) with a cost function that aims to simultaneously reduce the control energy and the end-effector tracking error for different types of trajectories, and they operate in discrete time, thus accounting for inherent process delays. Simulation and laboratory implementation results show the superior performance of the CTC in both cases: in simulation, the average end-effector positioning error is reduced by 51.55% and the average RMS power by 46.44%; in the laboratory experiments, the average end-effector positioning error is reduced by 43.29% and the average RMS power by 53.49%, even in the presence of possible model uncertainties and system disturbances. Full article

With the rapid development of high-voltage DC (HVDC) power systems, accurate measurement of surface electrostatic potential on insulating components has become critical for electric field assessment and insulation reliability. This paper proposes an electrostatic potential sensor based on cantilever micro-vibration modulation, which employs piezoelectric actuators to drive high-frequency micro-vibration of cantilever-type shielding electrodes, converting the static electrostatic potential into an alternating induced charge signal. An electrostatic induction model is established to describe the sensing principle, and the influence of structural and operating parameters on sensitivity is analyzed. Multi-physics coupled simulations are conducted to optimize the cantilever geometry and modulation frequency, aiming to enhance modulation efficiency while maintaining a compact sensor structure. To validate the effectiveness of the proposed sensor, an electrostatic potential measurement platform for insulating components is constructed, obtaining response curves of the sensor at different potentials and establishing a compensation model for the working distance correction coefficient. The experimental results demonstrate that the sensor achieves a maximum measurement error of 0.92% and a linearity of 0.47% within the 1–10 kV range. Surface potential distribution measurements of a post insulator under DC voltage agreed well with simulation results, demonstrating the effectiveness and applicability of the proposed sensor for HVDC insulation monitoring. Full article

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) systems have been widely applied in various scenarios recently. This paper aims to maximize the total secure communication rate (SCR) of multiple users while ensuring the minimum beamforming gain towards sensing targets under the surveillance of multiple UAV warden swarms. To reduce the risk of detection, a novel type of artificial noise (AN) is introduced to interfere with swarm wardens. We conduct an analysis of the detection error probability (DEP) of these wardens and subsequently establish a mathematical model. In this model, the SCR is maximized subject to power, trajectory, sensing performance, and secure communication constraints. Since the problem is non-convex and the variables to be optimized are numerous and complex, we decompose the problem into three sub-problems. Then, an overall algorithm is proposed to solve these sub-problems separately. Simulation results demonstrate that the proposed scheme leads to a significant increase in the SCR. Moreover, the system exhibits highly stable performance in both communication and sensing tasks over time, indicating its robustness and reliability. Additionally, communication fairness among users is ensured, and energy efficiency is enhanced. Full article

We study numerically finite-sample power functions of nonparametric asymptotic tests for at most one change in the mean that are based on convergence in the distribution of sup- and integral functionals of an appropriately weighted and normalized tied-down partial sums process. For each test, a three-way trade-off is observed among its type I errors, power for detecting the change near the beginning or end of the sample, and power for detecting the change in the middle of the sample. By choosing suitable weight functions of a special form, we propose new sup- and integral tests that are shown to be nearly as powerful as the overall most powerful sup-test in the literature, regardless of where the change occurs in the sample. Moreover, the type I errors of the new tests are closer to the asymptotic significance level across various distributions and are lower and converge faster for distributions that are more asymmetric, heavy-tailed, or both. Full article

This study focuses on the optimization of modern extraction techniques for selected by-product materials, including apple, lemon, and tangerine peels, and onion skins, using artificial neural network (ANN) models. The extraction methods included ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) with water as the extractant, as well as maceration (MAC) with natural deep eutectic solvents (NADES). Key parameters, such as total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities, including reducing power (EC₅₀) and free radical scavenging capacity (IC₅₀), were evaluated to compare the efficiency of each method. Among the techniques, UAE outperformed both MAE and MAC in extracting bioactive compounds, especially from onion skins and tangerine peels, as reflected in the highest TPC, TFC, and antioxidant activity. UAE of onion skins showed the best performance, yielding the highest TPC (5.735 ± 0.558 mg CAE/g) and TFC (1.973 ± 0.112 mg RE/g), along with the strongest antioxidant activity (EC₅₀ = 0.549 ± 0.076 mg/mL; IC₅₀ = 0.108 ± 0.049 mg/mL). Tangerine peel extracts obtained by UAE also exhibited high phenolic content (TPC up to 5.399 ± 0.325 mg CAE/g) and strong radical scavenging activity (IC₅₀ 0.118 ± 0.099 mg/mL). ANN models using multilayer perceptron architectures with high coefficients of determination (r² > 0.96) were developed to predict and optimize the extraction results. Sensitivity and error analyses confirmed the robustness of the models and emphasized the influence of the extraction technique and by-product type on the antioxidant parameters. Principal component and cluster analyses showed clear grouping patterns by extraction method, with UAE and MAE showing similar performance profiles. Overall, these results underline the potential of UAE- and ANN-based modeling for the optimal utilization of agricultural by-products. Full article

Background: The intestinal microflora is a population of microorganisms that resides in the human gastrointestinal tract and is important in maintaining metabolic and immune homeostasis in the body. Bacteria residing in the intestine produce short-chain fatty acids (SCFAs), which communicate with, among other things, the brain–gut axis—disorders of which are one of the causes of MS-like pathologies. A particular property of SCFAs is the induction of regulatory T cells, which are finding their way into pioneering therapies for MS patients. The aim of the study is to evaluate SCFA secretion in patients with multiple sclerosis from the West Pomeranian region depending on the genotypes of rs778986 and rs3894326 polymorphisms of the FUT3 gene. Methods: The study group included 47 patients clinically diagnosed with MS. Genotyping was performed by real-time PCR using TaqMan probes. Analysis of short-chain fatty acids in faeces was performed on a quadrupole mass spectrometer coupled to a time-of-flight (QTOF) analyser coupled to an AB Sciex high-performance liquid chromatograph (UHPLC). Results: Statistical analysis did not reveal any statistically significant differences in the prevalence of the studied polymorphisms in MS patients compared to the healthy control group. It was observed that the intestinal microflora and SCFA production in MS patients may be disturbed, while the studied FUT3 gene polymorphisms probably do not have a significant effect on their concentrations. A statistical tendency towards higher caproic acid content in heterozygotes of the rs778986 polymorphism and higher valeric acid secretion in homozygotes of rs3894326 was demonstrated. Conclusions: In summary, the studied FUT3 gene polymorphisms are not overrepresented in patients with MS. The rs778986 FUT3 polymorphism may affect the caproic acid content in the faeces of patients with MS, and the rs3894326 polymorphism may affect valeric acid secretion. Due to the small sample size and sparse genotype groups, the study has limited power and negative findings may reflect Type II error; replication in larger cohorts is warranted. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

This study attempted to develop and validate a data-driven simulation model that integrates field-measured data to assess the power requirement and fuel consumption characteristics of a self-propelled collector. The collector is a hydrostatic transmission-based, crawler-type platform designed for garlic and onion harvesting, equipped with multiple hydraulic subsystems for collection and sorting. During field experiments, the power requirements of each subsystem and fuel flow rate were recorded, and Willans line method was applied to estimate engine power and subsystem power transmission efficiencies. Because many small agricultural machines do not support electronically instrumented engines (e.g., CAN-bus/ECU-based measurements), the proposed model was formulated as a data-driven, low-order representation derived from on-site measurements rather than a full physics-based model. Using the identified parameters, the simulation framework predicts engine power and fuel efficiency under various operating conditions. The simulation results exhibited high agreement with field data, achieving R² and mean absolute percentage error values of 0.935–0.981 and 1.79–4.18%, respectively, confirming reliable reproduction of real field performance. A comprehensive analysis of the simulation results revealed that both engine speed and travel speed significantly influence power distribution and fuel rate, while also indicating that hydraulic working power is the dominant contributor to total power demand at higher engine speeds. These findings provide practical guidance for improving the fuel efficiency of compact self-propelled collectors. Full article

Distribution network data may encounter random missing data caused by abnormal conditions and continuous missing data caused by natural disasters during gathering, transmission, and conversion. To address these problems, this paper proposes a missing data imputation method based on the Temporal Generative Adversarial Network with Gradient Penalty (TGAN-GP), which takes the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as its basic framework, replaces traditional fully connected layers with a Temporal Convolutional Network (TCN) in the generator’s core, leverages causal dilated convolution to efficiently capture the long-range temporal dependencies and periodicity of measurement data, and integrates residual connections to mitigate gradient vanishing and network degradation during deep training. For the discriminator, the method adopts a Long Short-Term Memory (LSTM) network, which enhances the evaluation of the temporal rationality of generated data and thereby further improves imputation accuracy. Finally, simulations were conducted on the IEEE 33-bus distribution network test system. Results show that under the random missing scenario (10% missing rate), the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) of node voltage magnitude imputation are as low as 0.00062 and 0.00051, those of node injected active power imputation are 0.00081 and 0.00065, and those of node injected reactive power imputation are 0.00082 and 0.00076. Under the continuous missing scenario, the RMSE and MAE of node voltage magnitude imputation are 0.00147 and 0.00122, those of node injected active power imputation are 0.00373 and 0.00268, and those of node injected reactive power imputation are 0.00314 and 0.00226. The imputation errors of all three data types are significantly lower than the comparison methods’. Full article

Strongly convex functions form a central subclass of convex functions and have gained considerable attention due to their structural advantages and broad applicability, particularly in optimization and information theory. In this paper, we investigate the class of strongly F-convex functions, which generalizes the classical notion of strong convexity by introducing an auxiliary convex control function F. We establish several fundamental structural characterizations of this class and provide a variety of nontrivial examples such as power, logarithmic, and exponential functions. In addition, we derive refined Jensen-type and Hermite–Hadamard-type inequalities adapted to the strongly F-convex concept, thereby extending and sharpening their classical forms. As applications, we obtain new analytical inequalities and improved error bounds for entropy-related quantities, including Shannon, Tsallis, and Rényi entropies, demonstrating that the concept of strong F-convexity naturally yields strengthened divergence and uncertainty estimates. Full article

Generalized censoring, combined with a power-based distribution, improves inferential efficiency by capturing more detailed failure-time information in complex testing scenarios. Conventional censoring schemes may discard substantial failure-time information, leading to inefficiencies in parameter estimation and reliability prediction. To address this limitation, we develop a comprehensive inferential framework for the alpha-power Weibull (APW) distribution under a generalized progressive hybrid Type-II censoring scheme, a flexible design that unifies classical, hybrid, and progressive censoring while guaranteeing test completion within preassigned limits. Both maximum likelihood and Bayesian estimation procedures are derived for the model parameters, reliability function, and hazard rate. Associated uncertainty quantification is provided through asymptotic confidence intervals (normal and log-normal approximations) and Bayesian credible intervals obtained via Markov chain Monte Carlo (MCMC) methods with independent gamma priors. In addition, we propose optimal censoring designs based on trace, determinant, and quantile-variance criteria to maximize inferential efficiency at the design stage. Extensive Monte Carlo simulations, assessed using four precision measures, demonstrate that the Bayesian MCMC estimators consistently outperform their frequentist counterparts in terms of bias, mean squared error, robustness, and interval coverage across a wide range of censoring levels and prior settings. Finally, the proposed methodology is validated using real-life datasets from engineering (electronic devices), clinical (organ transplant), and physical (rare metals) studies, demonstrating the APW model’s superior goodness-of-fit, reliability prediction, and inferential stability. Overall, this study demonstrates that combining generalized censoring with the APW distribution substantially enhances inferential efficiency and predictive performance, offering a robust and versatile tool for complex life-testing experiments across multiple scientific domains. Full article