Search Results (57)

Background: Patients after a transient ischemic attack (TIA) are at high risk of subsequent stroke. There are various scores that aim to accurately identify patients at the highest risk of stroke. However, without comparisons between these scores, it is still unknown which is the score with the best predictive utility. Our study aims to identify the risk stratification score with the highest utility to identify patients at high risk for stroke within 90 days after a TIA. Methods: The MEDLINE and Scopus databases were systematically searched on 1 December 2023 for observational cohort studies assessing the ability of a score to predict a stroke within the first 90 days from the index TIA event. Only studies that had a direct comparison of at least two scores were included. A random-effects network meta-analysis was performed. Sensitivity and specificity, along with relevant 95% credible intervals, and between-score and between-study heterogeneity were estimated. We also estimated relative sensitivities and relative specificities compared with the ABCD2 score. We ranked each score according to its predictive accuracy based on both sensitivity and specificity estimates, using the diagnostic odds ratio (DOR) and the summary receiver operating characteristic (SROC) curve. Results: Our systematic review highlighted 9 studies including 14 discrete cohorts. The performance of all scores to identify patients at high risk for stroke recurrence within 90 days following a TIA was low (pooled sensitivity range 48–64%, pooled specificity range 59–72%). In the network meta-analysis, we analyzed 6 studies with 11 discrete cohorts, including data from 8217 patients. The ABCD3-I score demonstrated the highest DOR, followed by the ESRS, ABCD, California, and ABCD2. The SROC curves demonstrate no significant differences in the performance of the scores, using the ABCD score as the common comparator. Conclusions: In this systematic review and network meta-analysis of observational cohort studies of patients who experienced TIA and were followed for the occurrence of subsequent stroke, we failed to identify a score performing significantly better for the prediction of stroke at 90 days. New models are needed for the prediction and stroke risk stratification following a TIA. Full article

When control room operators encounter mental fatigue, the accuracy of their work will decline. Accurately predicting the mental fatigue of industrial control room operators is of great significance for preventing operational mistakes. In this study, facial data of experimental participants were collected via cameras, and fatigue levels were evaluated using an improved Karolinska Sleepiness Scale (KSS). Subsequently, a dataset of fatigue samples based on facial features was established. A novel early-warning framework was put forward, framing fatigue prediction as a time series prediction task. Two innovative data processing techniques were introduced. Reverse data binning transforms discrete fatigue labels into continuous values through a random perturbation of ≤0.3, enabling precise temporal modeling. A fatigue-aware data screening method uses the 6 s rule and a sliding window to filter out transient states and preserve key transition patterns. Five prediction models, namely Light Gradient Boosting Machine (LightGBM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Transformer, and Attention-based Temporal Convolutional Network (Attention-based TCN), were evaluated using the collected dataset of fatigue samples based on facial features. The results indicated that LightGBM demonstrated outstanding performance, with an accuracy rate reaching 93.33% and an average absolute error of 0.067. It significantly outperformed deep learning models. Moreover, its computational efficiency further verified its suitability for real-time deployment. This research integrates predictive modeling with industrial safety applications, providing evidence for the feasibility of machine learning in proactive fatigue management. Full article

In response to the growing demand for lightweight and high-efficiency industrial equipment, this study addresses the critical issue of lubrication failure in high-speed, heavy-duty gear reducers, which often leads to reduced transmission efficiency and premature mechanical damage. A three-dimensional transient multiphysics-coupled model of oil-jet lubrication is developed based on computational fluid dynamics (CFD). The model integrates the Volume of Fluid (VOF) multiphase flow method with the shear stress transport (SST) k−ω turbulence model. This framework enables the accurate capture of oil-jet interface fragmentation, reattachment, and turbulence-coupled behavior within the gear meshing region. A parametric study is conducted on oil injection velocities ranging from 20 to 50 m/s to elucidate the coupling mechanisms between geometric configuration and flow dynamics, as well as their impacts on oil film evolution, energy dissipation, and thermal management. The results reveal that the proposed method can reveal the dynamic evolution characteristics of the gear injection lubrication. Adopting an appropriately moderate injection velocity (30 m/s) improves oil film coverage and continuity, with the lubricant transitioning from discrete droplets to a dense wedge-shaped film within the meshing zone. Optimal lubrication performance is achieved at this velocity, where oil shear-carrying capacity and kinetic energy utilization efficiency are maximized, while excessive turbulent kinetic energy dissipation is effectively suppressed. Dynamic monitoring data at point P further corroborate that a well-tuned injection velocity stabilizes lubricant-velocity fluctuations and improves lubricant oil distribution, thereby promoting consistent oil film formation and more efficient heat transfer. The proposed closed-loop collaborative framework—comprising model initialization, numerical solution, and post-processing—together with the introduced quantitative evaluation metrics, provides a solid theoretical foundation and engineering reference for structural optimization, energy control, and thermal reliability design of gearbox lubrication systems. This work offers important insights into precision lubrication of high-speed transmissions and contributes to the sustainable, green development of industrial machinery. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

The time-domain electromagnetic method has been widely applied in mineral exploration, oil, and gas fields in recent years. However, its response characteristics remain unclear, and there is an urgent need to study the response characteristics of the borehole-surface transient electromagnetic(BSTEM) field. This study starts from the time-domain electric field diffusion equation and discretizes the calculation area in space using tetrahedral meshes. The Galerkin method is used to derive the finite element equation of the electric field, and the vector interpolation basis function is used to approximate the electric field in any arbitrary tetrahedral mesh in the free space, thus achieving the three-dimensional forward simulation of the BSTEM field based on the finite element method. Following validation of the numerical simulation method, we further analyze the electromagnetic field response excited by vertical line sources.. Through comparison, it is concluded that measuring the radial electric field is the most intuitive and effective layout method for BSTEM, with a focus on the propagation characteristics of the electromagnetic field in both low-resistance and high-resistance anomalies at different positions. Numerical simulations reveal that BSTEM demonstrates superior resolution capability for low-resistivity anomalies, while showing limited detectability for high-resistivity anomalies Numerical simulation results of BSTEM with realistic orebody models, the correctness of this rule is further verified. This has important implications for our understanding of the propagation laws of BSTEM as well as for subsequent data processing and interpretation. Full article

The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. The study focuses on the FDII of multiplicative faults in a DC motor and its electronic amplifier. To simulate such scenarios, a complete laboratory platform was developed for real-time FDII, using relay-based switching and custom LabVIEW software 2009. This platform enables real-time experimentation and represents an important component of the study. Two estimation-based fault detection (FD) algorithms were implemented: the Sliding Window Algorithm (SWA) for discrete-time models and a modified Sliding Integral Algorithm (SIA) for continuous-time models. The modification introduced to the SIA limits the data length used in least squares estimation, thereby reducing the impact of transient effects on parameter accuracy. Both algorithms achieved high model output-to-measured signal agreement, up to 98.6% under nominal conditions and above 95% during almost all fault scenarios. Moreover, the proposed fault isolation and identification methods, including a decision algorithm and an indirect estimation approach, successfully isolated and identified faults in key components such as amplifier resistors (R₁, R₉, R₁₂), capacitor (C₈), and motor parameters, including armature resistance (R_a), inertia (J), and friction coefficient (B). The decision algorithm, based on continuous-time model coefficients, demonstrated reliable fault isolation and identification, while the reduced Jacobian-based approach in the discrete model enhanced fault magnitude estimation, with deviations typically below 10%. Additionally, the platform supports remote experimentation, offering a valuable resource for advancing model-based FDII research and engineering education. Full article

The structural integrity of cement concrete pavements, paramount for ensuring traffic safety and operational efficiency, faces mounting challenges from the escalating burden of heavy-duty vehicular traffic. Precise characterisation of pavement dynamic responses under such conditions proves indispensable for implementing effective structural health monitoring and early warning system deployment. This investigation examines the triaxial dynamic response characteristics of cement concrete pavement subjected to low-speed, heavy-duty vehicular excitations, employing data acquired through in situ field measurements. A monitoring system incorporating embedded triaxial MEMS accelerometers was developed to capture vibration signals directly within the pavement structure. Raw data underwent preprocessing utilising a smoothing wavelet transform technique to attenuate noise, followed by empirical mode decomposition (EMD) and short-time energy (STE) analysis to scrutinise the time–frequency and energetic properties of triaxial vibration signals. The findings demonstrate that heavy, slow-moving vehicles generate substantial triaxial vibrations, with the vertical (Z-axis) response exhibiting the greatest amplitude and encompassing higher dominant frequency components compared to the horizontal (X and Y) axes. EMD successfully decomposed the complex signals into discrete intrinsic mode functions (IMFs), identifying high-frequency components (IMF1–IMF3) associated with transient vehicular impacts, mid-frequency components (IMF4–IMF6) presumably linked to structural and vehicle dynamics, and low-frequency components (IMF7–IMF9) representing system trends or ambient noise. The STE analysis of the selected IMFs elucidated the transient nature of axle loading, revealing pronounced, localised energy peaks. These findings furnish a comprehensive understanding of the dynamic behaviour of cement concrete pavements under heavy vehicle loads and establish a robust methodological framework for pavement performance assessment and refined axle load identification. Full article

Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends heavily on the decomposition level ($L$) and the wavelet filter technique selected. Hence, we examined the efficacy of wind predictions as a function of $L$and wavelet filters. In the proposed hybrid approach, differential evolution (DE) optimises the decomposition level of various wavelet filters (i.e., least asymmetric (LA), Daubechies (DB), and Morris minimum-bandwidth (MB)) using the maximal overlap discrete wavelet transform (MODWT), allowing for the decomposition of wind data into more statistically sound sub-signals. These sub-signals are used as inputs into the gated recurrent unit (GRU) to accurately capture wind speed. The final predicted values are obtained by reconciling the sub-signal predictions using multiresolution analysis (MRA) to form wavelet-MODWT-GRUs. Using wind data from three Wind Atlas South Africa (WASA) locations, Alexander Bay, Humansdorp, and Jozini, the root mean square error, mean absolute error, coefficient of determination, probability integral transform, pinball loss, and Dawid-Sebastiani showed that the MB-MODWT-GRU at $L=3$ was best across the three locations. Full article

This research presents a voting ensemble classification model to distinguish high impedance faults (HIFs) from other transients in a photovoltaic (PV) integrated microgrid (MG). Due to their low fault current magnitudes, sporadic incidence, and non-linear character, HIFs are difficult to detect with a conventional protective system. A machine learning (ML)-based ensemble classifier is used in this work to classify HIF more accurately. The ensemble classifier improves overall accuracy by combining the strengths of many rule-based models; this decreases the likelihood of overfitting and increases the robustness of classification. The ensemble classifier includes a classification process into two steps. The first phase extracts features from HIFs and other transient signals using the discrete wavelet transform (DWT) technique. A supervised discretisation approach is then used to discretise these attributes. Using discretised features, the rule-based classifiers like decision tree (DT), Java repeated incremental pruning (JRIP), and partial decision tree (PART) are trained in the second phase. In the classification step, the voting ensemble technique applies the rule of an average probability over the output predictions of rule-based classifiers to obtain the final target of classes. Under standard test conditions (STCs) and real-time weather circumstances, the ensemble technique surpasses individual classifiers in accuracy (95%), HIF detection success rate (93.3%), and overall performance metrics. Feature discretisation boosts classification accuracy to 98.75% and HIF detection to 95%. Additionally, the ensemble model’s efficacy is confirmed by classifying HIF from other transients in the IEEE 13-bus standard network. Furthermore, the ensemble model performs well, even with noisy event data. The proposed model provides higher classification accuracy in both PV-connected MG and IEEE 13 bus networks, allowing power systems to have effective protection against faults with improved reliability. Full article

This paper presents the development and validation of a high-fidelity, unsteady, computational fluid dynamics (CFD) model of the Purdue 3-Stage Axial Research Compressor. A grid convergence study assesses the spatial discretization accuracy of the single-passage, steady-state computational model. Additionally, the periodic-unsteady convergence of the unsteady signals of a multiple-passage transient blade row model was explored. Computational predictions were compared with experimental measurements to evaluate the efficacy of the various modeling decisions. Notably, transient blade row model calculations employing the Scale-Adaptive Simulation (SAS) formulation of Menter’s Shear Stress Transport (SST) turbulence model exhibited a significantly improved agreement with experimental data compared to steady-state calculations. Particularly, in conjunction with the SAS-SST turbulence model, the transient calculations significantly improved the spanwise (radial) mixing characteristics of the transient-average stagewise total temperature profiles. Spectral analyses of the transient signals compared with unsteady pressure measurements showed fundamental and second harmonic blade-passing frequency amplitudes matching within 5–7% in the embedded stage. This research underscores the importance of including accurate geometry, practical minimization of modeling assumptions using higher-fidelity physics models, comprehensive convergence assessment, and the comparison and validation of computational predictions with experimental measurements. Full article

Turbocharged internal combustion engines offer efficient power-to-weight ratios, aiding in fuel-saving efforts within the automotive industry. However, when the flow is low, compressors show various instabilities, i.e., stall and inlet recirculation, which have a negative influence on their performance. This paper uses transient numerical simulations to explore the inlet recirculation phenomenon in a turbocharger compressor. The Reynolds-Averaged Navier–Stokes equations and k-ω SST turbulence model were solved using ANSYS CFX. The numerical model was verified using the experimental data for the design speed line. Analysis of mesh independence was performed to assess the discretization uncertainty near the design and surge line points. The results indicate that the inlet recirculation appears for moderate flows lower than design conditions. It shows significant radial and streamwise growth as the flow decreases. The reversed flow area increases more intensely in the radial direction at medium mass flow rates, whereas the streamwise growth is more substantial at low mass flow rates. The reversed flow reached 27% of the total inlet area at the point on the surge line. It was accompanied by a 15.7% drop in efficiency between the points with weak and strong inlet recirculation. The presented research indicates significant changes in the size of the inlet recirculation zone in the circumferential direction. It reaches its highest intensity close to the angular position of the volute tongue. Full article

With the rapid development of the semiconductor industry, the demand for high-speed testing in the large-scale production of semiconductor devices and integrated circuit production lines continues to grow. As one of the key tools in semiconductor device performance testing and integrated circuit testing, source measure unit (SMU) plays a crucial role in high-precision transient response testing scenarios. In high-precision measurement scenarios, multiple measurements are often required and averaged to improve measurement accuracy, but this can slow down the measurement speed. This article proposes a measurement acceleration algorithm based on BA-Informer time series prediction to solve the problem of decreased measurement speed in high-precision measurement. On the one hand, this algorithm improves the encoder structure. Traditional time series prediction models may have limitations in handling long-term dependencies and trend extraction. BiRNN is an extended version of recurrent neural network (RNN), which consists of two directional RNN. One forward RNN processes data from the beginning to the end of the sequence, while the other reverse RNN processes data from the end to the beginning of the sequence. In the end, the outputs from both directions are merged at each time step. Compared to traditional one-way RNN, BiRNN can more effectively handle data with before and after dependencies. Based on its characteristics, this article integrates BiRNN into the encoder structure. This algorithm can simultaneously process input sequences from both positive and negative directions, effectively limiting the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. In this paper, BiRNN is integrated into the encoder structure, and the algorithm can simultaneously process input sequences from both positive and negative directions, more effectively capturing the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. This improvement enables the model to more accurately grasp the overall trend of data changes during prediction, thereby improving prediction accuracy. On the other hand, an attention discrete cosine transform (ADCT) module is introduced between the encoder and decoder to convert time-domain signals into frequency-domain representations. This not only reveals the spectral characteristics of the signal but also reduces data redundancy and improves the efficiency of subsequent processing by combining attention mechanisms. Finally, the algorithm performance is analyzed by analyzing the output characteristic curves of loads with different properties. The experiment shows that the prediction algorithm and the combination of measurement and prediction method proposed in this article save half of the measurement time by combining measurement and prediction while ensuring the same amount of data obtained, verifying the effectiveness of the proposed method. Full article

This review paper describes and compares the practical methods that make it possible to calculate an average value of apparent, active, and reactive (i.e., blind and distorted) power in each calculation step. In addition to two methods, $p–q$ and $ip–iq$, it deals with the application of the $id–iq$ method for determining power components’ mean values in a discrete step. The results are important and needed for the right dimensioning and sizing of power electronic and electrical systems (PEESs), which those power components produce. This is because the integral calculation for the mean values of the product of voltage u(t) and current i(t) always gives a value lower than the actual value of the apparent power. Using moving average and moving root mean square (rms) techniques (or digital filtering), one obtains the right values, although with a time delay. Using sliding filtering, these techniques calculate the average or rms values, respectively, of the power components in each step k. By calculating the moving average value of the power components in both transient and steady states (on/off as well), we achieve the correct design of the system. The transients for the three- and single-phase power electronic systems are modeled, simulated, and theoretically supported in this study. Any PEES can be determined and sized using the calculated data. The real-time HW simulator Plecs RT Box 1 and Matlab/Simulink 2024a simulations validate the comprehensive time waveform produced by the suggested method. Full article

This study employs the large-eddy simulation (LES) approach, together with the hybrid volume of fluid—discrete phase model, to examine the deformation and breakup of a water droplet impacted by a traveling shock wave. The research investigates the influence of Weber number on transient deformation and breakup characteristics. Particular focus is given to the detailed analysis of sub-droplet-size distributions, which are frequently overlooked in existing studies, providing a novel insight into droplet fragmentation dynamics. The predicted deformation and breakup patterns of droplets in the shear breakup regime align well with experimental data, validating the computational approach. Notably, LES is able to reproduce the underlying physical mechanisms, highlighting the significant role of recirculation zones and the progression of Kelvin–Helmholtz instabilities in droplet breakup. Additionally, it is shown that higher Mach numbers significantly amplify both cross-stream and streamwise deformations, leading to earlier breakup at higher airflow pressures. Increasing the Weber number from 205 to 7000 results in 25% reduction in the average size of the sub-droplets, indicating the strong influence of aerodynamic forces on droplet fragmentation. This comprehensive analysis, while aligning with experimental observations, also provides new insights into the complex dynamics of droplet breakup under post-shock conditions, highlighting the robustness and applicability of the proposed hybrid Eulerian–Lagrangian formulation for such advanced applications in fluid engineering. Full article

Complementary Metal-Oxide-Semiconductor (CMOS) image sensors (CISs), known for their high integration, low cost, and superior performance, have found widespread applications in satellite and space exploration. However, the readout circuits of pixel arrays are vulnerable to functional failures in complex or intense radiation environments, particularly due to transient γ radiation. Using Technology Computer-Aided Design (TCAD) device simulations and Simulation Program with Integrated Circuit Emphasis (SPICE) circuit simulations, combined with a double-exponential current source fault injection method, this study investigates the transient dose rate effect (TDRE) on a typical readout circuit of CISs. It presents the variations in the photoelectric signal under different dose rates and at different occurrence moments of the TDRE. The results show that, under low dose rates, the CIS readout circuit can still perform data acquisition and digital processing, with the photoelectric signal exhibiting some sensitivity to the occurrence moment. At high dose rates, however, the photoelectric signal not only remains sensitive to the occurrence moment but also shows significant discreteness. Further analysis of the CIS readout circuit sequence suggests that the occurrence moment is a critical factor affecting the circuit’s performance and should not be overlooked. These findings provide valuable insights and references for further research on the TDRE in circuits. Full article