Search Results (4,084)

In recent years, the rapid spread of electric vehicles (EVs) has intensified the competition to develop power units for EVs. In particular, improving the driving range of EVs has become a major topic, and in order to achieve this, many studies have been conducted on improving the efficiency of EV power units. In this study, we propose a multiple-inverter-drive permanent magnet synchronous motor based on an 8-pole, 48-slot structure, which is commonly used as an EV motor. The proposed motor is composed of two completely independent parallel inverters and windings, and intermittent operation is possible; that is, only one inverter and one parallel winding is used depending on the situation. In the proposed motor, we compare losses including stator iron loss, rotor iron loss, and magnet eddy current loss by PWM voltage inputs for some stator winding topologies, we show that the one-side winding arrangement is the most efficient during intermittent operation, and that it is more efficient than normal operation especially in the low-speed, low-torque range. Finally, through a vehicle-driving simulation considering the efficiency map including motor loss and inverter loss, we show that the intentional use of intermittent operation can improve electrical energy consumption. Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article

This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements–compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system’s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s. Full article

Driven by the environmental benefits associated with reduced greenhouse gas emissions, oil companies have intensified research efforts into reassessing the strategies used to meet the electrical demands of offshore production platforms. Among the various alternatives available, the deployment of onshore–offshore interconnections via High-Voltage Direct Current (HVDC) transmission systems has emerged as a promising solution, offering both economic and operational advantages. In addition to reliably meeting the electrical demand of offshore facilities, this approach enables enhanced operational flexibility due to the advanced control and regulation capabilities inherent to HVDC converter stations. Based on the use of interconnection through an HVDC link, aiming to evaluate the operation of the electrical system as a whole, this study focuses on evaluating dynamic events using the PSCAD software version 5.0.2 to analyze the direct online starting of a large induction motor and the sudden loss of a local synchronous generating unit. The simulation results are then analyzed to assess the effectiveness of both Grid-Following (GFL) and Grid-Forming (GFM) control strategies for the converters, while the synchronous generators are evaluated under both voltage regulation and constant power factor control operation, with a particular focus on system stability and restoration of normal operating conditions in the sequence of events. Full article

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous frameworks fail to reflect the discrete nature of actual load disconnections, while deterministic models inadequately capture the stochastic behavior of renewable generation and load variations. The proposed approach formulates load shedding as an integer optimization problem where variables are categorized as integer (load disconnection decisions at specific nodes) and continuous (voltages, power generation, and steady-state frequency), better reflecting practical power system operations. The key innovation combines integer load-shedding optimization with efficient uncertainty propagation through Unscented Transformation, eliminating the computational burden of Monte Carlo simulations while maintaining accuracy. Load and renewable uncertainties are modeled as normally distributed variables, and probabilistic constraints ensure operational limits compliance with predefined confidence levels. The methodology integrates Differential Evolution metaheuristics with Unscented Transformation for uncertainty propagation, requiring only 137 deterministic evaluations compared to 5000 for Monte Carlo methods. Validation on an IEEE 33-bus radial distribution system configured as an islanded microgrid demonstrates significant advantages over conventional approaches. Results show 36.5-fold computational efficiency improvement while achieving 95.28% confidence level compliance for frequency limits, compared to only 50% for deterministic methods. The integer formulation requires minimal additional load shedding (21.265%) compared to continuous approaches (20.682%), while better aligning with the discrete nature of real-world operational decisions. The proposed IM-POPF framework successfully minimizes total load shedding while maintaining frequency stability under uncertain conditions, providing a computationally efficient solution for real-time microgrid operation. Full article

Predictive maintenance (PdM) is a proactive strategy that enhances safety, minimizes unplanned downtime, and optimizes operational costs by forecasting equipment failures before they occur. This study presents a novel Field Programmable Gate Array (FPGA)-accelerated predictive maintenance framework for UAV engines using a Singular Value Decomposition (SVD)-optimized Long Short-Term Memory (LSTM) model. The model performs binary classification to predict the likelihood of imminent engine failure by processing normalized multi-sensor data, including temperature, pressure, and vibration measurements. To enable real-time deployment on resource-constrained UAV platforms, the LSTM’s weight matrices are compressed using Singular Value Decomposition (SVD), significantly reducing computational complexity while preserving predictive accuracy. The compressed model is executed on a Xilinx ZCU-104 FPGA and uses a pipelined, AXI-based hardware accelerator with efficient memory mapping and parallelized gate calculations tailored for low-power onboard systems. Unlike prior works, this study uniquely integrates a tailored SVD compression strategy with a custom hardware accelerator co-designed for real-time, flight-safe inference in UAV systems. Experimental results demonstrate a 98% classification accuracy, a 24% reduction in latency, and substantial FPGA resource savings—specifically, a 26% decrease in BRAM usage and a 37% reduction in DSP consumption—compared to the 32-bit floating-point SVD-compressed FPGA implementation, not CPU or GPU. These findings confirm the proposed system as an efficient and scalable solution for real-time UAV engine health monitoring, thereby enhancing in-flight safety through timely fault prediction and enabling autonomous engine monitoring without reliance on ground communication. Full article

A surface-wave-assisted, dual-band, circularly polarized reconfigurable intelligent surface is proposed that allows arbitrary beam-shaping capability within the [4.35 GHz–4.5 GHz] and [11.8 GHz–12.3 GHz] frequency bands. In particular, alongside the proposed physical design of the surface, a genetic algorithm-based design framework is introduced to enable the synthesis of complex radiation patterns beyond simple beam steering. It is shown that the phase profiles obtained from the proposed optimization scheme naturally lead to the excitation of surface waves, which facilitate arbitrary beam shaping by satisfying the local power conservation condition between the normally impinging and arbitrarily reflected waves. To physically construct the proposed surface, cascaded symmetric unit cells are employed to facilitate circular polarization operation and realize dual-band operation. Furthermore, varactor diodes are incorporated into the design of unit cells so that the reflection phase can be independently and continuously tuned across the two frequency bands, with a tuning range of 300 degrees. The versatility of the proposed surface is demonstrated through design examples that achieve (i) unidirectional beam steering, (ii) multi-directional beam steering, and (iii) sector-beam formation within each frequency band. Full article

To ensure the safety of nuclear power production, nuclear power plants deploy numerous sensors to monitor various physical indicators during production, enabling the early detection of anomalies. Efficient anomaly detection relies on complete sensor data. However, compared to conventional energy sources, the extreme physical environment of nuclear power plants is more likely to negatively impact the normal operation of sensors, compromising the integrity of the collected data. To address this issue, we propose an anomaly detection method for nuclear power data: Neural Normal Stochastic Process (NNSP). This method does not require imputing missing sensor data. Instead, it directly reads incomplete monitoring data through a sequentialization structure and encodes it as continuous latent representations in a neural network. This approach avoids additional “processing” of the raw data. Moreover, the continuity of these representations allows the decoder to specify supervisory signals at time points where data is missing or at future time points, thereby training the model to learn latent anomaly patterns in incomplete nuclear power monitoring data. Experimental results demonstrate that our model outperforms five mainstream baseline methods—ARMA, Isolation Forest, LSTM-AD, VAE, and NeutraL AD—in anomaly detection tasks on incomplete time series. On the Power Generation System (PGS) dataset with a 15% missing rate, our model achieves an F1 score of 83.72%, surpassing all baseline methods and maintaining strong performance across multiple industrial subsystems. Full article

Colorectal cancer (CRC) is one of the most prevalent and deadly neoplasms globally; this fact puts emphasis on the need for accurate molecular biomarkers for early detection and accurate prognosis. Circular RNAs (circRNAs) have recently emerged as very promising cancer biomarkers. In this study, we thoroughly examined whether the expression levels of circular transcripts of the protein arginine methyltransferase 1 (PRMT1) gene can predict the prognosis of patients diagnosed with colorectal adenocarcinoma, the most frequent type of CRC. Hence, a highly sensitive quantitative PCR (qPCR) assay was developed and applied to quantify circ-PRMT1 expression in cDNAs from 210 primary colorectal adenocarcinoma tissue specimens and 86 paired normal colorectal mucosae. Extensive biostatistical analysis was then performed to assess the potential prognostic power of circ-PRMT1. Significant overexpression of this molecule was observed in colorectal adenocarcinoma tissue samples in contrast to their non-cancerous counterparts. Moreover, higher circ-PRMT1 expression was correlated with poorer disease-free survival (DFS) and worse overall survival (OS) in colorectal adenocarcinoma patients. Interestingly, multivariate Cox regression analysis revealed that the prognostic value of the expression of this circRNA does not depend on other established prognostic factors included in the prognostic model. Furthermore, the stratification of patients based on TNM staging revealed that higher circ-PRMT1 levels were significantly related to shorter DFS and OS intervals, particularly in patients with colorectal adenocarcinoma of TNM stage II or III. In summary, this original research study provides evidence that circ-PRMT1 overexpression represents a promising molecular biomarker of poor prognosis in colorectal adenocarcinoma, not depending on other established prognostic factors such as TNM staging. Full article

Currently, the visual study of the structure of building materials and products is gradually supplemented by intelligent algorithms based on computer vision technologies. These algorithms are powerful tools for the visual diagnostic analysis of materials and are of great importance in analyzing the quality of production processes and predicting their mechanical properties. This paper considers the process of analyzing the visual structure of non-autoclaved aerated concrete products, namely their porosity, using the YOLOv11 convolutional neural network, with a subsequent prediction of one of the most important properties—thermal conductivity. The object of this study is a database of images of aerated concrete samples obtained under laboratory conditions and under the same photography conditions, supplemented by using the author’s augmentation algorithm (up to 100 photographs). The results of the porosity analysis, obtained in the form of a log-normal distribution of pore sizes, show that the developed computer vision model has a high accuracy of analyzing the porous structure of the material under study: Precision = 0.86 and Recall = 0.88 for detection; precision = 0.86 and recall = 0.91 for segmentation. The Hellinger and Kolmogorov–Smirnov statistical criteria, for determining the belonging of the real distribution and the one obtained using the intelligent algorithm to the same general population show high significance. Subsequent modeling of the material using the ANSYS 2024 R2 Material Designer module, taking into account the stochastic nature of the pore size, allowed us to predict the main characteristics—thermal conductivity and density. Comparison of the predicted results with real data showed an error less than 7%. Full article

Dynamic on-resistance (R_ON) of commercial GaN on Si normally off high-electron-mobility transistor (HEMT) devices is a very important parameter because it is responsible for conduction losses that limit the power conversion efficiency of high-power switching converters. Due to charge trapping effects, dynamic R_ON is always higher than in DC, a behavior known as current collapse. To study how short-time dynamics of charge trapping and release affects R_ON we use rectangular 0–5 V gate voltage pulses with durations in the 1 μs to 100 μs range. Measurements are first carried out for single pulses of increasing duration, and it is found that R_ON depends on both pulse duration and drain current I_D, being higher at shorter pulse durations and lower I_D. For a train of five pulses, R_ON decreases with pulse number, reaching a steady state after a time interval of 100 μs. The response to a five pulses train is compared to that of a square-wave signal to study the time evolution of R_ON toward a dynamic steady state. The DC R_ON is also measured, and it is a factor of ten smaller than dynamic R_ON at the same I_D. This confirms that a reduction in trapped charges takes place in DC as compared to the square-wave switching operation. Additional off-state stress tests at V_DS = 55 V reveal the presence of residual surface traps in the drain access region, leading to four times increase in R_ON in comparison to pristine devices. Finally, the dynamic R_ON is also measured by the double-pulse test (DPT) technique with inductive load, giving a good agreement with results from single-pulse measurements. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article

Physiological tremor analysis is a practical tool for assessing the neuromuscular impacts of sport-specific training. The purpose of this study was to examine and compare the physiological characteristics of lower limb resting postural tremor in athletes from Poland’s national speed skating team, following both sprint and endurance workouts. The study included 19 male, well-trained, elite athletes (with a mean age of 18 ± 3.1 years, body mass of 71.4 ± 10.1 kg, height of 178.5 ± 9.0 cm, and training experience of 12.6 ± 2.8 years) and a control group of 19 physically active but non-athlete men (with a mean age of 19 ± 2.3 years, body mass of 78.9 ± 12.1 kg, and height of 181.5 ± 11.0 cm). This group was assessed under resting conditions to provide baseline reference values for physiological tremor and to evaluate whether the neuromuscular tremor response is specific to trained athletes. Tremor amplitude and frequency were measured using an accelerometer, with data log-transformed to normalize the power spectrum distribution. Key findings indicate a significant effect of training condition on tremor amplitude in the low-frequency range (L(2_5); F_(1,18) = 38.42; p < 0.0001; η_p² = 0.68) and high-frequency range (L(9_14); F_(1,36) = 19.19; p < 0.0001; η_p² = 0.51). Post hoc analysis showed that tremor amplitude increased significantly after both sprint (p < 0.001) and endurance training (p < 0.001) compared to rest. No significant differences were observed between sprint and endurance training conditions for L(2_5) (p = 0.1014), but sprint training resulted in a greater increase in tremor in the high-frequency range (L(9_14); p < 0.0001). Tremor frequency (F(2_5) and F(9_14)) also increased significantly post-training. Notably, no differences were observed between limbs, indicating symmetrical neuromuscular adaptation. These findings highlight the utility of tremor analysis in monitoring neuromuscular fatigue and performance in speed skaters. Future research should explore the application of this method in broader athletic populations and evaluate its potential integration into training programs. Full article

Abnormal operations, such as faults occurring in an electrical power system (EPS), disrupt its balanced operation, posing potential hazards to human lives and the system’s equipment. Effective monitoring, control, protection, and coordination are essential to mitigate these risks. The complexity of these processes is further compounded by the presence of intermittent distributed energy resources (DERs) in active distribution networks (ADNs) with bidirectional power flow, which introduces a fast-changing dynamic aspect to the system. The deployment of phasor measurement units (PMUs) within the EPS as highly responsive equipment can play a pivotal role in addressing these challenges, enhancing the system’s resilience and reliability. However, synchrophasor measurement-based studies and analyses of power system phenomena may be hindered by the absence of PMU blocks in certain simulation tools, such as PSCAD, or by the existing PMU block in Matlab/Simulink R2021b, which exhibit technical limitations. These limitations include providing only the positive sequence component of the measurements and lacking information about individual phases, rendering them unsuitable for certain measurements, including unbalanced and non-symmetrical fault operations. This study proposes a new reliable PMU model in Matlab and tests it under normal and abnormal conditions, applying real-time simulation and controller-hardware-in-the-loop (CHIL) techniques. Full article

The desert locust (Schistocerca gregaria) represents one of the most destructive agricultural pests globally, renowned for its ability to form massive swarms that can devastate crops and threaten food security across vast regions. Despite the widespread application of the CRISPR/Cas9 gene-editing system in several insect orders, its utilization in locusts, particularly in the desert locust, has remained relatively unexplored. We established a CRISPR/Cas9-mediated gene-editing workflow for the desert locust using gene encoding for neuropeptide corazonin (Crz) as a target. We also analyzed the phenotypic and physiological characteristics of the mutant using paraffin sectioning, HE staining, and chitin staining techniques. Our findings revealed that while Crz knockout desert locusts were viable and maintained normal fertility, they exhibited striking phenotypic alterations, including albinism and a significant reduction in cuticle thickness. These observations not only highlight the functional role of Crz in pigmentation and cuticle development but also underscore the potential of CRISPR/Cas9 as a powerful tool for dissecting gene function in locusts. Furthermore, the successful application of CRISPR/Cas9 in desert locusts also paves the way for similar genetic studies in other non-model insects, expanding the scope of functional genomics in entomology. Full article