Search Results (345)

Motor imagery (MI) electroencephalograms (EEGs) are pivotal cortical potentials reflecting cortical activity during imagined motor actions, widely leveraged for brain-computer interface (BCI) system development. However, effectively decoding these MI EEG signals is often overshadowed by flawed methods in signal processing, deep learning methods that are clinically unexplained, and highly inconsistent performance across different datasets. We propose BCINetV1, a new framework for MI EEG decoding to address the aforementioned challenges. The BCINetV1 utilizes three innovative components: a temporal convolution-based attention block (T-CAB) and a spectral convolution-based attention block (S-CAB), both driven by a new convolutional self-attention (ConvSAT) mechanism to identify key non-stationary temporal and spectral patterns in the EEG signals. Lastly, a squeeze-and-excitation block (SEB) intelligently combines those identified tempo-spectral features for accurate, stable, and contextually aware MI EEG classification. Evaluated upon four diverse datasets containing 69 participants, BCINetV1 consistently achieved the highest average accuracies of 98.6% (Dataset 1), 96.6% (Dataset 2), 96.9% (Dataset 3), and 98.4% (Dataset 4). This research demonstrates that BCINetV1 is computationally efficient, extracts clinically vital markers, effectively handles the non-stationarity of EEG data, and shows a clear advantage over existing methods, marking a significant step forward for practical BCI applications. Full article

In this work, a comparative analysis of polymer test specimens from different types of filaments, manufactured using FDM technology, was performed. A tensile strength test was executed on test specimens after 3D additive printing, made from different groups of materials—PLA, PLA Wood, PETG, PC, PA6, ASA, CPE HG100 and FilaFlex SEBS. Test specimens from the same materials were subjected to accelerated aging, after which they were tested again for tensile strength. The results of all tests were analyzed and compared. Full article

Large turbine generators rely on shaft earthing brushes to safely divert harmful shaft currents to ground, protecting bearings from electrical damage. This paper presents a novel deep learning-based diagnostic framework to detect and classify faults in shaft earthing brushes of large turbine generators. A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. In the approach, raw time-domain signals are converted into informative time–frequency representations, which serve as input to a CNN model trained to distinguish normal and faulty conditions. The framework was evaluated using data from a fleet of large-scale generators under various brush fault scenarios (e.g., increased brush contact resistance, loss of brush contact, worn out brushes, and brush contamination). Experimental results demonstrate high fault detection accuracy (exceeding 98%) and the reliable identification of different fault types, outperforming conventional threshold-based monitoring techniques. The proposed deep learning framework offers a novel intelligent monitoring solution for predictive maintenance of turbine generators. The contributions include the following: (1) the development of a specialized deep learning model for shaft earthing brush fault diagnosis, (2) a systematic methodology for feature extraction from shaft current signals, and (3) the validation of the framework on real-world fault data. This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities. Full article

Proton and heavy ion irradiation experiments were carried out on Cascode GaN HEMT devices. Results show that device degradation from heavy ion irradiation is more significant than from proton irradiation. Under proton irradiation, obvious device degradation occurred. Low-frequency noise testing revealed a notable increase in internal defect density, reducing channel carrier concentration and mobility, and causing electrical performance degradation. Under heavy ion irradiation, devices suffered from single-event burnout (SEB) and exhibited increased leakage current. Failure analysis of post-irradiation devices showed that those with leakage current increase had conductive channels without morphological changes, while burned out devices showed obvious damage between the gate and drain regions. SRIM simulation indicated that ionization energy loss-induced electron–hole pairs and displacement damage from nuclear energy loss were the main causes of degradation. Sentaurus TCAD simulation of heavy ion irradiated GaN HEMT devices confirmed the mechanisms of leakage current increase and SEB. Full article

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article

Staphylococcus aureus (S. aureus) is a major pathogen responsible for mastitis in dairy cows and can contaminate raw milk, thereby posing significant health risks to consumers. The emergence of methicillin-resistant S. aureus (MRSA) has further heightened public health concerns due to its antibiotic resistance and infectious potential. In this study, we examined the prevalence, virulence genes, antimicrobial resistance, spa types, and biofilm formation of S. aureus isolates from dairy farms in Guangxi Province, China. Among 242 randomly selected samples, 37 S. aureus strains were identified (15.3% infection rate), including 67.5% MRSA. Antibiotic resistance was observed in 78.4% of isolates, with 35.1% exhibiting multidrug resistance (MDR). Enterotoxin gene analysis showed sea as the most common (67.6%), followed by ser (54.1%) and seh (51.4%), whereas seb and selj were absent. All isolates formed biofilms in vitro, with 64.8% showing strong biofilm-forming ability. Staphylococcal protein A (spa) typing classified the 37 S. aureus strains into 11 spa types, with t030 being the most prevalent (43.2%). These findings indicate that S. aureus is moderately prevalent in raw milk, often carrying multiple virulence genes, forming robust biofilms, and showing antimicrobial resistance. The MRSA that is “latent” in raw milk reminds us of the need for monitoring at the farm level. Full article

Stretchable organic field-effect transistors (OFETs), with inherent flexibility, versatile sensing mechanisms, and signal amplification properties, provide a unique device-level solution for the real-time, in situ detection of trace gaseous pollutants. However, serious challenges remain regarding the synergistic optimization of OFET gas sensor production preparation, mechano-electrical properties, and gas-sensing performance. Although the introduction of microstructures can theoretically provide OFETs with enhanced sensing performance, the high-precision process required for microstructure fabrication limits scale-up. Herein, a straightforward hybrid solvent strategy is proposed for regulating the intrinsic microstructure of the organic semiconductor layer, with the aim of constructing an ultrasensitive PDVT-10/SEBS fully stretchable OFET NO₂ sensor. The binary solvent system induces the formation of nanoneedle-like structures in the PDVT-10/SEBS organic semiconductor, which achieves a maximum mobility of 2.71 cm² V⁻¹ s⁻¹, a switching current ratio generally exceeding 10⁶, and a decrease in mobility of only 30% at 100% strain. Specifically, the device exhibits a response of up to 77.9 × 10⁶ % within 3 min and a sensitivity of up to 1.4 × 10⁶ %/ppm, and it demonstrates effective interference immunity, with a response of less than 100% to nine interferences. This work paves the way for next-generation wearable smart sensors. Full article

The U-shaped Metal-Oxide-Semiconductor Field-Effect Transistor (UMOS or trench FET) is one of the most widely used semiconductor power devices worldwide, increasingly replacing the traditional vertical double-diffused MOSFET (DMOSFET) in various applications due to its superior electrical performance. However, a detailed experimental comparison of ion-induced Single-Event Burnout (SEB) in similarly rated silicon (Si) UMOS and DMOS devices remains lacking. This study presents a comprehensive experimental comparison of ion-induced charge collection mechanisms and SEB susceptibility in similarly rated Si UMOS and DMOS devices. Charge collection mechanisms due to alpha particles from ²⁴¹Am radiation source are analyzed, and SEB cross sections induced by heavy ions from particle accelerators are directly compared. The implications of the unique gate structure of Si UMOSFETs on their reliability in harsh radiation environments are discussed based on technology computer-aided design (TCAD) simulations. Full article

Reducing the environmental impact is a key reason for developing recyclable insulation materials for high-voltage industries. In this study, polypropylene (PP) blends were prepared via melt mixing with styrene–ethylene–butylene–styrene (SEBS), a thermoplastic elastomer, to improve breakdown strengths at various cooling speeds. A systematic investigation was conducted to evaluate the influence of crystal size, degree of crystallinity, and nucleation growth rate on the breakdown strength. Crystallization behavior was analyzed using isothermal and non-isothermal methods based on the Avrami model. Increasing SEBS content reduced crystallinity, with the lowest nucleation growth rate observed at 35% SEBS. Breakdown strength correlated with crystallization behavior and was further validated by Weibull distribution method. Notably, PP/SEBS blends containing 35% SEBS exhibited the highest breakdown strength of 66.4 kV/mm at a cooling speed of 10 °C/mm. This improvement reflected a reduction in the degree of crystallinity from 36.0% to 22.9% and the lowest growth rate constant (k) at 35% SEBS. Furthermore, the predicted lifetime of PP/SEBS blend containing 35% SEBS, calculated using the oxidation induction time and the Arrhenius equation, was 42 years. These findings demonstrate that SEBS content and cooling rate effectively modulate crystallization and breakdown strength, enabling recyclable PP/SEBS with XLPE-comparable performance for sustainable high-voltage insulation. Full article

The performance of heterophase immunoassays is often limited by the kinetics of analyte binding. This problem is partially solved by bead-based assays, which are characterized by rapid diffusion in the particle suspension. However, at low analyte concentrations, the binding rate is still low. Here, we demonstrate a further improvement of analyte binding kinetics in bead-based immunoassays by simultaneously concentrating both an analyte and magnetic beads in a compact spatial region where binding occurs. The analyte is electrophoretically concentrated in a flow cell where beads are magnetically retained and dragged along the channel by viscous force. The flow cell is integrated with a microarray-based signal detection module, where beads with bound analyte scan the microarray surface and are retained on it by single specific interactions, assuring ultra-high sensitivity of the method. Thus, a continuous flow assay system is formed. Its performance is demonstrated by simultaneous detection of model pathogen biomarkers, cholera toxin (CT) and staphylococcal enterotoxin B (SEB), with a detection limit of 0.1 fM and response time of under 10 min. The assay is capable of real-time online sample monitoring, as shown by a 12 h long continuous flow analysis of tap water for SEB and CT. Full article

The integrated stress response (ISR) is a key regulator of cell survival, promoting apoptosis through the effector protein CHOP in instances of prolonged or severe stress. The ISR’s role in the initiation and progression of epithelial malignancies has been investigated; however, the ISR has not been evaluated in ocular adnexal sebaceous carcinoma (SebCA). Though uncommon, mortality rates of up to 40% have been reported, and the mechanisms underlying SebCA tumorigenesis remain unresolved; however, c-MYC upregulation has been documented. Our objective was to determine the role of MYC in modulating the ISR in the Meibomian gland. Human Meibomian gland epithelial cells (HMGECs) were subject to both pharmacologic and genetic manipulations of MYC expression. Cytotoxicity, proliferation, and changes in protein and gene expression were assessed. Conditionally MYC-overexpressing mice were subject to topical 4-hydroxytamoxifen (4-OHT) induction of the eyelids prior to tissue harvest for histology, immunohistochemistry, immunoblotting, and qPCR. MYC-inhibited HMGECs exhibited dose-dependent decreased proliferation, increased CHOP expression, and increased apoptosis. Conversely, MYC-overexpressing HMGECs and Meibomian glands from 4-OHT-induced mice demonstrated suppressed CHOP expression, reduced apoptosis, and upregulated fatty acid synthase expression. These results suggest that MYC inhibition induces the ISR and promotes apoptosis, while MYC induction suppresses CHOP expression. High MYC expression may, therefore, serve as a mechanism for SebCA to elude cell death by promoting lipogenesis. Full article

Zeolite Battery Research Africa (ZEBRA) batteries (Na-NiCl₂ solid electrolyte batteries, SEBs) have commercial applications in energy storage due to their low costs and recyclability, long lifetime, and high safety. In commercial ZEBRA batteries, Ni electrode and beta’’-alumina solid electrolyte (BASE) have a more than 70% share of the overall cell material costs. Na-ZnCl₂ all-liquid batteries (ALBs), which replace Ni with abundant and low-cost Zn and BASE electrolyte with molten salt electrolyte, could reduce costs and provide a longer lifetime and higher safety, making their application in grid storage promising. However, compared to SEBs, ALBs are in an early development stage, particularly for their molten salt electrolytes, which have a significant effect on the battery performance. Physical and chemical properties of the salt electrolyte like melting temperatures and solubilities of electrode materials (i.e., Na and Zn metal) are vital for the molten salt electrolyte selection and battery cell design and optimization. In this work, the binary and ternary phase diagrams of salt mixtures containing NaCl, CaCl₂, BaCl₂, SrCl₂, and KCl, obtained via FactSage simulation and DSC measurements, as well as the solubilities of electrode materials (Na and Zn metals), are presented and used for the selection of the molten salt electrolyte. Moreover, various criteria, considered for the selection of the molten salt electrolyte, include high electromotive force (EMF) for suitable electrochemical properties, low melting temperature for large charge/discharge range, low solubilities of electrode materials for low self-discharge, low material costs, and high material abundance for easy scale-up. Based on these criteria, the NaCl-CaCl₂-BaCl₂ and NaCl-SrCl₂-KCl salt mixtures are selected as the two most promising ALB molten salt electrolytes and suggested to be tested in the ALB demonstrators currently under development. Full article

This study aims to unveil the marine invertebrate Sidnyum elegans, a Mediterranean ascidian, as a natural resource for the early development of new treatments for triple-negative breast cancer (TNBC). Nine different fractions obtained via medium-pressure liquid chromatography (MPLC) of the butanol-soluble material of the ascidian were evaluated in proliferating MDA-MB-231 cells in a range of 10–50 µg/mL. Among them, the SEB-5 fraction was found to be the most effective in reducing cell proliferation and concomitantly inducing apoptosis, revealed via MTT assay and FACS analysis using Annexin V/PI dual staining. Furthermore, we investigated the effect of this fraction on cell cycle phases, revealing that SEB-5 can arrest the cells in the G0/G1 phase. This latter effect was then confirmed via transcriptomic analysis, showing that treatment with SEB-5 reduced the expression of cyclinB1, CDC25a, and CDK1. Finally, to evaluate the potential antimetastatic effect of SEB-5, a wound-healing assay was performed showing the ability of SEB-5 to reduce MDA-MB-231 cell migration. The chemical characterization of SEB-5 components was performed using liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS) and nuclear magnetic resonance (NMR) spectroscopy. This analysis revealed the presence of a terpenoid and polyketide-like compounds, including the alkyl sulfate 1 and phosphoeleganin 2, along with three novel phosphoeleganin-related products 3–5. Full article

In this work, the single-event burnout (SEB) effect and degradation behaviors induced by heavy-ion irradiation are investigated in a 120 V-rated transverse split-gate trench (TSGT) power metal-oxide-semiconductor field-effect transistor (MOSFET). Bismuth heavy-ions are used to conduct heavy-ion irradiation tests. The experimental results show that the SEB failure threshold voltage (V_SEB) of the tested sample is 72 V, which only accounts for 52.6% of the actual breakdown voltage of the device. The V_SEB value decreased with the increase in the flux. The simulation results show that the local “hot spot” formed after the incident heavy ion is an important reason for the drain current degradation of TSGT MOSFETs. To improve the single-event effect tolerance of TSGT MOSFETs, an SEB hardening method based on process optimization is proposed in this paper, which does not require additional customized epitaxial wafers. The simulation results show that, after SEB hardening, the V_SEB is increased to 115 V, which accounts for 89.1% of the breakdown voltage. Full article

The real-time detection of facial skin problems is crucial for improving dermatological health, yet its practical implementation remains challenging. Early detection and timely intervention can significantly enhance skin health while reducing the financial burden associated with traditional dermatological treatments. This paper introduces EM-YOLO, an advanced deep learning framework designed for embedded and distributed environments, leveraging improvements in YOLO models (versions 5, 7, and 8) for high-performance, real-time skin condition detection. The proposed architecture incorporates custom layers, including Squeeze-and-Excitation Block (SEB), Depthwise Separable Convolution (DWC), and Residual Dropout Block (RDB), to optimize feature extraction, enhance model robustness, and improve computational efficiency for deployment in resource-constrained settings. The proposed EM-YOLO model architecture clearly delineates the role of each architectural component, including preprocessing, detection, and postprocessing phases, ensuring a structured and modular representation of the detection pipeline. Extensive experiments demonstrate that EM-YOLO significantly outperforms traditional YOLO models in detecting facial skin conditions such as acne, dark circles, enlarged pores, and wrinkles. The proposed model achieves a precision of 82.30%, recall of 71.50%, F1-score of 76.40%, and mAP@0.5 of 68.80%, which are 23.52%, 32.7%, 29.34%, and 24.68% higher than standard YOLOv8, respectively. Furthermore, the enhanced YOLOv8 custom layers significantly improve system efficiency, achieving a request rate of 15 Req/s with an end-to-end latency of 0.315 s and an average processing latency of 0.021 s, demonstrating 51.61% faster inference and 200% improved throughput compared to traditional SCAS systems. These results highlight EM-YOLO’s superior precision, robustness, and efficiency, making it a highly effective solution for real-time dermatological detection tasks in embedded and distributed computing environments. Full article