Search Results (1,744)

This study introduces a machine learning–driven extended reality (XR) interaction framework that leverages electroencephalography (EEG) for decoding consumer intentions in immersive decision-making tasks, demonstrated through functional food purchasing within a simulated autonomous vehicle setting. Recognizing inherent limitations in traditional “Preference vs. Non-Preference” EEG paradigms for immersive product evaluation, we propose a novel and robust “Rest vs. Intention” classification approach that significantly enhances cognitive signal contrast and improves interpretability. Eight healthy adults participated in immersive XR product evaluations within a simulated autonomous driving environment using the Microsoft HoloLens 2 headset (Microsoft Corp., Redmond, WA, USA). Participants assessed 3D-rendered multivitamin supplements systematically varied in intrinsic (ingredient, origin) and extrinsic (color, formulation) attributes. Event-related potentials (ERPs) were extracted from 64-channel EEG recordings, specifically targeting five neurocognitive components: N1 (perceptual attention), P2 (stimulus salience), N2 (conflict monitoring), P3 (decision evaluation), and LPP (motivational relevance). Four ensemble classifiers (Extra Trees, LightGBM, Random Forest, XGBoost) were trained to discriminate cognitive states under both paradigms. The ‘Rest vs. Intention’ approach achieved high cross-validated classification accuracy (up to 97.3% in this sample), and area under the curve (AUC > 0.97) SHAP-based interpretability identified dominant contributions from the N1, P2, and N2 components, aligning with neurophysiological processes of attentional allocation and cognitive control. These findings provide preliminary evidence of the viability of ERP-based intention decoding within a simulated autonomous-vehicle setting. Our framework serves as an exploratory proof-of-concept foundation for future development of real-time, BCI-enabled in-transit commerce systems, while underscoring the need for larger-scale validation in authentic AV environments and raising important considerations for ethics and privacy in neuromarketing applications. Full article

Abnormal shutdown detection in oilfield pumping units presents significant challenges, including degraded image quality under low-light conditions, difficulty in detecting small or obscured targets, and limited capabilities for dynamic state perception. Previous approaches, such as traditional visual inspection and conventional image processing, often struggle with these limitations. To address these challenges, this study proposes an intelligent method integrating multi-scale feature enhancement and low-light image optimization. Specifically, a lightweight low-light enhancement framework is developed based on the Zero-DCE algorithm, improving the deep curve estimation network (DCE-Net) and non-reference loss functions through training on oilfield multi-exposure datasets. This significantly enhances brightness and detail retention in complex lighting conditions. The DAFE-Net detection model incorporates a four-level feature pyramid (P3–P6), channel-spatial attention mechanisms (CBAM), and Focal-EIoU loss to improve localization of small/occluded targets. Inter-frame difference algorithms further analyze motion states for robust “pump-off” determination. Experimental results on 5000 annotated images show the DAFE-Net achieves 93.9% mAP@50%, 96.5% recall, and 35 ms inference time, outperforming YOLOv11 and Faster R-CNN. Field tests confirm 93.9% accuracy under extreme conditions (e.g., strong illumination fluctuations and dust occlusion), demonstrating the method’s effectiveness in enabling intelligent monitoring across seven operational areas in the Changqing Oilfield while offering a scalable solution for real-time dynamic anomaly detection in industrial equipment monitoring. Full article

Organ transplantation has significantly progressed since the 1950s, with notable advancements in surgical procedures and immunosuppression. However, current organ preservation techniques, mainly static cold storage, have not evolved at the same pace and remain insufficient to prevent ischemic and oxidative damage. This damage, primarily caused by the cessation of aerobic metabolism, limits organ viability and transplant outcomes. In this study, we investigated whether supplementing a storage solution with a hemoglobin-based oxygen carrier (HBOC) could improve the condition of ex vivo rabbit kidneys by maintaining oxygenation and supporting aerobic metabolism. In a paired, randomized design, contralateral rabbit kidneys were preserved either in a Krebs-Ringer-based solution enriched with the polymerized hemoglobin OxyVita^®C (15 g/L, p50 4–6 mmHg, MW ≈ 17 MDa, pH adjusted to 7.4) or in an HBOC-free control solution. Physicochemical characterization of OxyVita^®C included oxygen equilibrium curves, zeta potential, polydispersity index, and dynamic light scattering. Biochemical markers (AST, ALT, LDH, K⁺, pH) and histopathological assessments were used to evaluate tissue integrity over 24 h. Histology was additionally stratified according to rinsing protocols (unwashed, NaCl single flush, triple flush), and tubular necrosis was scored by blinded pathologists. Group comparisons were analyzed using ANOVA with Tukey’s HSD test. The HBOC-enriched solution showed improved tissue preservation, higher cell survivability, and better histomorphological profiles, with significantly reduced tubular necrosis scores compared to controls. These findings suggest that active oxygen delivery via HBOCs offers a promising strategy to mitigate ischemic damage during ex vivo kidney storage. Limitations include the lack of transplantation outcomes and direct ROS quantification, which will be addressed in future work integrating hypothermic and normothermic machine perfusion. Full article

Remote Access Trojans (RATs) pose a serious cybersecurity risk due to their stealthy control over compromised systems. This study presents a detection framework that integrates host, network, and newly engineered behavioral features to enhance the identification of RATs. Two sets of experiments were performed: (i) using the original dataset only, and (ii) using an extended dataset with ten engineered features and importance analysis. The framework was evaluated on a public Kaggle dataset of an RAT and benign traffic. Eight machine learning classifiers were tested, including three baseline methods, four ensemble approaches, and a neural network. Results show that the engineered hybrid feature set substantially improves detection performance. Among the tested algorithms, Random Forest and MLP achieved the strongest performance, with accuracies of 98% and 97%, respectively, while Gradient Boosting and LightGBM also performed competitively. Performance was assessed using multiple metrics, and to gain deeper insight into model learning behavior, learning curves and Precision–Recall curves were analyzed. The results demonstrate how well hybrid feature modeling, neural networks, and ensemble machine learning techniques may improve RAT identification. In future work, exploring the use of explainable ML methods may improve the detection capabilities. Full article

Battery Energy Storage Systems (BESSs) are critical to smart grid functioning but are exposed to mounting cybersecurity threats with their integration into IoT and cloud-based control systems. Current solutions tend to be deficient in proper multi-class attack classification, secure encryption, and full integrity and power quality features. This paper proposes a comprehensive framework that integrates machine learning for attack detection, cryptographic security, data validation, and power quality control. With the BESS-Set dataset for binary classification, Random Forest achieves more than 98.50% accuracy, while LightGBM attains more than 97.60% accuracy for multi-class classification on the resampled data. Principal Component Analysis and feature importance show vital indicators such as State of Charge and battery power. Secure communication is implemented using Elliptic Curve Cryptography and a hybrid Blowfish–RSA encryption method. Data integrity is ensured through applying anomaly detection using Z-scores and redundancy testing, and IEEE 519-2022 power quality compliance is ensured by adaptive filtering and harmonic analysis. Real-time feasibility is demonstrated through hardware implementation on a PYNQ board, thus making this framework a stable and feasible option for BESS security in smart grids. Full article

In light of the growing significance of molecular biomarkers in central nervous system tumours, in this study, we aimed to comprehensively and quantitatively analyze the mRNA expression levels of DJ-1 (Parkinsonism-associated deglycase 7, PARK7), GDF15 (Growth Differentiation Factor 15), and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein) in glioma and meningioma tissues and to thoroughly evaluate the associations between these gene expression profiles and clinicopathological parameters. Real-time PCR (qRT-PCR) analyses performed on tumour tissues obtained from a total of 27 glioma and 18 meningioma patients revealed that these three genes exhibited significantly elevated expression compared to control samples. Despite their different cellular origins, statistically significant positive correlations were observed between the expression levels of DJ-1, GDF15, and MFGE8 and both tumour grade and the Ki-67 proliferation index (Ki-67 Pi) in both glioma and meningioma cases, indicating that higher gene expression is associated with increased tumour aggressiveness in both tumour types. Receiver operating characteristic (ROC) curve analyses further confirmed the diagnostic and prognostic potential of these genes. Additionally, protein–protein interaction networks involving the target genes were characterised, providing valuable insights into their molecular mechanisms. These findings suggest that DJ-1, GDF15, and MFGE8 may play a role in the aggressiveness, invasion, and proliferation of gliomas and meningiomas. Moreover, integrating these genes as molecular biomarkers into tumour classification systems may provide a foundation for the development of personalised and targeted therapeutic strategies, although further studies are needed to support this. Full article

Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. This study investigates the application of convolutional neural networks (CNNs) for the automated detection and classification of shading faults, including multiple severity levels, using current–voltage (I–V) curves. Four scenarios were simulated in Simulink: a healthy module and three levels of shading severity (light, moderate, and severe). The resulting I–V curves were transformed into grayscale images and used to train and evaluate several custom-designed CNN architectures. The goal is to assess the capability of CNN-based models to accurately identify shading faults and discriminate between severity levels. Multiple network configurations were tested, varying image resolution, network depth, and filter parameters, to explore their impact on classification accuracy. Furthermore, robustness was evaluated by introducing Gaussian noise at different levels. The best-performing models achieved classification accuracies of 99.5% under noiseless conditions and 90.1% under a 10 dB noise condition, demonstrating that CNN-based approaches can be both effective and computationally lightweight. These results underscore the potential of this methodology for integration into automated diagnostic tools for PV systems, particularly in applications requiring fast and reliable fault detection. Full article

Face recognition under uneven illumination conditions presents significant challenges, as asymmetric shadows often obscure facial features while overexposed regions lose critical texture details. To address this problem, a novel symmetry-aware illumination enhancement method named face shadow detection network (FSDN) is proposed, which features a nested U-Net architecture combined with Gaussian convolution. This method enables precise illumination intensity maps for the given face images through higher-order quadratic enhancement curves, effectively extending the low-light dynamic range while preserving essential facial symmetry. Comprehensive evaluations on the Extended Yale B and CMU-PIE datasets demonstrate the superiority of the proposed FSDN over conventional approaches, achieving structural similarity (SSIM) indices of 0.48 and 0.59, respectively, along with remarkably low face recognition error rates of 1.3% and 0.2%, respectively. The key innovation of this work lies in its simultaneous optimization of illumination uniformity and facial symmetry preservation, thereby significantly improving face analysis reliability under challenging lighting conditions. Full article

In this study, the geological disasters in Guizhou Province serve as the research object, and a systematic susceptibility evaluation is conducted in light of the province’s prominent problems with frequent geological disasters. The current research primarily focuses on the application of a single model, often with deficiencies in factor interpretation. It has not yet systematically integrated the advantages of the traditional information model and multiple machine learning algorithms, nor introduced interpretable methods to analyze the disaster mechanism deeply. In this study, the information value (IV) model is combined with machine learning algorithms—logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost)—to construct a coupling model to evaluate the susceptibility to geological disasters. Combined with the Bayesian optimization algorithm, the geological disaster susceptibility evaluation model is built. The confusion matrix and receiver operating characteristic (ROC) curve were used to evaluate the model’s accuracy. The Shapley Additive exPlanations (SHAP) method is used to quantify the contribution of each influencing factor, thereby improving the transparency and credibility of the model. The results show that the coupling models, especially the IV-XGB model, achieved the best performance (AUC = 0.9448), which significantly identifies the northern Wujiang River Basin and the central karst core area as high-risk areas and clarifies the disaster-causing mechanism of “terrain–hydrology–human activities” coupling. The SHAP method further identified that NDVI, land use type, and elevation were the predominant controlling factors. This study presents a high-precision and interpretable modeling method for assessing susceptibility to geological disasters, providing a scientific basis for disaster prevention and control in Guizhou Province and similar geological conditions. Full article

A comparative analysis of the photometric variability of the blazar 3C 273 and the quasar PDS 456 using multi-band data from ground- and space-based platforms (2015–2025) reveals contrasting behaviors. For 3C 273, a statistically significant secular dimming was detected in the ATLASc-band light curve $\left((5.6\pm 0.2)\times {10}^{-4}\mathrm{mag}{\mathrm{day}}^{-1}\right)$ and confirmed by Johnson–Cousins V-band photometry. Ten optical flares were identified, two coinciding with Fermi gamma-ray enhancements, suggesting a synchrotron origin linked to jet activity. A significant bluer-when-brighter trend ($\rho =-0.54$) was found relative to the o-band, and several color extrema align with gamma-ray activity, reinforcing the nonthermal interpretation. In contrast, PDS 456 exhibits a statistically significant secular brightening in the o-band $\left((-3.1\pm 0.2)\times {10}^{-5}\mathrm{mag}{\mathrm{day}}^{-1}\right)$ and 75 optical flares, four coinciding with UV flares observed by Swift/UVOT. The $c–o$ color index displays a non-Gaussian distribution with asymmetric reddening and blueing episodes. An extreme reddening event aligns with a strong UV flare, suggesting transient inner-disk heating. These results indicate jet-dominated variability in 3C 273 and disk-driven variability in PDS 456, highlighting distinct physical mechanisms in radio-loud versus radio-quiet active galactic nuclei. Full article

Optical spectroscopy is a versatile analytical technique with a diverse range of applications. Point-of-need systems are required to be affordable, miniaturized instruments that are easy to use. This paper proposes using an array of LEDs to create paired emitter detector diodes where commercial LEDs function as both a light source and detector. This system can measure the concentration of different chemicals via a set of discrete wavelengths. Calibration curves are presented for series of known concentrations of three water treatment chemicals using the K-matrix method. The spectral fingerprint identifies the chemical correctly with 99% accuracy using the Pearson correlation. Full article

This paper aims to explore whether astrophysical observations, primarily galaxy rotation curves, result from covarying coupling constants (CCC) rather than from dark matter. We have shown in earlier papers that cosmological observations, such as supernovae type 1a (Pantheon+), the small size of galaxies at cosmic dawn, baryon acoustic oscillations (BAO), the sound horizon in the cosmic microwave background (CMB), and time dilation effect, can be easily accounted for without requiring dark energy and dark matter when coupling constants are permitted to evolve in an expanding Universe, as predicted by Dirac, and the redshift is considered jointly due to the Universe’s expansion and Zwicky’s tired light (TL) effect. Here, we show that the CCC parameter α is responsible for generating the illusion of dark matter and dark energy, which we call α-matter and α-energy, and is influenced by the baryonic matter density distribution. While cosmologically α is a constant determined for the homogenous and isotropic Universe, e.g., by fitting Pantheon+ data, it can vary locally due to the extreme anisotropy of the matter distribution. Thus, in high baryonic density regions, one expects α-matter and α-energy densities to be relatively low and vice versa. We present its application to a few galaxy rotation curves from the SPARC database and find the results promising. Full article

Mild traumatic brain injury (mTBI) remains a clinical challenge, particularly in cases with normal computed tomography (CT) findings but persistent or evolving symptoms. Conventional diagnostic approaches relying solely on clinical criteria and neuroimaging often lack adequate sensitivity and may lead to unnecessary radiation exposure. Recent advances in biomarker research have identified several blood-based proteins such as glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein B (S100B), Tau protein, neuron-specific enolase (NSE), and neurofilament light chain (NFL) as potential tools for improving diagnostic precision and guiding clinical decisions. In this study, we synthesize current evidence evaluating the diagnostic and prognostic utility of these biomarkers using sensitivity, specificity, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). GFAP and UCH-L1 have shown high sensitivity in detecting intracranial lesions and are now FDA-cleared for emergency department triage within 12 h of injury. While S100B remains widely investigated, its low specificity limits its application beyond select clinical scenarios (i.e., in patients without polytrauma). Additionally, Tau, NSE, and NFL are emerging as prognostic markers, with studies suggesting associations with persistent symptoms and long-term neurocognitive outcomes. Overall, the integration of biomarker-based data into clinical workflows may enhance early mTBI diagnosis, reduce reliance on imaging, and enable individualized follow-up and prognostic stratification. Future research should refine optimal sampling windows and explore multimarker panels to maximize diagnostic and prognostic performance. Full article

A mill relining manipulator is key maintenance equipment for liners exchanged and operated by workers inside a grinding mill. To improve the operation efficiency and safety, real-time path planning and end deformation compensation should be performed prior to actual execution. This paper proposes a five-dimensional digital twin framework to realize virtual–real interaction between a physical manipulator and virtual model. First, a real-time digital twin scene is established based on OpenGL. The involved technologies include scene rendering, a camera system, the light design, model importation, joint control, and data transmission. Next, different solving methods are introduced into the service space for relining tasks, including a kinematics model, collision detection, path planning, and end deformation compensation. Finally, a user application is developed to realize real-time condition monitoring and simulation analysis visualization. Through comparison experiments, the superiority of the proposed path planning algorithm is demonstrated. In the case of a long-distance relining task, the planning time and path length of the proposed algorithm are 1.7 s and 15,299 mm, respectively. For motion smoothness, the joint change curve exhibits no abrupt variation. In addition, the experimental results between original and modified end trajectories further verified the effectiveness and feasibility of the proposed end effector compensation method. This study can also be extended to other heavy-duty manipulators to realize intelligent automation. Full article

Focusing on the discovery of new $\delta$ Scuti and $\gamma$ Doradus stars, we analyzed the Transiting Exoplanet Survey Satellite (TESS) light curves for 193,940 A-F stars selected from four legacy catalogs—the Henry Draper Catalogue (HD), the Smithsonian Astrophysical Observatory (SAO) Star Catalog, the Positions and Proper Motions Catalog (PPM), and the Bonner Durchmusterung (BD, including its extensions). Through visual inspection of light curve morphologies and periodograms, combined with evaluation of stellar parameters, we identified over 51,850 previously unreported variable stars. These include 15,380 $\delta$ Scuti, 18,560 $\gamma$ Doradus, 28 RR Lyrae stars, 260 heartbeat candidates, and 2645 eclipsing binaries, along with thousands of other variable types. Notably, over 4145 variables exhibit hybrid $\delta$ Scuti-$\gamma$ Doradus pulsations, and more than 380 eclipsing binaries feature pulsating primary components. This study reveals a substantial population of bright, previously undetected variables, providing a valuable resource for ensemble asteroseismology, binary evolution studies, and Galactic structure research. Our results also highlight the surprising richness in variability still hidden within well-known stellar catalogs and the continued importance of high-precision, time-domain surveys such as TESS. Full article