Search Results (3,314)

Background/Objectives: Emotion classification based on eye-movement features has become a widely adopted approach due to the simplicity of data acquisition and the strong association between ocular responses and emotional states. However, several challenges remain with regard to existing emotion recognition methods, including the relatively weak correlation between eye-movement features and emotional labels and the fact that the key features are not prominently presented. Methods: To address abovelimitations, this study proposes an improved CNN-transformer combined with enhanced deep canonical correlation analysis network (ICTD). The proposed method first performs preprocessing and reconstruction of raw eye-movement signals to extract informative features. Subsequently, convolutional neural networks (CNNs) and transformer architectures are employed to capture local and global feature, respectively. In addition, an incremental feature feedforward network is incorporated to enhance the transformer, enabling the model to assign higher importance to salient feature information. Finally, the extracted representations are processed through deep canonical correlation analysis based on cosine similarity in order to generate classification outcomes. Results: Experiments conducted on the SEED-IV, SEED-V, and eSEE-d datasets demonstrate that the proposed ICTD framework consistently outperforms baseline approaches and attains optimal classification results. (1) On the eSEE-d dataset, the results of three-category arousal and valence classification reach 81.8% and 85.2%, respectively; (2) on the SEED-IV dataset, the emotion four-category classification result reaches 91.2%; (3) finally, on the SEED-V dataset, the emotion five-category classification result reaches 85.1%. Conclusions: The proposed ICTD framework effectively improves feature representation and classification performance, showing strong potential for practical emotion recognition and physiological signal analysis. Full article

Pyroptosis is a pro-inflammatory form of programmed cell death mediated by inflammasomes and caspases and has been implicated in various inflammatory diseases. However, its function and regulatory role in dairy cows with clinical mastitis (CM) remain poorly understood. This study was conducted to investigate the differentially expressed proteins (DEPs) involved in biological processes (BPs) and the Kyoto Encyclopedia of Genes and Genomes pathways related to inflammasome-mediated pyroptosis based on proteomic data and to further explore their potential involvement in mastitis using in vivo and in vitro models. Histopathological analysis revealed morphological features consistent with pyroptosis in the mammary glands of CM-affected cows, including mammary epithelial cell (MEC) membrane disruption, increased reactive oxygen species production, elevated TUNEL–gasdermin D (GSDMD)-positive staining, and inflammatory cell infiltration. Proteomic profiling identified 276 DEPs and 17 BPs, among which NOD-like receptor family pyrin domain-containing 3 (NLRP3) was identified as a key candidate associated with cytokine production, immune defense, and inflammatory responses. Pathway enrichment analysis indicated that NLRP3, caspase-1 (CASP1), and GSDMD were enriched in the NOD-like receptor signaling pathway and were closely associated with mastitis. Immunohistochemical and molecular analyses demonstrated cytoplasmic localization and significant upregulation of NLRP3, CASP1, and GSDMD at both the mRNA and protein levels in CM-affected tissues. In both in vitro and in vivo models, a dose-dependent increase in the expression of pyroptosis-related targets and pro-inflammatory cytokines was observed with the progression of inflammation. Moreover, the pharmacological inhibition of CASP1 attenuated pyroptosis-associated changes and inflammatory responses in BMECs. Collectively, these findings suggest that the NLRP3–CASP1 axis is associated with inflammation-related pyroptosis in bovine mastitis and may represent a potential therapeutic target for clinical mastitis. Full article

The neuromuscular junction (NMJ) is responsible for transmitting neural signals that trigger muscle contraction. Muscle injuries cause damage to cellular structures and trigger local inflammatory processes. In this context, eccentric contraction was used as an experimental model because it involves excessive stretching, generating mechanical stress. Twenty-five adult male Wistar rats were distributed into groups: Control (C) (n = 5) and Injury (I) (n = 20). The protocol was performed on a treadmill and consisted of 18 sets/5 min/16 m/min speed, with intervals, and with a negative incline (−16º). The analyses consisted of histochemical techniques, such as myofibrillar ATPase and immunofluorescence (calcium channels, synaptophysin and α-bungarotoxin). Group I-0H showed alterations in the presynaptic region and an increase in Type I fibers. I-24H presented disorganization in the postsynaptic region. In I-4D, we observed the reorganization of neuromuscular activity, while I-7D presented greater density and cross-sectional area (CSA) of Type II fibers. It is concluded that the protocol promotes changes in NMJ structure and fiber distribution, mainly in I-24H. In I-4d, a reorganization of neuromuscular activity is observed, and in I-7D, a structural indicator consistent with recovery demonstrates the skeletal muscle’s ability to adapt to injury. Full article

Non-Intrusive Load Monitoring (NILM) enables appliance-level classification from aggregate electrical measurements and supports efficient energy management in smart buildings. However, the accuracy of existing NILM methods is often limited by the inability of conventional feature extraction techniques to capture nonlinear steady-state behavior. This study proposes a novel feature extraction framework for appliance classification, which integrates phase-space reconstruction (PSR) with 2-D Fourier series to derive geometry-based descriptors of appliance current waveforms. Unlike traditional signal-processing methods, the proposed approach utilizes the nonlinear geometric structure revealed by PSR and encodes it through Fourier descriptors, offering a discriminative, low-dimensional feature space suitable for classification using supervised machine learning algorithms. The method is evaluated on the high-resolution controlled single-appliance recordings from the COOLL dataset using the K-Nearest Neighbor (KNN) classifier. Extension to aggregated multi-appliance NILM scenarios would require additional stages such as event detection and load separation. Sensitivity analysis demonstrates that classification performance depends strongly on the choice of time delay and harmonic order, with optimal settings yielding an accuracy of up to 99.52% using KNN. The results confirm that larger time delays and a small number of harmonics effectively capture appliance-specific signatures. The findings highlight the effectiveness of PSR–Fourier-based geometric features as a robust alternative to conventional NILM feature extraction strategies. Full article

Drone-mounted ground-penetrating radar (GPR) systems offer new opportunities for integrating subsurface characterization into remote sensing workflows. However, the interaction between flight parameters, surface conditions, and vegetation characteristics remains poorly understood. This study investigates the impact of flight altitude, surface topography, crop presence, and canopy water content on the stability and interpretability of GPR signals collected using a drone. Field experiments were conducted under controlled conditions using agricultural plots with variable canopy cover and soil moisture regimes. Radargrams were processed to evaluate signal amplitude, reflection continuity, and attenuation patterns in relation to terrain slope and vegetation structure derived from co-registered RGB drone imagery. The results reveal that lower flight altitudes and smoother surfaces yield higher signal coherence and greater subsurface penetration, while increased canopy water content and biomass reduce signal strength and clarity. Integrating drone-based GPR observations with surface spectral and thermal data improved discrimination between soil and vegetation-induced signal distortions. The findings highlight the potential of drone–GPR systems as a complementary layer in a multi-sensor remote sensing framework for precision agriculture, environmental monitoring, and 3D soil mapping. Full article

Background/Objectives: Tenosynovial giant cell tumours (TGCT) are a group of mesenchymal tumours involving the synovium, bursae, and tendon sheaths, comprising two subtypes: nodular and diffuse. Although predominantly benign, diffuse forms can be locally aggressive, resulting in bone destruction. The pathogenesis of TGCTs is still poorly understood. The aim of this study was to systematically review the current literature on the factors, mechanisms, and markers involved in TGCT disease, focussing on their potential role in bone destruction. Methods: This systematic review was conducted using the PRISMA guidelines. A search was performed using PubMed, Scopus, and Cochrane Library, and all original scientific research into mechanisms/pathways/signalling involving TGCTs was included. Results: After the review process, 51 studies were included for data extraction. Extracted data included authorship, publication year, patient numbers and aetiology (nTGCT/dTGCT), demographics, investigative methods, and studied biological factors, mechanisms, and markers. Cross-tabulation of reported elements revealed 159 unique factors, with most appearing only once. Eight elements were reported five or more times: CSF1, CD68, Ki-67, MMP9, CD163, TRAP, TNF-α, and IL-1β. Although representing just 5% of all identified factors, these appeared in 69% of the included studies, highlighting their prominence in the literature. Conclusions: Apart from the well-known osteoclastogenesis factor CSF1, inflammatory cytokines (TNF-α and IL-1β) and monocyte–macrophage lineage makers (CD68, CD163) are signalling pathways key to TGCT disease progression and associated bone destruction. Full article

Traumatic brain injury (TBI) can lead to persistent adverse outcomes, including cognitive and emotional dysfunction, with recent estimates indicating that up to 50% of individuals with mild TBI experience long-term symptoms. Growing evidence suggests that biological sex influences TBI outcomes and recovery trajectories; however, the molecular underpinnings driving these sex-specific differences remain poorly understood. In this study, a preclinical TBI model was used to directly compare chronic glutamatergic alterations in adult male and female Sprague Dawley rats. To define frontocortical molecular signatures associated with sex-specific glutamatergic dysfunction, proteomic analyses were conducted. Proteomic data revealed dysregulation of key pathways, cellular processes, and molecular regulators involved in excitatory signaling and synaptic function in both sexes. Biomarker profiling identified a single common biomarker between males and females, along with multiple biomarkers unique to each sex. Furthermore, two key brain regions highly susceptible to TBI, the prefrontal cortex and hippocampal subregions, were examined for chronic alterations in key glutamatergic signaling proteins, including N-methyl-D-aspartate (NMDA) receptors and the excitatory synaptic marker postsynaptic density protein 95 (PSD95). Immunofluorescence analyses revealed both sex- and region-specific alterations in the expression of NMDA receptor subunits, as well as in PSD95. Notably, many of these changes were concentrated within the hippocampal subregions, suggesting long-term dysregulation of hippocampal glutamatergic circuitry following injury. Together, these findings indicate the emergence of chronic sex-specific pathophysiology in glutamate signaling after TBI and highlight the importance of incorporating sex as a biological variable in the development of precision medicine-based therapeutic strategies for TBI. Full article

Results of a methodological case study of low-level clouds in the atmosphere using a 1064 nm fiber lidar are presented. The lidar experiment was carried out in Daejeon, Republic of Korea, in January–March 2025. The study’s primary objective was to ascertain the vertical extinction coefficient profiles pertaining to tenuous, low-altitude cloud formations via implementation of a refined Sequential Lidar Signal Processing Algorithm (SLSPA). The SLSPA incorporates statistical estimation theory to assess signal and measurement error. Cloud extinction coefficient profiles are estimated within the SLSPA utilizing the modified Klett–Fernald inversion algorithm. The SLSPA adaptation is required (a) to evaluate the accuracy of Q-switch laser-based lidar sounding signal deconvolution, (b) to mitigate the impact of the lidar form factor on measurement results, (c) to account for aerosol extinction coefficient variability within the cloud in the modified inversion algorithm (MIA), and (d) to evaluate multiple scattering effect correction in the MIA. Theoretical and experimental aspects of the modified SLSPA are considered sequentially in the present work. The experimental results presented here are based on datasets sampled from the entire array of experimental data obtained during the measurement period. Full article

Quantum Encryption in Phase Space (QEPS) is a physical-layer encryption framework that harnesses the quantum-mechanical properties of coherent states to secure optical communications against both classical and quantum computational threats. By applying randomized phase shifts, displacements, or their dynamic combinations—implemented as unitary transformations in phase space—QEPS disrupts the phase reference essential for coherent detection, establishing aphase synchronization barrier. This review synthesizes the theoretical foundations, security mechanisms, and experimental progress of the QEPS framework, encompassing its three principal variants: the round-trip Quantum Public Key Envelope (QPKE) protocol—a public-key-like scheme built upon phase randomization (QEPS-p), the symmetric phase-only QEPS-p, and the displacement-based QEPS-d. Experimental validations demonstrate that authorized users achieve bit-error rates (BERs) below the forward-error-correction threshold, whereas eavesdroppers are confined to BERs near 50%, equivalent to random guessing—all while utilizing standard coherent optical transceivers at data rates up to 200 Gb/s over 80 km of fiber. We further examine QEPS’s robustness to channel impairments, its seamless compatibility with existing digital signal processing (DSP) pipelines, and its distinctive position within the post-quantum cryptography landscape. Finally, we outline key challenges and future research directions toward deploying QEPS as a practical, quantum-resistant security layer for next-generation optical networks. Full article

Three-dimensional reconstruction using unmanned aerial vehicle (UAV) platforms has been extensively utilized in various fields. While conventional techniques such as oblique photogrammetry can produce mesh models with geographical references, they often require substantial computational resources. Although recent studies have attempted to incorporate camera pose parameters into the emerging 3D Gaussian Splatting (3DGS), these methods often treat georeferencing as a post-processing step or rely on global bundle adjustment, which may propagate systematic errors and compromise final accuracy. This work integrates georeferencing as an intrinsic constraint during 3DGS training, enabling simultaneous optimization of geographic and photometric accuracy. The core of our approach lies in introducing a similarity transformation matrix T connecting the local model space with the global geographic coordinate system, along with a dedicated geographic loss function. Geographic coordinates are transformed via T before reprojection to compute the loss function. It was demonstrated that GeoRefGS presents a viable solution for efficiently integrating georeferenced information into 3DGS. Indeed, the proposed framework achieves an improvement of approximately 3.31 dB in peak signal-to-noise ratio while maintaining distance errors below 0.054 m, enabling reliable geographically referenced 3D reconstruction in substantially less time compared to conventional photogrammetric approaches. Full article

Rolling bearings exhibit nonlinear and non-stationary fault signals under complex working conditions, rendering single-modal representation insufficient for accurate diagnosis. To address this limitation, this paper proposes a novel parallel multimodal fusion fault diagnosis model based on a Gated Recurrent Unit (GRU), a Residual Network (ResNet), and a Convolutional Block Attention Module (CBAM). First, a systematic multimodal representation selection framework is introduced, identifying the Markov Transition Field (MTF) as the optimal two-dimensional (2D) image modality due to its superior texture clarity and noise resistance compared to other methods. Second, parallel dual-branch architecture is designed to simultaneously process heterogeneous data. The 1D-GRU branch captures long-range temporal dependencies directly from raw vibration signals, while the 2D ResNet-CBAM branch extracts deep spatial features from the MTF images, adaptively focusing on key fault regions. These heterogeneous features are then fused through concatenation to retain complementary diagnostic information. Experimental validation on the Case Western Reserve University (CWRU) dataset demonstrates that the proposed model achieves a 99.57% accuracy in a 10-classification task. Furthermore, it exhibits significant parameter efficiency and outstanding robustness, with the accuracy decreasing by no more than 1.2% under noise interference and cross-load scenarios, comprehensively outperforming existing single-modal and advanced fusion methods. Full article

In NOMA-enabled CR systems, superposed PU signals with unequal power levels and independent activity significantly complicate spectrum sensing and channel state discrimination. To address this issue, ML-based sensing exploits spectrum-domain features to perform channel state classification. However, the ML-based methods remain limited under independent PU activity and suffer from the performance tradeoff issue since the spectrum sensing constraints are not explicitly incorporated into the learning process. In this paper, we propose an OCL method that aligns LightGBM multiclass training with spectrum sensing objectives and leverages eigenvalue-based features to capture discriminative signal patterns under dynamic NOMA transmission. The cost-sensitive learning strategy is used to guide the classifier while the objective-driven tuning is used to optimize hyperparameters toward spectrum sensing objectives. To evaluate the overall performance toward $P_d$ and $P_{fa}$, we propose an overall sensing ability score by adopting the SPOTIS method. As a result, the proposed OCL method achieves the highest overall sensing ability scores with an average score of 0.638, outperforming EBSS-RF at 0.610 and FBSS-LR at 0.221. Under challenging signal pattern discrimination conditions, the OCL method improves the overall sensing ability score by 6.26% and 0.9 under different power coefficients compared to EBSS-RF, highlighting its effectiveness in addressing the performance tradeoff issue. Full article

This article presents the design and implementation of a 6–18 GHz GaN monolithic microwave integrated circuit (MMIC) power amplifier (PA). A two-stage cascaded reactive matching network structure based on transistor stacking technology is employed to achieve circuit gain, and a multi-cell combination is used in the final stage to simultaneously achieve high power and high efficiency. For demonstration, a prototype of the proposed PA with an area of 4.5 × 3.4 mm² is fabricated in a 0.1 µm GaN-on-Si high-electron-mobility transistor (HEMT) process. The measured results of the GaN PA show a small signal gain of 25–29 dB, an output power of 40.8–42.5 dBm, and a power-added efficiency (PAE) of 27–38% in the operating frequency range of 6–18 GHz. Full article

The integration of renewable energy and power electronics has intensified the occurrence of complex power quality disturbances (PQDs), which increasingly threaten grid stability. To address the challenges of multi-class PQD identification under noisy conditions, this paper proposes a novel framework that combines an enhanced time–frequency analysis method with an optimized AdaBoost decision tree. The main contributions are three-fold: (1) We develop an instantaneous frequency adaptive Fourier synchrosqueezing transform (IFAFSST) equipped with a custom adaptive operator that aligns closely with the frequency modulation patterns in PQD signals, thereby improving time–frequency energy localization. (2) The IFAFSST outputs are decomposed into low-frequency and high-frequency components, from each of which a set of 16 discriminative features is extracted. (3) An improved AdaBoost classifier is introduced, incorporating forward feature selection and Hyperband-based hyperparameter optimization to enhance classification performance. Hyperband accelerates the optimization process by dynamically allocating computing resources and iteratively eliminating suboptimal configurations, thereby enabling efficient determination of the optimal hyperparameters. The method proposed in this paper achieved an accuracy rate of 99.50% on simulated data containing 30 dB white noise and 98.30% on hardware platform data. This framework can effectively handle 23 types of interference, including seven types of single interference, 12 types of double compound interference, three types of triple compound interference, and one type of quadruple compound interference. It performs particularly well in identifying composite interference scenarios. This research has made a significant contribution to power quality analysis, providing a powerful solution with high accuracy and practical applicability, and offering great potential for the implementation of smart grid monitoring systems and the integration of renewable energy. Full article

Weak structured signal enhancement and detection in multi-sensor environments remains challenging due to severe noise interference and the heterogeneity of sensing modalities, which often renders traditional signal processing and conventional deep learning models ineffective. To address these limitations, this study proposes the Convolutional Attentional weak structured signal enhancement and detection Network (CA-WSDN), an end-to-end framework that integrates multi-scale 1D convolution for hierarchical temporal feature extraction with a SE-style cross-channel attention mechanism for adaptive multi-scale feature enhancement across heterogeneous sensor channels. The multi-scale branches capture transient and long-range temporal patterns, while the attention module dynamically emphasizes informative channels and suppresses noise-dominated features, thereby enhancing weak fault-related components. Experiments on simulated medical-equipment monitoring data under ultra-low SNR conditions (−5 dB to 0 dB) demonstrate the model’s robustness and generalization capability. CA-WSDN achieves an SNRI of 8.12 dB at ultra-low SNR experimental setting SNR and 95.1% diagnostic accuracy, outperforming all baseline algorithms. The results indicate that CA-WSDN provides an effective and scalable solution for weak structured signal enhancement and detection in complex noise-flooded multi-sensor systems, offering strong potential for industrial and medical monitoring applications. Full article