Search Results (37)

Multimodal medical image fusion plays a critical role in enhancing diagnostic accuracy by integrating complementary information from different imaging modalities. However, existing methods often suffer from issues such as unbalanced feature fusion, structural blurring, loss of fine details, and limited global semantic modeling, particularly in low signal-to-noise modalities like PET. To address these challenges, we propose PPMF-Net, a novel progressive and parallel deep learning framework for PET–MRI image fusion. The network employs a hierarchical multi-path architecture to capture local details, global semantics, and high-frequency information in a coordinated manner. Specifically, it integrates three key modules: (1) a Dynamic Edge-Enhanced Module (DEEM) utilizing inverted residual blocks and channel attention to sharpen edge and texture features, (2) a Nonlinear Interactive Feature Extraction module (NIFE) that combines convolutional operations with element-wise multiplication to enable cross-modal feature coupling, and (3) a Transformer-Enhanced Global Modeling module (TEGM) with hybrid local–global attention to improve long-range dependency and structural consistency. A multi-objective unsupervised loss function is designed to jointly optimize structural fidelity, functional complementarity, and detail clarity. Experimental results on the Harvard MIF dataset demonstrate that PPMF-Net outperforms state-of-the-art methods across multiple metrics—achieving SF: 38.27, SD: 96.55, SCD: 1.62, and MS-SSIM: 1.14—and shows strong generalization and robustness in tasks such as SPECT–MRI and CT–MRI fusion, indicating its promising potential for clinical applications. Full article

In the N$\nu$DEx collaboration, a high-pressure gas TPC is being developed to search for the neutrinoless double beta decay. The use of electronegative ⁸²SeF₆ gas mandates an ion-TPC. The reconstruction of the z coordinate is to be realized by exploiting the feature of multiple species of charge carriers. As the initial stage of the development, we studied the properties of the ${\mathrm{SF}}_6$ gas, which is non-toxic and has a similar molecular structure to ${\mathrm{SeF}}_6$. In the paper, we present the measurement of drift velocities and mobilities of the majority and minority negative charge carriers found in ${\mathrm{SF}}_6$ at a pressure of 750 Torr, slightly higher than the local atmospheric pressure. The reduced fields range between 3.0 and 5.5 Td. This was performed using a laser beam to ionize the gas inside a small TPC, with a drift length of 3.7 cm. A customized charge-sensitive amplifier was developed to read out the anode signals induced by the slowly drifting ions. The closure test of the reconstruction of the z coordinate using the difference in the velocities of the two carriers was also demonstrated. Full article

This work presents an approach based on signal processing and artificial intelligence (AI) to identify the pre-insertion resistor (PIR) and main contact instants during the operation of high-voltage SF6 circuit breakers to help improve the settings of controlled switching and attenuate transients. For this, the current and voltage signals of a real Brazilian substation are used as AI inputs, considering the noise and interferences common in this type of environment. Thus, the proposed modeling considers the signal preprocessing steps for feature extraction, the generation of the dataset for model training, the use of different machine learning techniques to automatically find the desired points, and, finally, the identification of the best moments for controlled switching of the circuit breakers. As a result, the models evaluated obtained good performance in the identification of operation points above 93%, considering precision and accuracy. In addition, valuable statistical notes related to the controlled switching condition are obtained from the circuit breakers evaluated in this research. Full article

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value. Full article

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R² up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity. Full article

Background: Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting. Methods: We propose a contrastive-based pre-training approach that teaches the network to perform patient normalization without requiring a direct comparison to a reference sample. We then use the contrastive pre-trained encoder as a favorable initialization for classification. To train the classifiers, we efficiently use available data and reduce overfitting through a multitask framework with margin delineation and cancer diagnosis tasks. We evaluate the model over 67 patients using 10-fold cross-validation and evaluate significance using paired, one-tailed t-tests. Results: The proposed approach achieves a sensitivity of 82.08% and specificity of 75.92% on the cancer diagnosis task with a sensitivity of 91.83% and specificity of 79.31% for margin delineation as an auxiliary task. In comparison to existing approaches, our method significantly outperforms a support vector machine (SVM) implemented with either sequential feature selection (SFS) (p = 0.0261) or L1 loss (p = 0.0452) when considering the average of sensitivity and specificity. Specifically, the proposed approach increases performance by 2.75% compared to the L1 model and 4.87% compared to the SFS model. In addition, there is a significant increase in specificity of 8.34% compared to the baseline autoencoder model (p = 0.0070). Conclusions: Our method effectively trains deep learning models for small data applications when existing, large pre-trained models are not suitable for fine-tuning. While we designed the network for a specific imaging modality, we report the development process so that the insights gained can be applied to address similar challenges in other non-traditional imaging modalities. A key contribution of this paper is a neural network framework for multi-spectral fluorescence lifetime-based tissue discrimination that performs patient normalization without requiring a reference (healthy) sample from each patient at test time. Full article

Interactions among leukocytes and leukocytes with immune-associated auxiliary cells represent an essential feature of the immune response that requires the involvement of cell adhesion molecules (CAMs). In the immune system, CAMs include a wide range of members pertaining to different structural and functional families involved in cell development, activation, differentiation and migration. Among them, β₂ integrins (LFA-1, Mac-1, p150,95 and α_Dβ₂) are predominantly involved in homotypic and heterotypic leukocyte adhesion. β2 integrins bind to intercellular (I)CAMs, actin cytoskeleton-linked receptors belonging to immunoglobulin superfamily (IgSF)-CAMs expressed by leukocytes and vascular endothelial cells, enabling leukocyte activation and transendothelial migration. β2 integrins have long been viewed as the most important ICAMs partners, propagating intracellular signalling from β2 integrin-ICAM adhesion receptor interaction. In this review, we present previous evidence from pioneering studies and more recent findings supporting an important role for ICAMs in signal transduction. We also discuss the contribution of immune ICAMs (ICAM-1, -2, and -3) to reciprocal cell signalling and function in processes in which β2 integrins supposedly take the lead, paying particular attention to T cell activation, differentiation and migration. Full article

In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker’s operating mechanism. In this study, the 72.5 kV SF₆ circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms. Full article

Follicular fluid (FF) ensures a safe environment for oocyte growth and maturation inside the ovarian follicle in mammals. In each cycle, the large dominant follicle (LF) contains the oocyte designated to be ovulated, whereas the small subordinate follicles (SFs) of the same wave will die through atresia. In cows, the oocytes from the SF, being 2 mm in size, are suitable for in vitro reproduction biotechnologies, and their competence in developing an embryo depends on the size of the follicles. FF contains proteins, metabolites, fatty acids, and a multitude of extracellular vesicles (ffEVs) of different origins, which may influence oocyte competence through bidirectional exchanges of specific molecular cargo between follicular cells and enclosed oocytes. FF composition evolves along with follicle growth, and the abundance of different lipids varies between the LF and SF. Here, significant differences in FF lipid content between the LFs and SFs within the same ovary were demonstrated by MALD-TOF mass spectrometry imaging on bovine ovarian sections. We then aimed to enlighten the lipid composition of FF, and MALDI-TOF lipid profiling was performed on cellular, vesicular, and liquid fractions of FF. Differential analyses on the abundance of detected lipid features revealed specific enrichment of phospholipids in different ffEV types, such as microvesicles (MVs) and exosomes (Exo), compared to depleted FF. MALDI-TOF lipid profiling on MVs and Exo from the LF and SF samples (n = 24) revealed that more than 40% of detected features were differentially abundant between the groups of MVs and Exo from the different follicles (p < 0.01, fold change > 2). Glycerophospholipid and sphingolipid features were more abundant in ffEVs from the SFs, whereas different lysophospholipids, including phosphatidylinositols, were more abundant in the LFs. As determined by functional analysis, the specific lipid composition of ffEVs suggested the involvement of vesicular lipids in cell signaling pathways and largely contributed to the differentiation of the dominant and subordinate follicles. Full article

This article aims to aid in exploring and forecasting hydrothermal minerals and gold deposits in Central Saudi Arabia (SA), with a focus on structural contexts. Remote sensing (RS) and satellite-based Bouguer gravity (SBG) data were integrated in order to create a mineral prediction map for the researched location. Data from the Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) were transformed and enhanced using a variety of approaches. The delineation of hydrothermal alteration zones (HAZs) and highlighting of structural discontinuities in the OLI data were made possible using band ratios and oriented principal component analysis (PCA). Additionally, the underlying structural features were successfully exposed by processing the SBG using a variety of edge detection techniques, like the analytical signal (AS), total horizontal derivative (THD), tilt angle (TA), horizontal tilt angle (TDX), theta map (TM), horizontal derivative of the tilt derivative (HD_TDR), horizontal gradient of the tilt angle (HGTA), tilt angle of the analytical signal (TAAS), and soft sign function (SF). As a result, more prominent lineaments were found in the NW–SE, NNW–SSE, NE–SW, and NNE–SSW directions than in the N–S and E–W directions. The GIS incorporated surface/subsurface geological structure density maps with zones of hydrothermal alteration. It was found that the lineaments derived from the analysis of the RS and SBG data were more in line with the HAZs, which demonstrated the common connection between alteration zones and deep lineaments. The findings revealed a mineral prediction map with extremely low to extremely high probabilities. Overall, combining RS and SBG data effectively identified probable mineralization sites associated with hydrothermal processes and made it easier to create this study’s final predictive mineralization map. Full article

Myelodysplastic/myeloproliferative neoplasms (MDS/MPN) are hematological disorders characterized by both proliferative and dysplastic features. According to the 2022 International Consensus Classification (ICC), MDS/MPN consists of clonal monocytosis of undetermined significance (CMUS), chronic myelomonocytic leukemia (CMML), atypical chronic myeloid leukemia (aCML), MDS/MPN with SF3B1 mutation (MDS/MPN-T-SF3B1), MDS/MPN with ring sideroblasts and thrombocytosis not otherwise specified (MDS/MPN-RS-T-NOS), and MDS/MPN-NOS. These disorders exhibit a diverse range of genetic alterations involving various transcription factors (e.g., RUNX1), signaling molecules (e.g., NRAS, JAK2), splicing factors (e.g., SF3B, SRSF2), and epigenetic regulators (e.g., TET2, ASXL1, DNMT3A), as well as specific cytogenetic abnormalities (e.g., 8 trisomies, 7 deletions/monosomies). Clinical studies exploring therapeutic options for higher-risk MDS/MPN overlap syndromes mostly involve hypomethylating agents, but other treatments such as lenalidomide and targeted agents such as JAK inhibitors and inhibitors targeting PARP, histone deacetylases, and the Ras pathway are under investigation. While these treatment modalities can provide partial disease control, allogeneic bone marrow transplantation (allo-BMT) is the only potentially curative option for patients. Important prognostic factors correlating with outcomes after allo-BMT include comorbidities, splenomegaly, karyotype alterations, and the bone marrow blasts percentage at the time of transplantation. Future research is imperative to optimizing therapeutic strategies and enhancing patient outcomes in MDS/MPN neoplasms. In this review, we summarize MDS/MPN diagnostic criteria, biology, and current and future treatment options, including bone marrow transplantation. Full article

The external-cavity quantum cascade laser (ECQCL) is an ideal mid-infrared (MIR) spectral light source for determining large molecular-absorption spectral features with broad transition bands. For this paper, a gas sensor system was developed using a broadband tunable ECQCL and a direct absorption spectroscopy detection scheme with a short path absorption cell of 29.6 cm. For spectral signal detection, a cheap and miniaturized quartz crystal tuning fork- (QCTF) based light detector was used for laser signal detection. The characteristics of the QCTF detector were theoretically simulated and experimentally observed. To demonstrate this sensing technique, sulfur hexafluoride (SF₆) was selected as the analyte, which can be used as an effective indicator to identify fault-types of gas-insulated electrical equipment. Preliminary results indicated that a good agreement was obtained between experimentally observed data and reference spectra according to the NIST database and previous publications, and the gas sensor system showed a good linear response to SF₆ gas concentration. Finally, Allan–Werle deviation analysis indicated that detection limits of 1.89 ppm for SF₆ were obtained with a 1 s integration time, which can be further improved to ~0.38 ppm by averaging up to 131 s. Full article

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver’s driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method. Full article

Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF₆ gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF₆ HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs. Full article

Metal magnetic memory testing (MMMT) is a nondestructive testing technique that can detect early signs of damage in components. Many scholars have studied the effect of uniaxial stress on the self-magnetic-leakage field (SMLF)’s strength. Nevertheless, there is still insufficient research on the combined action of bending and shear. We studied the law of distribution of the magnetic signal,$\Delta H_{\mathrm{SF}}\left(\mathrm{y}\right)$, at different stress parts of a steel box girder and the quantitative relationship between the magnetic characteristic parameters and the external load. The results showed that the MMMT could accurately detect the early stress concentration zone (SCZ) and predict the final buckling zone of steel box girders. It could be judged that the corresponding parts of the steel box girder had entered the elastic-plastic working stage by the reverse change of the $\Delta H_{\mathrm{SF}}\left(\mathrm{y}\right)$-F and${\left|H_{\mathrm{SF}}\left(\mathrm{y}\right)\right|}_{a }$-F curve trends, this feature could be used as an early warning sign before the steel box girder was deformed or destroyed. The fitted${\left|H_{\mathrm{SF}}\left(\mathrm{y}\right)\right|}_{ave }$-F linear expression could be used as the expression between the magnetic signal and the shear capacity. All the evaluation methods were expected to provide a basis for effectively evaluating the stress state of steel box girders with the MMMT method. Full article