Search Results (3,133)

This paper proposes a rolling bearing fault diagnosis method based on HFMD and a dual-branch parallel network, aiming to address the issue of diagnostic accuracy being compromised by the disparity in data quality across different source domains due to sparse feature separation in rolling bearing acoustic signals. Traditional methods face challenges in feature extraction, sensitivity to noise, and difficulties in handling coupled multi-fault conditions in rolling bearing fault diagnosis. To overcome these challenges, this study first employs the HawkFish Optimization Algorithm to optimize Feature Mode Decomposition (HFMD) parameters, thereby improving modal decomposition accuracy. The optimal modal components are selected based on the minimum Residual Energy Index (REI) criterion, with their time-domain graphs and Continuous Wavelet Transform (CWT) time-frequency diagrams extracted as network inputs. Then, a dual-branch parallel network model is constructed, where the multi-scale residual structure (Res2Net) incorporating the Efficient Channel Attention (ECA) mechanism serves as the temporal branch to extract key features and suppress noise interference, while the Swin Transformer integrating multi-stage cross-scale attention (MSCSA) acts as the time-frequency branch to break through local perception bottlenecks and enhance classification performance under limited resources. Finally, the time-domain graphs and time-frequency graphs are, respectively, input into Res2Net and Swin Transformer, and the features from both branches are fused through a fully connected layer to obtain comprehensive fault diagnosis results. The research results demonstrate that the proposed method achieves 100% accuracy in open-source datasets. In the experimental data, the diagnostic accuracy of this study demonstrates significant advantages over other diagnostic models, achieving an accuracy rate of 98.5%. Under few-shot conditions, this study maintains an accuracy rate no lower than 95%, with only a 2.34% variation in accuracy. HFMD and the dual-branch parallel network exhibit remarkable stability and superiority in the field of rolling bearing fault diagnosis. Full article

In Global Navigation Satellite System (GNSS)-denied environments, navigation using signals of opportunity (SOP) from Low Earth Orbit (LEO) satellites is considered a feasible alternative. Compared with single-constellation systems, multiple-constellation LEO systems offer improved satellite visibility and geometric diversity, which enhances positioning continuity and accuracy. To allocate weights among heterogeneous observations, prior studies have employed the Helmert variance component estimation (HVCE) method, which iteratively determines relative weight ratios of different observation types through posterior variance estimation. HVCE enables error modeling and weight adjustment without prior noise information but is highly sensitive to outliers, making it vulnerable to their impact. This study proposes a Robust HVCE-based dual-constellation weighted positioning method. The approach integrates prior weighting based on satellite elevation, observation screening based on characteristic slopes, HVCE, and IGG-III robust estimation to achieve dynamic weight adjustment and suppress outliers. Experimental results over a 33.9 km baseline demonstrate that the proposed method attains Two-Dimensional (2D) and Three-Dimensional (3D) positioning accuracies of 12.824 m and 23.230 m, corresponding to improvements of 29% and 16% over conventional HVCE weighting, respectively. It also outperforms single-constellation positioning and equal-weighted fusion, confirming the effectiveness of the proposed approach. Full article

In recent years, electrocardiogram (ECG) biometrics has received extensive attention and achieved a series of exciting results. In order to achieve optimal ECG biometric recognition, it is crucial to effectively process the original ECG signals. However, most existing methods only focus on extracting features from one-dimensional time series, limiting the discriminability of individual identification to some extent. To overcome this limitation, we propose a novel framework that integrates dual-level features, i.e., 1D (time series) and 2D (relative position matrix) representations, through collaborative embedding, dimensional attention weight learning, and projection matrix learning. Specifically, we leverage collective matrix factorization to learn the shared latent representations by embedding dual-level features to fully mine these two kinds of features and preserve as much information as possible. To further enhance the discrimination of learned representations, we preserve the diverse information for different dimensions of the latent representations by means of dimensional attention weight learning. In addition, the learned projection matrix simultaneously facilitates the integration of dual-level features and enables the transformation of out-of-sample queries into the discriminative latent representation space. Furthermore, we propose an effective and efficient optimization algorithm to minimize the overall objective loss. To evaluate the effectiveness of our learned latent representations, we conducted experiments on two benchmark datasets, and our experimental results show that our method can outperform state-of-the-art methods. Full article

Dual-specificity protein phosphatases (DUSPs) are a family of proteins that dephosphorylate both phospho-serine/threonine and phospho-tyrosine residues of Mitogen-Activated Protein Kinases (MAPKs). MAPKs are involved in a large number of cellular processes, including proliferation, differentiation, apoptosis, and stress responses. Therefore, dysregulation or improper functioning of the MAPK signalling is involved in the onset and progression of several diseases, including cancer. Likewise, dysregulation of DUSPs markedly affects cancer biology. The importance of MAPKs in the modulation of tumour development has been known for a long time, and MAPKs are consistently used as molecular targets for cancer therapy. However, in the last decade, DUSPs have acquired a greater interest as possible therapeutic targets to regulate MAPK activity and to prevent resistance mechanisms to MAPK-targeting therapies. Moreover, the possibility of exploiting DUSPs as biomarkers for the diagnosis and prognosis of specific types of cancer is also emerging. In this review, we report what is known in the literature on the role of DUSPs in cancer onset and progression, focusing on those targeting the extracellular signal-regulated kinases (ERKs), in particular ERK1/2 and ERK5 conventional MAPKs. The specific role of each ERK-targeting DUSP in supporting or hampering cancer progression in the context of different types of cancer is also discussed. Full article

Underwater fish image segmentation is the key technology to realizing intelligent fisheries and ecological monitoring. However, the problems of light attenuation, blurred boundaries, and low contrast caused by complex underwater environments seriously restrict the segmentation accuracy. In this paper, RetinalConet, an underwater fish segmentation network based on bionic retina dual-channel and multi-module cooperation, is proposed. Firstly, the bionic retina dual-channel module is embedded in the encoder to simulate the separation and processing mechanism of light and dark signals by biological vision systems and enhance the feature extraction ability of fuzzy target contours and translucent tissues. Secondly, the dynamic prompt module is introduced, and the response of key features is enhanced by inputting adaptive prompt templates to suppress the noise interference of water bodies. Finally, the edge prior guidance mechanism is integrated into the decoder, and low-contrast boundary features are dynamically enhanced by conditional normalization. The experimental results show that RetinalCoNet is superior to other mainstream segmentation models in the key indicators of mDice, reaching 82.3%, and mIou, reaching 89.2%, and it is outstanding in boundary segmentation in many different scenes. This study achieves accurate fish segmentation in complex underwater environments and contributes to underwater ecological monitoring. Full article

Urban traffic congestion remains a major contributor to vehicle emissions and travel inefficiency, prompting the need for adaptive and intelligent traffic management systems. In response, we introduce DeepSIGNAL-ITS (Deep Learning Signal Intelligence for Adaptive Lights in Intelligent Transportation Systems), a unified framework that leverages real-time traffic perception and learning-based control to optimize signal timing and reduce congestion. The system integrates vehicle detection via the YOLOv8 architecture at roadside units (RSUs) and manages signal control using Proximal Policy Optimization (PPO), guided by global traffic indicators such as accumulated vehicle waiting time. Secure communication between RSUs and cloud infrastructure is ensured through Transport Layer Security (TLS)-encrypted data exchange. We validate the framework through extensive simulations in SUMO across diverse urban settings. Simulation results show an average 30.20% reduction in vehicle waiting time at signalized intersections compared to baseline fixed-time configurations derived from OpenStreetMap (OSM). Furthermore, emissions assessed via the HBEFA-based model in SUMO reveal measurable reductions across pollutant categories, underscoring the framework’s dual potential to improve both traffic efficiency and environmental sustainability in simulated urban environments. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, synaptic dysfunction, and neuronal loss. Although amyloid-β plaques and neurofibrillary tangles have been the historical hallmarks of AD pathology, growing evidence highlights microglial-mediated neuroinflammation as a central driver of disease onset and progression. This review aims to provide an updated overview of the dual roles of microglia in AD, from their protective functions to their contribution to chronic inflammation and neurodegeneration. Methods: This review synthesizes findings from recent experimental and clinical studies to examine the molecular mechanisms underlying microglial activation and dysfunction in AD. Key areas of focus include microglial signaling pathways, gut–brain axis interactions, and immunometabolic regulation. The review also evaluates emerging immunomodulatory therapeutic strategies designed to restore microglial homeostasis. Results: Recent studies reveal that microglia undergo a dynamic transition from a homeostatic to a reactive state in AD, contributing to sustained neuroinflammation and impaired clearance of pathological aggregates. Molecular mechanisms such as TREM2 signaling, NLRP3 inflammasome activation, and metabolic reprogramming play critical roles in this process. Additionally, gut microbiota alterations and systemic inflammation have been shown to influence microglial function, further exacerbating disease pathology. Conclusions: Targeting microglial dysfunction through immunomodulatory strategies holds promise as a disease-modifying approach in AD. Therapeutic avenues under investigation include natural compounds, synthetic modulators, immunotherapies, and microbiota-based interventions. A deeper mechanistic understanding of microglial regulation may open new translational pathways for the development of effective treatments for AD. Full article

Objective: Insulin resistance (IR) is a complex and multifactorial disorder that contributes to type 2 diabetes and cardiovascular disease. MicroRNAs (miRNAs) play important roles in diverse developmental and disease processes. However, the molecular mechanisms of IR are unclear. This paper aims to explore the role of miRNA in regulating IR and to elucidate the mechanisms responsible for these effects. Methods: IR models were created by feeding a high-fat diet (HFD) to mice or stimulating 3T3-L1 cells with palmitate. Twelve weeks of HFD trigger weight gain, leading to lipid accumulation and insulin resistance in mice. The expression profiles of miRNAs in adipose tissues (AT) from the HFD-induced mouse models were analyzed. The relationship between miR-221-3p and SOCS1 was determined using dual luciferase reporter gene assays. Metabolic alterations in AT were investigated by real-time PCR and Western blot. Results: miR-221-3p was significantly increased in AT. HFD-induced disturbances in glucose homeostasis were aggravated by miR-221-3p upregulation. The inhibition of miR-221-3p promoted insulin sensitivity including reduced lipid accumulation and the disruption of glucose metabolism. Of note, the 3′-UTR of SOCS1 was found to be a direct target of miR-221-3p. The SOCS1 inhibitor attenuated miR-221-3p-induced increases in IRS-1 phosphorylation, AKT phosphorylation, and GLUT4. miR-221-3p was considered to be involved in the PI3K/AKT signaling pathway, thus leading to increased insulin sensitivity and decreased IR in HFD-fed mice and 3T3-L1 adipocytes. Conclusions: The miR-221-3p/SOCS1 axis in AT plays a pivotal role in the regulation of glucose metabolism, providing a novel target for treating IR and diabetes. Full article

Cardiotonic steroids (CTS), including the endogenous compound ouabain, modulate neuronal Na/K-ATPase (NKA) activity in a concentration-dependent manner, affecting neuronal survival and function. While high concentrations of ouabain are neurotoxic, endogenous levels of 0.1–1 nM exert neuroprotective effects and influence intracellular signaling. However, the effects of physiologically relevant ouabain concentrations on excitatory synaptic transmission remain unclear. In this study, we examined how 1 nM ouabain affects synaptic responses in rat hippocampal CA1 neurons. Using whole-cell patch-clamp recordings of evoked excitatory postsynaptic currents (EPSCs) and extracellular recordings of field excitatory postsynaptic potentials (fEPSPs), we found that ouabain enhances excitatory synaptic transmission, increasing EPSC amplitude and fEPSP slope by 35–50%. This effect was independent of NMDA receptor (NMDAR) activity. Ouabain reduced the magnitude of NMDAR-dependent long-term potentiation (LTP), but still augmented fEPSPs when applied after LTP induction. This implies separate additive mechanisms. These observations exhibit that ouabain, at concentrations corresponding to endogenous levels, facilitates basal excitatory synaptic transmission while partially suppressing LTP. We propose that ouabain exerts dual modulatory effects in hippocampal networks via distinct synaptic mechanisms. Full article

Infrared dim and small target detection aims to accurately localize targets within complex backgrounds or clutter. However, under extremely low signal-to-noise ratio (SNR) conditions, single-frame detection methods often fail to effectively detect such targets. In contrast, multi-frame detection can exploit temporal cues to significantly improve the probability of detection (Pd) and reduce false alarms (Fa). Existing multi-frame approaches often employ 3D convolutions/RNNs to implicitly extract temporal features. However, they typically lack explicit modeling of target motion. To address this, we propose a Dual Encoder–Decoder Multi-Frame Infrared Small Target Detection Network with Motion Encoding (DEMNet) that explicitly incorporates motion information into the detection process. The first multi-level encoder–decoder module leverages spatial and channel attention mechanisms to fuse hierarchical features across multiple scales, enabling robust spatial feature extraction from each frame of the temporally aligned input sequence. The second encoder–decoder module encodes both inter-frame target motion and intra-frame target positional information, followed by 3D convolution to achieve effective motion information fusion. Extensive experiments demonstrate that DEMNet achieves state-of-the-art performance, outperforming recent advanced methods such as DTUM and SSTNet. For the DAUB dataset, compared to the second-best model, DEMNet improves Pd by 2.42 percentage points and reduces Fa by 4.13 × 10⁻⁶ (a 68.72% reduction). For the NUDT dataset, it improves Pd by 1.68 percentage points and reduces Fa by 0.67 × 10⁻⁶ (a 7.26% reduction) compared to the next-best model. Notably, DEMNet demonstrates even greater advantages on test sequences with SNR ≤ 3. Full article

Food-derived carbon dots (F-CDs) are a novel class of carbon-based nanomaterials unintentionally generated during common thermal food processing techniques, such as baking, roasting, frying, and caramelization. These nanostructures exhibit unique optical and chemical properties, including photoluminescence, high aqueous solubility, and tunable surface functionality, making them increasingly relevant to both food science and biomedical research. Recent studies have highlighted their ability to interact with biological systems, particularly the gut microbiota, a critical determinant of host metabolism, immunity, and overall health. This review critically summarizes the current understanding of F-CDs, including their mechanisms of formation, analytical detection methods, and physicochemical properties. It explores their biological fate in the gastrointestinal tract, encompassing absorption, distribution, metabolism, and excretion, with a focus on their stability and cellular uptake. Special attention is given to the interaction between F-CDs and the gut microbiota, where evidence suggests both beneficial (e.g., anti-inflammatory, antioxidant) and detrimental (e.g., dysbiosis, inflammatory signaling) effects, depending on the CD type, dose, and exposure context. Additionally, this review addresses toxicological concerns, highlighting gaps in long-term safety data, standardized detection methods, and regulatory oversight. The dual role of F-CDs—as potential modulators of the microbiota and as emerging dietary nanomaterials with uncharted risks—underscores the need for further interdisciplinary research. Future efforts should aim to refine detection protocols, assess chronic exposure outcomes, and clarify structure–function relationships to enable the safe and responsible application of these nanomaterials in food and health contexts. Full article

This study introduces a dual-filter X-ray image enhancement technique designed to elevate the quality of radiographic images of the knee, breast, and wrist, employing the Cream and Bosso algorithms. Our quantitative analysis reveals significant improvements in bone, edge definition, and contrast (p < 0.001). The processing parameters are derived from the relationship between entropy metrics and the filtering parameter d. The results demonstrate contrast enhancements for knee radiographs and for wrist radiographs, while maintaining acceptable noise levels. Comparisons are made with CLAHE techniques, unsharp masking, and deep-learning-based models. This method is a reliable and computationally efficient approach to enhancing clinical diagnosis in resource-limited settings, thereby improving robustness and interpretability. Full article

High-dynamic airborne gravimetry faces critical challenges from platform-induced noise contamination. Conventional filtering methods exhibit inherent limitations in simultaneously achieving dynamic tracking capability and spectral fidelity. To overcome these constraints, this study proposes a Kalman–FIR fusion filtering (K-F) method, which is validated through engineering implementation on the GIPS-1A airborne gravimeter platform. The proposed framework employs a dual-stage strategy: (1) An adaptive state-space framework employing calibration coefficients (S_x, S_y, S_z) continuously estimates triaxial acceleration errors to compensate for gravity anomaly signals. This approach resolves aliasing artifacts induced by non-stationary noise while preserving low-frequency gravity components that are traditionally attenuated by conventional FIR filters. (2) A window-optimized FIR post-filter explicitly regulates cutoff frequencies to ensure spectral compatibility with downstream processing workflows, including terrain correction. Flight experiments demonstrate that the K-F method achieves a repeat-line internal consistency of 0.558 mGal at 0.01 Hz—a 65.3% accuracy improvement over standalone FIR filtering (1.606 mGal at 0.01 Hz). Concurrently, it enhances spatial resolution to 2.5 km (half-wavelength), enabling the recovery of data segments corrupted by airflow disturbances that were previously unusable. Implemented on the GIPS-1A system, K-F enables precision mineral exploration and establishes a noise-suppressed paradigm for extreme-dynamic gravimetry. Full article

MEMS switches offer great advantages over solid-state and conventional electromechanical switches, including a compact size and high isolation. This paper presents a novel silicon-to-silicon (Si-to-Si) MEMS switch featuring two suspended actuated platforms for DC power switching applications. The proposed design uniquely incorporates dual suspended chevron actuators, enabling bidirectional actuation, enhancing force generation, and improving overall switching performance. Leveraging the robustness of silicon, this Si-to-Si contact switch aims to enhance the reliability of MEMS-based DC power switches. Testing of a fabricated device in the PiezoMUMPs process demonstrated that a 2 μm initial contact gap closes at 1.1 V_DC, with a total actuation power of 246 mW. The switch exhibits a linear voltage–current response up to 5 mA of switching current and achieves a minimum contact resistance of ~294 ± 2 Ω, one of the lowest reported for Si-to-Si contacts. This low contact resistance is attributed to the suspended contact platforms, which mitigate misalignment. The measured response time was 4 ms for turn-on and 2.5 ms for turn-off. This switch withstood a breakdown voltage of up to 376 V across the 2 µm contact gap. Moreover, the 200 nm thick oxide layer separating the actuation and signal lines exhibited breakdown at 183 V. These findings highlight the potential of the switch for high-voltage applications and pave the way for further enhancements to improve its reliability in harsh environments. Full article

To overcome the limitations of low texture accuracy in traditional sculpture color restoration methods, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) model incorporating a dual attention mechanism (spatial and channel attention) and a channel converter to enhance restoration quality. First, the theoretical foundations of the DCGAN algorithm and its key components (generator, discriminator, etc.) are systematically introduced. Subsequently, a DCGAN-based application model for sculpture color restoration is developed. The generator employs a U-Net architecture integrated with a dual attention module and a channel converter, enhancing both local feature representation and global information capture. Meanwhile, the discriminator utilizes an image region segmentation approach to optimize the assessment of consistency between restored and original regions. The loss function follows a joint optimization strategy, combining perceptual loss, adversarial loss, and structural similarity index (SSIM) loss, ensuring superior restoration performance. In the experiments, mean square error (MSE), peak signal-to-noise ratio (PSNR), and SSIM were used as evaluation metrics, and sculpture color restoration tests were conducted on an Intel Xeon workstation. The performance of the proposed model was compared against the traditional DCGAN and other restoration models. The experimental results demonstrate that the improved DCGAN outperforms traditional methods across all evaluation metrics, and compared to traditional DCGAN, the proposed model achieves significantly higher SSIM and PSNR, while reducing MSE. Compared to other restoration models, PSNR and SSIM are further enhanced, MSE is reduced, and the visual consistency between the restored and undamaged areas is significantly improved, with richer texture details. Full article