Search Results (1,266)

Tendinopathies are a significant challenge in musculoskeletal medicine, with current treatments showing variable efficacy. Electromagnetic transduction therapy (EMTT) has emerged as a promising therapeutic approach, but its biological effects on tendon cells remain largely unexplored. Here, we investigated the effects of EMTT on primary cultured human tenocytes’ behavior and functions in vitro, focusing on cellular responses, senescence-related pathways, and molecular mechanisms. Primary cultures of human tenocytes were established from semitendinosus tendon biopsies of patients undergoing anterior cruciate ligament (ACL) reconstruction (n = 6, males aged 17–37 years). Cells were exposed to EMTT at different intensities (40 and 80 mT) and impulse numbers (1000–10,500). Cell viability (MTT assay), proliferation (Ki67), senescence markers (CDKN2a/INK4a), migration (scratch test), cytoskeleton organization (immunofluorescence), and gene expression (RT-PCR) were analyzed. A 40 mT exposure elicited minimal effects, whereas 80 mT treatments induced significant cellular responses. Repeated 80 mT exposure demonstrated a dual effect: despite a moderate decrease in overall cell vitality, increased Ki67 expression (+7%, p ≤ 0.05) and significant downregulation of senescence marker CDKN2a/INK4a were observed, suggesting potential senolytic-like activity. EMTT significantly enhanced cell migration (p < 0.001) and triggered cytoskeletal remodeling, with amplified stress fiber formation and paxillin redistribution. Molecular analysis revealed upregulation of tenogenic markers (Scleraxis, Tenomodulin) and enhanced Collagen I and III expressions, particularly with treatments at 80 mT, indicating improved matrix remodeling capacity. EMTT significantly promotes tenocyte proliferation, migration, and matrix production, while simultaneously exhibiting senolytic-like effects through downregulation of senescence-associated markers. These results support EMTT as a promising therapeutic approach for the management of tendinopathies through multiple regenerative mechanisms, though further studies are needed to validate these effects in vivo. Full article

Tissue engineering enables autologous reconstruction of human tissues, addressing limitations in tissue availability and immune compatibility. Several tissue engineering techniques, such as self-assembly, rely on or benefit from extracellular matrix (ECM) secretion by fibroblasts to produce biomimetic scaffolds. Models have been developed for use in humans, such as skin and corneas. Ascorbic acid (vitamin C, AA) is essential for collagen biosynthesis. However, AA is chemically unstable in culture, with a half-life of 24 h, requiring freshly prepared AA with each change of medium. This study aims to demonstrate the functional equivalence of 2-phospho-L-ascorbate (2PAA), a stable form of AA, for tissue reconstruction. Dermal, vaginal, and bladder stroma were reconstructed by self-assembly using tissue-specific protocols. The tissues were cultured in a medium supplemented with either freshly prepared or frozen AA, or with 2PAA. Biochemical analyses were performed on the tissues to evaluate cell density and tissue composition, including collagen secretion and deposition. Histology and quantitative polarized light microscopy were used to evaluate tissue architecture, and mechanical evaluation was performed both by tensiometry and atomic force microscopy (AFM) to evaluate its macroscopic and cell-scale mechanical properties. The tissues produced by the three ascorbate conditions had similar collagen deposition, architecture, and mechanical properties in each organ-specific stroma. Mechanical characterization revealed tissue-specific differences, with tensile modulus values ranging from 1–5 MPa and AFM-derived apparent stiffness in the 1–2 kPa range, reflecting the nonlinear and scale-dependent behavior of the engineered stroma. The results demonstrate the possibility of substituting AA with 2PAA for tissue engineering. This protocol could significantly reduce the costs associated with tissue production by reducing preparation time and use of materials. This is a crucial factor for any scale-up activity. Full article

The sparse iterative covariance-based estimation (SPICE) method has recently gained significant attraction in the field of scanning radar super-resolution imaging because of its angular resolution enhancement capability. However, it is unable to preserve the target profile, and the estimator is constrained by high computational complexity and memory consumption. In this paper, a grid-updating split SPICE-TV algorithm is presented. The method allows for the efficient updating of reconstruction results with both contour and resolution, and a recursive grid-updating implementation framework of the split SPICE-TV has the capability to reduce the computational complexity. First, the scanning radar angular super-resolution problem is transformed into a constrained optimization problem by simultaneously employing sparse covariance fitting criteria and TV regularization constraints. Then, the split Bregman method is employed to derive an efficient closed-form solution to the problem. Ultimately, the matrix inversion problem is transformed into an online iterative equation to reduce the computational complexity and memory consumption. The superiority of the proposed method is verified by simulation and experimental data. Full article

At present, the redundant structures are one of the most effective methods for solving magnetic levitation bearing coil failure. Coil failure causes residual effective magnetic poles to form different support structures and even asymmetrical structures. For the magnetic bearing with redundant structures, how to construct the electromagnetic force (EMF) that occurs under different support structures to achieve support reconstruction is the key to realizing fault tolerance control. To reveal the support reconstruction mechanism of magnetic bearing with a redundant structure, firstly, this paper takes a single-degree-of-freedom magnetic suspension body as an example to conduct a linearization theory analysis of the offset current, clarifying the concept of the current distribution matrix (CDM) and its function; then, the nonlinear EMF mode of magnetic bearing with an eight-pole is constructed, and it is linearized by using the theory of bias current linearization. Furthermore, the conditions of no coils fail, the 8th coil fails, and the 6–8th coils fail are considered, and, with the maximum principle function of EMF, the corresponding current matrices are obtained. Meanwhile, based on the CDM, the corresponding magnetic flux densities were calculated, proving that EMF reconstruction can be achieved under the three support structures. Finally, with the CDM and position control law, a fault-tolerant control system was constructed, and the simulation of the magnetic bearing with a redundant structure was carried out. The simulation results reveal the mechanism of support reconstruction with three aspects of rotor displacement, the value and direction of currents that occur in each coil. The simulation results show that, in the 8-pole magnetic bearing, this study can achieve support reconstruction in the case of faults in up to two coils. Under the three working conditions of wireless no coil failure, the 8th coil fails and the 6–8th coils fail, the current distribution strategy was adjusted through the CDM. The instantaneous displacement disturbance during the support reconstruction process was less than 0.28 μm, and the EMF after reconstruction was basically consistent with the expected value. Full article

Intrusion detection systems are crucial for cybersecurity applications. Network traffic data originate from diverse terminal sources, exhibiting multi-view feature spaces, while the collection of unknown intrusion data is costly. Current one-class classification (OCC) approaches are mainly designed for single-view data. Multi-view OCC approaches usually require collecting multi-view traffic data from all sources and have difficulty detecting intrusion independently in each view. Furthermore, they commonly ignore the potential subcategories in normal traffic data. To address these limitations, this paper utilizes the Broad Learning System (BLS) technique and proposes an intrusion detection framework based on a multi-view cluster structure guided one-class BLS-autoencoder (IDF-MOCBLSAE). Specifically, a multi-view co-association matrix optimization objective function with doubly-stochastic constraints is first designed to capture the cross-view cluster structure. Then, a multi-view cluster structure guided one-class BLS-autoencoder (MOCBLSAEs) is proposed, which learns the discriminative patterns of normal traffic data by preserving the cross-view clustering structure while minimizing the intra-view sample reconstruction errors, thereby enabling the identification of unknown intrusion data. Finally, an intrusion detection framework is constructed based on multiple MOCBLSAEs to achieve both individual and ensemble intrusion detection. Through experimentation, IDF-MOCBLSAE is validated on real-world network traffic datasets for multi-view one-class classification tasks, demonstrating its superiority over state-of-the-art one-class approaches. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

We propose a novel approach for clustering problem, which refers to as Graph Regularized Orthogonal Subspace Non Negative Matrix Factorization (GNMFOS). This type of model introduces both graph regularization and orthogonality as penalty terms into the objective function. It not only obtains the uniqueness of matrix decomposition but also improves the sparsity of decomposition and reduces computational complexity. Most importantly, using the idea of iteration under weak orthogonality, we construct an auxiliary function for the algorithm and obtain convergence proof to compensate for the lack of convergence proof in similar models. The experimental results show that compared with classical models such as GNMF and NMFOS, our algorithm significantly improves clustering performance and the quality of reconstructed images. Full article

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent development in accurate mask extraction from images, we introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks, bypassing the reliance on point cloud quality. In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. We use 3D Gaussian splatting (3DGS) to render silhouettes of the graph and directly optimize the nodes and the adjacency matrix to fit given multi-view silhouettes in a differentiable manner. Furthermore, we use a minimum spanning tree (MST) algorithm during the optimization loop to regularize the graph to a tree structure. Experiments on synthetic and real-world plants show consistent improvements in completeness and structural accuracy over existing point-cloud-based and heuristic baseline methods. Full article

Single-pixel imaging (SPI) is a significant branch of computational imaging. Owing to the high sensitivity, low cost, and wide spectrum, it acquires extensive applications across various domains. Nevertheless, multiple measurements and long reconstruction time constrain its application. The application of neural networks has significantly improved the quality of reconstruction, but there is still a huge space for improvement in performance. SAE and Unet have different advantages in the field of SPI. However, there is no method that combines the advantages of these two networks for SPI reconstruction. Therefore, we propose the EMNP-SPI method for SPI reconstruction using SAE and Unet networks. The SAE makes use of the measurement dimension information and uses the group inverse to obtain the decoding matrix to enhance its generalization. The Unet uses different size convolution kernels and attention mechanisms to enhance feature extraction capabilities. Simulations and experiments confirm that our proposed enhanced multi-network prior method can significantly improve the quality of image reconstruction at low measurement rates. Full article

Millimeter-wave (mmWave) MIMO systems have emerged as a key enabling technology for next-generation wireless networks, addressing the growing demand for ultra-high data rates through the utilization of wide bandwidths and large-scale antenna configurations. Beyond communication capabilities, these systems offer inherent advantages for integrated sensing applications, particularly in scenarios requiring precise object detection and localization. The sparse mmWave channel in the beamspace domain allows fewer radio-frequency (RF) chains by selecting dominant beams, boosting both communication efficiency and sensing resolution. However, existing channel estimation methods, such as learned approximate message passing (LAMP) networks, rely on computationally intensive iterations. This becomes particularly problematic in large-scale system deployments, where estimation inaccuracies can severely degrade sensing performance. To address these limitations, we propose a low-complexity channel estimator using a non-iterative reconstruction network (NIRNet) with a learning-based selection matrix (LSM). NIRNet employs a convolutional layer for efficient, non-iterative beamspace channel reconstruction, significantly reducing computational overhead compared to LAMP-based methods, which is vital for real-time sensing. The LSM generates a signal-aware Gaussian measurement matrix, outperforming traditional Bernoulli matrices, while a denoising network enhances accuracy under low SNR conditions, improving sensing resolution. Simulations show the NIRNet-based algorithm achieves a superior normalized mean squared error (NMSE) and an achievable sum rate (ASR) with lower complexity and reduced training overhead. Full article

This paper evaluates the time-dependent fragility and post-earthquake residual seismic performance of existing steel frame columns in offshore atmospheric environments. Based on experimental research, the seismic failure mechanism and deterioration laws of the seismic behavior of corroded steel frame columns were revealed. A finite element analysis (FEA) method for steel frame columns, which considers corrosion damage and ductile metal damage criteria, is developed and validated. A parametric analysis in terms of service age and design parameters is conducted. Considering the impact of environmental erosion and aging, a classification criterion for damage states for existing steel frame columns is proposed, and the theoretical characterization of each damage state is provided based on the moment-rotation skeleton curves. Based on the test and numerical analysis results, probability distributions of the fragility function parameters (median and logarithmic standard deviation) are constructed. The evolution laws of the fragility parameters with increasing service age under each damage state are determined, and a time-dependent fragility model for existing steel frame columns in offshore atmospheric environments is presented through regression analysis. At a drift ratio of 4%, the probability of complete damage to columns with 40, 50, 60, and 70-year service ages increased by 18.1%, 45.3%, 79.2%, and 124.5%, respectively, compared with columns within a 30-year service age. Based on the developed FEA models and the damage class of existing columns, the influence of characteristic variables (service age, design parameters, and damage level) on the residual seismic capacity of earthquake-damaged columns, namely the seismic resistance that can be maintained even after suffering earthquake damage, is revealed. Using the particle swarm optimization back-propagation neural network (PSO-BPNN) model, nonlinear mapping relationships between the characteristic variables and residual seismic capacity are constructed, thereby proposing a residual seismic performance evaluation model for existing multi-aged steel frame columns in an offshore atmospheric environment. Combined with the damage probability matrix of the time-dependent fragility, the expected values of the residual seismic capacity of existing multi-aged steel frame columns at a given drift ratio are obtained directly in a probabilistic sense. The results of this study lay the foundation for resistance to sequential earthquakes and post-earthquake functional recovery and reconstruction, and provide theoretical support for the full life-cycle seismic resilience assessment of existing steel structures in earthquake-prone areas. Full article

In remote sensing, object classification often suffers from severe degradation caused by atmospheric turbulence and low-signal conditions. Traditional image reconstruction approaches are computationally expensive and fragile under such conditions. In this work, we propose a novel image-free classification framework using single-pixel imaging (SPI), which directly classifies targets from 1D measurements without reconstructing the image. A learnable sampling matrix is introduced for structured light modulation, and a hybrid CNN-Transformer network (Hybrid-CTNet) is employed for robust feature extraction. To enhance resilience against turbulence and enable efficient deployment, we design a $(N+1)\times L$ hybrid strategy that integrates convolutional and Transformer blocks in every stage. Extensive simulations and optical experiments validate the effectiveness of our approach under various turbulence intensities and sampling rates as low as 1%. Compared with existing image-based and image-free methods, our model achieves superior performance in classification accuracy, computational efficiency, and robustness, which is important for potential low-resource real-time remote sensing applications. Full article

Topological orientation structures in chiral nematic liquid crystals, such as torons, exhibit promising optical properties and are of increasing interest for applications in photonic devices. However, despite this attention, their polarization and phase dynamics during formation remain insufficiently explored. In this work, we investigate the dynamic optical response of a toron generated by focused femtosecond infrared laser pulses. A custom-designed polarization holographic microscope is employed to simultaneously record four polarization-resolved interferograms in a single exposure. This enables the real-time reconstruction of the Jones matrix, providing a complete description of the local polarization transformation introduced by the formation of the topological structure. The study demonstrates that torons can facilitate spin–orbit coupling of light in a manner analogous to q-plates, highlighting their potential for advanced vector beam shaping and topological photonics applications. Full article