Search Results (33,284)

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

In recent years, most proposed digital image encryption algorithms have primarily focused on encrypting raw pixel data, often neglecting the integration with image compression techniques. Image compression algorithms, such as JPEG, are widely utilized in internet applications, highlighting the need for encryption methods that are compatible with compression processes. This study introduces an innovative color image encryption algorithm integrated with JPEG compression, designed to enhance the security of images susceptible to attacks or tampering during prolonged transmission. The research addresses critical challenges in achieving an optimal balance between encryption security and compression efficiency. The proposed encryption algorithm is structured around three key compression phases: Discrete Cosine Transform (DCT), quantization, and entropy coding. At each stage, the algorithm incorporates advanced techniques such as block segmentation, block replacement, DC coefficient confusion, non-zero AC coefficient transformation, and RSV (Run/Size and Value) pair recombination. Extensive simulations and security analyses demonstrate that the proposed algorithm exhibits strong robustness against noise interference and data loss, effectively meeting stringent security performance requirements. Full article

This study presents a comprehensive computational analysis of flow rate efficiency during uranium extraction via the In Situ Recovery method. Using field data from a deposit located in Southern Kazakhstan, a series of mathematical models were developed to evaluate the distribution and balance of leaching solution. A reactive transport model incorporating uranium dissolution kinetics and acid–rock interactions were utilized to assess the accuracy of both traditional and proposed methods. The results reveal a significant spatial imbalance in sulfuric acid distribution, with up to 239.1 tons of acid migrating beyond the block boundaries. To reduce computational demands while maintaining predictive accuracy, two alternative methods, a streamline-based and a trajectory-based approach were proposed and verified. The streamline method showed close agreement with reactive transport modeling and was able to effectively identify the presence of intra-block reagent imbalance. The trajectory-based method provided detailed insight into flow dynamics but tended to overestimate acid overflow outside the block. Both alternative methods outperformed the conventional approach in terms of accuracy by accounting for geological heterogeneity and well spacing. The proposed methods have significantly lower computational costs, as they do not require solving complex systems of partial differential equations involved in reactive transport simulations. The proposed approaches can be used to analyze the efficiency of mineral In Situ Recovery at both the design and operational stages, as well as to determine optimal production regimes for reducing economic expenditures in a timely manner. Full article

This article presents the results of the tribological performance of 20MnCr5 (1.7147) tool steel countersamples produced by Direct Metal Laser Sintering (DMLS), as a potential material for inserts or working layers of sheet metal forming tools. Tribological tests were performed using a roller-block tribotester. The samples were sheet metals made of materials with significantly different properties: Inconel 625, titanium-stabilised stainless steel 321, EN AW-6061 T0 aluminium alloy, and pure copper. The samples and countersamples were analysed in terms of their wear resistance, coefficient of friction (COF), changes in friction force during testing, and surface morphology after tribological contact under dry friction conditions. The tests were performed on DMLSed countersamples in the as-received state. The largest gain of countersample mass was observed for the 20MnCr5/EN AW-6061 T0 friction pair. The sample mass loss in this combination was also the largest, amounting to 19.96% of the initial mass. On the other hand, in the 20MnCr5/Inconel 625 friction pair, no significant changes in the mass of materials were recorded. For the Inconel 625 sample, a mass loss of 0.04% was observed. The basic wear mechanism of the samples was identified as abrasive wear. The highest friction forces were observed in the 20MnCr5/Cu friction pair (COF = 0.913) and 20MnCr5/EN AW-6061 T0 friction pair (COF = 1.234). The other two samples (Inconel 625, 321 steel) showed a very stable value of the friction force during the roller-block test resulting in a COF between 0.194 and 0.213. Based on the changes in friction force, COFs, and mass changes in friction pair components during wear tests, it can be concluded that potential tools in the form of inserts or working layers manufactured using 3D printing technology, the DMLS method, without additional surface treatment can be successfully used for forming sheets of 321 steel and Inconel 625. Full article

In this study, an effective strengthening method was investigated to improve the seismic performance of masonry brick walls. The strengthening method comprised the use of shotcrete, which was applied in both glass fiber-reinforced and unreinforced forms for steel plates and tie rods. Thirteen wall specimens constructed with vertical perforated masonry block bricks were tested under diagonal compression in accordance with ASTM E519 (2010). Reinforcement plates with different thicknesses (1.5 mm, 2 mm, and 3 mm) were anchored using 6 mm diameter tie rods. A specially designed steel frame and an experimental loading program with controlled deformation increments were employed to simulate the effects of reinforced concrete beam frame system on walls under the effect of diagonal loads caused by seismic loads. In addition, numerical simulations were conducted using three-dimensional finite element models in Abaqus Explicit software to validate the experimental results. The findings demonstrated that increasing the number of tie rods enhanced the shear strength and overall behavior of the walls. Steel plates effectively absorbed tensile stresses and limited crack propagation, while the fiber reinforcement in the shotcrete further improved wall strength and ductility. Overall, the proposed strengthening techniques provided significant improvements in the seismic resistance and energy absorption capacity of masonry walls, offering practical and reliable solutions to enhance the safety and durability of existing masonry structures. Full article

Wildland fires are a common and destructive natural disaster in Alaska. Recent active fires in Alaska were assessed and analysed for their associated synoptic-scale climatic conditions in this study. Hotspot (HS) data from satellite observations over the past 20 years since 2004 (total number of HS = 300,988) were used to identify active fire-periods, and the occurrence of Rossby wave breaking (RWB) was examined using various weather maps. Analysis results show that there are 13 active fire-periods of which 7 active fire-periods are related to RWB. The total number of HSs during the seven RWB-related fire-periods was 164,422, indicating that about half (54.6%) of the recent fires in Alaska occurred under fire weather conditions related to RWB. During the RWB-related fire-periods, two hotspot peaks with different wind directions occurred. At the first hotspot peak, southwesterly wind blew from high-pressure systems in the Gulf of Alaska. In the second hotspot peak, the Beaufort Sea High (BSH) supplied strong easterly wind into Interior Alaska. It was suggested that changes in wind direction during active fire-period and continuously blowing winds from BSH may affect fire propagation. It is hoped that this study will stimulate further research into active fires related to RWBs in Alaska. Full article

In today’s oncology, immunotherapy arises as a potent complement for conventional cancer treatment, allowing for obtaining better patient outcomes. B7-H3 (CD276) is a member of the B7 protein family, which emerged as an attractive target for the treatment of various tumors. The molecule modulates anti-cancer immune responses, acting through diverse signaling pathways and cell populations. It has been implicated in the pathogenesis of numerous malignancies, including melanoma, gliomas, lung cancer, gynecological cancers, renal cancer, gastrointestinal tumors, and others, fostering the immunosuppressive environment and marking worse prognosis for the patients. B7-H3 targeting therapies, such as monoclonal antibodies, antibody–drug conjugates, and CAR T-cells, present promising results in preclinical studies and are the subject of ongoing clinical trials. CAR-T therapies against B7-H3 have demonstrated utility in malignancies such as melanoma, glioblastoma, prostate cancer, and RCC. Moreover, ADCs targeting B7-H3 exerted cytotoxic effects on glioblastoma, neuroblastoma cells, prostate cancer, and craniopharyngioma models. B7-H3-targeting also delivers promising results in combined therapies, enhancing the response to other immune checkpoint inhibitors and giving hope for the development of approaches with minimized adverse effects. However, the strategies of B7-H3 blocking deliver substantial challenges, such as poorly understood molecular mechanisms behind B7-H3 protumor properties or therapy toxicity. In this review, we discuss B7-H3’s role in modulating immune responses, its significance for various malignancies, and clinical trials evaluating anti-B7-H3 immunotherapeutic strategies, focusing on the clinical potential of the molecule. Full article

Background: Hip fractures represent a major clinical challenge, particularly in elderly and frail patients, where postoperative pain control must balance effective analgesia with motor preservation to facilitate early mobilization. Various regional anesthesia techniques are used in this setting, including the pericapsular nerve group (PENG) block, fascia iliaca compartment block (FICB), femoral nerve block (FNB), and quadratus lumborum block (QLB), yet optimal strategies remain debated. Objectives: To systematically review the efficacy, safety, and clinical applicability of major regional anesthesia techniques for pain management in hip fractures, including considerations of fracture type, surgical approach, and functional outcomes. Methods: A systematic literature search was conducted following PRISMA 2020 guidelines in PubMed, Scopus, Web of Science, and the virtual library of the Hospital Central de la Defensa “Gómez Ulla” up to March 2025. Inclusion criteria were RCTs, systematic reviews, and meta-analyses evaluating regional anesthesia for hip surgery in adults. Risk of bias in RCTs was assessed using RoB 2.0, and certainty of evidence was evaluated using the GRADE approach. Results: Twenty-nine studies were included, comprising RCTs, systematic reviews, and meta-analyses. PENG block demonstrated superior motor preservation and reduced opioid consumption compared to FICB and FNB, particularly in intracapsular fractures and anterior surgical approaches. FICB and combination strategies (PENG+LFCN or sciatic block) may provide broader analgesic coverage in extracapsular fractures or posterior approaches. The overall risk of bias across RCTs was predominantly low, and certainty of evidence ranged from moderate to high for key outcomes. No significant safety concerns were identified across techniques, although reporting of adverse events was inconsistent. Conclusions: PENG block appears to offer a favorable balance of analgesia and motor preservation in hip fracture surgery, particularly for intracapsular fractures. For extracapsular fractures or posterior approaches, combination strategies may enhance analgesic coverage. Selection of block technique should be tailored to fracture type, surgical approach, and patient-specific functional goals. Full article

Fluorescent dyes, such as cyanine dyes, are widely used in fluorescence-imaging-guided tumor therapy due to their high absorbance and fluorescence quantum yield. However, challenges persist in optimizing the performance of fluorescent nanoparticles, particularly due to the aggregation-caused quenching (ACQ) effect of cyanine dyes. Here, a novel counterion construction strategy is introduced using cyanine dye as a model ACQ dye. Through dynamic-controlled flash nanoprecipitation, fluorescent nanoparticles (CyINPs) with tunable structures are developed, investigating the effects of various factors, including counterions, block copolymers, and dye concentrations, on CyINPs’ stability and fluorescence enhancement. The optimized CyINPs with good water solubility show a 21-fold increase in fluorescence intensity and a 3.5-fold increase in encapsulation efficiency compared to CyINPs prepared by a thermodynamic-driven method. Under the efforts of polymers and counterions, dyes are separated, which reduces the impact of the ACQ effect and results in stronger fluorescence intensity, providing insights into improving nanoparticle biocompatibility and energy utilization efficiency. Full article

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article

This study focused on the poly(DL-lactide-co-glycolide)-block-poly(ethylene glycol)-block-poly(DL-lactide-co-glycolide) (PLGA-PEG-PLGA) triblock copolymer, which was recently reported as a novel material for polymeric nanoparticles to replace poly(DL-lactide-co-glycolide) (PLGA) as a drug carrier for prednisolone (PSL), and aimed to evaluate the efficacy of PSL-loaded PLGA-PEG-PLGA nanoparticles (NPs) against allergic contact dermatitis (ACD). PSL-loaded PLGA-PEG-PLGA NPs were prepared using the nanoprecipitation method, and their particle size distribution and mean particle size were measured using dynamic light scattering. 1-Fluoro-2,4-dinitrobenzene (DNFB) was used to create a mouse model of contact hypersensitivity (CHS). PSL-loaded PLGA-PEG-PLGA NPs were administered before sensitization with DNFB, and the therapeutic effect was evaluated by quantifying intracutaneous TNF-α and IL-4 levels suing ELISA. When PSL-loaded PLGA-PEG-PLGA NPs were administered before sensitization, TNF-α expression and IL-4 statements were significantly lower in the PSL-loaded PLGA-PEG-PLGA NP group than in the non-treated group. No significant difference was observed between the PSL-loaded PLGA-PEG-PLGA NP and PSL-loaded ointment groups, even though the steroid dose was 40 times lower than in the PSL-containing ointment. These results suggest that PSL-loaded PLGA-PEG-PLGA NPs may have a better effect in the treatment of ACD than PSL-loaded PLGA NPs. Full article

Object detection in remote sensing imagery is critical in environmental monitoring, urban planning, and land resource management. However, the task remains challenging due to significant scale variations, arbitrary object orientations, and complex background clutter. To address these issues, we propose a novel orientation module (SOAM Block) that jointly models object scale and directional features while exploiting geometric symmetry inherent in many remote sensing targets. The SOAM Block is constructed upon a lightweight and efficient Adaptive Multi-Scale (AMS) Module, which utilizes a symmetric arrangement of parallel depth-wise convolutional branches with varied kernel sizes to extract fine-grained multi-scale features without dilation, thereby preserving local context and enhancing scale adaptability. In addition, a Strip-based Context Attention (SCA) mechanism is introduced to model long-range spatial dependencies, leveraging horizontal and vertical 1D strip convolutions in a directionally symmetric fashion. This design captures spatial correlations between distant regions and reinforces semantic consistency in cluttered scenes. Importantly, this work is the first to explicitly analyze the coupling between object scale and orientation in remote sensing imagery. The proposed method addresses the limitations of fixed receptive fields in capturing symmetric directional cues of large-scale objects. Extensive experiments are conducted on two widely used benchmarks—DOTA and HRSC2016—both of which exhibit significant scale variations and orientation diversity. Results demonstrate that our approach achieves superior detection accuracy with fewer parameters and lower computational overhead compared to state-of-the-art methods. The proposed SOAM Block thus offers a robust, scalable, and symmetry-aware solution for high-precision object detection in complex aerial scenes. Full article

Serial block-face scanning electron microscopy (SBEM) is a powerful technique for three-dimensional ultrastructural analysis of biological samples, though its application to in vitro cultured human cells remains underutilized. In this study, we present an optimized SBEM sample preparation protocol using human dermal fibroblasts and induced pluripotent stem cells (iPSCs). The method includes key modifications to the original protocol, such as using only glutaraldehyde for fixation and substituting the toxic cacodylate buffer with a less hazardous phosphate buffer. These adaptations result in excellent preservation of cellular ultrastructure, with high contrast and clarity, as validated by transmission electron microscopy (TEM). The loss of natural cell morphology resulted from fixation during passage, when cells formed a precipitate, rather than from fixation directly within the culture medium. The protocol is time-efficient, safe, and broadly applicable to both stem cells and differentiated cells cultured under 2D conditions, providing a valuable tool for ultrastructural analysis in diverse biomedical research settings. Full article

Background/Objectives: The Fanconi anemia (FA) pathway is essential for the repair of DNA interstrand crosslinks and maintenance of genomic stability. Germline loss of FA pathway function in the inherited Fanconi anemia syndrome leads to increased DNA damage and a range of clinical phenotypes, including a heightened risk of head and neck squamous cell carcinoma (HNSCC). Non-synonymous FA gene mutations are also observed in up to 20% of sporadic HNSCCs. The mechanistic target of rapamycin (mTOR) is known to stimulate cell growth, anabolic metabolism including protein synthesis, and survival following genotoxic stress. Methods/Results: Here, we demonstrate that FA− deficient (FA−) HNSCC cells exhibit elevated intracellular amino acid levels, increased total protein content, and an increase in protein synthesis indicative of enhanced translation. These changes are accompanied by hyperactivation of the mTOR effectors translation initiation factor 4E Binding Protein 1 (4E-BP1) and ribosomal protein S6. Treatment with the mTOR inhibitor rapamycin reduced the phosphorylation of these targets and blocked translation specifically in FA− cells but not in their isogenic FA− proficient (FA+) counterparts. Rapamycin-mediated mTOR inhibition sensitized FA− but not FA+ cells to rapamycin under nutrient stress, supporting a therapeutic metabolism-based vulnerability in FA− cancer cells. Conclusions: These findings uncover a novel role for the FA pathway in suppressing mTOR signaling and identify mTOR inhibition as a potential strategy for targeting FA− HNSCCs. Full article