Abstract ：

Lung cancer stands as the leading cause of cancer related mortality worldwide. Precise and automated identification of lung nodules through 3D Computed Tomography (CT) scans is an essential part of screening for lung cancer effectively. Due to the small size of pulmonary nodules and the close correlation between neighboring slices of 3D CT images, most of the existing methods only consider the characteristics of a single slice, thus easily lead to insufficient detection accuracy of pulmonary nodules. To solve this problem, this paper proposes a Channel Shuffle Slice-Aware Network (CSSANet), which aims to fully exploit the spatial correlation between slices and effectively utilize the intra-slice features and inter-slice contextual information to achieve accurate detection of lung nodules. Specifically, we design a Group Shuffle Attention module (GSA module) to fuse the inter-slice feature in order to enhance the discrimination and extraction of corresponding shape information of distinct nodules in the same group of slices. Experiments and ablation study on a publicly available LUNA16 dataset demonstrate that the proposed method can enhance the detection sensitivity effectively. The Competition Performance Metric (CPM) score of 89.8 % is superior over other representative detection models. © 2024

Keyword ：

Pulmonary diseases Lung cancer Image correlation Diagnosis Computerized tomography

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Abstract ：

Preterm birth a common and severe pregnancy complications, causing significant health, development, and economic problems. Accurate diagnosis of imminent labor for women with threatened preterm labor (TPL) is crucial. Electrohysterography (EHG), which represents uterine myometrial electrical activity, is a potential tool for predicting preterm birth. Increased cell synchronization is fundamental to generating high-intensity and coordinated uterine myometrial electrical activity as labor approaches. The present work aimed to evaluate the synchronization measures from multichannel EHG signals to predict labor in less than 24 h (time to delivery, TTD non-labor, imminent > non-imminent). Indeed, wPLI in the FWH bandwidth exhibited a positive correlation with gestational age (p © 2024 Elsevier Ltd

Keyword ：

Inverse problems Obstetrics Diagnosis

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Traditional semiconductor used as channel materials in driving transistors suffer from significant performance degradation as the semiconductor thickness is reduced. The two-dimensional (2D) materials with smooth, dangling-bond-free surfaces, represented by graphene, can be alternatives. Graphene boasts several advantages, including structural stability, ultra-thin thickness, near-total transparency, exceptional flexibility, and high mobility. Therefore, graphene field-effect transistors (GFETs) in the paper are used to drive Micro-light-emitting diodes (Micro-LEDs), key elements in next-generation advanced displays due to their high resolution, high brightness, high contrast, etc. Importantly, this study addresses the two major bottlenecks i.e. Micro-LEDs' mass transfer and graphene transfer. That is, monolithically integrated devices of Micro-LED and its driver GFET are designed and fabricated, bypassing the issue of traditional Micro-LEDs' mass transfer. For the first time, transfer-free method by plasma-enhanced chemical vapor deposition (PECVD) is used to grow graphene directly on GaN Micro-LED samples and prepared graphene transistors. This approach avoids doping and damage to the graphene during the transfer process, significantly shortens the growth time, and improves the fabrication efficiency. The devices possess broad applications potential and compatibility with semiconductor planar processes. This study paves the way for the transfer-free growth of graphene and the integration of Micro-LEDs with 2D materials transistors.

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Permanent ground deformation hazards, such as fault displacement, liquefaction-induced settlement, and landslides, pose a severe threat to the integrity of buried pipelines. In this study, three-dimensional numerical simulations are performed to investigate the horizontal lateral soil-pipeline interaction in medium-dense sand and to identify the failure mechanisms of the surrounding sand for different pipeline depth-diameter ratios. The ultimate bearing capacity of sand around the pipeline is evaluated for different soil failure mechanisms. Moreover, a simplified analytical model of the soil-pipeline under lateral movement is proposed based on the identified soil failure mechanisms. Consequently, an analytical solution for the ultimate bearing capacity of the soil under lateral motion of the buried pipeline is derived based on the limit-state equilibrium. The results obtained from the analytical solution indicate that at the limit state, the soil around a shallowly buried pipeline forms a ruptured surface extending to the ground surface with a logarithmic spiral failure surface. The lateral ultimate bearing capacity increases as the pipeline burial depth-diameter ratio increases until it reaches a constant value at a certain critical depth-diameter ratio. As the pipeline depth-diameter ratio increases, the pipeline displacement that causes shear failure of the soil also gradually increases. It is demonstrated that the proposed analytical solution well predicts the soil ultimate lateral bearing capacity for pipelines installed shallowly in medium and dense sand. Furthermore, the ultimate bearing capacity of pipelines in sand is evaluated by Chinese and some international codes. The disparity between results from different codes is attributed to the variation in empirical lateral bearing capacity coefficients used in the respective codes. © 2024

Keyword ：

Fracture mechanics Landslides Pipeline codes Water pipelines Pipeline laying

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In this article, the 3D Voigt-regularized Magnetohydrodynamic equations are considered, for which it is unknown if the uniqueness of weak solution exists. First, we prove that the uniform global attractor exists by constructing an evolutionary system. Then singular limits of this system are established. Namely, when a certain regularization parameter disappears, the convergence of global attractors is shown between the 3D autonomous Voigt-regularized Magnetohydrodynamic equations and Magnetohydrodynamic equations. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

Keyword ：

Magnetoplasma Magnetohydrodynamics

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The combination of carbon-based material adsorption and photocatalytic oxidation is a crucial approach to eliminate natural organic substances and mitigate membrane fouling induced by them. In this investigation, Bi2O3-TiO2 (BT) was incorporated with three varieties of carbon-based substances (such as powdered activated carbon (PAC), ordered mesoporous carbon CMK-3 (OMCs), and carbon nanotubes (CNTs)) and employed as a dual-layer coating on the surface of the ultrafiltration membrane to reduce membrane fouling induced by natural organics during the treatment of actual water bodies. Under light conditions, the BT/CMK-3 coating outperforms BT/PAC and BT/CNTs coatings in reducing membrane fouling, with a flux recovery rate of up to 80.4 % and an irreversible fouling rate as low as 19.5 %, while also improving the removal rates of UV254 and DOC to 55.2 % and 77.9 %, respectively. Based on the analyses of three-dimensional fluorescence PARAFAC and liquid chromatography, the BT/CMK-3 coating efficiently removes humic substances and protein-like compounds via adsorption and photocatalytic oxidation, achieving a 24.1 % removal rate of humic acid and a 93.7 % removal rate of low molecular weight neutral substances. Under the synergy of CMK-3 adsorption and BT photocatalytic oxidation, reactive oxygen species effectively degraded and transformed the membrane foulants enriched by CMK-3, reducing and increasing the Zeta potential and free energy on the membrane surface to -4.2 mV and 15.1mJ/m2, respectively. The interaction force between the foulant and the membrane changed into a repulsive force, and the fouling mechanism changed from complete blockage to intermediate blockage, thereby shortening the transition time. This advancement provides significant promise for the development and broader application of photocatalytic membrane filtration technology.

Keyword ：

Membrane fouling Natural organic matter Photocatalytic Ordered mesoporous carbon

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A Sybil attack involves a Malicious vehicle node stealing fake identities to continuously generate fake vehicles on the road, creating an illusion of congestion and interfering with the normal traffic flow of legitimate vehicles. In the current traffic environment, vehicles cannot perform real-time authentication, allowing highly stealthy Malicious vehicle nodes to continue attacking and significantly impact traffic. Given the rapidly changing network topology in vehicular networks, high precision and speed are required for attack detection methods. This paper proposes a three-class classification method for Sybil vehicles, Malicious vehicles, and Normal vehicles based on BSM packets. This method utilizes multiple features and employs a sliding window with the Random Forest algorithm for classification. Compared with deep learning methods, this method has the advantages of strong interpretability and fast detection speed. Experiments demonstrate that this method achieves fast detection speeds and high accuracy. With a window size of 2, the method achieves precision and recall both greater than 94%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keyword ：

Decision trees Traffic congestion Deep learning Vehicular ad hoc networks

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Latent diffusion models have achieved significant success in point cloud generation recently, where the diffusion process is constructed under a low-dimensional but efficient latent space. However, existing methods usually overlook the differences between consistency information and offset information in the point clouds, leading to difficulty in accurately learning both the overall shape and the offset of points on shape simultaneously. To address this issue, we propose a decomposed latent diffusion model that separately captures consistency information and offset information in the latent space with feature decoupling. To learn effective consistency information, the consistency constraint among different point clouds with a shape is imposed in the latent space. Then, based on the decomposed features, we further design a geometry diffusion model. We predict key points with consistency information to guide the diffusion model. Therefore, the diffusion model can achieve comprehensive and strong geometry feature extraction. Experiments show that our method achieved state-of-the-art generation performance on the ShapeNet dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Keyword ：

3D modeling Digital elevation model

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The poor adhesion of asphalt-granite limits the application of granite. This research aims to enhance the adhesion of asphalt and granite by establishing chemical connections between the hydroxyl groups on granite surface and the incompletely reacted isocyanate groups in polyurethane (PU) modified asphalt. For this purpose, end-capped PU modified asphalt (PUMA) was prepared, the adhesion mechanism between PUMA and granite was analyzed by Raman spectroscopy, the adhesion properties between PUMA and granite at different curing periods were evaluated by boiling and pull-out tests, and the wettability of PUMA on granite and its moisture sensitivity were evaluated using surface free energy (SFE) method. The findings suggested that the incompletely reacted isocyanate groups in PUMA can establish a chemical connection with the hydroxyl groups on the surface of granite. The adhesion between asphalt and granite can be greatly improved by the addition of PU, and this improvement is amplified as the curing time is extended. When the curing time is extended to 3 days, the fracture mode between asphalt and granite changed from adhesive failure to cohesive failure, and after 15 days of curing, the coating ratio of PUMA on granite following the water boiling test can be raised by 28.11% in comparison to the base asphalt. The water damage resistance of PUMA-granite system can be increased by adding PU and the adhesion work and combination ratio of the freshly prepared PUMA were increased by 55.91% and 79.45%, respectively, as compared to base asphalt. This study has great significance for enhancing the adhesion between asphalt and granite and encouraging granite application.

Keyword ：

Granite Polyurethane modified asphalt (PUMA) Interfacial adhesion End-capped polyurethane

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Retinal diseases such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness without timely diagnosis and treatment. Optical coherence tomography (OCT) has been widely utilized to detect retinal diseases because of its non-contact and non-invasive imaging peculiarities. Due to the lack of ophthalmic medical resources, automatic analyzing and diagnosing retinal OCT images is necessary with computer-aided diagnosis algorithms. In this study, we propose a lightweight retinal OCT image classification model integrating convolutional neural network (CNN) and Transformer to classify various retinal diseases with few parameters of the model. Local lesion features extracted by CNN can be encoded with the whole OCT image through the Transformer, which improves the classification ability. A convolutional block attention module is also integrated into our model to enhance the representational power. Compared with several classical models, our model achieves the best accuracy of 0.9800 and recall of 0.9799 with the least number of parameters and prediction time for an image on the OCT-C8 dataset. Moreover, on the OCT2017 dataset, our model outperforms the four state-of-the-art models except almost equal to another, achieving an average accuracy, precision, recall, specificity and F1-score of 0.9985, 0.9970, 0.9970, 0.9990, and 0.9970. Simultaneously, the number of parameters of our model has been reduced to just 1.28 M, and the average prediction time for an image is only 2.5 ms. © 2024 Elsevier Ltd

Keyword ：

Ophthalmology Optical coherence tomography Convolutional neural networks

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