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This study proposes a novel text classification model, MBConv-CapsNet, to address large-scale text data classification issues in the Internet era. Integrating the advantages of Mobile Inverted Bottleneck Convolutional Networks and Capsule Networks, this model comprehensively considers text sequence information, word embeddings, and contextual dependencies to capture both local and global information about the text effectively. It transforms from the original text matrix to a more compact and representative feature representation. A Capsule Network is designed to adaptively adjust the importance of different feature channels, including N-gram convolutional layers, selective kernel network layers, primary capsule layers, convolutional capsule layers, and fully connected capsule layers, aiming to enhance the model’s ability to capture semantic information of text across different feature channels. The use of the sparsemax function instead of the softmax function for dynamic routing within the Capsule Network directs the network’s focus more on capsules contributing significantly to the final output, reducing the impact of noise and redundant information, and further improving the classification performance. Experimental validation on multiple publicly available text classification datasets demonstrates significant performance improvements of the proposed method in binary classification, multi-classification, and multi-label text classification tasks, exhibiting better generalization capability and robustness.

In the domain of object detection, small object detection remains a pressing challenge, as existing approaches often suffer from limited accuracy, high model complexity, and difficulty meeting lightweight deployment requirements. In this paper, we propose PCPE-YOLO, a novel object detection algorithm, specifically designed to address these difficulties. First, we put forward a dynamically reconfigurable C2f_PIG module. This module uses a parameter-aware mechanism to adapt its bottleneck structures to different network depths and widths, reducing parameters while maintaining performance. Next, we introduce a Context Anchor Attention mechanism that boosts the model’s focus on the contexts of small objects, thereby improving detection accuracy. In addition, we add a small object detection layer to enhance the model’s localization capability for small objects. Finally, we integrate an Efficient Up-Convolution Block to sharpen decoder feature maps, enhancing small object recall with minimal computational overhead. Experiments on VisDrone2019, KITTI, and NWPU VHR-10 datasets show that PCPE-YOLO significantly outperforms both the baseline and other state-of-the-art methods in precision, recall, mean average precision, and parameters, achieving the best precision among all compared approaches. On VisDrone2019 in particular, it achieves improvements of 3.8% in precision, 5.6% in recall, 6.2% in mAP50, and 5% in F1 score, effectively combining lightweight design with high small object detection performance and providing a more efficient and reliable solution for small object detection in real-world applications.

The rapid development of industrial Internet has resulted in significant changes in industrial manufacturing system architecture. In this study, a system architecture suitable for small‐scale personalized production lines is proposed. The core of this architecture is a three‐layer structure comprising the Internet layer, data layer, and field control layer. The system facilitates the transmission of user‐specific product demands from the Internet to the production line, where the production equipment achieves personalized production through motion control. This system architecture for production lines integrates industrial Internet and smart manufacturing technologies, reducing the complexity of the enterprise system architecture and enhancing the flexibility of the system compared to large‐scale personalized production systems. The effectiveness of this system architecture was validated using a flexible yogurt filling production line and lays a foundation for the transformation of industrial manufacturing from the tradition system to a personalized production. A three‐layer system architecture applicable to small‐scale personalization production lines is proposed. The architecture reduces the complexity of the enterprise system architecture and increases the flexibility of the system compared to mass personalization production systems. The effectiveness of this system has been proven in a flexible production line for yogurt filling.

A multiscale regulation strategy has been demonstrated for synthetic energy storage enhancement in a tetragonal tungsten bronze structure ferroelectric. Grain refining and second-phase precipitation (perovskite phase) are introduced in the BaSrTiNb 2-x Ta x O 9 ceramics by regulating the composition and sintering process. Disordered polarization and distribution, chemical inhomogeneity, and insulating boundary layers are achieved to provide the fundamental structural origin of the relaxation characteristic, high breakdown strength, and superior energy storage performance. Thus, an ultrahigh energy storage density of 12.2 J cm −3 with an low energy consumption was achieved at an electric field of 950 kV cm −1 . This is the highest known energy storage performance in tetragonal tungsten bronze-based ferroelectric. Notably, this ceramic shows remarkable stability over frequency, temperature, and cycling electric fields. This work brings new material candidates and structure design for developing of energy storage capacitors apart from the predominant perovskite ferroelectric ceramics. The authors enhance energy storage performance in tetragonal tungsten bronze structure ferroelectrics using a multiscale regulation strategy. By adjusting the composition and sintering process of BaSrTiNb 2-x Ta x O 9 ceramics, they introduce grain refinement and perovskite second-phase precipitation.

The aim of this study was to investigate the energy evolution characteristics and an ontological model of the deformation of coal under different water contents. Uniaxial compression tests were conducted for coal with different water contents, and the analyses were based on the energy principle and the principle of minimum energy dissipation. The results showed that the physical properties of the coal specimens were different under different water contents, the peak strain was positively correlated with water content, and the compressive strength and elastic modulus were negatively correlated with water content. Additionally, the compressive strength and elastic modulus of the coal specimens showed a steep and subsequent slow-change trend. From an energy perspective, the higher the water content of the coal specimens, the higher their energy dissipation at the peak; the smaller the limiting elastic strain energy, the lower the absorbed energy. The principle of minimum energy dissipation was used to deduce the energy evolution and mechanical properties of coal body damage under different water contents, deriving the initial and critical values of damage. The water content of the coal specimens was positively correlated with their initial and critical values of damage, and the relationship with water content was nonlinear. This result was used to establish a stress–strain ontology model for coal rocks with different water contents under uniaxial compression. The model is an improvement over traditional ontology models, addressing the problem of low accuracy in simulations of materials at the compaction stage.

Objective Sarcopenia, a condition characterized by the gradual decline of muscle mass, strength, and function, is a key indicator of malnutrition in cancer patients and has been linked to poor prognoses in oncology. Sarcopenia is commonly assessed by measuring the skeletal muscle index (SMI) of the third lumbar spine (L3) using computed tomography (CT). This meta-analysis aimed to explore the relationship between low SMI and clinicopathological features, as well as prognosis, in individuals with endometrial cancer (EC). Methods Data from various databases including PubMed, Embase, Cochrane, Medline, and Web of Science were searched up until October 20th, 2024. Studies that investigated the association of low SMI and EC survival or clinicopathological characteristics were included. Pooled effect sizes were reported as hazards ratio (HR), odds ratios (ORs) or weighted mean difference (WMD). The quality and risk of bias in the studies were evaluated using the Newcastle-Ottawa Scale (NOS) and the Quality In Prognosis Studies (QUIPS), and the study was registered on PROSPERO (CRD42024509949) before commencing the search. Results A total of 218 studies were identified across all five databases, with 11 studies meeting the criteria for qualitative and quantitative analysis, involving 1588 patients. The findings of our meta-analysis demonstrated a significant link between low SMI and progression-free survival [ P =  0.002; HR: 1.62, 95% CI: 1.20–2.17]. Low SMI was also associated with a BMI < 25 ( P  < 0.00001; OR: 4.55, 95% CI: 3.01–6.87), FIGO stage ( P  = 0.04; OR: 1.33, 95% CI: 1.01–1.75), pathology grades ( P  = 0.001; OR: 1.77, 95% CI: 1.26–2.49), and the endometrioid pathological type ( P  = 0.01; OR: 0.68, 95% CI: 0.51–0.92). However, no significant correlation was found between low SMI and 5-year overall survival, serous pathological type, recurrence, length of hospital stay, intraoperative complications, and postoperative complications. All the included studies scored ≥ 7 on the NOS, indicating relatively high-quality evidence. Conclusions The meta-analysis highlighted the association between low SMI and unfavorable clinical features and outcomes in EC patients, emphasizing the importance of early diagnosis and appropriate management of sarcopenia assessed by low SMI to enhance prognoses in EC patients.

Background A growing body of research shows that drug monomers from traditional Chinese herbal medicines have antineuroinflammatory and neuroprotective effects that can significantly improve the recovery of motor function after spinal cord injury (SCI). Here, we explore the role and molecular mechanisms of Alpinetin on activating microglia‐mediated neuroinflammation and neuronal apoptosis after SCI. Methods Stimulation of microglia with lipopolysaccharide (LPS) to simulate neuroinflammation models in vitro, the effect of Alpinetin on the release of pro‐inflammatory mediators in LPS‐induced microglia and its mechanism were detected. In addition, a co‐culture system of microglia and neuronal cells was constructed to assess the effect of Alpinetin on activating microglia‐mediated neuronal apoptosis. Finally, rat spinal cord injury models were used to study the effects on inflammation, neuronal apoptosis, axonal regeneration, and motor function recovery in Alpinetin. Results Alpinetin inhibits microglia‐mediated neuroinflammation and activity of the JAK2/STAT3 pathway. Alpinetin can also reverse activated microglia‐mediated reactive oxygen species (ROS) production and decrease of mitochondrial membrane potential (MMP) in PC12 neuronal cells. In addition, in vivo Alpinetin significantly inhibits the inflammatory response and neuronal apoptosis, improves axonal regeneration, and recovery of motor function. Conclusion Alpinetin can be used to treat neurodegenerative diseases and is a novel drug candidate for the treatment of microglia‐mediated neuroinflammation. Alpinetin alleviates the inflammatory response and neuronal toxicity caused by activated microglia via targeting the JAK2/STAT3 pathway, and ultimately promotes functional recovery in SCI rats.

With increasing coal mining depths, water-rock interactions exacerbate the mechanical degradation of coal-rock masses and geological disaster risks. Investigating the mechanical properties and energy evolution mechanisms of water-bearing sandstone is crucial for ensuring safe mining operations. To address the existing research gap in analyzing energy evolution mechanisms of water-saturated rock masses from a macroscopic perspective and the lack of exploration into energy mechanisms at critical failure points at the mesoscale, this study employs the particle discrete element software PFC3D to establish numerical models of sandstone with varying water contents. Combined with uniaxial compression tests and energy calculation principles, the mechanical degradation laws and energy evolution characteristics of sandstone under water-rock interactions are systematically investigated. The results indicate that the mechanical properties of sandstone exhibit significant degradation with prolonged immersion time, where compressive strength and elastic modulus gradually decrease with increasing water content. Energy evolution during sandstone deformation and failure can be divided into three stages: elastic energy storage, crack propagation energy dissipation, and sudden energy release at failure. Water immersion significantly reduces energy absorption efficiency during the elastic storage stage and increases energy dissipation rates during crack propagation. Mesoscale crack development analysis reveals that water accelerates the extension of initial fractures and the initiation of new cracks, while higher water content promotes a transition from localized to diffuse crack distribution. Additionally, the energy thresholds at critical failure points and failure modes of samples with different water contents show significant correlations, revealing the dynamic regulatory mechanism of water-induced weakening effects on energy accumulation and release in sandstone. These findings provide theoretical support for safe mining and dynamic disaster prevention in deep water-rich coal seams.

Herbig Ae/Be stars (HAeBes) are critical tracers of intermediate- and high-mass star formation, yet their census remains incomplete compared to low-mass young stellar objects like T-Tauri stars. To expand the known population, we systematically searched for HAeBes in LAMOST DR7 low-resolution spectra. Following Sun et al., we applied Uniform Manifold Approximation and Projection for dimensionality reduction and Support Vector Machine classification, identifying ∼240,000 spectra with potential Hα emission. After removing contaminants (nonstellar objects, extragalactic sources, cataclysmic variables, and Algol systems) and restricting to B/A-type stars, we obtained 1835 candidates through 2MASS/WISE visual inspection. Spectral energy distribution analysis confirmed 143 sources with infrared excess (J band or longer wavelengths), including 92 known HAeBes. From the remaining 51 candidates, we classified 26 with strong infrared excess as new HAeBes. Color-index analysis of confirmed HAeBes and classical Ae/Be stars (CAeBes) revealed that the (K − W1)0 versus (W2 − W3)0 diagram effectively separates these populations: CAeBes predominantly occupy (K − W1)0 ≤ 0.5 and (W2 − W3)0 ≤ 1.1, while other regions trace transition disks ((K − W1)0 < 0.5 and (W2 − W3)0 > 1.1), globally depleted disks ((K − W1)0 > 0.5 and (W2 − W3)0 < 1.1), and Class I/Flat/II HAeBes ((K − W1)0 > 0.5 and (W2 − W3)0 > 1.1). More importantly, the HAeBes exhibit a clear evolutionary gradient on this diagram, with those in the Class III, Class II, Flat-SED, and Class I evolutionary stages being effectively distinguished by concentric ellipses that are roughly centered at (0, 0) with semimajor axes of a = 1.5, a = 3.0, and a = 4.0, and a semimajor to semiminor axis ratio of 1.6:1.

The aim of this study is to establish and validate an optimal explainable prediction model based on a machine learning (ML) approach to predict the short-term prognosis in high on-treatment platelet reactivity (HTPR) individuals with acute ischaemic stroke (AIS). Using individual basic characteristics, blood test indices, and the CYP2C19 genotype, a model to predict a poor functional prognosis (modified Rankin scale score ≥ 3) was constructed based on ML models, including logistic regression, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and light gradient boosting machine. On this basis, global and local interpretability techniques were used to interpret selected ML models and explore the risk factors affecting the short-term prognosis of AIS in patients with HTPR. In this study, the performance of the model was futher evaluated through sensitivity analysis and subgroup analysis. A total of 515 AIS patients with HTPR were retrospectively enrolled, and approximately 129 (25%) had a poor outcome in the short term. Among the 6 ML models, RF performed best in discriminative ability in terms of area under the curve (0.84 [0.71–0.97]), accuracy (0.80 [0.71–0.89]), and precision (0.71 [0.61–0.81], which are far superior to the other models. Interpretability techniques showed that high levels of diastolic blood pressure, blood urea nitrogen, homocysteine, C-reactive protein, white blood cells, and CYP2C19 poor metabolizers were significant predictors of a poor prognosis of AIS in patients with HTPR. The risk prediction model for AIS patients with HTPR based on RF algorithms has high predictive power. By applying interpretability methods, the model’s transparency and clinical usability were enhanced, offering a reference for the clinical prevention and treatment of HTPR.