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This study examines Chinese university students’ adoption of generative artificial intelligence (GenAI) tools by integrating the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and AI literacy dimensions into a hybrid framework. Survey data from 1006 students across various majors and regions are analyzed using partial least squares structural equation modeling. Notably, AI literacy (i.e., students’ AI ethics, evaluation, and awareness) positively affect their attitudes, subjective norms, and perceived behavioral control, although the influence patterns vary according to the literacy dimension. Perceived privacy risks reduce AI trust, which mediates adoption behavior. Overall, core TPB pathways are validated, with behavioral intentions significantly predicting students’ actual use. Gender and regional differences moderate the key relationships. The results of this study suggest that enhancing students’ ethical and evaluative competencies, building user trust, and addressing privacy concerns could promote generative AI integration in education.

In the realm of railroad transportation, the switch sliding baseplate constitutes one of the most crucial components within railroad crossings. Wear defects occurring on the switch sliding baseplate can give rise to issues such as delayed switch operation, inflexible switching, or even complete failure, thereby escalating the risk of train derailment. Consequently, the detection of wear defects on the switch sliding baseplate is of paramount importance for enhancing traffic efficiency and guaranteeing the safety of train switching operations. Micro-cutting defects, which are among the most significant defects resulting from wear, exhibit complex and diverse morphological and characteristic features. Traditional random sampling methods struggle to capture their detailed characteristics, leading to inadequate accuracy and robustness in the detection process. To address the above-mentioned issues, the YOLOv5s algorithm has been refined and subsequently applied to the detection of micro-cutting defects generated by wear on the switch sliding baseplate. The experimental results demonstrate that, in comparison with the currently prevalent mainstream target detection algorithms, the improved model can attain optimal recall rates R, mAP@0.5, and mAP@0.5:0.95. Specifically, when contrasted with the original YOLOv5s algorithm, the improved model witnesses significant enhancements in its precision rate P, the recall rate R, mAP@0.5, and mAP@0.5:0.95, with increments of 1.26%, 5.6%, 9.1%, and 8.92%, respectively. These improvements fully corroborate the performance of the proposed model in the context of micro-cutting defect detection.

Multiple logistic regression analysis revealed that patients with NP were less likely to achieve pain relief (odds ratio [OR]: 3.531, 95% confidence interval [CI]: 1.155-10.789; p=0.027). The reported prevalence of axSpA is between 0.32% and 1.4% in different surveys.1 Chronic back pain is a prominent and early feature of axSpA.2,3 Pain is associated with functional limitation and disease activity which seriously affects the quality of life and mental health of patients.4,5 Eighty-to-ninety percent of axSpA patients consider pain to be the primary symptom to be treated and improved urgently.6 Moreover, the Assessment of SpondyloArthritis international Society (ASAS) defines improvement of symptoms, a reduction of pain as an important target in axSpA management.7 Pain relief can be achieved by pharmacological and non-pharmacological treatments, in rare cases, including surgical methods. The sensitivity and specificity of Chinese version of the DN4 were 82.7% and 97.1%, respectively.20 A set of instruments was developed by the ASAS to measure health outcomes in axSpA.21,22 Disease activity was assessed using the erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), and Patient Global Assessment (PGA). Demographic and other clinical characteristics include age, sex, body mass index (BMI), employment, income, presence of smoking habits or drinking habits, human leukocyte antigen B27 (HLA-B27), peripheral joint pain, and prescribed medications were collected by questionnaires and from electronic medical records.

Nonlinear dynamical index can measure the complexity for a single time scale of the series, and when combined with coarse-grained methods, multiple time scales can be obtained to extract more information. In this study, a new coarse-grained method called refined composite variable-step multiscale (RCVM) is proposed, which obtains more subseries by setting different initial points and step lengths and thus extracts more potential information; moreover, in order to get a nonlinear dynamical index value with stronger stability, this study proposes the multimapping dispersion entropy (MDE) by averaging multiple classes of effective mapping approaches on the basis of dispersion entropy; by combining MDE and RCVM processing, RCVM-MDE is proposed to be used as a new nonlinear dynamical index, which can reflect the complexity of the series at multiple scales. The results of the four classes of chaotic simulated signals show that RCVM-MDE is not only able to detect the series nonlinear dynamic changes, but also has a very high stability; the results of three classes of real-world signals demonstrate the differentiability of RCVM-MDE compared to other commonly used entropies, as well as the best classification effect.

The tooth surface structure of spiral bevel gear is complex and requires high machining accuracy. In order to reduce the tooth form deformation of heat treatment, this paper proposes a reverse adjustment correction model of tooth cutting for heat treatment tooth form deformation of spiral bevel gear. Based on the Levenberg–Marquardat method, a stable and accurate numerical solution for the reverse adjustment amount of the cutting parameters is solved. Firstly, a mathematical model of the tooth surface of spiral bevel gears was established based on the cutting parameters. Secondly, the effect law of each cutting parameter on tooth form was studied by using the method of small variable perturbation. Finally, based on the tooth form error sensitivity coefficient matrix, a reverse adjustment correction model of tooth cutting is established to compensate the heat treatment tooth form deformation by reserving the tooth cutting allowance in the tooth cutting stage. The effectiveness of the reverse adjustment correction model of tooth cutting was verified through experiments on reverse adjustment of tooth cutting processing. The experimental results show that the accumulative tooth form error of the spiral bevel gear after heat treatment is 199.8 μm, which is reduced by 67.71%, and the maximum tooth form error is 8.7 μm, which is reduced by 74.75%, after reverse adjustment of cutting parameters. This research can provide technical support and a theoretical reference for heat treatment tooth form deformation control and high-precision tooth cutting processing of spiral bevel gears.

Ultra-high-performance concrete (UHPC) has gained significant attention as a construction material owing to its exceptional mechanical properties and durability. Steel fibers are widely utilized as a reinforcement material for UHPC. Achieving excellent bond and tensile performances is considered to be a predominant issue for the utilization of steel fiber reinforcement. This comprehensive review presents recent research progress on the bond and tensile properties of steel-fiber-reinforced UHPC. First, an overview of the experimental methods for evaluating pullout and tensile performance is provided. Subsequently, the factors influencing these properties are discussed in detail. The review then comprehensively examines several analytical models for steel-fiber-reinforced UHPC, ranging from traditional approaches to innovative methods such as artificial neural network models, genetic algorithms, deep learning methods, inverse analysis, and micromechanical damage models. Furthermore, the correlations between pullout behavior, tensile performance, and flexural strength are explored in detail. Finally, the review addresses essential considerations and summarizes various modification techniques for improving the pullout and tensile performances, including physical and chemical methods of modifying the steel fiber surface and UHPC matrix. This review serves as a valuable reference for researchers and engineers in relevant fields, promoting further research and application of steel fiber-reinforced UHPC.

Harnessing the cGAS‐STING DNA sensing pathway in dendritic cells (DCs) to enhance anti‐tumor immunity in immunosuppressive pancreatic tumors remains a significant challenge. While manganese (Mn 2+ ) enhances cGAS sensitivity to DNA, the precise mechanisms and potential of Mn‐DNA complexes in this process are unclear. Here, this work introduces a strategy to encapsulate Mn‐DNA complexes into DC‐derived immunogenic extracellular vesicle (EV DC @Mn‐DNA) to trigger robust anti‐tumor immunity. This work shows that Mn 2+ induces tumor cell‐DNA transition to Z‐DNA, strengthening cGAS binding and promoting its phase condensation for optimal activation in DCs. Using this newly developed Raft‐Ultra method, this work engineers immunogenic EVs derived from tumor cell lysate‐pulsed DCs, loaded with Mn‐DNA complexes (EV DC @Mn‐DNA). These EVs efficiently deliver Mn‐DNA complexes to DCs, activating the cGAS‐STING pathway both in vitro and in vivo. In animal models, EV DC @Mn‐DNA administration enhances vascular function, as evidenced by increased blood flow and perfusion, improved anti‐PD‐L1 delivery, reduced hypoxia, and elevated endothelial cell‐ICAM1 expression, which facilitates T cell adhesion. This approach expands the intratumoral population of activated DCs and T cells and promotes the formation of larger tertiary lymphoid structures, ultimately suppressing orthotopic pancreatic tumor growth. Overall, this EV DC @Mn‐DNA strategy reprograms intratumoral DCs, restores vascular‐immune homeostasis, and potentiates anti‐tumor immunity in pancreatic cancer.

Background Several systematic reviews and meta-analyses demonstrated the association between depression and the risk of coronary heart disease (CHD), but the previous reviews had some limitations. Moreover, a number of additional studies have been published since the publication of these reviews. We conducted an updated meta-analysis of prospective studies to assess the association between depression and the risk of CHD. Methods Relevant prospective studies investigating the association between depression and CHD were retrieved from the PubMed, Embase, Web of Science search (up to April 2014) and from reviewing reference lists of obtained articles. Either a random-effects model or fixed-effects model was used to compute the pooled risk estimates when appropriate. Results Thirty prospective cohort studies with 40 independent reports met the inclusion criteria. These groups included 893,850 participants (59,062 CHD cases) during a follow-up duration ranging from 2 to 37 years. The pooled relative risks (RRs) were 1.30 (95% CI, 1.22-1.40) for CHD and 1.30 (95% CI, 1.18-1.44) for myocardial infarction (MI). In the subgroup analysis by follow-up duration, the RR of CHD was 1.36 (95% CI, 1.24-1.49) for less than 15 years follow-up, and 1.09 (95% CI, 0.96-1.23) for equal to or more than 15 years follow-up. Potential publication bias may exist, but correction for this bias using trim-and-fill method did not alter the combined risk estimate substantially. Conclusions The results of our meta-analysis suggest that depression is independently associated with a significantly increased risk of CHD and MI, which may have implications for CHD etiological research and psychological medicine.

Electromagnetic interference pollution has raised urgent demand for the development of electromagnetic interference shielding materials. Transition metal carbides (MXenes) with excellent conductivity have shown great potential in electromagnetic interference (EMI) shielding materials, while the poor mechanical strength, flexibility, and structural stability greatly limit their further applications. Here, cellulose nanofibers and sodium alginate are incorporated with MXene nanosheets as flexible matrices to construct strong and flexible mussel-like layered MXene/Cellulose nanofiber/Sodium Alginate composite films, and nickel ions are further introduced to induce metal coordination crosslinking of alginate units. Benefited from the dual-crosslinked network structure of hydrogen bonding and metal coordination, the tensile strength, Young’s modulus, and toughness of the MXene/cellulose nanofiber/nickel alginate composite film are significantly increased. After subsequent reduction by ascorbic acid, excess nickel ions are reduced to nickel nanoparticles and uniformly dispersed within the highly conductive composite film, which further improved its hysteresis loss effect toward the incident electromagnetic waves. Consequently, the MXene/cellulose nanofiber/nickel alginate-Ni composite film presents a considerably enhanced electromagnetic interference shielding effectiveness (47.17 dB) at a very low thickness of 29 µm. This study proposes a feasible dual-crosslinking and subsequent reduction strategy to synergistically enhance the mechanical properties and electromagnetic interference shielding performance of MXene-based composite materials.

Distant metastasis remains the leading cause of mortality in breast cancer, yet comprehensive population-based evaluations of metastatic site combinations and their survival implications are limited. This study aimed to explore the clinicopathological determinants and prognostic outcomes of site-specific and multi-organ metastases in breast cancer using SEER data. A total of 200,558 female breast cancer patients diagnosed between 2014 and 2023 were extracted from the SEER database. Logistic regression was used to assess associations between clinicopathological features and metastatic patterns. Kaplan–Meier analysis and Cox proportional hazards models were applied to evaluate overall survival (OS) across different metastatic site combinations. Among patients with distant metastasis classified into 15 common metastatic patterns, bone was the most common metastatic site (21.3%), followed by lung (16.1%), liver (9.2%), and brain (2.9%). Molecular subtypes showed distinct organotropism: HR+/HER2 − tumors were prone to bone-only metastasis, whereas HER2-positive and triple-negative subtypes were more likely to involve visceral and brain metastases. Multi-organ metastases, especially combinations including the brain (e.g., brain + liver + lung), were associated with the poorest prognosis (median OS: 4.0 months). Younger age (≤ 40 years), higher histological grade (Grade III), and tumor location in the axillary tail or unspecified regions were independently associated with increased metastatic risk. Grade III tumors exhibited broader visceral spread and significantly worse survival compared to lower-grade tumors. This is the first population-based study to systematically characterize 15 metastatic site combinations and their survival outcomes across molecular subtypes. The findings highlight the heterogeneity of breast cancer metastasis and underscore the need for subtype-specific, site-targeted surveillance strategies and prognostic assessment tools.