Explore the vast range of titles available.

In the contemporary entrepreneurial environment increasingly shaped by artificial intelligence, the Artificial intelligence (AI) literacy of entrepreneurs plays an essential role in enhancing entrepreneurial resilience. However, the underlying mechanisms that explain this relationship remain inadequately explored. Grounded in the Conservation of Resources (COR) theory, this study examines the effect of entrepreneurs’ AI literacy on entrepreneurial resilience, specifically assessing the mediating effect of AI anxiety and the moderating effect of social support. Data were obtained from 330 Chinese entrepreneurs using a two-wave survey design. The analysis revealed a significant positive association between AI literacy and entrepreneurial resilience. Furthermore, AI anxiety mediates this relationship, suggesting that AI literacy enhances entrepreneurial resilience by alleviating AI anxiety. Moreover, all three forms of social support were found to buffer the negative effect of AI anxiety on resilience. Notably, the moderating effects of subjective support and support utilization are significantly stronger than those of objective support, highlighting the distinct roles of different support types. These findings deepen theoretical insight into the psychological pathways linking AI literacy to resilience and offer practical guidance for entrepreneurs in the AI era.

PurposeThis study empirically evaluates the effect of China’s 2016 Green Financial System (GFS) framework on corporate green development, focusing on the role of green investment in achieving sustainability.Design/methodology/approachThis study uses a quasinatural experiment design to combine difference-in-difference and propensity score matching methods for analysis. It examines 799 polluting and 1,130 nonpolluting firms from 2013 to 2020, enabling a comprehensive assessment of the GFS framework’s influence.FindingsThis study affirms a statistically significant positive influence of the GFS framework on escalating green investment levels in polluting firms. Robust sensitivity analyses, encompassing parallel trend assessment, entropy balancing test, and alternative proxies, corroborate these findings. A mediation analysis identifies the implementation of an environmental management system as the potential underlying mechanism. A cross-sectional analysis identifies high financial slack, high profitability, mandatory CSR regulations, and marketization level as the influencing factors.Research limitations/implicationsThe study’s findings have critical implications for policymakers, regulators, and companies. Demonstrating the effectiveness of the GFS framework in driving green investment underscores the importance of aligning financial systems with sustainability goals.Originality/valueThis study contributes novel empirical evidence on the positive effect of China’s GFS framework on corporate green development. The quasinatural experiment design, coupled with comprehensive sensitivity analyses, strengthens the robustness of the findings.

Accumulating evidence indicates that ectopic expression of non‐coding RNAs are responsible for breast cancer progression. Increased non‐coding RNA PVT1, the host gene of microRNA‐1207‐5p (miR‐1207‐5p), has been associated with breast cancer proliferation. However, how PVT1 functions in breast cancer is still not clear. In this study, we show a PVT1‐derived microRNA, miR‐1207‐5p, that promotes the proliferation of breast cancer cells by directly regulating STAT6. We first confirm the positive correlated expression pattern between PVT1 and miR‐1207‐5p by observing consistent induced expression by estrogen, and overexpression in breast cancer cell lines and breast cancer patient specimens. Moreover, silence of PVT1 also decreased miR‐1207‐5p expression. Furthermore, increased miR‐1207‐5p expression promoted, while decreased miR‐1207‐5p expression suppressed, cell proliferation, colony formation, and cell cycle progression in breast cancer cell lines. Mechanistically, a novel target of miR‐1207‐5p, STAT6, was identified by a luciferase reporter assay. Overexpression of miR‐1207‐5p decreased the levels of STAT6, which activated CDKN1A and CDKN1B to regulate the cell cycle. We also confirmed the reverse correlation of miR‐1207‐5p and STAT6 expression levels in breast cancer samples. Therefore, our findings reveal that PVT1‐derived miR‐1207‐5p promotes the proliferation of breast cancer cells by targeting STAT6, which in turn controls CDKN1A and CDKN1B expression. These findings suggest miR‐1207‐5p might be a potential target for breast cancer therapy. our findings demonstrate that PVT1‐derived miR‐1207‐5p promotes the proliferation of breast cancer cells by targeting STAT6, which in turn controls CDKN1A and CDKN1B expression. These findings suggest miR‐1207‐5p might be a potential target for breast cancer therapy.

The construction and enhancement of ecological niches are essential for small and medium-sized enterprises (SMEs), with digital platforms serving as key carriers for achieving niche improvement. However, SMEs encounter a “distinctiveness–conformity” paradox when leveraging digital platforms: they are expected to sustain differentiation to attract resource tilt while simultaneously integrating into the platform ecosystem to obtain a sense of belonging and complementary resources. Grounded in optimal distinctiveness theory, this study analyzes questionnaire data from 383 Chinese SMEs embedded in digital platforms. Results show that digital platform capabilities (integration and reconfiguration) enhance SMEs ecological niches through organizational agility and platform eco-embeddedness. Polynomial regression and response surface analyses reveal that balanced improvement in organizational agility and eco-embeddedness significantly strengthens niche enhancement, whereas imbalance between the two weakens it. This research clarifies how SMEs leverage digital platform capabilities to advance their ecological niches, offering theoretical and practical insights for achieving strategic balance between distinctiveness and conformity in digital platform ecosystems.

The emergence of multimodal medical imaging technology greatly increases the accuracy of clinical diagnosis and etiological analysis. Nevertheless, each medical imaging modal unavoidably has its own limitations, so the fusion of multimodal medical images may become an effective solution. In this paper, a novel fusion method on the multimodal medical images exploiting convolutional neural network (CNN) and extreme learning machine (ELM) is proposed. As a typical representative in deep learning, CNN has been gaining more and more popularity in the field of image processing. However, CNN often suffers from several drawbacks, such as high computational costs and intensive human interventions. To this end, the model of convolutional extreme learning machine (CELM) is constructed by incorporating ELM into the traditional CNN model. CELM serves as an important tool to extract and capture the features of the source images from a variety of different angles. The final fused image can be obtained by integrating the significant features together. Experimental results indicate that, the proposed method is not only helpful to enhance the accuracy of the lesion detection and localization, but also superior to the current state-of-the-art ones in terms of both subjective visual performance and objective criteria.

Grounded in regulatory focus theory, this study investigates the double-edged sword effect of generative AI adoption on sustainable entrepreneurial intentions and its underlying mechanisms. A questionnaire-based survey was conducted among 357 business students from public universities in China. The results reveal that generative AI adoption exerts a double-edged effect: it enhances sustainable entrepreneurial intentions by strengthening sustainable entrepreneurial self-efficacy through a promotion-focused pathway, while simultaneously undermining such intentions by heightening sustainable entrepreneurial fear of failure via a prevention-focused pathway. Moreover, artificial intelligence literacy moderates these relationships, amplifying the positive influence of generative AI adoption on entrepreneurial self-efficacy and attenuating its negative effect on fear of failure. This study enhances understanding of sustainable entrepreneurship amid the rise in generative AI, extends regulatory focus theory, and informs the development of AI-integrated sustainability education in academic institutions.

Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recurrence in HCC patients using databases (TCGA, AMC and Inserm) and machine learning methods and obtain the gene signature that could predict early relapse of HCC. Statistical methods, such as feature selection, survival analysis and Chi-Square test in R software, were used to analyze and select mutant genes related to disease free survival (DFS), race and vascular invasion. In addition, whole-exome sequencing was performed on 10 HCC patients recruited from our center, and the sequencing results were compared with the databases. Using the databases and machine learning methods, the prediction model of recurrence was constructed and optimized, and the selected mutant genes were verified in the test group. The accuracy of prediction was 74.19%. Moreover, these 10 patients from our center were used to verify these mutant genes and the prediction model, and a success rate of 80% was achieved. Collectively, we discovered recurrence-related genes and established recurrence prediction model of recurrence for HCC patients, which could provide significant guidance for clinical prediction of recurrence.

This paper proposes an efficient adaptive variant of a quadratic penalty accelerated inexact proximal point (QP-AIPP) method proposed earlier by the authors. Both the QP-AIPP method and its variant solve linearly set constrained nonconvex composite optimization problems using a quadratic penalty approach where the generated penalized subproblems are solved by a variant of the underlying AIPP method. The variant, in turn, solves a given penalized subproblem by generating a sequence of proximal subproblems which are then solved by an accelerated composite gradient algorithm. The main difference between AIPP and its variant is that the proximal subproblems in the former are always convex while the ones in the latter are not necessarily convex due to the fact that their prox parameters are chosen as aggressively as possible so as to improve efficiency. The possibly nonconvex proximal subproblems generated by the AIPP variant are also tentatively solved by a novel adaptive accelerated composite gradient algorithm based on the validity of some key convergence inequalities. As a result, the variant generates a sequence of proximal subproblems where the stepsizes are adaptively changed according to the responses obtained from the calls to the accelerated composite gradient algorithm. Finally, numerical results are given to demonstrate the efficiency of the proposed AIPP and QP-AIPP variants.

Hydrogels are extensively explored as biomaterials for tissue scaffolds, and their controlled fabrication has been the subject of wide investigation. However, the tedious mechanical property adjusting process through formula control hindered their application for diverse tissue scaffolds. To overcome this limitation, we proposed a two-step process to realize simple adjustment of mechanical modulus over a broad range, by combining digital light processing (DLP) and post-processing steps. UV-curable hydrogels (polyacrylamide-alginate) are 3D printed via DLP, with the ability to create complex 3D patterns. Subsequent post-processing with Fe 3+ ions bath induces secondary crosslinking of hydrogel scaffolds, tuning the modulus as required through soaking in solutions with different Fe 3+ concentrations. This innovative two-step process offers high-precision (10 μm) and broad modulus adjusting capability (15.8–345 kPa), covering a broad range of tissues in the human body. As a practical demonstration, hydrogel scaffolds with tissue-mimicking patterns were printed for cultivating cardiac tissue and vascular scaffolds, which can effectively support tissue growth and induce tissue morphologies.

Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, and in severe cases, can result in life-threatening conditions such as bone cancer. In this paper, a hybrid model HRD (Human-Resnet50-Densenet121) based on deep learning and human participation is proposed to efficiently identify disease features by classifying X-ray images. Feasibility testing of the model was conducted using the MURA dataset, with metrics such as accuracy, recall rate, F1-score, ROC curve, Cohen’s kappa, and AUC values employed for evaluation. Experimental results indicate that, in terms of model accuracy, the hybrid model constructed through a combination strategy surpassed the accuracy of any individual model by more than 4%. The model achieved a peak accuracy of 88.81%, a maximum recall rate of 94%, and the highest F1-score value of 87%, all surpassing those of any single model. The hybrid model demonstrates excellent generalization performance and classification accuracy.