Xiangnan University

Sohail A., Khan M.A., Ullah H., Alnowibet K.A., Li Y., Chu Y.

Zhang W., Lan Y., Lin A., Xiao M.

Qiu X., Deng H., Li P., He W.

Digital intelligence refers to the integration of digital technologies and intelligent digitalization, which encompasses the development and application of data as a new productive force for societal benefit. Blended teaching has become essential in adapting to the educational advancements of the digital intelligence era. The deep integration of digital intelligence technology and classroom teaching has become the new norm of education and teaching reform. As the core technology of artificial intelligence (AI), a knowledge graph can express the knowledge system visually, integrate educational resources, collect learning process data to realize the monitoring and diagnosis of the learning process and meet the diversified needs of intelligent education applications. It is an effective tool for deeply integrating nursing education and AI. As healthcare technology continues to evolve and knowledge is rapidly updated, students need to continually learn and update their knowledge base so as to keep up with professional development. Therefore, this study explored the practical strategy of digital–intelligent blended teaching of Surgical Nursing by reshaping and integrating the course content, sorting out the course knowledge system, and constructing the course knowledge graph to enable students to systematically master the learning content and access high-quality learning resources. It provided a reference for the digital transformation of higher education, with a view to promoting the consolidation of students’ mastery of basic knowledge, improving students’ clinical thinking and comprehensive ability to implement holistic care for patients, and cultivating students’ ability to acquire and use cutting-edge knowledge.

Li L., Li H., He L.

The digital economy has penetrated various fields of the social economy, including the healthcare industry, and the high-quality development of hospitals requires high-quality middle-level managers. Most middle-level managers in Chinese university affiliated hospitals come from medical technology backbones, and the phenomenon of “double shoulders” is common. They are tired of coping and lack professional knowledge in management. Constructing a scientific competency model for middle-level managers in Chinese university affiliated hospitals is not only a reference for cultivating middle-level managers in Chinese university affiliated hospitals, but also of great significance for improving the quality of middle-level managers in Chinese university affiliated hospitals. The article extracts the competence qualities of middle-level managers in Chinese university affiliated hospitals through literature analysis and grounded theory and uses fuzzy analytic hierarchy process to determine the weights of various elements of competence, constructing a competence model for middle-level managers. The results show that the competency qualities of middle-level managers in Chinese university affiliated hospitals can be summarized into 26 competency qualities, including five dimensions: digital technology ability, hospital decision-making ability, hospital management ability, personal characteristics, and knowledge learning ability. Among them, hospital management ability and decision-making ability are the most important dimensions.

Li D., Jiang S., Wen J., Wei R., Ouyang X.

Diversification is one of the important strategies in the growth process of enterprises. At present, the academic community has not yet reached a unanimous conclusion on the relationship between diversification and corporate performance. Taking 31 China’s A-share pharmaceutical listed companies from 2018 to 2022 as a sample, this paper empirically analyzes the impact of diversification on the performance of pharmaceutical companies. Empirical results show that overall, diversification has a negative impact on the return on equity (ROE) of a company, but it is not significant. However, from the perspective of different views, related diversification leads to a significant decrease in the company’s ROE, while unrelated diversification leads to a significant increase in the company’s ROE. Using return on assets (ROA) as a substitute variable for ROE to conduct a robustness test on the model, the results remain consistent. This indicates that overall diversified operations have a negative effect on the performance of pharmaceutical companies, but it is not significant. Related diversified operations have a significant negative impact on the performance of pharmaceutical companies, while unrelated diversified operations have a significant positive impact on the performance of pharmaceutical companies. Based on the empirical results, the performance improvement strategies of pharmaceutical manufacturing enterprises are proposed from the perspectives of maintaining moderate diversification and focusing on building core competitiveness. This study enriches the research content of diversification, provides support for the evaluation of the implementation effects of diversified strategies for pharmaceutical companies, and can also provide a basis for how to choose diversified strategies.

Hou L., Tan J., Ou Z., Zhang N.

This paper relies on the data of high-tech listed companies in Guangdong Province during the period from 2012 to 2022. With the return on total assets as the explained variable, financial subsidies as the explanatory variable, and enterprise age, enterprise scale, asset-liability ratio, and enterprise growth as control variables, a panel data regression model is constructed to empirically examine the influence of financial subsidies on the performance of high-tech enterprises. The findings indicate that financial subsidies exhibit a notable positive impact on the performance of high-tech enterprises within Guangdong Province and have passed the robustness test. Based on the research conclusions, targeted suggestions and countermeasures are proposed from both the government and enterprises to jointly propel the high-quality development of high-tech enterprises.

Xie H., Tan T., Li Q., Li T.

To explore the application value of multidimensional radiomics based on ultrasound imaging in assessing the HER-2 status of breast cancer. We retrospectively analyzed the ultrasound imaging, clinical, and laboratory data of 850 breast cancer patients from two centers. During the study, we first utilized automation technology to accurately delineate the tumor region of interest (ROI) in breast ultrasound imaging. Subsequently, the intra-tumoral ROI was automatically expanded by 1 cm and 2 cm to obtain larger areas including the peritumoral tissues, and further generated three-dimensional volumes of interest (VOI) within and around the tumor. Through the K-means clustering method, we identified the sub-regions of interest within the ROI and extracted corresponding radiomic features using the pyradiomics toolkit. Additionally, we employed an advanced Vision Transformer (VIT) model to perform deep radiomic feature extraction on the ROI. Based on feature selection, we utilized various machine learning algorithms for modeling and analysis to assess the HER-2 status of breast cancer. After comprehensive comparison and evaluation of multiple models, we found that the diagnostic model based on multidimensional feature fusion exhibited excellent diagnostic performance in assessing the HER-2 status of breast cancer. In the training set, the model achieved an accuracy of 0.949 and an AUC value of 0.990 (95% CI: 0.986–0.995), with outstanding key performance indicators such as sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. The model showed good generalization in the test set, with accuracy 0.747, AUC 0.848 (95% CI: 0.791–0.904), and sensitivity 0.911. Specificity was slightly lower, but other indicators remained high, and the F1 score was 0.703. Calibration and clinical decision curves further confirmed the model’s effectiveness and reliability. This study fully demonstrates that multidimensional breast ultrasonography-based radiomic features can effectively assess the HER-2 status of breast cancer. This finding not only provides new evidence for early diagnosis of breast cancer but also offers new ideas and methods for personalized treatment planning and prognosis assessment.

Qian Z., Huang Q., Wang F., Lei Q., He Z., Wu Q., Wu M., Gao J.

AbstractA simple, rapid, and green high-performance liquid chromatography-mass spectrometry (HPLC-MS) method was developed for determination of tauroursodeoxycholic acid (TUDCA), taurocholic acid (TCA), and taurochenodeoxycholic acid (TCDCA) in bio-transformed Jindanfen (BTJDF), which is obtained from chicken bile through a bioconversion process. The sample was prepared using water. The HPLC separation was operated on a poroshell 120 EC-C18 column with a 2.0 min gradient elution procedure. Detection was performed on a single quadrupole mass spectrometer in negative mode with selected ion monitoring mode (SIM). This developed HPLC-MS method presented good linearity (r > 0.997) and sensitivity (limit of quantification, 30.0–80.0 pg) for three analytes. The relative standard deviations (RSDs) for precision, repeatability, and stability were all below 3.00%. The matrix effects and average recoveries of three analytes were 91.2–97.9% (RSDs < 1.50%) and 95.4–103% (RSDs < 3.00%), respectively. The average contents of TUDCA, TCA, and TCDCA in ten batches of samples were 33.8, 13.2, and 20.5%, respectively. Finally, the greenness of the developed method was validated by Analytical Eco-Scale and Complex-GAPI. The developed method was proved to be an eco-friendly, effective, and reliable approach for assaying the three cholic acids in BTJDF, which is help to improve the quality evaluation level of the BTJDF industry.

Zhou Z., Wu H., Li Y., Kang C., Wu Y.

This paper studies a p-layers deep learning artificial neural network (DLANN) for European multi-asset options. Firstly, a p-layers DLANN is constructed with undetermined weights and bias. Secondly, according to the terminal values of the partial differential equation (PDE) and the points that satisfy the PDE of multi-asset options, some discrete data are fed into the p-layers DLANN. Thirdly, using the least square error as the objective function, the weights and bias of the DLANN are trained well. In order to optimize the objective function, the partial derivatives for the weights and bias of DLANN are carefully derived. Moreover, to improve the computational efficiency, a time-segment DLANN is proposed. Numerical examples are presented to confirm the accuracy, efficiency, and stability of the proposed p-layers DLANN. Computational examples show that the DLANN’s relative error is less than 0.5% for different numbers of assets d=1,2,3,4. In the future, the p-layers DLANN can be extended into American options, Asian options, Lookback options, and so on.

Liu S., Lei T., Tan Y., Huang X., Zhao W., Zou H., Su J., Zeng J., Zeng H.

Abstract Background The global rise in multidrug-resistant Acinetobacter baumannii infections poses a significant healthcare challenge. Bacteriophage offer a promising alternative to antibiotics for treating A. baumannii infections. Phage tail fiber and spike proteins are essential for host recognition, with some exhibiting depolymerase activity that aids in degrading the bacterial cell wall, facilitating infection. Detailed studies of the functional domains responsible for depolymerase activity and receptor-binding in phage tail fiber/spike proteins are a crucial step toward developing effective phage treatments. Results A total of 32 functional domains were identified across 313 tail fiber and spike proteins from 204 publicly available Acinetobacter baumannii phages using InterPro and AlphaFold3. Domains associated with depolymerase function were Pectin lyase-like domain (PLD), phage_tailspike_middle domain (PTMD), Transglycosidases domain (TGD), and SGNH hydrolase domain (SHD). These domains were primarily found in phages from the Autographiviridae family, specifically within the Friunavirus genus. The predominant PLD domain displayed high variability, with its sequence conserved only in a 25-amino-acid region among two closely related fiber/spike protein lineages. All enzymatic domains exhibit high sequence diversity yet retain structural stability, which is essential for enzymatic function. As for receptor-binding domains, four types of pyocin_knob domains (PKD) were initially identified, characterized by unique β-sheet and α-helix configurations. Each type of PKD exhibited distinct potential receptor-binding sites, primarily located within the α-helix region, and was closely associated with the Obolenskvirus genus, as well as the Autographiviridae and Straboviridae families. The G3DSA:2.60.40.3940 domain, exhibiting minor structural variations, was predominantly found in phages of the Obolenskvirus genus. Additionally, a novel Obo-β-sandwich structure, identified as a potential receptor-binding domain, was discovered within Obolenskvirus genus cluster. The structural diversity of these receptor-binding domains accounts for their interactions with various receptors. Conclusions This research deepens the understanding of the relationship between A. baumannii phage genera and the functional domains within their tail fiber/spike proteins, emphasizing the compatibility between structural characteristics and functional roles. The data obtained could serve as a reference for the targeted modification of phages or their tail fiber/spike proteins, enhancing their therapeutic applications.

Hua Y., Cheng M., Zhou Q., Liu Q., Huang S., Ning J., Cheng T., Deng Y.

With the acceleration of aging, more and more elderly people choose to supplement with nutritional supplements, and human pursuit of nutrition and health has never stopped. However, the selection of nutritional supplements in the current market is often too complex, and this situation can also make it difficult for many people to find products that are suitable for themselves. This article designs a personalized nutritional supplement recommendation system based on linear regression, support vector machine, and naive Bayesian methods in machine learning technology. It can automatically help users find suitable nutritional supplements in the market. By designing a system framework and applying algorithms to data collection and preprocessing, the applicability of a personalized nutrition supplement recommendation system based on machine learning technology is evaluated through data comparison. Research has found that personalized nutrition recommendation systems can effectively help consumers choose products that are suitable for them, and have a positive impact on maintaining physical health. The application of linear regression based on machine learning technology in personalized nutritional supplement recommendation systems is far more accurate than traditional methods, support vector machines, and naive Bayesian methods, with linear regression accuracy reaching 96%.

Qian Z., He Z., Lian D., Huang Q., Tan G., Xie J.

Background: Flavonoid is a type of active constituent in herbs and always used as the quality control markers of herbal medicines. Owing to the extensive diversity of flavonoids, numerous reference compounds are necessitated for the analysis of flavonoids, and some are usually very expensive, which engenders challenges in the analysis of flavonoids in herbal medicines. Consequently, the development of a simple, rapid, and reference compounds saving method is important for the determination of flavonoids in herbal medicines. Objective: In order to develop a high-performance liquid chromatography (HPLC) method for the determination of 5 flavonoids (mangiferin, hesperidin, baicalin, buddleoside, and rutin) in five herbal medicines (Anemarrhenae rhizome, Sophorae flos, Citri reticulatae pericarpium, Scutellariae radix, and Chrysanthemi indici flos) with rutin. Methods: Five herbal medicine samples were prepared according to the Chinese Pharmacopoeia which includes ultrasound and reflux methods. The separation of the sample was performed on a PoroShell 120 EC-C18 (4.6 mm×100 mm, 2.7 µm) by gradient elution with 0.1% formic acid and acetonitrile at a flow rate of 1.0 mL/min. The wavelengths were set as follows: Anemarrhenae rhizome (363 nm), Sophorae flos (256 nm), Citri reticulatae pericarpium (236 nm), Scutellariae radix (263 nm), Chrysanthemi indici flos (354 nm). Results: The method validation showed that the established HPLC method was accurate and stable for quantitative analysis of flavonoids in five herbal medicines. The comparative analysis revealed that the determination results of the current HPLC method and Chinese Pharmacopoeia method are consistent, exhibiting less than 1% relative error. Remarkably, the developed HPLC method needs one cheapest reference compound (rutin) and costs 8 min for sample HPLC analysis. Conclusion: The developed HPLC method for quantitative analysis of five flavonoids in five herbal medicines is simple, rapid, and reference compound saving, which provides a good alternative method for quality control of flavonoids in herbal medicines.

Jiang L., Zhang M., Hu S.

IntroductionThe digital economy is reshaping economic structures and resource flows, but its role in enhancing resource allocation efficiency within the framework of green development has not yet been fully explored.MethodsThis paper analyzes panel data from 257 Chinese cities to investigate how the digital economy influences regional resource allocation efficiency, with a focus on the mediating role of green innovation.ResultsThe results show that the digital economy significantly improves resource allocation efficiency, with nonlinear effects characterized by increasing marginal returns. Mechanism tests reveal that the digital economy optimizes resource allocation by fostering green innovation. Heterogeneity analysis indicates that in eastern regions and areas with strong environmental regulations, the digital economy promotes both substantive and strategic green innovations, leading to higher efficiency. In mid-western regions, only substantive green innovation has a significant effect, while regions with weak environmental regulations experience less pronounced impacts.DiscussionThis study offers insights into leveraging green innovation to achieve efficient resource allocation and strengthen environmental resilience in the digital era.

Wu J., Yi J., Zhang Y.

Long J., Huang H.