Bankruptcy Prediction Model in China: Prediction of Financial Distress in Listed companies of Chinese Manufacturing Industry

The establishment of prediction model for financial distress is not only for the interests of the wide stakeholders, but also to maintain a healthy and stable environment for the development of stock market. This paper starts from the definition and drivers initiating financial distress and then draws support from previous scholars on developing the model. This paper targets “ST” companies in Chinese manufacturing industry in 2017. 24 ST companies and 24 non-ST companies are identified as control groups. Logistic regression is applied to build financial distress warning model. With introduction of state ownership into the model, with the aim to explore potential influence imposed by government to protect state-owned companies, this paper injects new vitality and impetus into research on prediction of financial distress. Important contribution of this paper includes discovering financial indicators that play an important role in prediction of financial distress, refining previous logistic model by extending time scale and undertaking more detailed analysis on manufacturing industry. The paper finally provides an appropriate model for prediction of financial distress for listed companies in Chinese manufacturing industry

The relationship between future self-continuity and intention to use Internet wealth management: The mediating role of tolerance of uncertainty and trait anxiety

This study aimed to analyze the mediating effect of tolerance of uncertainty (TU) and trait anxiety (TA) on future self-continuity (FSC) and intention to use Internet wealth management (IUIWM) systems. A questionnaire survey was distributed online and a total of 388 participants completed questionnaire, The questionnaire included the following scales: Chinese version of the FSC, Intention to Use the Internet Wealth Management, TU, and TA. Pearson correlation was used to investigate the correlation coefficient between variables while the sequential regression method was used to analyze relationship between variables. To analyze the collected data, the SPSS 26.0 was used. A two-step procedure was applied to analyze the mediation effect. Confirmatory factor analysis (CFA) was conducted to test the measurement model. Afterward, the Maximum Likelihood method was used for path analysis, and the Bias-corrected Bootstrap method was used to investigate determine the estimated value and confidence interval of the mediating effect

Classification of knee osteoarthritis based on quantum-to-classical transfer learning

Quantum machine learning takes advantage of features such as quantum computing superposition and entanglement to enable better performance of machine learning models. In this paper, we first propose an improved hybrid quantum convolutional neural network (HQCNN) model. The HQCNN model was used to pre-train brain tumor dataset (MRI) images. Next, the quantum classical transfer learning (QCTL) approach is used to fine-tune and extract features based on pre-trained weights. A hybrid quantum convolutional network structure was used to test the osteoarthritis of the knee dataset (OAI) and to quantitatively evaluate standard metrics to verify the robustness of the classifier. The final experimental results show that the QCTL method can effectively classify knee osteoarthritis with a classification accuracy of 98.36%. The quantum-to-classical transfer learning method improves classification accuracy by 1.08%. How to use different coding techniques in HQCNN models applied to medical image analysis is also a future research direction

Stabilization mechanism of water-in-oil emulsions by medium- and long-chain diacylglycerol: post-crystallization vs. pre-crystallization

The restriction of using trans-fatty acid is driving the food industries to develop natural, healthy and efficient emulsifiers for the fabrication of water-in-oil (W/O) emulsions. In this work, medium- and long-chain diacylglycerol (MLCD) with high nutritional features and surface activities was used for the preparation of emulsion. The influence of crystallization procedures (pre- or post-crystallization) on the emulsions’ stability was examined in terms of the change in droplet size distribution (DSD), sedimentation, microstructure and thermal properties. The sedimentation and coalescence of emulsions were reduced when higher amount (8%, w/w) of MLCD was used. The post-crystallized emulsions showed narrower DSD and less sedimentation compared to the pre-crystallized emulsions. Pre-crystallized emulsion prepared using shear speed of 10,000 rpm showed improved stability due to the reduction of crystal size. MLCD was able to form typical interfacial crystal shells in post-crystallized emulsions whereas only large crystals were formed in the continuous phase in the pre-crystallizations. Therefore, the post-crystallized emulsions had higher thickness and sedimentation was effectively reduced. The findings in this work could be the basis for the future application of MLCD and provide insights on how the physical stabilities of emulsions can be affected when different crystallization processes are employed

Circular RNA hsa_circ_001783 regulates breast cancer progression via sponging miR-200c-3p

Increasing evidence suggests circular RNAs (circRNAs) exert critical functions in tumor progression via sponging miRNAs (microRNAs). However, the role of circRNAs in breast cancer remains unclear. Here we systematically analyzed the circular RNAs in breast cancer based on their characteristic in sponging disease-specific miRNAs and identified hsa_circ_001783 as a top ranked circRNA in our computation and verified its high expression in both breast cancer cells and cancer tissue. A higher level of hsa_circ_001783 was significantly correlated with heavier tumor burden and poorer prognosis of patients with breast cancer. Knockdown of this circRNA remarkably inhibited the proliferation and invasion of breast cancer cells. Importantly, hsa_circ_001783 promoted progression of breast cancer cells via sponging miR-200c-3p. Taken together, hsa_circ_001783 may serve as a novel prognostic and therapeutic target for breast cancer

Effect of Salvianolic Acid b and Paeonol on Blood Lipid Metabolism and Hemorrheology in Myocardial Ischemia Rabbits Induced by Pituitruin

The purpose of this study was to determine the therapeutic effect of salvianolic acid b and paeonol on coronary disease. The ischemia myocardial animal model is induced by administering pituitrin (20 μg·kg−1) intravenously via the abdominal vein. A combination of salvianolic acid b and paeonol (CSAP) (5, 10 and 15 mg/kg BW) was administrated to experimental rabbits. Biochemical indices were evaluated during six weeks of intervention. We found that the compound of salvianolic acid b and paeonol (5, 10 and 15 mg/kg BW) can markedly and dose-dependently reduce fibrinogen and malonaldehyde levels, increase the HDL level, improve blood viscosity and plasma viscosity in rabbits. In addition, the medicine can still reduce the ratio of NO/ET and the contents of lactate dehydrogenase (LDH) and creatine phosphokinase (CPK) in a dose-dependent manner. This study demonstrates that compound of salvianolic acid b and paeonol (5, 10 and 15 mg/kg BW) can improve the blood hemorrheology, decrease oxidative injury and repair the function of blood vessel endothelium, and subsequently prevent the development of Coronary disease