Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

A new approach to intelligent fault diagnosis of rotating machinery

Abstract: This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy entropies, are extracted to acquire more fault characteristic information. Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into ANFIS to identify different abnormal cases. The proposed approach is applied to fault diagnosis of rolling element bearings, and testing results show that the proposed approach can reliably recognise different fault categories and severities. Moreover, the effectiveness of the proposed feature selection method is also demonstrated by the testing results.

The COVID-19 Pandemic and Physical Activity

Abstract: The SARS-CoV-2-caused COVID-19 pandemic has resulted in a devastating threat to human society in terms of health, economy, and lifestyle. Although the virus usually first invades and infects the lung and respiratory track tissue, in extreme cases, almost all major organs in the body are now known to be negatively impacted often leading to severe systemic failure in some people. Unfortunately, there is currently no effective treatment for this disease. Pre-existing pathological conditions or comorbidities such as age are a major reason for premature death and increased morbidity and mortality. The immobilization due to hospitalization and bed rest and the physical inactivity due to sustained quarantine and social distancing can downregulate the ability of organs systems to resist to viral infection and increase the risk of damage to the immune, respiratory, cardiovascular, musculoskeletal systems and the brain. The cellular mechanisms and danger of this “second wave” effect of COVID-19 to the human body, along with the effects of aging, proper nutrition, and regular physical activity, are reviewed in this article.

Enhanced valley splitting in monolayer WSe2 due to magnetic exchange field

Abstract: Exploiting the valley degree of freedom to store and manipulate information provides a novel paradigm for future electronics. A monolayer transition-metal dichalcogenide (TMDC) with a broken inversion symmetry possesses two degenerate yet inequivalent valleys, which offers unique opportunities for valley control through the helicity of light. Lifting the valley degeneracy by Zeeman splitting has been demonstrated recently, which may enable valley control by a magnetic field. However, the realized valley splitting is modest (∼0.2 meV T-1). Here we show greatly enhanced valley spitting in monolayer WSe2, utilizing the interfacial magnetic exchange field (MEF) from a ferromagnetic EuS substrate. A valley splitting of 2.5 meV is demonstrated at 1 T by magnetoreflectance measurements and corresponds to an effective exchange field of ∼12 T. Moreover, the splitting follows the magnetization of EuS, a hallmark of the MEF. Utilizing the MEF of a magnetic insulator can induce magnetic order and valley and spin polarization in TMDCs, which may enable valleytronic and quantum-computing applications.

Customer churn prediction using improved balanced random forests

Abstract: Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests.