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Long-term monitoring of resting tremor is key to assess the status of patients suffering from Parkinson's disease (PD), which is of vital importance for reasonable medication. The detection and quantification of resting tremor in reported works rely heavily on specified movements and are not appropriate for long-term monitoring in real-life condition. The purpose of this study is to develop a detection model for long-term monitoring of resting tremor and explore an effective indicator for tremor quantification. This study included long-term acceleration data from PD patients and proposed a resting tremor detection model based on machine learning classifiers and Synthetic Minority Oversampling Technique (SMOTE). Four machine learning classifiers, K-Nearest Neighbor (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Support Vector Machine (SVM), were compared. Furthermore, an indicator called tremor timing ratio (TTR) was defined and calculated for tremor quantification. The detection model with RF classifier achieved the highest overall accuracy of 94.81%. The sample entropy of the acceleration signal was proved most influential in the classification by exploring the feature importance. Through the Kruskal-Wallis test and the Mann-Whitney U test, the TTR had a strong correlation with the subscore of resting tremor in Unified Parkinson Disease Rating Scale (UPDRS). Such two-step evaluation process for resting tremor can detect the tremor effectively and is expected to be applied in long-term monitoring of PD patients in daily life to realize a more comprehensive assessment of PD. [ABSTRACT FROM AUTHOR]

This paper takes the supply chain perspective to study the choice of selling model for manufacturers and e-tailers. To accomplish our objective, we consider three selling models, including reselling, agency selling, and mixed selling. By comparing and analyzing the equilibrium outcomes of the three selling models, we obtain some beneficial results. These results show that manufacturers and e-tailers cannot manage to make a profit at the same time whether they choose the reselling model or agency selling model. Our results also show that the mixed selling model may improve supply chain performance, as long as the cross-price elasticity is not low. Especially when the cross-price elasticity is comparatively high, a manufacturer and an e-tailer in vertical competition can achieve a profit-Pareto-improving situation, regardless of the market share of the e-tailer. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of the lithium-ion battery industry, the massive upstream consumption of mineral raw materials has led to great crises and challenges in balancing global supply and demand. A diversity of trading partners can effectively alleviate the pressure of supply and demand for raw materials; so, it is particularly important for trading countries to find suitable trading partners. This study takes nine important minerals contained in the main element raw materials of lithium-ion batteries as the research object, and the international trade data range from 2011 to 2020. The study combines complex networks and motifs and links prediction methods to predict the potential international trade relations of the nine minerals based on the actual network topology. We summarize three trade link rules as follows. (1) The predicted links have a timeliness requirement, and they generally turn into actual trade links within 3 years. (2) When looking for trading partners, trading countries tend to establish long-term and stable trade relations. (3) The existing trade relations will promote the establishment of predicted trade relations. In addition, we find that successfully predicted links will promote the formation of important motifs in the network. This study can provide a theoretical basis for trading countries to find suitable trading partners and can strengthen the safety of trading countries in the trade of the main element minerals for lithium-ion batteries. [ABSTRACT FROM AUTHOR]

Objective. To investigate patients' preference and the attitude towards physician attire in an internal medicine clinic in China. Methods. This study was conducted from 1 January 2021 to 30 June 2021 in a tertiary care hospital in China. We surveyed 126 patients in the hospital with 6 sets of pictures of commonly worn physician attires in the hospital setting with a two-part questionnaire. The first part listed respondent demographics to collect basic information. The second part of the questionnaire was administered to adult patients who received care in the internal medicine clinics (outpatients). Survey forms collected demographic data (age, gender, patient age, education, marital status, and employment status), asked questions regarding 6 specific attires (scrubs, scrubs and white coat, casual, casual and white coat, business suit, and formal and white coat), and behavioral items (professional, responsible, reliable, knowledgeable, succession rate, and medical safety), finally, to assess the preference of attire on overall perception. Results. Scrubs and white coat scored the highest through 6 domains about physicians' attire (professional, reliable, responsible, knowledgeable, medical safety, and succession rate, p < 0.05 for all comparisons). A casual suit without a white coat was the least preferred across the surveyed attributes. There was a significant preference gap between wearing a white coat and not wearing a white coat (p < 0.001). Physician attire to white coat was considered as more professional, reliable, responsible, knowledgeable, having greater medical safety, and a higher success rate than attires without white coat. Conclusion. Patients felt that the physician wearing a white coat was better than other attires. Scrubs and white coat was the most popular attire. Hospital or related authorities may promote the standardized wearing of white coats, leading in a greater patient-physician relationship. [ABSTRACT FROM AUTHOR]