John W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

Abstract. John W. Creswell was previously a professor in educational psychology in the University of Nebraska–Lincoln. He moved to the University of Michigan in 2015 as a professor in the Department of Family Medicine. He has published many articles and close to 27 books on mixed methods. Professor Creswell is also one of the founding members of the Journal of Mixed Methods Research. He was a Fulbright scholar in South Africa in 2008 and Thailand in 2012. In 2011, he served as a visiting professor in the School of Public Health of Harvard University. In 2014, he became the Chairman of the Mixed Methods International Research Association. Professor Creswell has a personal website called “Mixed Methods Research” at http://johnwcreswell.com/. The site contains the information about his background, his own blog, consulting works and published books. He also posted replies questions from academic researchers and practitioners in the blog.Keywords. Research design, Methodology, Methods.JEL. A20, B40, B49

Kappa-Opioid Receptors in the Caudal Nucleus Tractus Solitarius Mediate 100 Hz Electroacupuncture-Induced Sleep Activities in Rats

Previous results demonstrated that 10 Hz electroacupuncture (EA) of Anmian acupoints in rats during the dark period enhances slow wave sleep (SWS), which involves the induction of cholinergic activity in the caudal nucleus tractus solitarius (NTS) and subsequent activation of opioidergic neurons and μ-receptors. Studies have shown that different kinds of endogenous opiate peptides and receptors may mediate the consequences of EA with different frequencies. Herein, we further elucidated that high-frequency (100 Hz)-EA of Anmian enhanced SWS during the dark period but exhibited no direct effect on rapid eye movement (REM) sleep. High-frequency EA-induced SWS enhancement was dose-dependently blocked by microinjection of naloxone or κ-receptor antagonist (nor-binaltorphimine) into the caudal NTS, but was affected neither by μ- (naloxonazine) nor δ-receptor antagonists (natatrindole), suggesting the role of NTS κ-receptors in the high-frequency EA-induced SWS enhancement. Current and previous results depict the opioid mechanisms of EA-induced sleep

Cogging Torque Reduction of Interior Permanent-Magnet Synchronous Motors by Finite-Element Method

The cogging torque of a permanent-magnet motor is an oscillatory torque that always induces vibration, acoustic noise, possible resonance and speed ripples, and its minimization is a major concern for electric motor designers. This paper presents an effective approach for the cogging torque reduction of interior permanent-magnet motors by modifying the magnet span angle of the rotor and the shoe depth and shoe ramp of the stator. The cogging torque is calculated by employing a commercial finite-element analysis software Ansoft/Maxwell. The results show that the peak value of the cogging torque for the modified design decreases 50% in comparison with that of the original design

An Behavioral Finance Analysis Using Learning Vector Quantization in the Taiwan Stock Market Index Future

There are various types of trading behavior in the stock market. And the buying or selling activities in many investment strategies are influenced by numerous factors respectively, such as fundamental analysis, macroeconomic analysis, and news analysis. Consequently, various factors will reflect on market price. Random Walk in financial engineering is not the focus in this paper. Otherwise, the importance of the technique analysis about Taiwan Stock Index Futures will be emphasized in this research. It is the intention of this paper to investigate the information content of Open, High, Low, Close prices in the previous trading day and relative higher and lower points in the prior period of the current trading day, as well as their prices in analyzing Taiwan Stock Index Future. The predictability of Learning Vector Quantizationl Network can clearly be seen from the empirical result

FedBA: Non-IID Federated Learning Framework in UAV Networks

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs