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In the Industrial Revolution 4.0 (IR 4.0), the role of higher education (HE) stays crucial for the development of highly skilled human resources in the multi-service sector. Indeed, the pharmaceutical education quality in higher education is significant for building a quality workforce in the healthcare service sectors. Additionally, the increasingly global education market required the ongoing increase of quality in higher education. This study investigates the driving factors of student satisfaction in Pharmacy education at private universities in Vietnam. Both qualitative and quantitative approaches are used in this study. For quantitative research, the surveyed questionnaire is developed and is the primary instrument. Besides, focus groups, semi-structured in-depth interviews with experts, and meta-analysis are applied instruments for qualitative research. The sample is 265 students from 3 private universities. The driving factors of student satisfaction are (1) Educational program, (2) University Reputation, (3) Business Interaction, (4) Extracurricular activities, and (5) Teaching facilitators activities. Eventually, the authors propose the suggestions for both academic and practical perspectives; simultaneously, the limitation is discussed for further research.

The Electrocardiography classifier is an essential tool for helping doctors in diagnosing early heart problems. This paper proposes with an electrocardiography classifier for analyzing accuracy in case of non-long-tail effect. Data are obtained from MIT-BIH arrhythmia database. Therefore, a discrete wavelet transform decomposition algorithm is employed for feature extraction and a principal component analysis is used for dimension reduction of data. In addition, the heart beat can be classified using a neural network method. In order to evaluate the classifier accuracy, the confusion matrix and Receiver Operating Characteristic curve are applied.

This paper provided a method to investigate motor control areas and distinguish between different activities of right and left hemisphere brain by analysis signal using function NearInfrared Spectroscopy(fNIRS) technique. Brain signals were measured on three subjects with the task of lifting weights 5 kg. Data were pre-processing using filter smoothing Savitzky Golay. Then, the signal was analyzed using wavelet decomposition algorithm with Bior5.5 function to produce the wavelet coefficients. An average algorithm of the wavelet coefficients was applied to predict motor control area as well as distinguish between active left hemisphere and right hemisphere.

This paper presented a hierarchical sliding mode controller for an inverted pendulum system. The structure characteristic of the inverted pendulum system could be treated as two subsystems. According to the decomposition of cart-inverted pendulum system we select sliding mode surfaces for both subsystems. The sliding mode surfaces of the two subsystems were defined at first. The sliding mode surface of one subsystem was selected as the first layer sliding mode surface. The second layer sliding mode surface was constructed by the first layer sliding mode surface and the sliding mode surface of the other subsystem. The hierarchical sliding mode control law was deduced in terms of Lyapunov stability theorem. Finally, simulation results showed the validity of this control method and its robustness for external disturbances.

This paper presented the method of creating virtual models of power supply laboratory in the Matlab environment based on the physical model of LD Didactic GmbH Company. A study created virtual models that had all the features of the physical model. Thus, experiment on the virtual models was similar to experiment on the physical models.