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The online teaching and learning method can implement synchronous and asynchronous processes for theory courses using digital platforms through digital education systems. Implementing online teaching and learning for laboratory courses presents challenges, as synchronous is infeasible due to the requirement for specialized tools, equipment, and experimental materials. An asynchronous teaching and learning model can be used instead. In this study, we proposed an Asynchronous online teaching and learning model tailored for practice laboratory-based courses in engineering and technology education. This model is named “Development of a Laboratory-Based Learning Model via a Digital Platform Environment.” This model is proposed based on the student-feedback interaction model, and called the online feedback practice model. The proposed model is experimented with teaching and learning laboratory-based subjects. The experiment involved two groups of 200 students, including 100 students in the control group (face-to-face practice) and 100 students in the experimental group (online feedback practice). Both groups participated in a final evaluation through direct in-class assessments. The research results demonstrated that the model yields higher effectiveness than the traditional method, based on evaluating statistical analysis to compare average scores, outcomes, achievement rate, and assignment completion rate of two student groups. The model also gathered feedback from instructors teaching in the same field regarding the course's learning outcomes, which have been validated to meet AUN-QA standards. The survey results are also statistically evaluated and calculated through means, variances, standard deviations, and hypotheses using t-tests to demonstrate the effectiveness of the proposed model.

This paper studies energy-efficiency (EE) power allocation for cognitive radio MIMO-OFDM systems. Our aim is to minimize energy efficiency, measured by "Joule per bit" metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non-convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energyefficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency-division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by "Joule per bit" metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non-convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]