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This paper aims to develop a conceptual framework for evaluating the impact of motivational factors on the work results of lecturers at Vietnam National University, Hanoi (VNU), one of two leading multidisciplinary and multi-sectoral national universities in Vietnam. This study has considered wages and other benefits (WB), training and development (TD), working environment (WE) and working motivation (WM) as motivational factors, and proposed a structural model of the impact of motivational factors on the work results of lecturers at VNU. The empirical analysis used data from the survey data of 321 university lecturers. Comprehensive, valid, and reliable tools (SPSS 26 and SmartPLS 3.0 software) are used to evaluate rigorous statistical tests including convergence validity, discriminatory validity, reliability, and average variance extracted to analyze and verify the gathered data, and the hypotheses developed. The result of path analysis shows that four motivational factors constitute a structured system with different degrees of influence on the work results of lecturers. There is also a positive relationship between the motivational factors and the work results of lecturers. As a result, it can be concluded that all hypotheses developed are supported. Several recommendations are further suggested to improve the performance of lecturers at VNU.

Feasibility analysis algorithms are based on particular metrics such as processor utilization, load factor, processor demand, response-times, etc. The design of efficient algorithms for computing these metrics is a major issue in real-time scheduling theory. In this paper we propose two FPTASs (fully-polynomial time approximation schemes) for checking feasibility of static-priority tasks subjected to release jitters executed upon a uniprocessor platform. We then use these FPTASs for computing two upper bounds of worst-case response-times. Lastly, we show that these bounds do not achieve constant error bounds in comparison with values computed by an exact worst-case responsetime analysis (performed in pseudo-polynomial time), and we present numerical experiments.