Resampling for Order Estimation of Autoregressive Models with Missing Data

In this article, we consider the order estimation of autoregressive models with incomplete data using the expectation–maximization (EM) algorithm-based information criteria. The criteria take the form of a penalization of the conditional expectation of the log-likelihood. The evaluation of the penalization term generally involves numerical differentiation and matrix inversion. We introduce a simplification of the penalization term for autoregressive model selection and we propose a penalty factor based on a resampling procedure in the criteria formula. The simulation results show the improvements yielded by the proposed method when compared with the classical information criteria for model selection with incomplete data.     

Perceptions of undergraduate pharmacy students on plagiarism in three major public universities in Egypt

The survey aimed to capture the perceptions of undergraduate pharmacy students towards plagiarism in three major public universities in Cairo, Egypt: Helwan, Ain-Shams, and Cairo Universities. This was a paper-based self-administrated survey study. The questionnaire was validated by both content and face validation. The final survey form captured the knowledge of the students on plagiarism in terms of definitions, attitudes, and practices. Four hundred and fourteen students, 320 females and 94 males, participated in the study. There was a significant difference between the students who knew the definition of plagiarism among the three universities with p-value = .01. More than half of the participants (67%) claimed that they had no previous education or training on plagiarism. However, after being informed about plagiarism, most of them agreed that plagiarism should be regarded as stealing and a punishment. Additionally, poor study skills and the ease of copying and pasting from the Internet were identified by the majority of the students to be the leading causes of plagiarism. Pharmacy students need to be more educated on plagiarism and its consequences on research and educational ethics. Finally, more strict policies should be incorporated to monitor and control plagiarism in undergraduate sections.     

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Research in assembly optimisation is presently inclined towards integrative measures. Several benefits of simultaneously optimised Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) have been highlighted by researchers to have better solution quality, shorter time-to-market, and minimalised error during planning. Recently, several efforts have been made to realise integrated assembly optimisation. However, none of the published research considered the two-sided assembly line problem. This paper presents an integrated ASP and ALB optimisation in a two-sided assembly environment (2S-ASPLB), which is mainly adopted in automotive assembly process. In this study, the 2S-ASPLB problem was formulated and optimised using Multi-Objective Multi-Verse Optimiser (MOMVO) by considering line efficiency, reorientation penalty, and tool change as the optimisation objectives. The computational experiments were conducted in a few stages, beginning with the identification of the best decoding approach for 2S-ASPLB. Next, the best MOMVO coefficient was studied, followed by comparing MOMVO performance with well-established multi-objective optimisation algorithms. Finally, a case study problem was presented to demonstrate applicability of the proposed model and algorithm in real-life problem. The results indicated that the priority factor (PF) decoding approach had better performance compared with others. Meanwhile, in comparison with well-established algorithms, MOMVO performed better in convergence and solution distribution. The case study results indicated the applicability of proposed 2S-ASPLB model and algorithm to improve line efficiency in assembly line. The main contribution of the research is a new 2S-ASPLB model and optimisation scheme, which can assist manufacturer in designing better assembly layout.

     

The willingness to emigrate in six MENA countries: The role of post-revolutionary stress

This paper studies the determinants of emigration from six Middle East and North Africa (MENA) countries in light of the Arab Spring of 2011. The aim is to determine if the economically unfortunate events which occurred as a result of the Arab Spring, resulted in a brain drain for many countries. The paper's analysis is conducted using the Arab Transformation Project dataset of the year 2014 by employing an ordered probit model. The paper's main conclusion is that sentiments of unhappiness appear to be the primary determinant of the willingness to emigrate. Other post-revolutionary feelings include lack of trust and political and democratic discontent, which highly encourage the willingness to emigrate. In addition, socio-economic factors, such as being young, male, and highly educated, contribute to the willingness to emigrate. However, married individuals are less likely to consider emigration.     

Responsible Management Education in Time of Crisis: A Conceptual Framework for Public Business Schools in Egypt and Similar Middle Eastern Context

Public Organization Review - Recent studies show that the adoption of RME scenarios is still a matter of concern for non-western countries ((Mousa et al., Journal of Management Development...