Longitudinal profiles of bounded outcome scores as predictors for disease activity in rheumatoid arthritis patients: a joint modeling approach

A variety of statistical approaches have been suggested in the literature for the analysis of bounded outcome scores (BOS). In this paper, we suggest a statistical approach when BOSs are repeatedly measured over time and used as predictors in a regression model. Instead of directly using the BOS as a predictor, we propose to extend the approaches suggested in [16 E. Lesaffre, D. Rizopoulos, and R. Tsonaka, The logistics-transform for bounded outcome scores, Biostatistics 8 (2007), pp. 72–85. doi: 10.1093/biostatistics/kxj034[Crossref], [PubMed], [Web of Science ®] , [Google Scholar],21 M. Molas and E. Lesaffre, A comparison of the three random effects approaches to analyse repeated bounded outcome scores with an application in a stroke revalidation study, Stat. Med. 27 (2008), pp. 6612–6633. doi: 10.1002/sim.3432[Crossref], [PubMed], [Web of Science ®] , [Google Scholar],28 R. Tsonaka, D. Rizopoulos, and E. Lesaffre, Power and sample size calculations for discrete bounded outcome scores, Stat. Med. 25 (2006), pp. 4241–4252. doi: 10.1002/sim.2679[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]] to a joint modeling setting. Our approach is illustrated on longitudinal profiles of multiple patients’ reported outcomes to predict the current clinical status of rheumatoid arthritis patients by a disease activities score of 28 joints (DAS28). Both a maximum likelihood as well as a Bayesian approach is developed.     

Recurrence relations between product moments of order statistics

A general result for obtaining recurrence relations between product moments of order statistics is established and this result is used to determine the recurrence relations between product moments of some doubly truncated distributions. The examples considered are Weibull, exponential, Pareto, power function and Cauchy distributions.     

The causes of gender diversity in Malaysian large firms

The issue of women’s representation at the decision-making level in Malaysia has received special attention from the Government since 2004, the year in which it adopted a policy requiring that 30 % of the posts at the decision-making level in the public sector be filled by women. In 2011, the policy was extended to the private sector where 30 % of listed firms’ board seats are to be allocated to women with 2016 being the deadline for compliance. To this end, this paper aims at examining the factors that determine the appointment of women to the boards of Malaysian large firms. Large firms were chosen in this study because they have the resources and the capacity to adopt the policy more readily than smaller firms. The results reveal that gender diversity is positively associated with board size and the presence of family on the board. That is, the larger the board, the more likely it is that women sit on it. The fact that the presence of women on the board is associated with the presence of one or more family members on the board means that the appointment of women to the board is very much influenced by family ties rather than commercial reasons. The results also reveal a positive association between board independence and the proportion of women directors. Further, it is found that board independence is associated positively with the presence of independent women directors. Finally, the results show that firm performance is negatively associated with gender diversity. That is, firms with low financial performance are more likely to have women on their boards. Hence, taken altogether, the evidence suggests that the appointment of women to the board is very much driven by tokenism and family connection rather than by the business case.     

Cellular Bucket Brigades on U‐Lines with Discrete Work Stations

It is challenging to maximize and maintain productivity of a U‐line with discrete stations under the impact of variability. This is because maximizing productivity requires assigning workers to suitable tasks and maintaining productivity requires sufficient flexibility in task assignment to absorb the impact of variability. To achieve this goal, we propose an operating protocol to coordinate workers on the U‐line. Under the protocol the system can be configured such that its productivity is maximized. Workers are allowed to dynamically share work so that the system can effectively absorb the impact of variability. Analysis based on a deterministic model shows that the system always converges to a fixed point or a period‐2 orbit. We identify a sufficient condition for the system to converge to the fixed point. Increasing the number of stations improves productivity only under certain circumstances. The improvement is most significant when the number of stations in each stage increases from one to two, but further dividing the U‐line into more stations has diminishing return. Simulations based on random work velocities suggest that our approach significantly outperforms an optimized, static work allocation policy if variability in velocity is large.     

Detection of outliers in high-dimensional data using nu-support vector regression

Support Vector Regression (SVR) is gaining in popularity in the detection of outliers and classification problems in high-dimensional data (HDD) as this technique does not require the data to be of full rank. In real application, most of the data are of high dimensional. Classification of high-dimensional data is needed in applied sciences, in particular, as it is important to discriminate cancerous cells from non-cancerous cells. It is also imperative that outliers are identified before constructing a model on the relationship between the dependent and independent variables to avoid misleading interpretations about the fitting of a model. The standard SVR and the μ-ε-SVR are able to detect outliers; however, they are computationally expensive. The fixed parameters support vector regression (FP-ε-SVR) was put forward to remedy this issue. However, the FP-ε-SVR using ε-SVR is not very successful in identifying outliers. In this article, we propose an alternative method to detect outliers i.e. by employing nu-SVR. The merit of our proposed method is confirmed by three real examples and the Monte Carlo simulation. The results show that our proposed nu-SVR method is very successful in identifying outliers under a variety of situations, and with less computational running time.     

Deaf identity construction in Malaysia

This paper focuses on the identity creation processes of Deaf people in Malaysian society using an intersectionality approach to issues of identity, disability, and Deaf studies. Deaf people belong to a marginalized community and typically experience stereotyped, prejudiced, and stigmatized treatment from the hearing community, which has a serious impact on their social and personal identity development. Without sufficient information about the process of identity creation for Deaf people, the hearing community cannot reduce inequality nor can it increase social integration and stability for the Deaf community. There is a paucity of research about this identity creation process in Malaysia, and few studies have used identity-related theories and Deaf studies to examine the issue. This study uses a constructivist point of view, employing an interpretive phenomenological epistemology in order to construct insightful accounts of participants’ personal and interpersonal experience in mainstream society. The data was analyzed using methods associated with grounded theory and constant comparative analysis in order to reveal the extent to which they identify themselves, that is linguistic minority or PWD. The findings suggest that, on the one hand, mainstream society categorizes Deaf people as Persons with Disabilities rather than as a linguistic minority; on the other hand, without realizing it, Deaf people typically accept society's evaluation by identifying with it and playing the expected roles of PWD.     

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.