Construction practitioners recognise that rework is undesirable due to the detrimental effects. While rework literature has examined rework causation factors in construction projects, the problem continues to plague the industry resulting in poor delivery performance. To better understand this phenomenon and given the scarcity of Malaysian-based rework study, a questionnaire survey involving 130 Malaysian construction practitioners (consultants, contractors and clients) were undertaken to obtain feedback about nature, implications, causes and solutions for rework. Data were analysed using descriptive statistical techniques to prioritise the variables studied. From the analyses, rework is a causal factor for delays and cost overruns, higher wastage and productivity inhibitor. The findings revealed the leading causes of rework are poor quality management, improper planning, lack of communication, design changes and poor subcontractor management. Some practical rework minimisation approaches are also suggested to better manage and prevent rework towards enhanced project performance.

Flow visualization is commonly used in studying fluid dynamics since fluids such as air, water and wind have invisible flow patterns due to its transparent property. In this era of big data, flow visualization is important to provide a compelling method of exploring, analysing and presenting large-scale flow field data. Flow visualization provides better insight into the fluid properties in space and time. This paper intends to provide an overview on the development of flow visualization methods such as spot noise, line integral convolution (LIC), texture advection, streamline placement, etc. from early 2000 to recent years. The reviewed methods will be compared and evaluated to determine the optimal approach of flow visualization.

Human aging is the main social and medicinal challenge as it is among the important factor lead to many diseases. Exploring the underneath of the aging process will give advantages for human. As the network of protein-protein interaction (PPI) in this study is dynamic, we are able to observe topological changes of network over time and make prediction of the key players of aging. The dynamic networks consist of thirteen elderly ages, they are within 80 -99 years. Integration of static protein-protein interaction network data with microarray human brain gene expression is an approach to construct the dynamic network. The network topological features are then will be used to carry out prediction using Support Vector Machine (SVM), artificial neural network (ANN) and K-Nearest Neighbour (KNN) models. This paper aims to provide a computational approach within the aging related proteins prediction by network topological features. SVM outperform the two others method in predicting aging-related proteins by reaching Area Under ROC Curve (AUC) value of 74.6%.

Autistic Spectrum Disorder (ASD) is a variant of neurodevelopment and behavioural impairments that mostly affected the children and adolescence. The screening of ASD in adulthood is regularly more challenging as it may overlap with the mental-health disorder symptoms. In this paper, 3-Satisfiability data mining approach with hybrid clonal selection algorithm in Hopfield neural network is proposed. The core impetus of this research is to investigate the feasibility and effectiveness of the proposed model in the task of Autistic Spectrum Disorder detection. The performance of the proposed approach will be compared with the state-of-the-art 3-Satisfiability data mining with exhaustive search algorithm in Hopfield neural network. The simulations were carried out on the Autistic Spectrum Disorder (ASD) among adults' data sets provided by UCI machine learning repository. The comparison results depicted overall improvements of accuracy and robustness by the proposed model in Autistic Spectrum Disorder (ASD) diagnosis. This research will provide the useful insight as it can be further applied in the diagnosis of more vigilant diseases.

DNA splicing system is modelled by the process of recombinant DNA that generates a language which is known as a splicing language. In DNA splicing system, molecules are cut and recombined with the existence of restriction enzymes and a ligase to produce new molecules. The molecules resulting from the splicing system depict the splicing language using formal language theory. In nature, the sequences of restriction enzyme can be recognised as palindromic or non-palindromic, where palindrome is a sequence of string which reads the same forwards and backwards. Research on splicing languages from DNA splicing systems has been done based on palindromic and non-palindromic restriction enzymes. In this research, the relation between DNA splicing language and concepts in automata theory is discussed. The splicing languages from the splicing system with one cutting site each of two palindromic or two non-palindromic restriction enzymes are deduced from the grammars. Then, the graphical representations of the languages generated by the grammars depicting the splicing languages are visualised as automata diagrams.

Real world problems are often described using mathematical models.The construction of these models often give rise to fuzzy differential equations where imprecise and vagueness are common in real world.Finding real world solutions require finding solutions to various type of fuzzy differential equations, which have gained popularity in modelling real life phenomena.Most of fuzzy differential equations that describe real world problems cannot be solved analytically.Thus, researchers focus on finding numerical methods that can be made as accurately as possible.Numerical methods for solving ordinary differential equations can be adapted to solve fuzzy differential equations.In this study, we aim to implement Runge-Kutta method of order six to solve wide ranges of fuzzy differential equations.The efficiency and accuracy of the method will be tested using various stepsizes.Numerical results will be further tested against existing methods in literatures.The implementation of Runge-Kutta method of higher order proves to be more accurate and able to solve various types of fuzzy differential equations.

A problem of steady laminar magnetohydrodynamic (MHD) Casson flow over a non-linear stretching/shrinking sheet with suction and slip boundary condition is considered. The governing nonlinear partial differential equations are transformed into a system of nonlinear ordinary differential equations using a similarity transformation. The transformed ordinary differential equations are then solved numerically using the bvp4c function in MATLAB software. Dual solutions are found for a certain range of the governing parameter. The effects of the suction parameter and the slip boundary condition on the skin friction and heat transfer coefficients as well as the velocity and temperature profiles are presented and discussed.

Acacia species was introduced as the commercial species in Sabah for the production of pulp and paper.

Two-phase flow is the interaction of two different matters, namely fluid and solid that contributes to an intriguing model in the boundary layer flow problem. It is can be precisely defined as the binary mixture of the fluid and spherical particles that involve interaction between both phases on the flow region. The fascinating class of non-Newtonian fluid, particularly Casson, Williamson and Jeffrey models are further considered for fluid phase. The mathematical formulation on the respective models are presented and discussed. The adiabatic two phases is engaged throughout the entire flow process by means of no phase changes occurred. The governing equations for the proposed models are presented herein and need to undergo the boundary layer approximation as way of tackling its difficulty into a solvable form by using the order of magnitude analysis.

Project selection is a process of evaluating a set of proposals and hence selecting the most potential project to be completed.This process is a challenging task for decision makers since the process involves many conflicting criteria and due to the limited resources, not all the projects can be implemented.Therefore, Condorcet Voting Theory based on Analytical Hierarchy Process (AHP) method is used to solve the project selection problems.The Condorcet Voting Theory based on AHP is a method to solve the multicriteria decision making (MCDM) problems where the voters will cast their votes to choose between two candidates.The quantitative ratio of the votes given to the candidates will be further used to construct the pairwise comparison matrix instead of the 9-points Saaty scale in the traditional AHP approach.The objectives of this study are to solve the project selection problems using Condorcet Voting Theory based on AHP method and to do comparison between AHP method and Condorcet Voting Theory based on AHP method.A real-life empirical data on the factors that contribute to road accidents was used to demonstrate the application of Condorcet Voting Theory based on AHP method.Decision makers will be able to make a judgment in a simple way by using voting method as the decision makers or the voters will cast their votes to select or not to select the alternatives.