This review provides an intensive overview of flame retardant coating systems. The occurrence of flame due to thermal degradation of the polymer substrate as a result of overheating is one of the major concerns. Hence, coating is the best solution to this problem as it prevents the substrate from igniting the flame. In this review, the descriptions of several classifications of coating and their relation to thermal degradation and flammability were discussed. The details of flame retardants and flame retardant coatings in terms of principles, types, mechanisms, and properties were explained as well. This overview imparted the importance of intumescent flame retardant coatings in preventing the spread of flame via the formation of a multicellular charred layer. Thus, the intended intumescence can reduce the risk of flame from inherently flammable materials used to maintain a high standard of living.

Involvement of important engineering applications in the interaction of free-moving objects with dispersed multi-phase flows are numerous in recent years. However, due to the challenge and complexity, few studies have been reported and modeling approaches remain very limited. This paper reviews the use of discrete particle models (DPMs) for the study of the fluid flow phenomena and elaborates several different types of multiple phases. The efficiency of particle retention and computational time were considered in determining the most appropriate multiphase model. Therefore, the dynamic modeling of multi-scale systems requires a method to solve the problem of structural fluid interaction (FSI) in the particle fluid flow.

The usage of glass fiber reinforced polymer (GFRP) composite cross-arms in transmission towers is relatively new compared to wood timber cross-arms. In this case, many research works conducted experiments on composite cross-arms, either in coupon or full-scale size. However, none performed finite element (FE) analyses on full-scale composite cross-arms under actual working load and broken wire conditions. Thus, this work evaluates the performance of glass fiber reinforced polymer (GFRP) composite cross-arm tubes in 275 kV transmission towers using FE analysis. In this study, the performance analysis was run mimicking actual normal and broken wire conditions with five and three times more than working loads (WL). The full-scale assembly load test experiment outcomes were used to validate the FE analysis. Furthermore, the mechanical properties values of the GFRP composite were incorporated in simulation analysis based on the previous experimental work on coupons samples of GFRP tubes. Additionally, parametric studies were performed to determine the ultimate applied load and factor of safety for both normal and broken wire loading conditions. This research discovered that the GFRP composite cross-arm could withstand the applied load of five times and three times working load (WL) for normal and broken wire conditions, respectively. In addition, the factor of safety of tubes was 1.08 and 1.1 for normal and broken wire conditions, respectively, which can be considered safe to use. Hence, the composite cross-arms can sustain load two times more than the design requirement, which is two times the working load for normal conditions. In future studies, it is recommended to analyze the fatigue properties of the composite due to wind loading, which may induce failure in long-term service.

Cross arm of the transmission tower supports the transmission conductor. This paper is a review of previous studies on Glass-Fiber Reinforced Polymer (GFRP) cross arm and properties of fibreglass as an outdoor application. Based on the previous study, this paper reveals the durability and degradation of GFRP as performance cross arm in transmission tower. It was discovered that the behaviour of mechanical strength decreases while the GFRP are exposed to the outdoor environment, thus in general GFRP structures are capable to withstand into extreme/cyclic environment conditions. In addition, the GFRPs alone has others beneficial which generally more resilient compared to other conventional materials on strength which regard to high strength and stiffness-to-weight ratio, corrosion resistance, chemical stability, and the capability to be sustain in harsh environment and possible to longer the serviceability and perform as a good insulator in lightning impulse strength. It can be concluded that fibreglass cross arm has significant properties as a replacement for traditional conventional timber cross arm due to its durability in moisture effect.

Gardening is an activity that requires fully care and time which two main factors of nowadays people lack of due to fast moving daily lifestyle. Growing a plant requires a person to monitor several parameters such as water, oxygen, nutrient, light, temperature and support for the plant. In conjunction to overcome the lack of commitment for the people to enjoy their hobby in gardening, an automatic plant pot is introduced. This paper discusses the studies of plant growth which later will be used as the criteria to design and develop a prototype of automatic plant pot. From the studies three outstanding elements were used as the main criteria to design a prototype of automatic plan pot which are water, nitrogen and temperature. These three elements need to be fully automated control to meet the objective that this prototype can help people to do gardening with less care and attention.

This study present of fly ash generation at generated one the power plant in Malaysia. The main purpose of this research to predict the generation of fly ash in future years by a method. This prediction is important so that fly ash generated could be used in a beneficial way. Prediction of fly ash was done by using two types of software which are Neural Network Toolbox, MATLAB and IBM SPSS Statistics 23, Linear Regression. Among these two methods, IBM SPSS Statistics 23, Linear Regression is found to be the most effective way to predict the generation of fly ash in the future by using five year’s best fit linear regression equation compared to Neural Network Toolbox, MATLAB.