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The development of high performance cheetah skin structure for the first time and its use as a filler for polyethylene composite. The huge improvement in mechanical and physical performance of the composite make it a material of choice for different applications.

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.

Deoxyribonucleic Acid (DNA) has been recently found to be an efficient renewable and environmentally-friendly flame retardant. In this work, for the first time, we have used waste DNA from fishing industry to modify clay structure in order to increase the clay interactions with epoxy resin and take benefit of its additional thermal property effect on thermo-physical properties of epoxy-clay nanocomposites. Intercalation of DNA within the clay layers was accomplished in a one-step approach confirmed by FT-IR, XPS, TGA, and XRD analyses, indicating that d-space of clay layers was expanded from ~1.2 nm for pristine clay to ~1.9 nm for clay modified with DNA (d-clay). Compared to epoxy nanocomposite containing 2.5%wt of Nanomer I.28E organoclay (m-clay), it was found that at 2.5%wt d-clay loading, significant enhancements of ~14%, ~6% and ~26% in tensile strength, tensile modulus, and fracture toughness of epoxy nanocomposite can be achieved, respectively. Effect of DNA as clay modifier on thermal performance of epoxy nanocomposite containing 2.5%wt d-clay was evaluated using TGA and cone calorimetry analysis, revealing significant decreases of ~4000 kJ/m2 and ~78 kW/m2 in total heat release and peak of heat release rate, respectively, in comparison to that containing 2.5%wt of m-clay.

Thermally reduced graphene nanoplatelets were covalently functionalised via Bingel reaction to improve their dispersion and interfacial bonding with an epoxy resin. Functionalised graphene were characterized by microscopic, thermal and spectroscopic techniques. Thermal analysis of functionalised graphene revealed a significantly higher thermal stability compared to graphene oxide. Inclusion of only 0.1 wt% of functionalised graphene in an epoxy resin showed 22% increase in flexural strength and 18% improvement in storage modulus. The improved mechanical properties of nanocomposites is due to the uniform dispersion of functionalised graphene and strong interfacial bonding between modified graphene and epoxy resin as confirmed by microscopy observations.

The ubiquity of the Internet and Web has led to the emergency of several Web search engines with varying capabilities. A weakness of existing search engines is the very extensive amount of hits that they can produce. Moreover, only a small number of web users actually know how to utilize the true power of Web search engines. Therefore, there is a need for searching infrastructure to help ease and guide the searching efforts of web users toward their desired objectives. In this paper, we propose a context-based meta-search engine and discuss its implementation on top of the actual Google.com search engine. The proposed meta-search engine benefits the user the most when the user does not know what exact document he or she is looking for. Comparison of the context-based meta-search engine with both Google and Guided Google shows that the results returned by context-based meta-search engine is much more intuitive and accurate than the results returned by both Google and Guided Google.

The modelling and simulation approach is employed to develop an intelligent energy management system for hybrid electric vehicles. The aim is to optimize fuel consumption and reduce emissions. An analysis of the role of drivetrain, energy management control strategy and the associated impacts on the fuel consumption with combined wind/drag, slope, rolling, and accessories loads are included.

This paper presents an energy management system to reduce the energy consumption of a vehicle when its air conditioning system is in use. The system controls the mass flow rate of the air by dynamically adjusting the blower speed and air-gates opening under various heat and loads circumstances. Simulations were conducted for a travelling vehicle operating the air conditioning system without and with the developed energy management system. The results show that the comfort temperature within the cabin room is achieved for reduced amount of energy consumption.

To exploit the benefits offered by parallel HEVs, an intelligent energy management model is developed and evaluated in this paper. Despite most existing works, the developed model incorporates combined wind/drag, slope, rolling, and accessories loads to minimise the fuel consumption under varying driving conditions. A slope prediction unit is also employed. The engine and the electric motor can output power simultaneously under a heavy-load or a slopped road condition. Two simulation were conducted namely slopped-windy-prediction and slopped-windy-prediction-hybrid. The results indicate that the vehicle speed and acceleration is smoother where the hybrid component was included. The average fuel consumption for the first and second simulations were 7.94 and 7.46 liter/100 km, respectively.

An intelligent energy management system (IEMS) is developed to improve fuel efficiency of an internal combustion engine vehicle. It helps determine the best approach to run the engine system through dynamically analysing various factors relating to vehicle. The energy balance technique is implemented and utilised. The simulation outcome of the IEMS is compared against that of a conventional system under the same driving factors. The results show that the IEMS reduces the fuel consumption around 5.6% for the tested conditions.