Recently, the attraction on the bio-composite (known as green composites) materials has significantly increased due to the potential of being substitute to conventional materials used in manufacturing industries. Bio-composite materials are produced with natural fibres or natural resins instead of synthesized fibres (carbon, glass, etc fibres) or resins (poly vinyl alcohol, epoxy, etc resins ). The bio-based fibres such as jute, sisal, flax, hemp, bamboo, hair, wool, silk etc., are obtained from plants or animals. Also, natural matrix materials such as natural rubber, polyester, etc., are produced from plants. The advantages of bio-composites such as the ease of disposal and being able to composted characteristics of bio-composites after the expiration date which is not generally possible with conventional synthetic materials, being renewable, sustainable have attracted many researcher. Furthermore, the comparable mechanical properties of bio-composites make feasible for application to many different products. This study reviews the, recent trends, mechanical and chemical properties, and application of bio-composites in recent years.

The objective of this paper was to predict the failure load in single lap adhesive joints subjected to tensile loading by using artificial neural networks. Experimental data obtained from the literature cover the single lap adhesive joints with various geometric models under the tensile loading. The data are arranged in a format such that two input parameters cover the length and width of bond area in single lap adhesive joints and the corresponding output is the ultimate failure load. An artificial neural network model was developed to estimate relationship between failure loads by using geometric dimensions of bond area as input data. A three-layer feedforward artificial neural network that utilized Levenberg–Marquardt learning algorithm model was used in order to train network. It was observed that artificial neural network model can estimate failure load of single lap adhesive joints with acceptable error. Mean absolute percentage error and Nash–Sutcliffe coefficient of efficiency values of both training and testing data were 3.523 and 3.524 and 0.997 and 0.992, respectively. The results showed that the artificial neural network is an efficient alternative method to predict the failure load of single lap adhesive joints. Also estimated results are in very good agreement with the experimental data

The current study investigates the fuel properties, performance and emission characteristics in a variable compression ratio (VCR) diesel of the biodiesel produced from the waste cooking oil (WCO). The WCO samples were collected from the university and converted to biodiesel fuel with a two-step transesterification reaction. The fuel property tests showed that the properties of the WCO biodiesel were within the biodiesel standards. Diesel, WCO biodiesel and diesel-WCO biodiesel blend (B20) was used as fuel in a VCR engine. The performance and emission characteristics of the engine were measured at two different compression ratios (14:1 and 16:1) under partial load conditions. The experimental results showed that WCO biodiesel slightly decreased the brake thermal efficiency and thus increased specific fuel consumption. Biodiesel usage improved CO emissions up to 21,75% compared to diesel fuel. But, biodiesel usage increased CO 2 and NO x emission due to higher combustion temperature and extra oxygen content of the biodiesel.