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Skin is an important organ and many studies have been carried out to understand its functions and behaviour. Nevertheless, there is still lack of reliable data and theory that could best define skin deformation behaviour. Therefore, this paper aims to quantify the biomechanical properties of bovine under uniaxial tension skin utilising experiment-numerical approach. Bovine skin samples were tested according to ASTM D2209-00 standard to obtain stress-stretch data. Based on the experiment data, a programme is written using Matlab to quantify and determine the bovine skin biomechanical properties. The Ogden parameters are found to be µ = 0.4 and α = 4.6. These values are important for future reference and therefore proving that the current study is significant and has contributed to the pool of knowledge in the area of skin biomechanics.  Index Terms-biomechanical properties, bovine skin, ogden constitutive model

NiTi is categorized as a Shape Memory Alloy (SMA) that has been commercially studied and used in biomedical industry due to two main unique properties, Pseudoelastic (PE) and Shape Memory Effect (SME). Combined with biomimetic properties to human bone, NiTi has the potential to be applied as implants by engineered manufacturing process. The common manufacturing by casting has some challenges in order to obtain intrinsic and miscellaneous design of NiTi parts leash to explore more using powder metallurgy (PM) method that expected to get the porous structure. This paper aims to provide an overview of processing NiTi by conventional PM method which could contribute in focusing porous part that suits for biomedical and implants.

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Current Harumanismango farming technique in Malaysia still mostlydepends on the farmers' own expertise to monitor the crops from the attack ofpests and insects. This approach is susceptible to human errors, and thosewho do not possess this skill may not be able to detect the disease at the righttime. As leaf diseases seriously affect the crop's growth and the quality of theyield, this study aims to develop a recognition system that detects thepresence of disease in the mango leaf using image processing technique.First, the image is acquired through a smartphone camera; once it has beenpre-processed, it is then segmented in which the RGB image is converted toan HSI image, then the features are extracted. Lastly, the classification ofdisease is done to determine thetype of leaf disease. The proposed systemeffectively detects and classify the disease with an accuracy of 68.89%. Thefindings of this project will contribute to farmers and society's benefit, andresearchers can use the approach to address similar issues in future works.

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