Dengue fever (DF) and the potentially fatal dengue haemorrhagic fever (DHF) are continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Support Vector Machine (SVM) in predicting future dengue outbreak. Datasets used in the undertaken study includes data on dengue cases provided by the Health Department in Kelantan, Malaysia. Data scaling were applied to normalize the range of features before being fed into the training model. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear function. The SVM with RBF kernel function was superior to the other techniques because it obtains the highest prediction accuracy of 85%. The polynomial is an alternative model that can achieve a high prediction performance in terms of sensitivity (76%) and specificity (87%).

Protein hydrolysates from angelwing clam were obtained by enzymatic hydrolysis using bromelain. The bitterness of hydrolysates was evaluated based on the degree hydrolysis (DH), sensory analysis, molecular weight distribution and functional group. By using 3 % of enzyme substrate ratio bromelain resulted in high DH value at 12.57 % when angelwing clam was hydrolysed for 2 hours. Sensory analysis showed that angelwing hydrolysate was bitter. Angelwing hydrolysate had molecular weight below 50 kDa. The lower molecular weight indicated that the protein has been degraded into smaller peptide chains which contribute to bitter taste. Moreover, the high peak of amine group in angelwing hydrolysate (3385.6 cm -1 ) suggested that bitterness exists. Angelwing hydrolysate had higher protein content, lower fat content and had good water holding capacity than the flesh. This result suggested that angelwing hydrolysate could be useful as food ingredient even though bitter taste developed after the hydrolysis. Thus, debittering should be considered in order to pave the way for full utilization of angelwing clam hydrolysate as a food ingredient.Keywords: angelwing clam, sensory, hydrolysate, bromelain, bitterness, physicochemical properties Abstrak Hidrolisat protein daripada kerang mentarang diperolehi daripada hidrolisis proses menggunakan enzim bromelain. Kepahitan hidrolisat dinilai berdasarkan tahap hidrolisis (DH), analisis deria, pengedaran berat molekul dan kumpulan berfungsi. Dengan menggunakan 3 % daripada nisbah substrat enzim bromelain menyebabkan nilai DH tinggi pada 12.57 % apabila kerang mentarang telah dihidrolisiskan selama 2 jam. Analisis deria menunjukkan kerang mentarang hidrolisat adalah pahit. Hidrolisat kerang mentarang mempunyai berat molekul di bawah 50 kDa. Berat molekul yang rendah menunjukkan bahawa protein yang telah dipecahkan kepada rantaian peptida yang lebih kecil menyumbang kepada rasa pahit. Selain itu, kumpulan amina yang mempunyai puncak yang tinggi dalam mentarang hidrolisat (3385.6 cm -1 ) menunjukkan bahawa kepahitan wujud. Hidrolisat kerang mentarang mempunyai kandungan protein yang lebih tinggi, kandungan lemak yang lebih rendah dan mempunyai keupayaan pegangan air yang lebih baik daripada daging. Hasil keputusan ini menunjukkan bahawa kerang mentarang hidrolisat boleh dimanfaatkan sebagai bahan makanan walaupun rasa pahit hadir selepas hidrolisis. Oleh itu, penyah-pahitan perlu dipertimbangkan untuk membuka jalan kepada penggunaan penuh kerang mentarang hidrolisat sebagai bahan makanan.

Cancer can be defined as an uncontrolled and unregulated growth of cell in human body and it can be diagnosed in all level of age, races and also both genders. High level of awareness and adequate knowledge of warning signs of cancer might have great impact in surviving the disease. A cross-sectional study was done to determine the level of knowledge in identifying warning signs of cancer among students in Universiti Teknologi MARA (UiTM) Kota Bharu Campus and to examine the factors that contribute significantly to the level of knowledge. There are 9 independent variables involved in this study; gender, CGPA, father’s education level, mother’s education level, family’s monthly income, health insurance status, family history of having cancer, BM, and age. The dependent variable in this study is level of knowledge in identifying warning signs of cancer where it is categorized into 0 and 1; 0 denoting low knowledge and 1 denoting high knowledge. The finding shows that most of the respondents have low level of knowledge (84.9%). Multiple logistic regression analysis was performed to identify the determinants of level of knowledge in identifying warning signs of cancer. Overall, there were two significant variables (family monthly income and family history of having cancer) found in logistic regression analysis. There are several recommendations highlighted in this study such as the strategies to communicate the warning signs of cancer to the public.