In view of the usefulness and a great importance of the kinetic equation in specific chemical engineering problems, we discuss the numerical solution of a simple fuzzy fractional kinetic equation applied for the hemicelluloses hydrolysis reaction. The fuzzy approximate solution is derived based on the Legendre polynomials to the fuzzy fractional equation calculus. Moreover, the complete error analysis is explained based on the application of fuzzy Caputo fractional derivative. The main advantage of the present method is its superior accuracy which is obtained by using a limited number of Legendre polynomials. The method is computationally interesting, and the numerical results demonstrate the effectiveness and validity of the method for solving fuzzy fractional differential equations.

Protein-Protein Interaction (PPI) is a network of protein interconnections which regulates most of the biological methods. A sound state of biota largely depends on synchronized interactions between protein molecules, and any aberrant interactions between protein molecules may lead to complications, including cervical leukemia, tuberculosis, and other neural disorders. In PPI investigation, a plethora of computational methods have been developed over the years to analyze and predict PPI conclusively; however, a majority of these techniques proved to be strenuous and expensive. Therefore, the need for faster, accurate, and critical analysis of PPI warrants the adoption of Machine Learning (ML) methods such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest Model (RFM). These classifiers are useful in PPI unfolding in terms of amino acid sequence data. The SVM classifier, in particular, is serviceable in solving a majority of complex classification problems producing robust results in a reasonable time frame. This publication summarizes some state-of-art SVM based PPI investigations and challenges incurred in the application of the SVM method.

In this study, different types of graphene, including single-, few-, and multi-layer graphene, were grown on a stainless steel (SS) mesh coated with Cu catalyst by using the chemical vapor deposition (CVD) method. Even though the SS mesh consisted of different types of metals, such as Fe, Ni, and Cr, which can also be used as catalysts, the reason for coating Cu catalyst on the SS surface had been related to the nature of the Cu, which promotes the growth of graphene with high quality and quantity at low temperature and time. The reaction temperature and run time, as the most important parameters of the CVD method, were varied, and thus led to the synthesis of different layers of graphene. Moreover, the presence of single-, few-, and multi-layer graphene was confirmed by employing two techniques, namely transmission electron microscopy (TEM) and Raman spectroscopy. On top of that, electron dispersive X-ray (EDX) was further applied to establish the influence of the CVD parameters on the growth of graphene.

In this research, natural nanomaterials including cellulose nanocrystal (CNC), nanofiber cellulose (NFC), and synthetic nanoparticles such as carbon nanofiber (CNF) and carbon nanotube (CNT) with different structures, sizes, and surface areas were produced and analyzed. The most significant contribution of this study is to evaluate and compare these nanomaterials based on the effects of their structures and morphologies on their electrochemical, biomedical, and thermal properties. Based on the obtained results, the natural nanomaterials with low dimension and surface area have zero cytotoxicity effects on the living cells at 12.5 and 3.125 μg/ml concentrations of NFC and CNC, respectively. Meanwhile, synthetic nanomaterials with the high surface area around 15.3–21.1 m2/g and significant thermal stability (480 °C–600 °C) enhance the output of electrode by creating a higher surface area and decreasing the current flow resistance.