Motilal Nehru National Institute of Technology Allahabad

About: Motilal Nehru National Institute of Technology Allahabad is a education organization based out in Allahabad, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 2475 authors who have published 5067 publications receiving 61891 citations. The organization is also known as: NIT Allahabad & Motilal Nehru Regional Engineering College.

Marine Fungi: A Source of Potential Anticancer Compounds.

Abstract: Metabolites from marine fungi have hogged the limelight in drug discovery because of their promise as therapeutic agents. A number of metabolites related to marine fungi have been discovered from various sources which are known to possess a range of activities as antibacterial, antiviral and anticancer agents. Although, over a thousand marine fungi based metabolites have already been reported, none of them have reached the market yet which could partly be related to non-comprehensive screening approaches and lack of sustained lead optimization. The origin of these marine fungal metabolites is varied as their habitats have been reported from various sources such as sponge, algae, mangrove derived fungi, and fungi from bottom sediments. The importance of these natural compounds is based on their cytotoxicity and related activities that emanate from the diversity in their chemical structures and functional groups present on them. This review covers the majority of anticancer compounds isolated from marine fungi during 2012-2016 against specific cancer cell lines.

Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey

Abstract: Since neural networks have universal approximation capabilities, therefore it is possible to postulate them as solutions for given differential equations that define unsupervised errors. In this paper, we present a wide survey and classification of different Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural network techniques, which are used for solving differential equations of various kinds. Our main purpose is to provide a synthesis of the published research works in this area and stimulate further research interest and effort in the identified topics. Here, we describe the crux of various research articles published by numerous researchers, mostly within the last 10 years to get a better knowledge about the present scenario.

Multi-objective optimization of Nd:YAG laser cutting of thin superalloy sheet using grey relational analysis with entropy measurement

Abstract: This paper presents a hybrid optimization approach for the determination of the optimum laser cutting process parameters which minimize the kerf width, kerf taper, and kerf deviation together during pulsed Nd:YAG laser cutting of a thin sheet of nickel-based superalloy SUPERNI 718 (an equivalent grade to Inconel 718). A hybrid approach of Taguchi methodology and grey relational analysis has been applied to achieve better cut qualities within existing resources. The input process parameters considered are oxygen pressure, pulse width, pulse frequency, and cutting speed. A higher resolution based L27 orthogonal array has been used for conducting the experiments for both straight and curved cut profiles. The designed experimental results are used in grey relational analysis and the weights of the quality characteristics are determined by employing the entropy measurement method. The significant parameters were obtained by performing analysis of variance (ANOVA). The optimized parameters for straight and curved laser cut profiles have been compared. On the basis of optimization results it has been found that the optimal parameter level suggested for straight cut profiles are not valid for curved cut profiles. The application of the hybrid approach for straight cuts has reduced Kt and Kd by 52.37% and 17%, respectively. For curved cuts the approach has reduced Kw and Kt by 8.45% and 44.44%, respectively. The results have also been verified by running confirmation tests.

Heavy metal detoxification and tolerance mechanisms in plants: Implications for phytoremediation

Abstract: Heavy metals, such as cobalt, copper, manganese, molybdenum, and zinc, are essential in trace amounts for growth by plants and other living organisms. However, in excessive amounts these heavy meta...