Jawaharlal Nehru Technological University, Hyderabad

About: Jawaharlal Nehru Technological University, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 3956 authors who have published 5277 publications receiving 48435 citations. The organization is also known as: JNTU Hyderabad & Nagarjuna Sagar Engineering College.

Plant fibre based bio-composites: Sustainable and renewable green materials

Abstract: The abundant availability and accessibility of plant fibres are the major reasons for an emerging new interest in sustainable technology. While focusing on the composite materials, the main points to be considered are environment friendliness and light weight, with high specific properties. This century has witnessed remarkable achievements in green technology in the field of materials science through the development of high-performance materials made from natural resources is increasing worldwide. Plant fibres are a kind of renewable resources, which have been renewed by nature and human ingenuity for thousands of years. The greatest challenge in working with plant fibre reinforced composites (PFRCs) is their large variation in properties and characteristics. A PFRCs properties are influenced by a number of variables, including the fibre type, environmental conditions, processing methods, and modification of the fibre. A detailed systematic review on these sustainable and renewable green materials is presented in this paper. The overall characteristics of plant fibres used in bio-composites, including source, type, structure, composition, as well as properties, will be reviewed. Finally, the review will conclude with recent developments and future trends of PFRCs as well as key issues that need to be addressed and resolved.

A Comparative Review of Natural and Synthetic Biopolymer Composite Scaffolds.

Abstract: Tissue engineering (TE) and regenerative medicine integrate information and technology from various fields to restore/replace tissues and damaged organs for medical treatments. To achieve this, scaffolds act as delivery vectors or as cellular systems for drugs and cells; thereby, cellular material is able to colonize host cells sufficiently to meet up the requirements of regeneration and repair. This process is multi-stage and requires the development of various components to create the desired neo-tissue or organ. In several current TE strategies, biomaterials are essential components. While several polymers are established for their use as biomaterials, careful consideration of the cellular environment and interactions needed is required in selecting a polymer for a given application. Depending on this, scaffold materials can be of natural or synthetic origin, degradable or nondegradable. In this review, an overview of various natural and synthetic polymers and their possible composite scaffolds with their physicochemical properties including biocompatibility, biodegradability, morphology, mechanical strength, pore size, and porosity are discussed. The scaffolds fabrication techniques and a few commercially available biopolymers are also tabulated.

Acute Effects of Hexavalent Chromium on Survival, Oxygen Consumption, Hematological Parameters and Some Biochemical Profiles of the Indian Major Carp, Labeo rohita

Abstract: An attempt has been made in the present investigation to determine the acute toxicity of hexavalent chromium and its toxicological effects on survival, physiological, hematological and biochemical parameters of the widely consumed Indian major carp, Labeo rohita. Short-term acute toxicity tests were performed adopting renewal bioassay technique (USEPA, 1975) over a period of 96 h, using different concentrations of potassium dichromate to the fish and the 96 h LC[50] value was found to be 111.45 mg/l (Cr(+6) as 39.40 mg/l). ANOVA results showed that the normal respiratory activity of the fish was significantly affected and there is a depression in the metabolic rate at the end of 24, 48, 72 and 96 h exposure. The metal also induced significant decrease (p<0.001) in the hematological parameters of the fish like total erythrocyte count, hemoglobin percent and absolute value Mean cell hemoglobin (MCH) both at the end of 24h and 96 h exposure indicating anemia. Appreciable decline in the biochemical profiles such as total glycogen, total lipids and total protein contents of the fish was also observed. However, the decrease in protein content was significant only at the end of 96 h. This study reflects the extent of the toxic effects of hexavalent chromium and the metal induced cumulative deleterious effects at various functional levels in the widely consumed freshwater fish, Labeo rohita.

A comparative analysis of training methods for artificial neural network rainfall-runoff models

Abstract: This paper compares various training methods available for training multi-layer perceptron (MLP) type of artificial neural networks (ANNs) for modelling the rainfall-runoff process. The training methods investigated include the popular back-propagation algorithm (BPA), real-coded genetic algorithm (RGA), and a self-organizing map (SOM). A SOM was used to first classify the input-output space into different categories and then develop feed-forward MLP models for each category using BPA. The daily average rainfall and streamflow data derived from an existing catchment were employed to develop all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the approach of first classifying the input-output space into different categories using SOM and then developing separate ANN models for different classes trained using BPA performs better than the approach of developing a single ANN rainfall-runoff model trained using BPA. The ANN rainfall-runoff model trained using RGA was able to provide a better generalization of the complex, dynamic, non-linear, and fragmented rainfall-runoff process in comparison with the other approaches investigated in this study. It has been found that the RGA trained ANN model significantly outperformed the ANN model trained using BPA, and was also able to overcome certain limitations of the ANN rainfall-runoff model trained using BPA reported by many researchers in the past. It is noted that the performances of various ANN models should to be evaluated using a wide variety of statistical performance indices rather than relying on a few global error statistics normally employed that are similar in nature to the global error minimized at the output layer of an ANN.