Informatics Publishing Limited

Kamla Uniyal , N. S. K. Harsh , D. P. Uniyal , Sanjay Verma

Abstract

An outbreak of a new canker and dieback disease was recorded in Acacia nilotica seedlings. The causal organism was identified as Fusarium lateritium f. sp. acaciae f. sp. nov. and perithecial stage belongs to Gibberella. The disease was controlled by three sprays of mixture of Carbendazim and Copper oxychloride and mixture of Carbendazim and Dithane M-45.

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Sanjay Verma ¹, Ashok Sharma ¹, Sarita Sharma ¹, Rajan K. Priyadarshi ¹

Affiliations
1 Department of Chemical Engineering, Ujjain Engineering Collage, Ujjain (M.P.), IN

Abstract

In drinking water treatment plants, chlorination is done for the disinfection. On one hand chlorine addition provides residual protection against recontamination of water in the distribution with pathogenic micro-organism but at the same time it reacts with natural organic matter (NOM) present in water to form certain by-products, which are harmful in long term consumption. Trihalomethanes (THMs) is one such group of by products which contains CHCl₃, CHCl₂Br, CHClBr₂ and CHBr₃. In drinking water formation of THMs is a function of pH, temperature, reaction time, total organic carbon (TOC), chlorine dose etc. Choosing important parameters to model the formation of THMs is useful alternative to chemical analysis. This paper presents the application of two empirical models for simulating and forecasting THMs concentrations within drinking water. The first is a linear autoregressive model with external inputs, known as ARX; the second is a non-linear artificial neural network (ANN) model. The results demonstrate the potential of an ANN model, which has a unique ability to detect non-linear complex relationships between data. In evaluating all the given data, simulation results show a similar performance for the linear and non-linear models. However, for specific water treatment conditions (very high and very low chlorine doses, pH and TOC), the ANN model gives better predictions than the ARX model.

Keywords

Residual Chlorine, Drinking Water, Neural Networks, Arx.

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Ajay Sharma ¹, C. D. Khare ¹, Anurag Garg ², Sanjay Verma ³

Affiliations
1 EX Department, R.G.P.V. Bhopal University, IN
2 S.V.I.T, Indore, IN
3 I.P.S. Gwalior, IN

Abstract

This paper provide new low power solutions for Very Large Scale Integration (VLSI) designers. Especially, we focus on leakage power reduction. Although leakage power was negligible at 0.18μ technology and in nano scale technology, such as 0.07μ, leakage power is almost equal to dynamic power consumption. This paper presents heretofore unexplored methods for low-power VLSI design. In particular, the Low Leakage approach provides what may be the best solution for VLSI designers concerned about the twin problems of low static power and maintenance of VLSI logic state during sleep mode. For such a two-headed problem, the Low Leakage approach can provide two orders of magnitude (100X) or more static power reduction over the best prior approach; however, there is a cost potentially quite small-in terms of delay increase and area overhead. In short, Low Leakage principles provide heretofore unknown Pareto points for consideration in VLSI design.

Keywords

CMOS, Low Leakage, Static Power Dissipation.

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