A systematic study on natural radioactivity in bore well water samples of Kodagu district, India was carried out with the use of LED fluorimetric and radon emanometric techniques. Uranium concentration in the studied water samples varied from 0.013 to 0.083 Bq l-1 with an average of 0.046 Bq l-1. Radon concentration in these samples varied from 6.38 to 30.69 Bq l-1 with an average of 13.49 Bq l-1. The total dose due to U and 222Rn concentration in these water samples varied from 33.93 to 158.46 μSvy-1 with an average of 69.97 μSvy-1. The average annual effective dose due to U and 222Rn concentrations in bore well water samples are below the limiting values recommended by EPA, WHO and AERB. Estimated concentrations of uranium and radon were within the recommended MCL by USEPA.

[1]. UNSCEAR, 2000 "Annex A: Dose assessment methodologies; Annex B: Exposures from natural radiation sources," United Nations Scientific Committee on the Effects of Atomic Radiation, United States.
[2]. NRC, 1983 "Uranium." In: Drinking Water and Health vol. 5. Washington, DC, United States: National Academy Press, 1983.
[3]. Hess, C. T.; Michel, J.; Harton, T. R.; Procured, H. M.; Coniglio, W. A., (1985). The occurrences of radioactivity in public water supplies in United States., Health Phys., 48 (5), 553- 86. [4]. Hakonson-Hayes, A.C., Fresquez, P.R. and Whicker, F.W., 2002. Assessing potential risks from exposure to natural uranium in well water. Journal of environmental radioactivity, 59(1), pp.29-40. [5]. Bleise, A., Danesi, P.R. and Burkart, W., 2003. Properties, use and health effects of depleted uranium (DU): a general overview. Journal of environmental radioactivity, 64(2), pp.93-112.

Nowadays renewable energy sources (RES) are integrated to the grid to fulfil the increased deman to energy.............

[1]. Mukhtair Singh, Vinod Khadikar, Ambrish Chandra and Rajiv K. Mehra, "Grid interconnection of Renewable..............."

The Geomagnetically induced current adversely effects the power system. Main impact of geomagnetically induced current is on power transformer and also HVDC converter transformer. Because of the interaction of the solar storm on earth, there is a occurrence of DC bias current. Many harmful effects are observed in the HVDC converter transformer. The invasion of geomagnetically induced current into the converter transformer is through neutral wire which will result into the saturation of converter transformer core saturation..............

[1]. J.A.Patel,R.S.Mehta,S.B.Rathod,V.N.Rajput,K.S.Pandya, "Analysis of Gepmagnetically Induced Current in Transformer", International Conference on Electrical,Electronics and Optimization Techniques (ICEEOT)-2016.
[2]. Xue ling Ma,Ju Wen,Lian guang Liu,Chao Wang, "Simulation Study on Converter Transformer Saturation Characteristics due to GIC",International Conference on Electricity Distribution,China,2010.
[3]. Yong Li,Longfu Luo,Dajiang He,Christian Rehtanz,Dechang Yang,Sven Riberg, "Study on the Effects of the DC Bias on the Harmonic Characteristic of the New Converter Transformer",IEEE,2010.
[4]. Dechang Yang,Yong Li,Christian Rehtanz,Longfu Luo,Jiazhu Xu, "Study on Harmonic Losses of Inductive Filtering Converter Transformer(IFCT) in HVDC System,IEEE,2010.
[5]. R.Girgis,K.Vedante,K.Gramme, "Effects of Geomagnetically Induced Currents on Power Transformers and Power Systems",CIGRE 2012.

The increasing demand of energy in the world has causes the pollution and devastation of environment and also depletion of the resources. It imminent that cannot be avoided, however, There is agitation and confrontation from country to country which later realised that only energy efficiency practices is the means of minimizing the impact to the environment, but developing countries like Nigeria centered only on end users. These studies see how energy efficiency will be built into energy generation processes and the benefit to the environment, economic growth and development of a country.

[1]. ABB Incorporation(2011). Enhancing Productivity and Energy Efficiency Oil Refineries, Terminals and
Downstream Complexes. www.abb.co.uk/refining.
[2]. ABB Inc. (2007), Energy Efficiency in Power Grid. www.abb.co.uk
[3]. Caiflin Bergh (2012). Energy Efficiency in the South Africa Crude Oil Refining Industry; Drives, Barriers and
Opportunities.
[4]. Dan Staniaszek and Eoin Lees, (2012). Determining Energy Savings for Energy Efficiency Obligation Schemes
[5]. Feynman, Richard (1964). The Feynman Lectures on Physics; Volume 1. U.S.A: Addison Wesley. ISBN 0-201-
02115-3

The Empirical Mode Decomposition (EMD) is a basic building block of Hilbert-Huang Transformation. The main principle behind EMD is to decompose a sound wave into its intrinsic mode function (IMF). The time domain analysis is fairly described with EMD breaking down. The basis is derived from the same sound wave only. The analysis is useful for affected respiratory sound waves, which are non-linear and non-stationary in nature. Huang and Wu established another milestone in the area of EMD called Ensemble Empirical Mode Decomposition (EEMD). The EEMD is now can be used to interpolate a sound wave from the affected waveform.............

[1]. Hai-Lin Feng, Yi-Ming Fang, Xuan-Qi Xiang, Jian Li," A Data-Driven Noise Reduction Method and Its Application for the Enhancement of StressWave Signals" ScientificWorld Journal article id 353081 , 2012 .
[2]. Tsung-Ying Sun Chan-Cheng Liu ; Jyun-Hong Jheng ; "An efficient noise reduction algorithm using empirical mode decomposition and correlation measurement" Intelligent Signal Processing and Communications Systems, Feb2008.
[3]. Yunchao Gao Enfang Sang ; Zhengyan Shen "Comparison of EMD and Complex EMD in Signal Processing" image and Signal Processing, 2008.
[4]. Shelja Kumari, Himanshu Sharma, and Taruna Sharma, "Broadband Signal Noise Reduction by Various Techniques" in IJERD, 2013, paper 7.7, p. 49.

With the increase in the number of coal based thermal power plants in India, generation of coal ash has reached enormous proportions. India at present produces around 120 Million tonnes of Ash per annum. The power requirements of the country are rapidly increasing with increase in growth of the industrial sectors. India depends on Thermal power as its main source (around 80% of power produced is thermal power), as a result the quantity of Ash produced shall also increase. Indian coal on an average has 35 % Ash and this is one of the prime factors which shall lead to increased ash production and hence, Ash utilization problems for the country. Out of the total ash produced. Fly ash contributes to a small percentage, majority being Pond ash and bottom ash. Ash disposal involves design and installation of ash ponds............

[1]. Soil Mechanics and Foundation Engineering - B.C. Punmia
[2]. J. Breckling, Ed., "The Analysis of Directional Time Series: Applications to Wind Speed and Direction", ser. Lecture Notes in Statistics. Berlin, Germany: Springer, 1989, vol. 61.
[3]. Ashis Kumar Bera; Ambarish Ghosh; andAmalendu Ghosh "Compaction Characteristics of Pond Ash".
[4]. Flyash characterization With Reference to Geotechnical Applications- N. S. PANDIAN, Department of Civil Engineering, Indian Institute of Science, Bangalore R. E. Sorace, V. S. Reinhardt, and S. A. Vaughn, "High-speed digital-to-RF converter," U.S. Patent 5 668 842, Sept. 16, 1997.
[5]. G. A. Leonardsand B. Bailey"Pulverized Coal Ash as Structural Fill", Journal of Geotechnical Engineering Div., ASCE, 108, 517–531 (1982).

Artificial Neural Networks (ANNs) are nonlinear mapping structures which functions same as human brain. Modeling can be made stronger especially while the underlying data relationship is not known. ANNs may recognize and learn inter-related patterns between input data sets and related target values. After training, ANNs may be utilized to judge the output of new independent input data. Thus ANNs are used best for the modeling of membrane processes, like ultra filtration and microfiltration. This allows us to judge the permeate flux and membrane rejection as functions of process variables. The aim is modeling of membrane transport for protein filtration is to analyze membrane systems by means of ANNs. To analyze this different ANNs are developed with the help of Mat lab. [1a][9].

[1]. Sanjeev V. Tambe, Bhaskar D. Kulkarni and Pradeep Deshpande, Elements of Artificial Neural Network with Selected Applications in Chemical Engineering and Chemical & Biological Sciences, Simulation and Advanced Control Inc., Louisville, USA, 1995
[2]. Curcio, S... "Ultrafiltration of BSA in pulsating conditions: an artificial neural networks approach", Journal of Membrane Science, 20050115
[3]. Stefano Curcio, Germana Scilingo,Vincenza Calabro, Gabriele Iorio , Ultrafiltration of BSA in pulsating conditions-an artificial neural networks approach,Department of Chemical Engineering and Materials, University of Calabria. Journal of Membrane Science 246 (2005) 235–247
[4]. Mohammad A.Razavi. "Dynamic modeling of milk ultra filtration by artificial neural network", Journal of Membrane Science, 20030801
[5]. Albert S. Kim and Huaiqun Chen, Prediction of permeate flux decline in Cross flow membrane filtration of colloidal suspension: a radial basis function neural network approach, Department of Civil and Environmental Engineering, University of Hawaii at Manoa,Honolulu, Hawaii.

Burnishing process is a surface enhancement technique, which improves the surface finish of the component along with surface properties of the component. It is a cost effective process, mostly used in aerospace, biomedical, and automobile industries to improve reliability and performance of the component. In burnishing process, response parameters depends on burnishing process parameters, tool, and material on which burnishing is done. The key driving forces for newer production technologies and material development are strength to weight ratio of materials, performance and reliability improvement. The availability of appropriate manufacturing methods plays a vital role with respect to both material properties, cost. Authors tried to review the research papers of last four decades and progress of burnishing process is discussed

[1]. N. H. Tam, S CLoh and S. Miyazawa, ―Surface hardening by ball burnishing,‖ Tribol. Int., vol. 23, no. 6, pp. 413–417, 1991
[2]. T. Morimoto and K. Tamamura, ―Burnishing process using a rotating ball-tool — effect of tool material on the burnishing process,‖ Wear, vol. 147, no. 1, pp. 185–193, 1991
[3]. F. Klocke and J. Liermann, ―Roller burnishing of hard turned surfaces,‖ Int. J. Mach. Tools Manuf., vol. 38, no. 5–6, pp. 419–423, 1998.
[4]. F. J. Shiou and C. H. Chen, ―Freeform surface finish of plastic injection mold by using ball-burnishing process,‖ J. Mater. Process. Technol., vol. 140, no. 1–3 SPEC., pp. 248–254, 2003
[5]. B. C. Yeldose and B. Ramamoorthy, ―An investigation into the high performance of TiN-coated rollers in burnishing process,‖ J. Mater. Process. Technol., vol. 207, no. 1–3, pp. 350–355, 2008.

In this paper a development method to detect and classify the several power quality problems using the discrete wavelet transformation and artificial neural networks combined. There are several other methods in use to detect the same problem like Hilbert transform, Gabor transform, Gabor-Wigner transform, S transform, and Hilbert-Haung transform. The method of using wavelet and ANN includes the development of voltage waveforms of sampling rate and number of cycles, and also large number of power quality events with help of MATLAB software. The wavelet transformation and ANN tools used to get required coefficients. The obtained events of power quality monitored in each step to classify the particular event. These steps of the paper lead towards the automatic real time monitoring, detection and classification of power signals.

[1]. Power quality event classification: an overview and key issues D. Saxena*1, K.S. Verma# and S.N.Singh. International journal of engineering, science and technology vol 2 ,no 3 ,2010 pp. 186-199
[2]. MATLAB/Simulink based Modeling and simulation of Power quality Disturbances by S.Khokhar, A.A mohdZin , A.S mokhtar ,. 2014 IEEE