Column Removal of Trichloroethylene and Dichloromethane using Low Cost Activated Carbon

M. Radhika,Young-Seak Lee,K. Palanivelu 한국탄소학회 2010 Carbon Letters Vol.11 No.1

Coconut shell activated carbon (CSAC) was investigated for its ability in the removal of two neutral chlorinated organic compounds, namely trichloroethylene (TCE) and dichloromethane (DCM) from aqueous solution using a packed bed column. The efficiency of the prepared activated carbon was also compared with a commercial activated carbon (CAC). The important design parameters such as flow rate and bed height were studied. In all the cases the lowest flow rate (5 mL/min) and the highest bed height (25 cm) resulted in maximum uptake and per cent removal. The experimental data were analysed using bed depth service time model (BDST) and Thomas model. The regeneration experiments including about five adsorption-desorption cycles were conducted. The suitable elutant selected from batch regeneration experiments (25% isopropyl alcohol) was used to desorb the loaded activated carbon in each cycle.

Feature Selection Using Submodular Approach for Financial Big Data

Girija Attigeri,Manohara Pai M. M.,Radhika M. Pai 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.6

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is gettinghumungous and is termed as big data. The financial sector is one domain which needs to leverage the big databeing generated to identify financial risks, fraudulent activities, and so on. The design of predictive models forsuch financial big data is imperative for maintaining the health of the country’s economics. Financial data hasmany features such as transaction history, repayment data, purchase data, investment data, and so on. The mainproblem in predictive algorithm is finding the right subset of representative features from which the predictivemodel can be constructed for a particular task. This paper proposes a correlation-based method usingsubmodular optimization for selecting the optimum number of features and thereby, reducing the dimensionsof the data for faster and better prediction. The important proposition is that the optimal feature subset shouldcontain features having high correlation with the class label, but should not correlate with each other in thesubset. Experiments are conducted to understand the effect of the various subsets on different classificationalgorithms for loan data. The IBM Bluemix Big Data platform is used for experimentation along with the Sparknotebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsetsin significantly less execution time. The algorithm is also compared with the existing feature selection andextraction algorithms.

Duplex and Composite Coatings: A Thematic Review on Thermal Spray Techniques and Applications

M. Sathish,N. Radhika,Bassiouny Saleh 대한금속·재료학회 2023 METALS AND MATERIALS International Vol.29 No.5

Thermal spraying is a broad terminology for a coating process in which materials are deposited in a spray of finely distributedliquefied or semi-liquefied particulates to build layered deposition coatings. Duplex and composite coatings can be utilizedin the thermal spray technique to enhance the tribological properties of a broad range of engineering materials while savingmoney; service life extension, maintenance expenses, and replacement costs are lower than for single-layered coatings. As aresult, this paper critically analyses various pre-processing surface treatments for substrates, commonly used thermal sprayprocessing methods, and various post-processing surface treatments for thermally sprayed substrate materials. This paperalso includes a description of several characterization methodologies as well as some comprehensive prospective applicationsfor engineering components with challenges and scopes. Thus, this paper provides a fundamental understanding of surfacetreatments to researchers who use thermal spray coating techniques.

Time and Cost Analysis for Highway Road Construction Project Using Artificial Neural Networks

Naik, M. Gopal,Radhika, V. Shiva Bala Korea Institute of Construction Engineering and Ma 2015 Journal of construction engineering and project ma Vol.5 No.1

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

Isolation, identification and characterization of arsenic transforming exogenous endophytic Citrobacter sp. RPT from roots of Pteris vittata

Selvankumar, T.,Radhika, R.,Mythili, R.,Arunprakash, S.,Srinivasan, P.,Govarthanan, M.,Kim, Hyunook Springer-Verlag 2017 3 Biotech Vol.7 No.4

A New Marginal Color Image Water Marking Method based on Logical Operators

B. Vijaya Kumar,M.Radhika Mani,G. Roseline NesaKumari,Dr. V. Vijaya Kumar 보안공학연구지원센터 2009 International Journal of Security and Its Applicat Vol.3 No.4

Image hiding is a technique that embeds the images into a cover image which makes the watermarks imperceptible so that they can be securely transmitted to the receiver. In this paper, an effective color image watermarking method based on the marginal strategy is proposed. The method uses logical operators like AND and XOR on the nibble to hide the image with in the cover image. To test the robustness of the stego image the present method is applied on different kinds of cover images and the resulting perceptual quality is good.

Texture Classification Based on Random Threshold Vector Technique

B.V. Ramana Reddy,M.Radhika Mani,B.Sujatha,Dr.V.Vijaya Kumar 보안공학연구지원센터 2010 International Journal of Multimedia and Ubiquitous Vol.5 No.1

Evaluation of Growth, Carcass, Immune Response and Stress Parameters in Naked Neck Chicken and Their Normal Siblings under Tropical Winter and Summer Temperatures

Rajkumar, U.,Reddy, M.R.,Rao, S.V. Rama,Radhika, K.,Shanmugam, M. Asian Australasian Association of Animal Productio 2011 Animal Bioscience Vol.24 No.4

The performance of naked neck and normal chicken was evaluated with respect to growth, carcass, immune, biochemical and stress parameters under winter and summer seasons to assess the suitability of naked neck birds under high temperatures in the global scenario of climate change. The growth performance was significantly ($p{\leq}0.05$) higher in naked neck chicken in the summer season. The dressing percentage was significantly ($p{\leq}0.05$) higher in naked neck birds in both winter and summer season because of reduced plumage. The thigh, giblet and feather proportion significantly ($p{\leq}0.05$) varied between naked neck and normal chickens in summer season. The humeral immune response to sheep red blood cells (SRBC), Newcastle disease vaccine (NDV) and cutaneous basophil hypersensitivity (CBH) did not show any significant differences among the chicken groups. The protein and cholesterol concentration observed was within the normal ranges. The total cholesterol levels in plasma were significantly ($p{\leq}0.05$) lower in naked neck birds in both the seasons. H:L ratio was significantly ($p{\leq}0.05$) lower in summer season indicating less stress in naked neck chicken. Basophil and eosinophil concentration was significantly ($p{\leq}0.05$) higher in normal chicken in summer. The lipid peroxidation was higher in full feathered birds under summer stress. The enzyme glutathione reductase (GR) levels were significantly higher during the summer and varied significantly ($p{\leq}0.05$) between the normal and naked neck chicken in both seasons. The results indicated that the naked neck birds performed significantly better at high ambient temperatures with respect to growth, carcass and biochemical parameters. It was concluded that the ability of the naked neck chicken to adapt to high temperatures foresees a viable option for the biological mitigation of climate change.

Cortically projecting basal forebrain parvalbumin neurons regulate cortical gamma band oscillations

Kim, Tae,Thankachan, Stephen,McKenna, James T.,McNally, James M.,Yang, Chun,Choi, Jee Hyun,Chen, Lichao,Kocsis, Bernat,Deisseroth, Karl,Strecker, Robert E.,Basheer, Radhika,Brown, Ritchie E.,McCarley, National Academy of Sciences 2015 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF Vol.112 No.11

<P><B>Significance</B></P><P>When we are awake, purposeful thinking and behavior require the synchronization of brain cells involved in different aspects of the same task. Cerebral cortex electrical oscillations in the gamma (30–80 Hz) range are particularly important in such synchronization. In this report we identify a particular subcortical cell type which has increased activity during waking and is involved in activating the cerebral cortex and generating gamma oscillations, enabling active cortical processing. Abnormalities of the brain mechanisms controlling gamma oscillations are involved in the disordered thinking typical of neuropsychiatric disorders such as schizophrenia. Thus, these findings may pave the way for targeted therapies to treat schizophrenia and other disorders involving abnormal cortical gamma oscillations.</P><P>Cortical gamma band oscillations (GBO, 30–80 Hz, typically ∼40 Hz) are involved in higher cognitive functions such as feature binding, attention, and working memory. GBO abnormalities are a feature of several neuropsychiatric disorders associated with dysfunction of cortical fast-spiking interneurons containing the calcium-binding protein parvalbumin (PV). GBO vary according to the state of arousal, are modulated by attention, and are correlated with conscious awareness. However, the subcortical cell types underlying the state-dependent control of GBO are not well understood. Here we tested the role of one cell type in the wakefulness-promoting basal forebrain (BF) region, cortically projecting GABAergic neurons containing PV, whose virally transduced fibers we found apposed cortical PV interneurons involved in generating GBO. Optogenetic stimulation of BF PV neurons in mice preferentially increased cortical GBO power by entraining a cortical oscillator with a resonant frequency of ∼40 Hz, as revealed by analysis of both rhythmic and nonrhythmic BF PV stimulation. Selective saporin lesions of BF cholinergic neurons did not alter the enhancement of cortical GBO power induced by BF PV stimulation. Importantly, bilateral optogenetic inhibition of BF PV neurons decreased the power of the 40-Hz auditory steady-state response, a read-out of the ability of the cortex to generate GBO used in clinical studies. Our results are surprising and novel in indicating that this presumptively inhibitory BF PV input controls cortical GBO, likely by synchronizing the activity of cortical PV interneurons. BF PV neurons may represent a previously unidentified therapeutic target to treat disorders involving abnormal GBO, such as schizophrenia.</P>