Cu2+ uptake by Chlorococcum hemicolum - A Xeric Chlorophycean Alga

Bioremediation of copper by xeric chlorophycean bioremediator, Chlorococcum hemicolum was investigated. The growth rates at various concentrations of Cu2+ were assessed in terms of protein level and 8 mg L-1 (37.67 % level in growth kinetics) is the tolerance limit. Absorption/adsorption kinetics was estimated after 240 hrs of Cu2+ treatments. Absorptions were higher than adsorption with maximum accumulation factor (AF) of 1.40. The Cu2+ concentration and absorption were linearly related (r = 0.99; p>0.01). Other biochemical parameters like total sugar, chlorophyll and carotenoids were also quantified to correlate the state of metabolism and these exhibited reduction due to heavy metal stress

Automatic Irony Detection using Feature Fusion and Ensemble Classifier

With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods

Anomaly based Intrusion Detection using Modified Fuzzy Clustering

This paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. As a result, Intrusion Detection System has become an indispensable component of computer security. The proposed method consists of three steps: Pre-Processing, Feature Selection and Clustering. In pre-processing step, the duplicate samples are eliminated from the sample set. Next, principal component analysis is adopted to select the most discriminative features. In clustering step, the network samples are clustered using Robust Spatial Kernel Fuzzy C-Means (RSKFCM) algorithm. RSKFCM is a variant of traditional Fuzzy C-Means which considers the neighbourhood membership information and uses kernel distance metric. To evaluate the proposed method, we conducted experiments on standard dataset and compared the results with state-of-the-art methods. We used cluster validity indices, accuracy and false positive rate as performance metrics. Experimental results inferred that, the proposed method achieves better results compared to other methods

Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method

Social Networking sites have become popular and common places for sharing wide range of emotions through short texts. These emotions include happiness, sadness, anxiety, fear, etc. Analyzing short texts helps in identifying the sentiment expressed by the crowd. Sentiment Analysis on IMDb movie reviews identifies the overall sentiment or opinion expressed by a reviewer towards a movie. Many researchers are working on pruning the sentiment analysis model that clearly identifies and distinguishes between a positive review and a negative review. In the proposed work, we show that the use of Hybrid features obtained by concatenating Machine Learning features (TF, TF-IDF) with Lexicon features (Positive-Negative word count, Connotation) gives better results both in terms of accuracy and complexity when tested against classifiers like SVM, Naïve Bayes, KNN and Maximum Entropy. The proposed model clearly differentiates between a positive review and negative review. Since understanding the context of the reviews plays an important role in classification, using hybrid features helps in capturing the context of the movie reviews and hence increases the accuracy of classification

Acute metheamoglobinemia due to nitrobenzene poisoning: Case series

Nitrobenzene is a nitrite compound; its toxic effects are due to its ability to convert hemoglobin to\ud methaemoglobin by oxidizing iron. The clinical features of nitrobenzene poisoning vary based on the\ud concentration of methaemoglobin in blood. Immediate identification based on clinical features, odour of the\ud compound with supporting evidence of increased methaemoglobin levels will help in a timely intervention thus\ud preventing fatal outcome. Early haemodynamic and ventilator support along with administration of methylene\ud blue as an antidote has been proved crucial in saving some lives. An acute nitrobenzene poisoning presenting with\ud methaemoglobinemia is becoming quite common in this part of the country. Here authorsreport a series of cases\ud of nitrobenzene poisoning where immediate clinical evaluation, with repeated intravenous methylene blue saved\ud three patients, but two patients presenting late and with heavy exposure could not be save

Techniques, Tricks and Algorithms for Efficient GPU-Based Processing of Higher Order Hyperbolic PDEs

GPU computing is expected to play an integral part in all modern Exascale supercomputers. It is also expected that higher order Godunov schemes will make up about a significant fraction of the application mix on such supercomputers. It is, therefore, very important to prepare the community of users of higher order schemes for hyperbolic PDEs for this emerging opportunity. We focus on three broad and high-impact areas where higher order Godunov schemes are used. The first area is computational fluid dynamics (CFD). The second is computational magnetohydrodynamics (MHD) which has an involution constraint that has to be mimetically preserved. The third is computational electrodynamics (CED) which has involution constraints and also extremely stiff source terms. Together, these three diverse uses of higher order Godunov methodology, cover many of the most important applications areas. In all three cases, we show that the optimal use of algorithms, techniques and tricks, along with the use of OpenACC, yields superlative speedups on GPUs! As a bonus, we find a most remarkable and desirable result: some higher order schemes, with their larger operations count per zone, show better speedup than lower order schemes on GPUs. In other words, the GPU is an optimal stratagem for overcoming the higher computational complexities of higher order schemes! Several avenues for future improvement have also been identified. A scalability study is presented for a real-world application using GPUs and comparable numbers of high-end multicore CPUs. It is found that GPUs offer a substantial performance benefit over comparable number of CPUs, especially when all the methods designed in this paper are used.Comment: 73 pages, 17 figure

Phospholipase, proteinase, esterase and haemolytic activity of Candida species isolated from oral cavity and its antifungal susceptibility pattern

Background: Candida species is a normal commensal flora of human body inhabiting the skin, mucous membrane and gastro intestinal tract but may be associated with superficial and deep-seated fungal infections. The switch of Candida species from commensal to a potent pathogen, is facilitated by various extracellular hydrolytic enzymes. The aim of this study was to estimate the phospholipase, proteinase, haemolysin and esterase activity of Candida species and to determine the antifungal susceptibility. Methods: Total 100 isolates of Candida spp. were collected from diagnostic microbiology laboratories in central Kerala. Phospholipase, proteinase, esterase and haemolytic activity was determined by early defined methods of Price et al, Aoki et al, Walter Rudek and Manns et al, respectively. Results: C. tropicalis exhibited highest phospholipase, proteinase and esterase activity followed by C. albicans and C. krusei. C.albicans shows highest haemolytic activity followed by C. tropicalis and C. krusei. Conclusions: Extracellular enzymes, phospholipase, proteinase, esterase and haemolysin was detected among Candida species in the present study

Efficient Finite Difference WENO Scheme for Hyperbolic Systems with Non-Conservative Products

Higher order finite difference Weighted Essentially Non-Oscillatory (WENO) schemes have been constructed for conservation laws. For multidimensional problems, they offer high order accuracy at a fraction of the cost of a finite volume WENO or DG scheme of comparable accuracy. This makes them quite attractive for several science and engineering applications. But, to the best of our knowledge, such schemes have not been extended to non-linear hyperbolic systems with non-conservative products. In this paper, we perform such an extension which improves the domain of applicability of such schemes. The extension is carried out by writing the scheme in fluctuation form. We use the HLLI Riemann solver of Dumbser and Balsara (2016) as a building block for carrying out this extension. Because of the use of an HLL building block, the resulting scheme has a proper supersonic limit. The use of anti-diffusive fluxes ensures that stationary discontinuities can be preserved by the scheme, thus expanding its domain of applicability. Our new finite difference WENO formulation uses the same WENO reconstruction that was used in classical versions, making it very easy for users to transition over to the present formulation. For conservation laws, the new finite difference WENO is shown to perform as well as the classical version of finite difference WENO, with two major advantages:- 1) It can capture jumps in stationary linearly degenerate wave families exactly. 2) It only requires the reconstruction to be applied once. Several examples from hyperbolic PDE systems with non-conservative products are shown which indicate that the scheme works and achieves its design order of accuracy for smooth multidimensional flows. Stringent Riemann ... *Abstract truncated, see PDF*Comment: Accepted in Communications on Applied Mathematics and Computatio

DOES “CASH MANAGEMENT” INFLUENCE COMPANY TO MANAGE LIQUIDITY AND SPENDING

Successful cash management is essential to both corporate and personal financial success. A review of the main ideas, tactics, and advantages of cash management is given in this abstract. In order to maintain economic security, liquidity, and development, effective cash management which includes everything from transaction volume to long-term investments is essential. This abstract examines a number of aspects of cash management, such as anticipating cash flows, managing liquidity, and streamlining the process with technology and financial instruments. It drives into detail about how important it is to keep a sufficient cash balance to cover current expenses while optimising return on surplus funds. In addition to helping individuals and organizations take advantage of financial opportunities like investments, purchases, and debt