Pediatric Rotavirus Gastroenteritis: A 2 year Analysis to Understand Current Prevalence in Mumbai

Many studies have established the high prevalence of paediatric Rotavirus gastroenteritis in India. The importance of rapid diagnosis of rotavirus infection has also been stressed upon, to initiate prompt rehydration therapy and prevent unnecessary use of antibiotics .We undertook a retrospective analysis of 327 paediatric stool specimens to understand the current prevalence and seasonal distribution of cases in Mumbai and its surrounding areas. Overall Rotavirus positivity rate was 37.9 %, with peak positivity in winter seasons. Infections were more common upto 2 years of age. Incidence of bacterial and parasitic coinfections was low

Online Learning Models for Content Popularity Prediction In Wireless Edge Caching

Caching popular contents in advance is an important technique to achieve the low latency requirement and to reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process (PPP), optimal content placement caching probabilities are derived for known popularity profile, which is unknown in practice. In this paper, online prediction (OP) and online learning (OL) methods are presented based on popularity prediction model (PPM) and Grassmannian prediction model (GPM), to predict the content profile for future time slots for time-varying popularities. In OP, the problem of finding the coefficients is modeled as a constrained non-negative least squares (NNLS) problem which is solved with a modified NNLS algorithm. In addition, these two models are compared with log-request prediction model (RPM), information prediction model (IPM) and average success probability (ASP) based model. Next, in OL methods for the time-varying case, the cumulative mean squared error (MSE) is minimized and the MSE regret is analyzed for each of the models. Moreover, for quasi-time varying case where the popularity changes block-wise, KWIK (know what it knows) learning method is modified for these models to improve the prediction MSE and ASP performance. Simulation results show that for OP, PPM and GPM provides the best ASP among these models, concluding that minimum mean squared error based models do not necessarily result in optimal ASP. OL based models yield approximately similar ASP and MSE, while for quasi-time varying case, KWIK methods provide better performance, which has been verified with MovieLens dataset.Comment: 9 figure, 29 page

Quantum correlations and least disturbing local measurements

We examine the evaluation of the minimum information loss due to an unread local measurement in mixed states of bipartite systems, for a general entropic form. Such quantity provides a measure of quantum correlations, reducing for pure states to the generalized entanglement entropy, while in the case of mixed states it vanishes just for classically correlated states with respect to the measured system, as the quantum discord. General stationary conditions are provided, together with their explicit form for general two-qubit states. Closed expressions for the minimum information loss as measured by quadratic and cubic entropies are also derived for general states of two-qubit systems. As application, we analyze the case of states with maximally mixed marginals, where a general evaluation is provided, as well as X states and the mixture of two aligned states.Comment: 10 pages, 3 figure

DEVELOPMENT AND VALIDATION OF SPECTROPHOTOMETRIC AND ION PAIR CHROMATOGRAPHIC TECHNIQUE FOR ESTIMATION OF VALSARTAN AND HYDROCHLOROTHIAZIDE

Two new simple, sensitive, rapid, accurate and reproducible methods (UV-spectrophotometric and ion pair chromatography) have been developed for simultaneous estimation of valsartan (VAL) and hydrochlrothiazide (HCTZ) from their tablet dosage form. The first method involves multiwavelength spectrophotometric method (Method 1) in which interference of HCTZ at 245nm (wavelength for estimation of VAL) was removed by recording absorbance difference at 245nm and 301 nm whereas HCTZ was estimated directly from its absorbance at 316 nm at which VAL shows no absorbance. Linearity of the response was demonstrated by VAL in the concentration range of 5-45 g/ml with a square correlation coefficient (r2) of 0.9998. Linearity of the response was demonstrated by HCTZ in the concentration range of 2-18 g/ml with a square correlation coefficient (r2) of 0.9994. The second method utilizes ion pair chromatography (Method 2) on a HIQ sil ODS column (250 mm* 4.6 mm i.d.) using methanol: 0.0025 M orthophosphoric acid: (70:30 by volume) having pH 4.6: 0.1% hexane sulphonic acid as mobile phase with UV detection at 259nm over concentration range for VAL is 240-0 μg/ml, and for HCTZ is 75-0μg/ml. Losartan potassium was used as the internal standard. The suggested procedures were checked using laboratory prepared mixtures and were applied successfully for the analysis of their tablet dosage form. The results of analysis were statistically analysed. Both the methods were validated as per ICH Q2B guideline

Classification of Handwritten Digits using Machine Learning Techniques

ThenbspMNIST datasetnbsp(MixednbspNational Institute of Standards and Technologynbspdatabase) is a largenbspdatabasenbspof handwritten digits that is commonly used fornbsptrainingnbspvariousnbspimage processingnbspsystems. [1][2] The database is also widely used for training and testing in the field ofnbspmachine learning. The MNIST database contains 60,000 training images and 10,000 testing images. [3] In this paper, we aim to apply classification techniques to predict labels for records in the MNIST dataset using machine learning. In total, there are 10 labels ranging from 0-9. Classification will be done using Random Forest Classification Algorithm. We also aim to implement Principle Component Analysis to reduce the dimensionality of the data while retaining its variance. To this data, we aim to apply K Nearest Neighbors Classification Algorith

Pseudo Random Generator Based Public Key Cryptography

Advances in communication technology have seen strong interest in digital data transmission. However, illegal data access has become more easy and prevalent in wireless and general communication networks. In order to protect the valuable data from illegal access, different kinds of cryptographic systems have been proposed. In this paper, a new integrating channel coding and cryptography design communication systems is proposed. So we use cryptography as an error detection tool. In order to preserve the advantages of encryption and to improve its disadvantages, we place the encryptor before the encoder. The hamming encoder is used to select the generator matrix to be used as a block code to form the new system .In this the security of common cryptographic primitive i.e a key stream generator based on LFSR can be strengthened by using the properties of a physical layer.So, a passive eaves dropping will experience great difficulty in cracking the LFSR based cryptography system as the computational complexity of discovering the secret key increases to large extent. The analysis indicates that the proposed design possesses the following feature. Its security is higher than the conventional one with the channel encoder only. Privacy is more due to unknown random codes. As the applied codes are unknown to a hostile user, this means that it is hardly possible to detect the message of another user. Anti-jam performance is good. It overcomes the disadvantage of Chaos based cryptography system as input data is not extended and hence bandwidth is not wasted. Moreover, the computer simulation shows that the proposed system has a good ability in error detection especially when the SNR per bit is moderate high, and the detection ability is enhanced when the increased length of Hamming code is employed