KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution

Numerical Study On Oscillating Flow Over A Flat Plate Using Pseudo-Compressibility in Intermittent Turbulent Regime

A Computational Fluid Dynamic (CFD) in-house code is developed to study unsteady characteristics of incompressible oscillating boundary layer flow over a flat plate under laminar and intermittently turbulent condition using pseudo-compressible unsteady Reynolds Averaged Navier- Stokes (RANS) model. In the in-house code, the two-dimensional, unsteady conservation of mass and momentum equations are discretized using finite difference techniques which employs second order accurate (based on Taylor series) central differencing for spatial derivatives and second order Runge-Kutta accurate differencing for temporal derivatives. The in-house code employs Fully Explicit-Finite Difference technique (FEFD) to solve the governing differential equations of the mathematical model. In the study two different closure models are adopted, Chien’s (k–epsilon) and Jones and Launder (k–epsilon) turbulence model. For the purpose of validation and verification of the proposed pseudo-compressibility method, flow over a flat plate is chosen as benchmark case. The numerically predicted velocities are compared to experimentally observed velocity fields using Particle Image Velocimetry (PIV) in laminar regime. The verification of the proposed model is performed using Grid Convergence Index (GCI) method. The discretization errors observed are less than 5% which are within the acceptable range. Once verified and validated, the technique of pseudo-compressibility is use to simulate oscillating flow problem. The velocity fields predicted by the in-house code in laminar regime are compared to the one given by the analytical solution to Stokes’ second problem of oscillating flow. An intermittency equation (gamma ) is proposed which couples with Jones and Launder (k–epsilon) along with unsteady RANS equations to simulate intermittently turbulent oscillating boundary flows. Using the proposed unsteady pseudo-compressible NS and RANS models, numerical experiments were conducted for unsteady cases for Reynolds number (based on Stokes’ thickness) corresponding to laminar and intermittently turbulent flows, respectively. Predicted time-dependent velocity profiles and shear stress distributions are compared to LES results and experimental data. Turbulence properties during acceleration and deceleration phases are also predicted. The sudden rise in shear stress during the acceleration phase of the oscillation, indicating the onset of intermittence, is observed and discussed. Comparison of the results shows that the observed deviations between the velocity magnitudes predicted by the in-house code and experimental data are within the acceptable range for laminar and intermittently turbulent flow conditions. Based on the results of the present study, one can conclude that the proposed unsteady pseudo-compressible intermittent RANS model is capable of predicting the characteristics of oscillating external flows successfully

DISINVESTMENT IN INDIA: AN EMPIRICAL STUDY

ABSTRACT Disinvestment is a procedure whereby some parts (beyond 51%) of Public Sector enterprises (PSEs) are sold to private organizations or individuals. However, it is the government and not the Public Sector Units who receive money from disinvestment. The study has been conducted to achieve two basic objectives i.e.. To assess the disinvestment in last five (5) years, To find out the reasons of failure to achieve disinvestment Targets set by Government of India and to suggest some measures to be helpful in achieving disinvestment targets declared in the budget speech of every year. For the present study relevant data have been collected from secondary sources like annual reports of various websites of government of india and newspapers. This study concluded that Disinvestment targets are declared every year in budget speech but Government of India does not take any precautionary measures for achieving these targets, it does not frame any policy every year. Therefore, Disinvestment targets can only be met when Government review Disinvestment Policies time to time and Government should reduce Ministerial differences and it should also consider those years in which elections are to be held while deciding Disinvestment Targets

Integrative genomics reveals pathogenic mediator of valproate-induced neurodevelopmental disability

Prenatal exposure to the anti-seizure medication sodium valproate (VPA) is associated with an increased risk of adverse postnatal neurodevelopmental outcomes, including lowered intellectual ability, autism spectrum disorder and attention-deficit hyperactivity disorder. In this study, we aimed to clarify the molecular mechanisms underpinning the neurodevelopmental consequences of gestational VPA exposure using integrative genomics. First, we assessed the effect of gestational VPA on fetal brain gene expression using a validated rat model of valproate teratogenicity that mimics the human scenario of chronic oral valproate treatment during pregnancy at doses which are therapeutically relevant to the treatment of epilepsy. Two different rat strains were studied - inbred Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a model of genetic generalized epilepsy, and inbred Non-Epileptic Control rats. Female rats were fed standard chow or VPA mixed in standard chow for 2 weeks prior to conception and then mated with same-strain males. In the VPA-exposed rats maternal oral treatment was continued throughout pregnancy. Fetuses were extracted via C-section on gestational day 21 (one day prior to birth) and fetal brains were snap frozen and genome-wide gene expression data generated. We found that gestational VPA exposure via chronic maternal oral dosing was associated with substantial drug-induced differential gene expression in the pup brains, including dysregulated splicing, and observed that this occurred in the absence of evidence for significant neuronal gain or loss. The functional consequences of VPA-induced gene expression were explored using pathway analysis and integration with genetic risk data for psychiatric disease and behavioural traits. The set of genes down-regulated by VPA in the pup brains were significantly enriched for pathways related to neurodevelopment and synaptic function, and significantly enriched for heritability to human intelligence, schizophrenia and bipolar disorder. Our results provide a mechanistic link between chronic fetal VPA exposure and neurodevelopmental disability mediated by VPA-induced transcriptional dysregulation

An Analytical Study of Rumoured Tweets by Using Twitter Data

Earlier when the internet was not there, rumours were spread by word of mouth technique but in this era of technology where we have social networking sites like twitter, rumours can be spread easily and quickly and a situation of panic can arise. Twitter is an American online news and social networking service on which users finds the latest news and world events faster. It is used for communication, interaction withpeople, announcement of event etc. from breaking news to sports, politics and everyday interests, one can find this service very addictive and an easy way to gather information about a certain event. Businesses can also use it to build their own brands and for marketing. But the founders of twitter like jack Dorsey forgot one thing that every coin has two sides. While twitter is a great way to interact with the masses, it is also a home of spammers. Spamming is a very common thing on twitter. Spammers create twitter accounts to perform a variety of tasks like posting links with unrelated tweets and the speed at which these fake and malicious misinformation spread on twitter in a real-time emergencies always causing a huge flood of tweets on twitter. In this paper, we demonstrated an analytical study of those rumoured tweets by twitter data. Using some of the rumoured tweets posted during the Chennai flood in 2015 and some non-rumoured tweets, we trained a classifier. The ability to track rumours and predict their outcomes have many applications for journalists, emergency services, and thereforehelp in minimizing the impact of false and fake information on this twitter platform

Cross-protection against European swine influenza viruses in the context of infection immunity against the 2009 pandemic H1N1 virus : studies in the pig model of influenza

Pigs are natural hosts for the same influenza virus subtypes as humans and are a valuable model for cross-protection studies with influenza. In this study, we have used the pig model to examine the extent of virological protection between a) the 2009 pandemic H1N1 (pH1N1) virus and three different European H1 swine influenza virus (SIV) lineages, and b) these H1 viruses and a European H3N2 SIV. Pigs were inoculated intranasally with representative strains of each virus lineage with 6- and 17-week intervals between H1 inoculations and between H1 and H3 inoculations, respectively. Virus titers in nasal swabs and/or tissues of the respiratory tract were determined after each inoculation. There was substantial though differing cross-protection between pH1N1 and other H1 viruses, which was directly correlated with the relatedness in the viral hemagglutinin (HA) and neuraminidase (NA) proteins. Cross-protection against H3N2 was almost complete in pigs with immunity against H1N2, but was weak in H1N1/pH1N1-immune pigs. In conclusion, infection with a live, wild type influenza virus may offer substantial cross-lineage protection against viruses of the same HA and/or NA subtype. True heterosubtypic protection, in contrast, appears to be minimal in natural influenza virus hosts. We discuss our findings in the light of the zoonotic and pandemic risks of SIVs

Therapeutic Radionuclides: Making the Right Choice

Recently, there has been a resurgence of interest in nuclear medicine therapeutic procedures. Using unsealed sources for therapy is not a new concept; it has been around since the beginnings of nuclear medicine. Treatment of thyroid disorders with radioiodine is a classic example. The availability of radionuclides with suitable therapeutic properties for specific applications, as well as methods for their selective targeting to diseased tissue have, however, remained the main obstacles for therapy to assume a more widespread role in nuclear medicine. Nonetheless, a number of new techniques that have recently emerged, (e.g., tumor therapy with radiolabeled monoclonal antibodies, treatment of metastatic bone pain, etc.) appear to have provided a substantial impetus to research on production of new therapeutic radionuclides. Although there are a number of new therapeutic approaches requiring specific radionuclides, only selected broad areas will be used as examples in this article

Proof-of-concept that network pharmacology is effective to modify development of acquired temporal lobe epilepsy

Epilepsy is a complex network phenomenon that, as yet, cannot be prevented or cured. We recently proposed network-based approaches to prevent epileptogenesis. For proof of concept we combined two drugs (levetiracetam and topiramate) for which in silico analysis of drug-protein interaction networks indicated a synergistic effect on a large functional network of epilepsy-relevant proteins. Using the intrahippocampal kainate mouse model of temporal lobe epilepsy, the drug combination was administered during the latent period before onset of spontaneous recurrent seizures (SRS). When SRS were periodically recorded by video-EEG monitoring after termination of treatment, a significant decrease in incidence and frequency of SRS was determined, indicating antiepileptogenic efficacy. Such efficacy was not observed following single drug treatment. Furthermore, a combination of levetiracetam and phenobarbital, for which in silico analysis of drug-protein interaction networks did not indicate any significant drug-drug interaction, was not effective to modify development of epilepsy. Surprisingly, the promising antiepileptogenic effect of the levetiracetam/topiramate combination was obtained in the absence of any significant neuroprotective or anti-inflammatory effects as indicated by multimodal brain imaging and histopathology. High throughput RNA-sequencing (RNA-seq) of the ipsilateral hippocampus of mice treated with the levetiracetam/topiramate combination showed that several genes that have been linked previously to epileptogenesis, were significantly differentially expressed, providing interesting entry points for future mechanistic studies. Overall, we have discovered a novel combination treatment with promise for prevention of epilepsy

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Meta-Analysis of MicroRNAs Dysregulated in the Hippocampal Dentate Gyrus of Animal Models of Epilepsy.

The identification of mechanisms transforming normal to seizure-generating tissue after brain injury is key to developing new antiepileptogenic treatments. MicroRNAs (miRNAs) may act as regulators and potential treatment targets for epileptogenesis. Here, we undertook a meta-analysis of changes in miRNA expression in the hippocampal dentate gyrus (DG) following an epileptogenic insult in three epilepsy models. We identified 26 miRNAs significantly differentially expressed during epileptogenesis, and five differentially expressed in chronic epilepsy. Of these, 13 were not identified in any of the individual studies. To assess the role of these miRNAs, we predicted their mRNA targets and then filtered the list to include only target genes expressed in DG and negatively correlated with miRNA expression. Functional enrichment analysis of mRNA targets of miRNAs dysregulated during epileptogenesis suggested a role for molecular processes related to inflammation and synaptic function. Our results identify new miRNAs associated with epileptogenesis from existing data, highlighting the utility of meta-analysis in maximizing value from preclinical data