To model breakdown voltage using artificial neural networks of solid insulating materials

During manufacture, insulating materials may have voids which are source to electrical trees. Due to partial discharge, the insulating material degrades and breakdown occurs. The factors contributing to the breakdown are difficult to determine. As the equation describing the function is unknown, function estimation, which has some of its own useful properties, a major field of Artificial neural networks, is used. In this project using Artificial Neural Network, we develop models which intakes four different possible inputs that effect the breakdown which are the insulating sample thickness (t), void thickness (t1), void diameter(d) and the materials¡¦ permittivity (ƒÕr) predicts the breakdown voltage as a function of these four inputs. The Neural Network needs to be trained to be able to predict the Breakdown Voltage as close as possible. For the purpose of training , experimental data using a cylinder plane electrode system is used. The different dimensions used will be used to create the voids artificially. The parameters are selected after detail studying of the models as to which would generate best results. After the training is completed, the breakdown voltage as a function of the four input parameters is predicted. The results are very convincing as the error with which it is predicted is very less. Hence, this again proves the capability and effectiveness of using simulation models. MATLAB 2010 is used for doing the simulation process

Cleaved intracellular SNARE peptides are implicated in a novel cytotoxicity mechanism of botulinum serotype C

Recent advances in intracellular protein delivery have enabled more in-depth analyses of cellular functions. A specialized family of SNARE proteases, known as Botulinum Neurotoxins, blocks neurotransmitter exocytosis, which leads to systemic toxicity caused by flaccid paralysis. These pharmaceutically valuable enzymes have also been helpful in the study of SNARE functions. As can be seen in Figure 1A, SNARE bundle formation causes vesicle docking at the presynapse. Although these toxins are systemically toxic, no known cytotoxic effects have been reported with the curious exception of the Botulinum serotype C [1]. This enzyme cleaves intracellular SNAP25, as does serotype A and E, but also, exceptionally, cleaves Syntaxin 1. Using an array of lipid and polymer transfection reagents we were able to deliver different combinations of Botulinum holoenzymes into the normally unaffected, Neuro2A, SH-SY5Y, PC12, and Min6 cells to analyze the individual contribution of each SNARE protein and their cleaved peptide products

Young star clusters in interacting galaxies - NGC 1487 and NGC 4038/4039

We estimate the dynamical masses of several young (~10 Myr) massive star clusters in two interacting galaxies, NGC 4038/4039 ("The Antennae") and NGC 1487, under the assumption of virial equilibrium. These are compared with photometric mass estimates from K-band photometry and assuming a standard Kroupa IMF. The clusters were selected to have near-infrared colors dominated by red supergiants, and hence to be old enough to have survived the earliest phases of cluster evolution when the interstellar medium is rapidly swept out from the cluster, supported by the fact that there is no obvious Halpha emission associated with the clusters. All but one of the Antennae clusters have dynamical and photometric mass estimates which are within a factor ~2 of one another, implying both that standard IMFs provide a good approximation to the IMF of these clusters, and that there is no significant extra-virial motion, as would be expected if they were rapidly dispersing. These results suggest that almost all of the Antennae clusters in our sample have survived the gas removal phase as bound or marginally bound objects. Two of the three NGC 1487 clusters studied here have M_dyn estimates which are significantly larger than the photometric mass estimates. At least one of these two clusters, and one in the Antennae, may be actively in the process of dissolving. The process of dissolution contributes a component of non-virial motion to the integrated velocity measurements, resulting in an estimated M_dyn which is too high relative to the amount of measured stellar light. The dissolution candidates in both galaxies are amongst the clusters with the lowest pressures/densities measured in our sample.Comment: 17 pages, 14 Figures, A&A accepte

Security in Data Mining- A Comprehensive Survey

Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper

Therapeutic efficacy of anti-MMP9 antibody in combination with nab-paclitaxel-based chemotherapy in pre-clinical models of pancreatic cancer

Matrix metalloproteinase 9 (MMP9) is involved in the proteolysis of extracellular proteins and plays a critical role in pancreatic ductal adenocarcinoma (PDAC) progression, invasion and metastasis. The therapeutic potential of an anti-MMP9 antibody (αMMP9) was evaluated in combination with nab-paclitaxel (NPT)-based standard cytotoxic therapy in pre-clinical models of PDAC. Tumour progression and survival studies were performed in NOD/SCID mice. The mechanistic evaluation involved RNA-Seq, Luminex, IHC and Immunoblot analyses of tumour samples. Median animal survival compared to controls was significantly increased after 2-week therapy with NPT (59%), Gem (29%) and NPT+Gem (76%). Addition of αMMP9 antibody exhibited further extension in survival: NPT+αMMP9 (76%), Gem+αMMP9 (47%) and NPT+Gem+αMMP9 (94%). Six-week maintenance therapy revealed that median animal survival was significantly increased after NPT+Gem (186%) and further improved by the addition of αMMP9 antibody (218%). Qualitative assessment of mice exhibited that αMMP9 therapy led to a reduction in jaundice, bloody ascites and metastatic burden. Anti-MMP9 antibody increased the levels of tumour-associated IL-28 (1.5-fold) and decreased stromal markers (collagen I, αSMA) and the EMT marker vimentin. Subcutaneous tumours revealed low but detectable levels of MMP9 in all therapy groups but no difference in MMP9 expression. Anti-MMP9 antibody monotherapy resulted in more gene expression changes in the mouse stroma compared to the human tumour compartment. These findings suggest that anti-MMP9 antibody can exert specific stroma-directed effects that could be exploited in combination with currently used cytotoxics to improve clinical PDAC therapy

Functional magnetic resonance imaging of the mouse brain

Functional magnetic resonance imaging (fMRI) measuring a blood-oxygen-level dependent (BOLD) signal is the most commonly used neuroimaging tool to understand brain function in humans. As mouse models are one of the most commonly used neuroscience experimental models, and with the advent of transgenic mouse models of neurodegenerative pathologies, there has been an increasing push in recent years to apply fMRI techniques to the mouse brain. This thesis focuses on the development and implementation of mouse brain fMRI techniques, in particular to describe the mouse visual system. Multiple studies in the literature have noted several technical challenges in mouse fMRI. In this work I have developed methods which go some way to reducing the impact of these issues, and I record robust and reliable haemodynamic-driven signal responses to visual stimuli in mouse brain regions specific to visual processing. I then developed increasingly complex visual stimuli, approaching the level of complexity used in electrophysiology studies of the mouse visual system, despite the geometric and magnetic field constraints of using a 9.4T pre-clinical MRI scanner. I have also applied a novel technique for measuring high-temporal resolution BOLD responses in the mouse superior colliculus, and I used this data to improve statistical parametric mapping of mouse brain BOLD responses. I also describe the first application of dynamic causal modelling to mouse fMRI data, characterising effective connectivity in the mouse brain visual system. This thesis makes significant contributions to the reverse translation of fMRI to the mouse brain, closing the gap between invasive electrophysiological measurements in the mouse brain and non-invasive fMRI measurements in the human brain

A transform of complementary aspects with applications to entropic uncertainty relations

Even though mutually unbiased bases and entropic uncertainty relations play an important role in quantum cryptographic protocols they remain ill understood. Here, we construct special sets of up to 2n+1 mutually unbiased bases (MUBs) in dimension d=2^n which have particularly beautiful symmetry properties derived from the Clifford algebra. More precisely, we show that there exists a unitary transformation that cyclically permutes such bases. This unitary can be understood as a generalization of the Fourier transform, which exchanges two MUBs, to multiple complementary aspects. We proceed to prove a lower bound for min-entropic entropic uncertainty relations for any set of MUBs, and show that symmetry plays a central role in obtaining tight bounds. For example, we obtain for the first time a tight bound for four MUBs in dimension d=4, which is attained by an eigenstate of our complementarity transform. Finally, we discuss the relation to other symmetries obtained by transformations in discrete phase space, and note that the extrema of discrete Wigner functions are directly related to min-entropic uncertainty relations for MUBs.Comment: 16 pages, 2 figures, v2: published version, clarified ref [30

Optimal behavior of viscoelastic flow at resonant frequencies

The global entropy generation rate in the zero-mean oscillatory flow of a Maxwell fluid in a pipe is analyzed with the aim at determining its behavior at resonant flow conditions. This quantity is calculated explicitly using the analytic expression for the velocity field and assuming isothermal conditions. The global entropy generation rate shows well-defined peaks at the resonant frequencies where the flow displays maximum velocities. It was found that resonant frequencies can be considered optimal in the sense that they maximize the power transmitted to the pulsating flow at the expense of maximum dissipation.Comment: Paper accepted to be published in Phys. Rev.