Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics

The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources, dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out

On Butterfly effect in Higher Derivative Gravities

We study butterfly effect in $D$-dimensional gravitational theories containing terms quadratic in Ricci scalar and Ricci tensor. One observes that due to higher order derivatives in the corresponding equations of motion there are two butterfly velocities. The velocities are determined by the dimension of operators whose sources are provided by the metric. The three dimensional TMG model is also studied where we get two butterfly velocities at generic point of the moduli space of parameters. At critical point two velocities coincide.Comment: 16 pages, references adde

Cloud-based video analytics using convolutional neural networks.

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of distributed processing for training and inference. We propose a cloud‐based video analytics system based on an optimally tuned convolutional neural network to classify objects from video streams. The tuning of convolutional neural network is empowered by in‐memory distributed computing. The object classification is performed by comparing the target object with the prestored trained patterns, generating a set of matching scores. The matching scores greater than an empirically determined threshold reveal the classification of the target object. The proposed system proved to be robust to classification errors with an accuracy and precision of 97% and 96%, respectively, and can be used as a general‐purpose video analytics system

Digital video source identification based on green-channel photo response non-uniformity (G-PRNU)

This paper proposes a simple but yet an effective new method for the problem of digital video camera identification. It is known that after an exposure time of 0.15 seconds, the green channel is the noisiest of the three RGB colour channels [5]. Based on this observation, the digital camera pattern noise reference, which is extracted using only the green channel of the frames and is called Green-channel Photo Response Non-Uniformity (G-PRNU), is exploited as a fingerprint of the camera. The green channels are first resized to a standard frame size (512x512) using bilinear interpolation. Then the camera fingerprint is obtained by a wavelet based denoising filter described in [4] and averaged over the frames. 2-D correlation coefficient is used in the detection test. This method has been evaluated using 290 video sequences taken by four consumer digital video cameras and two mobile phones. The results show G- PRNU has potential to be a reliable technique in digital video camera identification, and gives better results than PRNU

Model Development for Prediction of Diabetic Retinopathy

This research focuses on presenting an empirical method to gather necessary data and then developing several models to predict the chance of diabetic retinopathy (proliferative and non-proliferative) by observing HbA1c, duration of disease and albumin excretion rate of diabetic patients. We gathered required knowledge from other studies that have investigated the relation of different risk factors and complications in diabetes. In order to create 1-1 models, curve fitting was performed by using two different software applications: Tiberius (Brierley 2011) and SPSS (IBM 2010), which work based on ANN and least square regression, respectively. To start producing the model, seven different patterns, i.e. linear, logarithmic, quadratic, cubic, power, s and exponential, have been chosen as the best regression options. Using R-squared, it can be clearly seen that the best selected regression models fit the data in all the dataset tables better than ANN, as well as the other six regression patterns

Synergic effect of chronic hepatitis C infection and beta thalassemia major with marked hepatic iron overload on liver fibrosis: a retrospective cross-sectional study

BACKGROUND: Increased hepatic iron is assumed to potentiate progression towards liver fibrosis in chronic hepatitis C virus (HCV) infection. In this study we have evaluated the potentiating effect of marked hepatic iron overload and chronic HCV infection on hepatic fibrosis in thalassemic patients. METHODS: Liver biopsies of one group of patients with beta thalassemia major and chronic HCV infection (group 1) was compared with two groups of patients (groups 2&3) with either chronic HCV infection or thalassemia major, respectively (20 patients in each group). Necroinflammation, fibrosis, and iron overload were graded and compared. RESULTS: Stage of fibrosis in group 1 patients was significantly higher than the other two groups (p < 0.05). Necroinflammatory grade was significantly lower, but iron score was significantly higher in thalassemic patients (group 3) in comparison to groups 1 and 2 (p < 0.05). CONCLUSION: Our results indicate that marked liver iron overload and HCV infection in thalassemic patients have potentiating effect on hepatic fibrogenesis

Higher order curvature information and its application in a modified diagonal Secant method

A secant equation (quasi-Newton) has one of the most important rule to find an optimal solution in nonlinear optimization. Curvature information must satisfy the usual secant equation to ensure positive definiteness of the Hessian approximation. In this work, we present a new diagonal updating to improve the Hessian approximation with a modifying weak secant equation for the diagonal quasi-Newton (DQN) method. The gradient and function evaluation are utilized to obtain a new weak secant equation and achieve a higher order accuracy in curvature information in the proposed method. Modified DQN methods based on the modified weak secant equation are globally convergent. Extended numerical results indicate the advantages of modified DQN methods over the usual ones and some classical conjugate gradient methods

The Evolution of Telecom Technologies: Current Trends and Near-Future Implications

A project commissioned by The Centre for Cross Border Studies with funding from eirco

Green and Sustainable Chemical Looping Plasma Process for Ammonia and Hydrogen Production

The overarching aim of this chapter is to propose a novel clean thermochemical process that harnesses thermal plasma technology to co-produce hydrogen and ammonia using a chemical looping process. The thermodynamic potential and feasibility of the process were demonstrated using a simulation of the system with aluminium and aluminium oxide as the oxygen and nitrogen carriers between the reactors. The effect of different operating parameters, such as feed ratio and temperature of the reactor, on the energetic performance of the process was investigated. Results showed that the nitridation and ammoniation reactors could operate at 6273 K) to reduce the alumina oxide to aluminium. The ratio of steam to aluminium nitride was identified as the key operating parameter for controlling the ammoniation reactor. Using a heat recovery unit, the extracted heat from the products was utilised to generate auxiliary steam for a combined cycle aiming at generating electricity for a thermal plasma reactor. It was demonstrated that the process can operate at an approximate self-sustaining factor ∼ 0.11, and an exergy partitioning fraction of up to 0.65. Integrating the process with solar photovoltaic showed a solar share of ∼32% without considering any battery storage units

Development and Evaluation of a Novel Pellet-Based Tablet System for Potential Colon Delivery of Budesonide

Budesonide, a potent glucocorticoid, is used for the treatment of inflammatory bowel diseases. Current available oral formulations of budesonide have low efficacy against ulcerative colitis because of the premature drug release in the upper part of the gastrointestinal tract. In this paper a pH- and time-controlled colon-targeted pellet-based tablet of budesonide was established. Pellet cores were prepared by extrusion-spheronization method and further coated with xanthan gum (barrier layer), Eudragit NE30D and L30D55 combination (inner layer), and Eudragit FS30 (as enteric layer) sequentially to achieve the required release profile. The coated pellets then compressed into tablets using inert tabletting granules of Cellactose or Pearlitol. Release studies, performed in simulated gastric, intestinal, and colon pH were used in sequence to mimic the gastrointestinal transit. The influence of formulation variables like barrier layer thickness, inner layer composition, and enteric coat thickness on drug release were investigated and the coated pellets that contained 12% weight gain in xanthan gum layer, Eudragit L30D55 and Eudragit NE30D with a ratio of 3 : 7 in inner layer with 30% weight gain and 25% weight gain in Eudragit FS layer were found to protect the drug release in stomach and small intestine and 83.35 ± 2.4 of budesonide was released at 24 h. The drug release from the tablets prepared using 40% Cellactose 80 as tableting excipient was found to be closely similar to that of uncompressed pellets