High performance computing using MPI and OpenMP on multi-core parallel systems

The rapidly increasing number of cores in modern microprocessors is pushing the current high performance computing (HPC) systems into the petascale and exascale era. The hybrid nature of these systems - distributed memory across nodes and shared memory with non-uniform memory access within each node - poses a challenge to application developers. In this paper, we study a hybrid approach to programming such systems - a combination of two traditional programming models, MPI and OpenMP. We present the performance of standard benchmarks from the multi-zone NAS Parallel Benchmarks and two full applications using this approach on several multi-core based systems including an SGI Altix 4700, an IBM p575+ and an SGI Altix ICE 8200EX. We also present new data locality extensions to OpenMP to better match the hierarchical memory structure of multi-core architectures.     

Fuzzy mean–variance–skewness portfolio selection models by interval analysis

In portfolio selection problem, the expected return, risk, liquidity etc. cannot be predicted precisely. The investor generally makes his portfolio decision according to his experience and his economic wisdom. So, deterministic portfolio selection is not a good choice for the investor. In most of the recent works on this problem, fuzzy set theory is widely used to model the problem in uncertain environments. This paper utilizes the concept of interval numbers in fuzzy set theory to extend the classical mean–variance (MV) portfolio selection model into mean–variance–skewness (MVS) model with consideration of transaction cost. In addition, some other criteria like short and long term returns, liquidity, dividends, number of assets in the portfolio and the maximum and minimum allowable capital invested in stocks of any selected company are considered. Three different models have been proposed by defining the future financial market optimistically, pessimistically and in the combined form to model the fuzzy MVS portfolio selection problem. In order to solve the models, fuzzy simulation (FS) and elitist genetic algorithm (EGA) are integrated to produce a more powerful and effective hybrid intelligence algorithm (HIA). Finally, our approaches are tested on a set of stock data from Bombay Stock Exchange (BSE).     

Biocompatible stimuli responsive superabsorbent polymer for controlled release of GHK-Cu peptide for wound dressing application

Superabsorbent polymer based on Polyaspartic acid (PASP) and 2-acrylamido-propane sulphonic acid (AMPS) has been synthesized to achieve maximum swelling with deionized water. The swelling properties of the polymer have been studied in different physiological fluids viz. saline and glucose. Best swelling has been observed in glucose solution. Studies at different pH have shown that the polymer has better swelling under extreme acidic and alkaline conditions. A parabolic curve has been observed. The polymer based on PASP and AMPS is sensitive to glucose and pH conditions. This property can be exploited for diabetic /chronic wound dressing. The polymer has also been evaluated for their blood compatibility, encapsulation efficiency and release of peptide GHK-Cu having dermatological application. The polymer and the encapsulated polymer structure have been studied using FTIR and SEM with EDAX. An encapsulation efficiency of 72.8% was achieved and a release of 88.46% was observed in 24h.     

Associative classification of mammograms using weighted rules

In this paper, we present a novel method for the classification of mammograms using a unique weighted association rule based classifier. Images are preprocessed to reveal regions of interest. Texture components are extracted from segmented parts of the image and discretized for rule discovery. Association rules are derived between various texture components extracted from segments of images and employed for classification based on their intra- and inter-class dependencies. These rules are then employed for the classification of a commonly used mammography dataset, and rigorous experimentation is performed to evaluate the rules’ efficacy under different classification scenarios. The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.     

Synthesis and modulation of the optical properties of carbon quantum dots using microwave radiation

Abstract

Carbon quantum dots have exhibited highly fluorescent characteristics as nanomaterials. Soluble in water and easily synthesized by multiple simple techniques, there are immense fabrication possibilities by permuting their properties via changing precursors, synthesis route, reaction parameters, etc. As economic and environment-friendly seed material, they are being viewed as an alternative to conventional fluorescent materials in myriad of applications including displays, cancer detection, drug delivery carriers in biomedicine, absorbing material in photovoltaics, etc. In this work, the hydrophilic carbon quantum dots were synthesized from the aqueous solution of citric acid and urea through microwave radiation for varying heat durations. The method is facile, faster and friendly to the environment without any need for high temperature and complicated chemical techniques. It was observed that the bandgap of the fabricated carbon quantum dots and its optical properties namely absorbance, photoluminescence enhanced with an increase in exposure of samples to heat up to an optimum limit, owing to the increase in density of states. However, further exposure to heat for longer duration degraded the absorbance and bandgap while photoluminescence gets saturated. Stokes’ shift revealed that all the synthesized carbon quantum dots possess stable emission. This was reconfirmed from consistent emission peak positions under varying excitation in the samples. The absorbance and PL spectrum exhibited by the synthesized dots makes it a suitable material for boosting the performance of organic solar cell.     

Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming

Hybrid systems are dynamical systems characterized by the simultaneous presence of discrete and continuous variables. Model‐based control of such systems is computationally demanding. To this effect, explicit controllers which provide control inputs as a set of functions of the state variables have been derived, using multiparametric programming mainly for the linear systems. Hybrid polynomial systems are considered resulting in a Mixed Integer Polynomial Programming problem. Treating the initial state of the system as a set of bounded parameters, the problem is reformulated as a multiparametric Mixed Integer Polynomial optimization (mp‐MIPOPT) problem. A novel algorithm for mp‐MIPOPT problems is proposed and the exact explicit control law for polynomial hybrid systems is computed. The key idea is the computation of the analytical solution of the optimality conditions while the binary variables are treated as relaxed parameters. Finally, using symbolic calculations exact nonconvex critical regions are computed. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3441–3460, 2016     

Effect of the shapes of nanostructures on the light absorption in organic thin films

We theoretically investigate the enhancement of optical absorption in organic thin films by embedding the periodic metallic nanostructures (NSs) inside the thin films. For the case study, one of the organic solar cell materials copper phthalocyanine (CuPc) has been used. Finite Different Time Domain technique has been used to illustrate the role of the several different shapes of the metallic NSs. It has been found that the NSs can result in broadband optical absorption enhancement. A study on the position of the metallic particles in the organic thin film and the effect of particle dimension on absorption has also been performed. A significant enhancement in the absorption in the wavelength range 350–550?nm has been observed. Furthermore, the investigation has been performed on the effect of the light source incident angle and polarization direction on the absorption in the organic thin films. The best incident angle is found at 75°.     

The software model checker Blast

Blast is an automatic verification tool for checking temporal safety properties of C programs. Given a C program and a temporal safety property, Blast either statically proves that the program satisfies the safety property, or provides an execution path that exhibits a violation of the property (or, since the problem is undecidable, does not terminate). Blast constructs, explores, and refines abstractions of the program state space based on lazy predicate abstraction and interpolation-based predicate discovery. This paper gives an introduction to Blast and demonstrates, through two case studies, how it can be applied to program verification and test-case generation. In the first case study, we use Blast to statically prove memory safety for C programs. We use CCured, a type-based memory-safety analyzer, to annotate a program with run-time assertions that check for safe memory operations. Then, we use Blast to remove as many of the run-time checks as possible (by proving that these checks never fail), and to generate execution scenarios that violate the assertions for the remaining run-time checks. In our second case study, we use Blast to automatically generate test suites that guarantee full coverage with respect to a given predicate. Given a C program and a target predicate p, Blast determines the program locations q for which there exists a program execution that reaches q with p true, and automatically generates a set of test vectors that cause such executions. Our experiments show that Blast can provide automated, precise, and scalable analysis for C programs.     

Strategic alliance formation and the effects on the performance of manufacturing enterprises from supply chain perspective

This study examines the antecedents of strategic alliance formation in manufacturing firms in China, the alliance effect on innovation capability and dyadic quality performance, and how these two organisational capabilities are related to the supply chain performance of Chinese manufacturing enterprises in Shanghai. We perform a series of statistical techniques including logistic regression analysis, multivariate analysis of variance (MANOVA) and multiple regression analysis for assessing the hypothesised relationships. Our findings indicate that relational stability and effective communication are significant antecedent factors influencing strategic alliance formation among Chinese manufacturing enterprises. Such alliance formation is found to benefit innovation capability and dyadic quality performance, which are significant contributors to the supply chain performance of Chinese manufacturers. We provide important theoretical and practical implications on these antecedents and consequences of strategic alliance formation leading to supply chain performance in the Chinese manufacturing context.     

Development of Cooling System for Gyrotron Collector

In this paper, the development of cooling system for 42 GHz, 200 kW gyrotron collector is presented. The design of the cooling duct has been finalized after different analyses such as, the fluid analysis, the thermal analysis, the structural analysis, etc. All analyses have been carried out by ANSYS software and the development of the cooling system based on the final design is performed.