The process of aerodynamic shape optimisation requires the development of intelligent models to address the stipulated design goals. The Direct Numeric Optimisation (DNO) approach is examined in this paper, which analyses the feasibility of a shape, in iteration until convergence based on defined objectives and constraints. The method is computationally intensive hence the components of the DNO architecture are defined, validated and modified to generate an efficient search optimisation model. Efficiency is enhanced by mapping the solution space for High-Altitude Long Endurance (HALE) airfoil design problem, through an inverse mapping of PARSEC airfoil shape variables over a series of benchmark profiles. Solution regions with aerodynamically infeasible shapes are identified and eliminated from the search process, to reduce computational time. A single-point airfoil optimisation with Gradient-Based method, over the defined search space is examined. Variations in base airfoils confirmed the solution space is highly multimodal and gradient methods merely locate the local optima. A Particle Swarm Optimisation (PSO) algorithm incorporating a double-mutation operator to mitigate sub-optimal solutions, for highly multimodal solution topologies was defined and validated. The swarm algorithm for airfoil shape optimisation confirmed the limited search flexibility of gradient methods, by establishing a global solution with a 16% reduction in drag. The swarm algorithm is computationally intense for shape optimisation. An Artificial Neural Network (ANN) is developed and validated with a relationship between the mapped PARSEC solution space and the aerodynamic coefficients of lift and drag established. A network sensitivity study indicated a double-layered network with 30 neurons for lift and 20 for drag is required to establish the aerodynamic coefficients with acceptable accuracy. The surrogate model is used for airfoil shape optimisation by replacing the flow solver from the DNO loop. Time savings are established with the aerodynamic performance of the output solution in line with the results of the direct PSO-Flow-Solver combination. Neural network simulations for fitness function approximation are prone to errors. Hence, future research will focus on developing a hybrid search methodology by integrating the flow solver and ANN in the DNO approach.

SYNOPSISThe polymerization of N-vinylcarbazole initiated with NO2 and SOz in dichloroethane has been studied. The kinetics of polymerization were followed gravimetrically. The polymerization is fast with NOz but a relatively slow rate was obtained with SO2. The polymerization with these gases appears to be initiated by a charge transfer mechanism.

The Direct Numerical Optimization (DNO) approach for airfoil shape design requires the integration of modules: a) A geometrical shape function; b) Computational flow solver and; c) Search model for shape optimization. These modules operate iteratively until convergence based on defined objectives and constraints. The DNO architecture is to be validated to ensure efficient optimization simulations and is the focus of this paper. The PARSEC airfoil shape function is first validated by observing the effect of design coefficients on airfoil geometry and aerodynamics. The design variables provide independent one-to-one control over airfoil geometry, for imposing shape constraints. The aerodynamic performance of PARSEC airfoils through variable perturbations, conform to established aerodynamic principles. It confirms the design flexibility of the shape function in providing direct control over airfoil geometry. The Particle Swarm Optimization (PSO) algorithm is introduced as the search agent. A PSO simulation requires userinputs to define the search pattern. A methodology is presented to validate these parameters on pre-defined benchmark mathematical functions. Self Organizing Maps (SOM) are applied to illustrate trade-offs between PSO search variables. An Adaptive Inertia Weight (APSO) scheme that dynamically alters the search path of the swarm by monitoring the position of the particles, provides an acceptable convergence. Validation tests indicated the maximum velocity of the particles is less than 1% of computational domain size for convergence. The DNO approach is computationally inefficient, thus a surrogate model to address this issue is presented. An Artificial Neural Network (ANN) model with a training dataset of 3000 airfoils is applied to develop a model that applies the PARSEC airfoil geometry variables as inputs and the equating aerodynamic coefficient as output. System validation with 1000 randomly generated airfoils indicated 70% of the simulated solutions were within 10% of actual solver run. Future research will involve reducing the percentage error of the surrogate model against the theoretical solution.

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Predicting the role of protein is one of the most challenging problems. There are few approaches available for the prediction of role of unknown protein in terms of drug target or vaccine candidate. We propose here Naïve Bayes probabilistic classifier, a promising method for reliable predictions. This method is tested on the proteins identified in our mass spectrometry based membrane protemics study of Leishmania donovani parasite that causes a fatal disease (Visceral Leishmaniasis) in humans all around the world. Most of the vaccine/drug targets belonging to membrane proteins are represented as key players in the pathogenesis of Leishmania infection. Analyses of our previous results, using Naïve Bayes probabilistic classifier, indicate that this method predicts the role of unknown/hypothetical protein (as drug target/vaccine candidate) significantly with higher precision. We have employed this method in order to provide probabilistic predictions of unknown/hypothetical proteins as targets. This study reports the unknown/hypothetical proteins of Leishmania membrane fraction as a potential drug targets and vaccine candidate which is vital information for this parasite. Future molecular studies and characterization of these potent targets may produce a recombinant therapeutic/prophylactic tool against Visceral Leishmaniasis. These unknown/hypothetical proteins may open a vast research field to be exploited for novel treatment strategies.

Purpose This study aims to explore how some organizational leaders are making successful attempts toward making “social contributions” toward the underprivileged or needy stakeholders in the society. This study suggests empirical themes about behavioral patterns of such organizational leaders and illustrates the need to transcend erstwhile “avoid harm” socially responsible leadership. Design/methodology/approach Data were collected on 52 Indian organizational leaders and were analyzed following the guidelines of constructionist grounded theory. Findings Findings suggested that organizational leaders who facilitated some form of social contributions in the life of different stakeholders had “unconditional adherence to social contributions,” they were “pertinacious about going the extra mile” to facilitate social contributions and at times their “existence as an enthusiastically innovative change agent” also facilitated social contributions to the lives of different stakeholders. Social implications This study might initiate discussion around the behavioral patterns of organizational leaders who are attempting to make society a better place by leading or facilitating social contributions. Identified exemplary behavioral patterns might encourage more exploratory studies directed toward the identification of other forms of socially contributive leadership attributes. Originality/value Emphasis on investigating corporate social responsibility (CSR) from individual-level perspective advocated the importance of the psychological foundation of CSR. This study empirically identifies behavioral patterns that characterize organizational leaders who had a strong commitment to make social contributions to society. Patterns identified corresponded to cognitive and behavioral approaches of organizational leaders that were instrumental in actualizing social contributions to the Indian society.

In Army Services, there are a number of valuable decisions that have to be taken for mission accomplishment. These decisions are very important and the choice of a weapon may be able to alter the outcome of a battle decisively. Among several such decisions one is to decide which weapons to deploy/assign over a given terrain. Recommender systems are intelligent applications to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services. This paper proposes the design of recommender system that automates the process of finding the appropriate type of weapon(s) that can be deployed over a terrain having certain characteristics. The user agent seeks recommendations, which are in the form of intuitionistic fuzzy set (IFS), from trustworthy peers and produces aggregated order of recommendations taking degree of trust on recommenders into consideration. Trust on recommender is also updated based on importance of recommendation given to the user. A prototype of the trust-based recommender system for modern tactical combat system has been designed and developed through which the user can get the recommendation to use a specific kind of weapon or a set of weapons that would be best-suited in a given type of terrain.

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