Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand-alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables.     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.     

Attack Resilient and Efficient Protocol based on Greedy Perimeter Coordinator Routing—Mobility Awareness for Preventing the Attack in the VANET

Wireless Personal Communications - A Greedy Perimeter Coordinator Routing and Mobility Awareness (GPCR-MA) vehicular routing is a widely accepted routing protocol for VANET (Vehicular Ad hoc...     

Structural,Electronic Properties,Hydrogen Bonding Analyses,and Biological Activity of Two Multiple Myeloma Drugs: Lenalidomide and Pomalidomide

In this article, we made a comparative study of two multiple myeloma drugs: lenalidomide and pomalidomide. We calculated and discussed their geometries at DFT/B3LYP method. Intra-molecular hydrogen bonding in these molecules is confirmed and characterized by QTAIM calculations. Electronic parameters along with HOMO-LUMO and MESP surfaces are calculated in order to compare their chemical reactivity. The effect of structure and bonding on electronic properties and biological activities is discussed and it is established that pomalidomide is more biologically active than lenalidomide. The phenyl ring of these molecules show teratogenic effects, making a possibility of finding another new class of drugs.     

A mathematical model and solution methods for rail freight transportation planning in an Indian food grain supply chain

This paper addresses the rail transportation of food grains undertaken by Food Corporation of India (FCI) to meet the requirements of the food security programme called Public Distribution System (PDS). The research focuses on improving the allocation of railway rakes transporting food grains to a set of storage warehouses. A penalty factor based approach is adopted to represent the considerations in transportation planning and three penalty factors such as rake penalty factor, weekly penalty factor and capacity utilization penalty factor are introduced for the purpose. The single source - multiple destination problem is formulated and solved using exact method to minimize the sum of these three penalty factor values, termed total penalty. Further, a heuristic named optimum rake allocation algorithm is developed and tested using a set of 35 problem instances. The proposed heuristic is found to be highly efficient in terms of solution quality and computation time. A case study of FCI Kerala Region is also carried out to validate the formulated model and the proposed heuristic. The work provides valuable insights into the practical issues encountered in rail freight transportation planning and proposes an effective solution methodology to address them.     

Diffusion of innovation model of consumer behaviour - Ideas to accelerate adoption of renewable energy sources by consumer communities in India

Renewable Energy (RE) sources form a minuscule portion of India’s overall Energy consumption today. India continues to rely on fast depleting fossil fuel and expensive Oil imports to satisfy the energy demands of the economy. But this is hardly sustainable and India has to quickly get RE sources to play a major role in servicing the energy needs of its population. Despite the best efforts the adoption of RE sources by consumer communities in India is patchy. This article will focus on what needs to be done to create a pull from the market for RE sources, by looking at Consumer Behaviour literature available in the area of Diffusion of Innovation[1]. Demand for RE sources from consumer communities must reach a tipping point[4] quickly; for the sector to take-off on its own and become a self-sustaining business.