Competitiveness and an Emerging Sector: The Russian Software Industry and its Global Linkages

This paper analyzes the Russian software industry in the context of trade in information technology services. We assess Russia's underlying sources of competitive edge in software, such as its scientific establishments, education system, diaspora and low costs, and identify the institutional impediments to growth. A survey reveals that foreign outsourcing contracts, a high value-added niche and high education levels characterize Russia's small, privately held software firms. A comparison with the Indian software industry underscores the structural differences in outsourcing relationships developed by the two countries and emphasizes that national advantages are complex amalgamations of many factors and need to be overtly marketed. Beyond the resources that give comparative and competitive advantage to a transitioning economy, a critical role is played by powerful industry organizations and by non-market state institutions that can level the economic field and inject credibility into market structures.     

Bubble Formation through Reaction at Liquid‐Liquid Interfaces

Slag foaming is an important phenomenon in steelmaking processes with both beneficial as well as negative effects. The present work is part of the wider project on the modelling of slag foaming, with special reference to dynamic conditions. Since bubble formation is the first step to foam formation, the present work was carried out in an attempt to simulate the bubble formation in slag/metal reactions in steelmaking processes by water‐modelling experiments. The bubble formation due to the gas produced through chemical reaction at the interface between oleic acid and sodium bicarbonate solution was systematically monitored. The chemical reaction rate was varied by varying the concentration of sodium bicarbonate. The bubbles were observed to be generated in the heavier aqueous phase just below the water‐oil interface. The bubbles penetrated the interface and escaped through the oil phase. The rate of the reaction was estimated from the volume of the gas that passed the water/oil interface. It was observed that the bubble formation and bubble growth mechanism were influenced by the reaction rate while the bubble size seemed to be unaffected by the reaction rate.     

Classification of heart rate data using artificial neural network and fuzzy equivalence relation

The electrocardiogram is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks, etc. may contain useful information about the nature of disease afflicting the heart. However, these subtle details cannot be directly monitored by the human observer. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the signal parameters, extracted and analysed using computers, are highly useful in diagnostics. This paper deals with the classification of certain diseases using artificial neural network (ANN) and fuzzy equivalence relations. The heart rate variability is used as the base signal from which certain parameters are extracted and presented to the ANN for classification. The same data is also used for fuzzy equivalence classifier. The feedforward architecture ANN classifier is seen to be correct in about 85% of the test cases, and the fuzzy classifier yields correct classification in over 90% of the cases.     

M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene

When occlusion is minimal, a single camera is generally sufficient to detect and track objects. However, when the density of objects is high, the resulting occlusion and lack of visibility suggests the use of multiple cameras and collaboration between them so that an object is detected using information available from all the cameras in the scene.In this paper, we present a system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized surveillance cameras located far from each other. The system is fully automatic, and takes decisions about object detection and tracking using evidence collected from many pairs of cameras. Innovations that help us tackle the problem include a region-based stereo algorithm capable of finding 3D points inside an object knowing only the projections of the object (as a whole) in two views, a segmentation algorithm using bayesian classification and the use of occlusion analysis to combine evidence from different camera pairs.The system has been tested using different densities of people in the scene. This helps us determine the number of cameras required for a particular density of people. Experiments have also been conducted to verify and quantify the efficacy of the occlusion analysis scheme.     

Quantum fast Fourier transform using multilevel atoms

We propose an implementation of the quantum fast Fourier transform algorithm in an entangled system of multilevel atoms. The Fourier transform occurs naturally in the unitary time evolution of energy eigenstates and is used to define an alternative wave-packet basis for quantum information in the atom. A change of basis from energy levels to wave packets amounts to a discrete quantum Fourier transform within each atom. The algorithm then reduces to a series of conditional phase transforms between two entangled atoms in mixed energy and wave-packet bases. We show how to implement such transforms using wave-packet control of the internal states of the ions in the linear ion-trap scheme for quantum computing.     

Investigating the Financial Implications and Viability of Diversified Water Supply Systems in an Urban Water Supply Zone

Water Resources Management - Diversified water supply schemes can reduce both peak demand and overall demand in the urban water supply network. Consequently, they provide benefits to both the water...     

Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae

At the chromosomal level of evolution, recombination is a major factor for genetic variations. However, recombination does not occur with equal frequency at various regions of genome. The recombination has the tendency to occur at specific region with higher frequency and with low frequency at other regions, and former regions are named as hot recombination regions whereas later are called cold regions for recombination. In this paper, we have developed supervised machine learning-based models using artificial neural network, support vector machine and Naïve Bayes for efficient and effective classification of such hot and cold recombination regions based on the nucleotide composition of sequences. All models were validated and tested using tenfold cross-validation. Furthermore, neural network model was validated using leave one out and random sampling techniques in addition to tenfold cross-validation. Moreover, models were evaluated using receiver-operating curve. Our results indicate that artificial neural network achieves the best result.