Search for Narrow-Width ttbar Resonances in ppbar Collisions at center of mass energy = 1.8 TeV

We present a preliminary result on a search for narrow-width resonances that decay into ttbar pairs using 130 pb^{-1} of lepton plus jets data in ppbar collisions at center of mass energy = 1.8 TeV. No significant deviation from Standard Model prediction is observed. 95% C.L. upper limits on the production cross section of the narrow-width resonance times its branching fraction to ttbar are presented for different resonance masses, M_X. We also exclude the existence of a leptophobic topcolor particle, X, with M_X < 560 GeV/c^2 for a width \Gamma_X = 0.012 M_X.Comment: 3 pages, 1 figure; Submitted for proceedings of 5th International Conference on Quark Confinement and Hadron spectrum, held in Italy, from 11-14 Sep., 200

Renewable Energy Options among Rural Households in Haryana and Himachal Pradesh: An Overview

In developing countries the energy problems are both widespread and serious. Lack of access to sufficient and sustainable supplies of energy impacts around 90% of the population of many developing countries. People are compelled to live without regular and good quality electricity supply. The rural population remains dependent on fuels such as animal dung, crop residues, fuel wood and charcoal to cook their daily meals. Without efficient, clean energy, people are undermined in their efforts to engage effectively in productive activities and improve their quality of life (Barnes and Floor, 1996). India is home to the largest rural population in the world with approx. 68.84% of the total population residing in rural areas (Census, 2011). In order to contribute to the overall development in India, access to modern energy and cleaner fuel for rural households is important. There is a need to bridge the access gap by expanding energy systems to meet the energy requirements of the fast growing population and mitigate the threat of climate change. The best possible solution to the energy poverty challenges lies in the shift towards sustainable energy technologies. In the present scenario, the uncontrollable increase in use of non-renewable energies such as fossil fuel, oil, natural gas has led to fluctuation of demand and supply. This negative energy balance for decades has forced India to purchase energy from other countries to fulfill the needs of the entire country. Hence, energy access is an important component of poverty alleviation and an indispensable element of sustainable human development. Government of India has initiated numerous development programmes, focusing on providing sustainable energy solutions to rural communities often deprived of clean and uninterrupted energy supply for their daily energy requirements. The study entitled ‘Renewable Energy Options among Rural Households\u27 was conducted in Haryana and Himachal Pradesh states. The outcomes of the study provide a roadmap for future programmes promoting the use of clean, efficient and modern energy technologies, to be implemented more effectively. Findings would further benefit the primary and secondary key stakeholders involved in research and development, formulation of policies and regulations, promoting sale and purchase and provide financial assistance to future energy programmes meant to popularize the use of Renewable Energy Technologies

Community-based Outlier Detection for Edge-attributed Graphs

The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. Beyond graph analysis tasks like graph query processing, link analysis, influence propagation, there has recently been some work in the area of outlier detection for information network data. Although various kinds of outliers have been studied for graph data, there is not much work on anomaly detection from edge-attributed graphs. In this paper, we introduce a method that detects novel outlier graph nodes by taking into account the node data and edge data simultaneously to detect anomalies. We model the problem as a community detection task, where outliers form a separate community. We propose a method that uses a probabilistic graph model (Hidden Markov Random Field) for joint modeling of nodes and edges in the network to compute Holistic Community Outliers (HCOutliers). Thus, our model presents a natural setting for heterogeneous graphs that have multiple edges/relationships between two nodes. EM (Expectation Maximization) is used to learn model parameters, and infer hidden community labels. Experimental results on synthetic datasets and the DBLP dataset show the effectiveness of our approach for finding novel outliers from networks

Separation of variables for a lattice integrable system and the inverse problem

We investigate the relation between the local variables of a discrete integrable lattice system and the corresponding separation variables, derived from the associated spectral curve. In particular, we have shown how the inverse transformation from the separation variables to the discrete lattice variables may be factorised as a sequence of canonical transformations, following the procedure outlined by Kuznetsov.Comment: 14 pages. submitted for publicatio

Career Path Suggestion using String Matching and Decision Trees

High school and college graduates seemingly are often battling for the courses they should major in order to achieve their target career. In this paper, we worked on suggesting a career path to a graduate to reach his/her dream career given the current educational status. Firstly, we collected the career data of professionals and academicians from various career fields and compiled the data set by using the necessary information from the data. Further, this was used as the basis to suggest the most appropriate career path for the person given his/her current educational status. Decision trees and string matching algorithms were employed to suggest the appropriate career path for a person. Finally, an analysis of the result has been done directing to further improvements in the model.Comment: 3 pages, 4 figures, 1 tabl

EMBEDDED IMPLEMENTATION OF EEG ANALYSIS USING INDEPENDENT COMPONENT APPROACH

Brain signals are important in diagnosing various disorders and abnormalities in the human body. These signals are recorded by scalp electrodes and are called as EEG signals. EEG signals are a mixture of signals from different brain regions which contain artefacts along with original information. These contaminated mixtures are analysed such that diagnosis of various diseases is possible. One of the effective methods available is Independent Component Analysis (ICA) for removing artefacts and for separation and analysis of the desired sources from within the EEGs. This paper focuses on the analysis of EEG signals using ICA approach. Two ICA algorithms- Pearson ICA and JADE ICA are analysed in this paper. Comparison of these ICA algorithms in removing artefacts from EEG has been carried out by simulation using MATLAB. Then the Pearson ICA algorithm simulation is done using Visual C#. The algorithm has been implemented in an Embedded Development Kit (EDK) using .NET Micro Framework and the results are presented