Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Search for dark matter produced with an energetic jet or a hadronically decaying W or Z boson at √s = 13 TeV

Abstract: A search for dark matter particles is performed using events with large missing transverse momentum, at least one energetic jet, and no leptons, in proton-proton collisions at root S = 13TeV collected with the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 12.9 fb(-1). The search includes events with jets from the hadronic decays of a W or Z boson. The data are found to be in agreement with the predicted background contributions from standard model processes. The results are presented in terms of simpli fi ed models in which dark matter particles are produced through interactions involving a vector, axial-vector, scalar, or pseudoscalar mediator. Vector and axial-vector mediator particles with masses up to 1.95TeV, and scalar and pseudoscalar mediator particles with masses up to 100 and 430 GeV respectively, are excluded at 95% con fi dence level. The results are also interpreted in terms of the invisible decays of the Higgs boson, yielding an observed (expected) 95% con fi dence level upper limit of 0.44 (0.56) on the corresponding branching fraction. The results of this search provide the strongest constraints on the dark matter pair production cross section through vector and axial-vector mediators at a particle collider. When compared to the direct detection experiments, the limits obtained from this search provide stronger constraints for dark matter masses less than 5, 9, and 550 GeV, assuming vector, scalar, and axial-vector mediators, respectively. The search yields stronger constraints for dark matter masses less than 200 GeV, assuming a pseudoscalar mediator, when compared to the indirect detection results from Fermi-LAT.

Investigation of the influential strength of factors on adoption of green supply chain management practices: An Indian mining scenario

Abstract: Rapid industrial development that leads to economic growth and massive employment generation needs intense support from mining industries which act as a downstream supply chain partner for an industry. The counter side of intensive and unregulated mining activities is the massive waste generation and environmental degradation. Waste produced by mining industries is acquired by their upstream supply chain partners. So, there is a growing pressure on mining companies to enhance their ecological performance. In this regard, green supply chain management (GSCM), emerged as an environmental strategy that not only improves the environmental performance of individual organizations, but also that of the entire supply chain which has also been accepted by industries. However, an exception is observed in the case of the mining industries in India. This can be attributed to the poor understanding of the involved factors. Hence, an attempt is made here to identify the drivers of GSCM and extract the causal relationship among them through the use of decision making trial and evaluation laboratory (DEMATEL). Further, the strength of influence of these drivers on each other as also on the entire system is investigated to prioritize the drivers according to their influential strength. The results of the study, explore ‘top management commitment’ and ‘competitiveness’ as the two most important drivers whereas ‘employee pressure’ is the least important driver.

Linear antenna array synthesis using cat swarm optimization

Abstract: Antenna arrays with high directivity and low side lobe levels need to be designed for increasing the efficiency of communication systems. A new evolutionary technique, cat swarm optimization (CSO), is proposed for the synthesis of linear antenna arrays. The CSO is a high performance computational method capable of solving linear and non-linear optimization problems. CSO is applied to optimize the antenna element positions for suppressing side lobe levels and for achieving nulls in desired directions. The steps involved in the problem formulation of the CSO are presented. Various design examples are considered and the obtained CSO based results are validated by comparing with the results obtained using particle swarm optimization (PSO) and ant colony optimization (ACO). The flexibility and ease of implementation of the CSO algorithm is evident from this analysis, showing the algorithm's usefulness in electromagnetic optimization problems.

An Evolutionary SVM Model for DDOS Attack Detection in Software Defined Networks

Abstract: Software-Defined Network (SDN) has become a promising network architecture in current days that provide network operators more control over the network infrastructure. The controller, also called as the operating system of the SDN, is responsible for running various network applications and maintaining several network services and functionalities. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential targets. Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to the Internet. As the control layer is vulnerable to DDoS attacks, the goal of this paper is to detect the attack traffic, by taking the centralized control aspect of SDN. Nowadays, in the field of SDN, various machine learning (ML) techniques are being deployed for detecting malicious traffic. Despite these works, choosing the relevant features and accurate classifiers for attack detection is an open question. For better detection accuracy, in this work, Support Vector Machine (SVM) is assisted by kernel principal component analysis (KPCA) with genetic algorithm (GA). In the proposed SVM model, KPCA is used for reducing the dimension of feature vectors, and GA is used for optimizing different SVM parameters. In order to reduce the noise caused by feature differences, an improved kernel function (N-RBF) is proposed. The experimental results show that compared to single-SVM, the proposed model achieves more accurate classification with better generalization. Moreover, the proposed model can be embedded within the controller to define security rules to prevent possible attacks by the attackers.