Hamiltonian vs Lagrangian Embedding of a Massive Spin-one Theory Involving 2-form Field

We consider the Hamiltonian and Lagrangian embedding of a first-order, massive spin-one, gauge non-invariant theory involving anti-symmetric tensor field. We apply the BFV-BRST generalised canonical approach to convert the model to a first class system and construct nil-potent BFV-BRST charge and an unitarising Hamiltonian. The canonical analysis of the St\"uckelberg formulation of this model is presented. We bring out the contrasting feature in the constraint structure, specifically with respect to the reducibility aspect, of the Hamiltonian and the Lagrangian embedded model. We show that to obtain manifestly covariant St\"uckelberg Lagrangian from the BFV embedded Hamiltonian, phase space has to be further enlarged and show how the reducible gauge structure emerges in the embedded model.Comment: Revtex, 13 pages, no figure, to appear in Int. J. Mod. Phys.

The Hydrodynamical Limit of Quantum Hall system

We study the current algebra of FQHE systems in the hydrodynamical limit of small amplitude, long-wavelength fluctuations. We show that the algebra simplifies considerably in this limit. The hamiltonian is expressed in a current-current form and the operators creating inter-Landau level and lowest Landau level collective excitations are identified.Comment: Revtex, 16 page

Automatic Discrimination of Abnormal Subjects Using the Visual Evoked Potential Spectral Components

Study of visual evoked potential (VEP) is one of the utilized methods in clinical diagnosis of ophthalmology and neurological disorders. The automatic detection of VEP spectral components is an important tool in the diagnosis of mental activity. This paper presents a novel computational approach using feedforward neural network to identify abnormal subjects from changes in spectral components. The output vector from the feedforward neural network is based on the VEP spectral components. The software was developed to identify mental state from the VEP spectral components using Matlab software package. Using this approach, it is possible to perform real-time abnormality identification accurately on personal computers

ANTIFUNGAL ACTIVITY OF SEAWEED ULVA LACTUCA L. EXTRACTED CRUDE PROTEIN AGAINST PATHOGENIC FUNGI

Objective: The objective of this study was to evaluate the antifungal activity of seaweed extracted protein against the pathogenic fungi. Methods: Antifungal activity of seaweed Ulva lactuca L. extracted protein was determined against pathogenic fungi such as Alternaria solani, Aspergillus clavatus, Aspergillus niger, Aspergillus flavus, and Fusarium oxysporum by disk diffusion Method. Then, the potentially active protein was determined using sodium dodecyl sulfate-polyacrylamide gel electrophoresis and circular dichroism (CD) spectroscopy. Results: Seaweeds extracted protein checked for the antifungal activity against A. clavatus, A. solani, and A. flavus better activity compared to standard amphotericin-B and CD spectroscopy. Analysis of the extracts divulges the presence of entire protein compounds. Conclusions: This study extracted seaweed protein sufficient of antifungal activity opposed to antifungal pathogens as compared with the standard. This is first report an activity of seaweed extracted protein against the plant and human pathogenic fungus bearing agricultural important

Numerical Simulations of Flow in a 3-D Supersonic Intake at High Mach Numbers

Numerical simulations of the compressible, 3-D non reacting flow in the engine inlet sectionof a concept hypersonic air-breathing vehicle are presented. These simulations have been carriedout using FLUENT. For all the results reported, the mesh has been refined to achieve areaaveragedwall y+ about 105. Mass flow rate through the intake and stagnation pressure recoveryare used to compare the performance at various angles of attack. The calculations are able topredict the mode of air-intake operation (critical and subcritical) for different angles of attack.Flow distortion at the intake for various angles of attack is also calculated and discussed. Thenumerical results are validated by simulating the flow through a 2-D mixed compression hypersonicintake model and comparing with the experimental data

Implementation of FOAF, AIISO and DOAP ontologies for creating an academic community network using semantic frameworks

Web 2.0 delivers the information which is then displayed in human readable content, omitting the crucial information which can be drawn from the data by the applications. Web 3.0 or semantic web is an extension to the current web, with an ambition to determine the drawbacks of the current web. The semantic web has already proven its influence in several communities around the globe, such as social media, music industry, healthcare domain, online blogs or articles, etc.; Among the several tools and technologies, ontologies or vocabularies are the foundation pillar for the semantic web. In this paper, the developed system aims at improving the collaboration and academic relations among staff which is directly related to our education community by providing a better networking platform which lets the agents discuss their achievements, titles, domain interests, and various other activities. Results have been analyzed to show how new facts, information can be implied from the presented knowledge of several agents and help generate a relationship graph by utilizing various semantic tools. The system discussed in this paper processes all the information in a format which can be understood by both humans and the machines, to interpret the underlying meaning about it and provide effective results

Reducing CPU Utilization & Improving Failover Time in Dual Controller SDN (Software Defined Network) Environment

A salient component of the network is failover. Largely, customers are accessing dual path controller software defined network (SDN) environment.  Subsequently, when one controller fizzles out the network communication can persist as a result of the other controller.   There will be no interruption in the network communication due to the failover setup which will be effective in case of failure of a controller.  In the absence of failover, the network communication will be obstructed.  This could be by cause of the system resources, system itself or routing policies or Distributed Denial of Services.  Generally, controller will support many switches. When SDN controller collapses controller will not be in sync with switches. After few attempts switches will failover to the secondary SDN controller. During this shutdown time the network communication will be obstructed.  Failover algorithm is the key to solve this issue and will facilitate the synchronization of the information with SDN controllers and enhance failover time.  Apparently, communication between both SDN controllers and switches is enhanced as well as reinforces the time of synchronization due to multiple SDN controllers.  CPU utilization is another problem in SDN environment as the new inflow packets need to be forwarded to control plane to find the action that need to be taken. Processing at both ends and having one to one mapping between new incoming connection requests and the packet from data plane to control plane increases the CPU utilization and there by more power consumption

End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics

Protein folding is the intricate process by which a linear sequence of amino acids self-assembles into a unique three-dimensional structure. Protein folding kinetics is the study of pathways and time-dependent mechanisms a protein undergoes when it folds. Understanding protein kinetics is essential as a protein needs to fold correctly for it to perform its biological functions optimally, and a misfolded protein can sometimes be contorted into shapes that are not ideal for a cellular environment giving rise to many degenerative, neuro-degenerative disorders and amyloid diseases. Monitoring at-risk individuals and detecting protein discrepancies in a protein's folding kinetics at the early stages could majorly result in public health benefits, as preventive measures can be taken. This research proposes an efficient pipeline for predicting protein folding kinetics with high accuracy and low memory footprint. The deployed machine learning (ML) model outperformed the state-of-the-art ML models by 4.8% in terms of accuracy while consuming 327x lesser memory and being 7.3% faster.Comment: Accepted for presentation at the 22nd International Conference on Machine Learning and Application

Joint Model of App-Based Ridehailing Adoption, Intensity of Use and Intermediate Public Transport (IPT) Consideration among Workers in Chennai City

You are viewing a past publication from the Good Systems Network Digest in July 2020.Office of the VP for Researc