Measurement of Magnetic Relaxation in the peak regime of V3Si

Magnetization relaxation measurements are carried out in the Peak effect regime of superconducting V3Si crystal, using Quantum Design SQUID magnetometer. Relaxation in the increasing field scan is logarithmic in time, consistent with the theory of flux creep. The relaxation on the decreasing field scan however exhibits athermal behavior which is predominantly governed by the flux avalanches triggered by the small external field perturbation experienced by the superconductor during measurement scan in an inhomogeneous field.Comment: PDF, 17 pages including 9 figure

Evaluating Asset-Pricing Models Using The Hansen-Jagannathan Bound: A Monte Carlo Investigation

We conduct Monte Carlo experiments to examine whether the bound proposed by Hansen and Jagannathan (1991) is a useful device for evaluating asset pricing models. Specifically, we use recently developed statistical tests, which are based on a 'distance' between the model and the Hansen-Jagannathan bound, to compute the rejection rates of true models. We provide finite-sample critical values for asset pricing models with time separable preferences, and show how they depend upon nuisance parameters—risk aversion and the rate of time preference. Further, we show that the finite-sample distribution of the test statistic associated with the risk-neutral case is extreme, in the sense that critical values based on this distribution will deliver type I errors no larger than intended—regardless of risk aversion or the rate of time preference. Extending the analysis to accommodate other preferences, we show that in the state non-separable case, the small-sample distributions of the test statistics are influenced significantly by the degree of intertemporal substitution, but not by attitudes toward risk. For habit formation preferences, the small-sample distributions are strongly influenced by the habit parameter. However, the maximal-size critical values for time-separable preferences are appropriate for habit formation as well as state non-separable preferences. We conclude that with these critical values the HJ bound is indeed a useful evaluation device. We then use the critical values to evaluate three asset pricing models using U.S. data. We find evidence against the time-separable model and mixed evidence on the remaining two models.

Depressed classes Assertion in Princely Mysore - A Study

The princely Mysore state, which came to be called a model state by the British and Ramrajya by Mahatma Gandhi, was unique in several respects. After the Rendition of the state to the Wodeyar Kings in 1881, the Maharajas and the Dewans embarked upon developmental activities in a big way. Political and economic changes resulted in social and educational changes among the depressed classes. While the depressed classes (Dalits) in other parts of India still lived in the dark ages, the depressed classes of Mysore in the 20th century underwent a process of socio-economic transformation. This resulted in their self-assertion in bigaray. The paper focuses on this issue

Multiservice Delivery in Wireless Networks Management

A Wireless Sensor Network is a self-configuring set of connections of tiny sensor nodes communicate in the middle of themselves using radio signals, and deployed in measure to sense, observe and identify with the physical world.WSN provide a bridge between the real physical and virtual worlds. Allow the ability to observe the previously unobservable at a fine resolution over large spatiotemporal scales. A join that execute different than typical behavior (drop packets, scare routing system and save their assets by not ahead the other node packets) is identified as selfish node. The multiservice delivery between the source-destination pairs in distributed selfish wireless networks (SeWN), where selfish relay nodes (RN) expose their selfish behaviors. Research focus evaluating the trust of a node group and excluding selfish nodes for improving the network performance. In the network connectivity of selfish wireless networks (SeWNs) constituted by selfish nodes (SeNs). Source transfer the multi-service delivery to destination through Relay Node (RN). At the time of transfer, the selfish relay nodes expose their selfish behavior by doing dropping multiservice. In this environment, the network need to establish the connection between source and destination, for that source need to find the optimal path. Concept of Node selfishness management is constructed to manage the RN’sto manage the RN’s node-selfishness information (NSI). It includes the degree of node-selfishness (DeNS), the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES). DeNs determines in terms of RN’s historical behaviors, DeIS defines in terms of its available resources and finally DeES described by means of the employed incentive mechanism and the quality-of-service (QoS) requirements. Over the spread node-selfishness administration, a path collection criterion is considered to select the most reliable and through path in terms of RNs’ DeISs precious by their accessible resources, and the optimal incentive are determined by the source to motivate forwarding multiservice of the RNs in the selected path. Simulation results show that this future model effectively manages the RNs’ NSI, and the most select path selection and the optimal incentives are determined

Student risk identification learning model using machine learning approach

Several challenges are associated with online based learning systems, the most important of which is the lack of student motivation in various course materials and for various course activities. Further, it is important to identify student who are at risk of failing to complete the course on time. The existing models applied machine learning approach for solving it. However, these models are not efficient as they are trained using legacy data and also failed to address imbalanced data issues for both training and testing the classification approach. Further, they are not efficient for classifying new courses. For overcoming these research challenges, this work presented a novel design by training the learning model for identifying risk using current courses. Further, we present an XGBoost classification algorithm that can classify risk for new courses. Experiments are conducted to evaluate performance of proposed model. The outcome shows the proposed model attain significant performance over stat-of-art model in terms of ROC, F-measure, Precision and Recall

Stable and Metastable vortex states and the first order transition across the peak effect region in weakly pinned 2H-NbSe_2

The peak effect in weakly pinned superconductors is accompanied by metastable vortex states. Each metastable vortex configuration is characterized by a different critical current density J_c, which mainly depends on the past thermomagnetic history of the superconductor. A recent model [G. Ravikumar, et al, Phys. Rev. B 61, R6479 (2000)] proposed to explain the history dependent J_c postulates a stable state of vortex lattice with a critical current density J_c^{st}, determined uniquely by the field and temperature. In this paper, we present evidence for the existence of the stable state of the vortex lattice in the peak effect region of 2H-NbSe_2. It is shown that this stable state can be reached from any metastable vortex state by cycling the applied field by a small amplitude. The minor magnetization loops obtained by repeated field cycling allow us to determine the pinning and "equilibrium" properties of the stable state of the vortex lattice at a given field and temperature unambiguously. The data imply the occurence of a first order phase transition from an ordered phase to a disordered vortex phase across the peak effect.Comment: 20 pages, 10 figures. Corresponding author: S. Ramakrishna