Dephasing time of composite fermions

We study the dephasing of fermions interacting with a fluctuating transverse gauge field. The divergence of the imaginary part of the fermion self energy at finite temperatures is shown to result from a breakdown of Fermi's golden rule due to a faster than exponential decay in time. The strong dephasing affects experiments where phase coherence is probed. This result is used to describe the suppression of Shubnikov-de Haas (SdH) oscillations of composite fermions (oscillations in the conductivity near the half-filled Landau level). We find that it is important to take into account both the effect of dephasing and the mass renormalization. We conclude that while it is possible to use the conventional theory to extract an effective mass from the temperature dependence of the SdH oscillations, the resulting effective mass differs from the $m^\ast$ of the quasiparticle in Fermi liquid theory.Comment: 14 pages, RevTeX 3.0, epsf, 1 EPS figur

Finding An Alternative Routing For Solid Waste Management

Finding the shortest route for transferring solid waste is one of the biggest challenges for every solid waste management organization. Globally, waste collection process represents 74% of operating costs, yet it has been given minor attention. In this research, Dijkstra Algorithm and Travelling Salesman Problem (TSP) using GIS Software 10.1 were used to optimize the route from Seberang Perai Industrial area to Ampang Jajar waste transfer station. Using the technique, we compare the existing and optimized route with the 24 companies from phases I, II and IV in the Seberang Perai Industrial area to waste transfer stations. Dijkstra and TSP are some of the classic algorithms in the GIS which are used to find the shortest route to reduce the operating cost. Carbon emission is an extended part of this study by comparing transportation fuel consumption between existing routes and optimized routes

Newborn care: Effectiveness of simulation training for staff nurses

A neonate is also called a newborn. Aim: To assess the effectiveness of simulation training on knowledge and practice regarding newborn care among staff nurses. Research design: A quasi experimental non randomized control group design was used. Sampling and sampling technique: Sixty staff nurses each  in experimental and control group a were selected by non probability purposive sampling for the study in Rohilkhand Medical college hospital and Varunarjun Medical College Hospital. Knowledge and practice was assessed by using structured knowledge questionnaire and practice checklist.. The intervention included the simulation training of neonatal resuscitation and teaching on  immediate and routine newborn care. Results and findings: The study findings revealed that the  mean post-test knowledge score was higher i.e. (31.66±1.71) than the mean pretest knowledge score i.e. (20.68 ±4.68) in the experimental group. It revealed that the mean post-test practice score was higher i.e.( 24.71±0.45) than the mean pretest practice  score i.e. (21.03 ±1.30) in the experimental group. Data  revealed that the mean experimental group knowledge score was higher (31.66±1.17) than the mean control group knowledge score (26.03 ± 3.66). The difference was found to be statistically  significant at p=0.05 level of significance

S-Type Random <i>k</i> Satisfiability Logic in Discrete Hopfield Neural Network Using Probability Distribution: Performance Optimization and Analysis

Recently, a variety of non-systematic satisfiability studies on Discrete Hopfield Neural Networks have been introduced to overcome a lack of interpretation. Although a flexible structure was established to assist in the generation of a wide range of spatial solutions that converge on global minima, the fundamental problem is that the existing logic completely ignores the probability dataset’s distribution and features, as well as the literal status distribution. Thus, this study considers a new type of non-systematic logic termed S-type Random k Satisfiability, which employs a creative layer of a Discrete Hopfield Neural Network, and which plays a significant role in the identification of the prevailing attribute likelihood of a binomial distribution dataset. The goal of the probability logic phase is to establish the logical structure and assign negative literals based on two given statistical parameters. The performance of the proposed logic structure was investigated using the comparison of a proposed metric to current state-of-the-art logical rules; consequently, was found that the models have a high value in two parameters that efficiently introduce a logical structure in the probability logic phase. Additionally, by implementing a Discrete Hopfield Neural Network, it has been observed that the cost function experiences a reduction. A new form of synaptic weight assessment via statistical methods was applied to investigate the effect of the two proposed parameters in the logic structure. Overall, the investigation demonstrated that controlling the two proposed parameters has a good effect on synaptic weight management and the generation of global minima solutions