Case Study On Power Factor Improvement

Abstract-Electrical Power constitutes a major component of the manufacturing cost in industry. In an electrical installation, power factor may become poor because of induction motors, welding machines, power transformers, voltage regulators, arc and induction furnaces, choke coils, neon signs etc. A poor power factor for the plant causes huge amount of losses, leading to thermal problem in switchgears. However power factor is controllable with a properly designed power factor improvement capacitors system. The power factor correction obtained by using capacitor banks to generate locally the reactive energy necessary for the transfer of electrical useful power, allows a better and more rational technical-economical management of the plants. This paper describes different aspects of power factor improvement in a typical industrial plant with the help of a case study

Fuzzy R&D portfolio selection of interdependent projects

AbstractGlobal competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA)

Regulation of Metabotropic Glutamate Receptor Internalization and Synaptic AMPA Receptor Endocytosis by the Postsynaptic Protein Norbin

Group I mGluRs have diverse functions in some fundamental neuronal processes, including modulation of synaptic plasticity; and dysregulation of these receptors could lead to various neuropsychiatric disorders. Trafficking of Group I mGluRs plays critical roles in controlling the precise spatiotemporal localization and activity of these receptors, both of which contribute to proper downstream signaling. Using “molecular replacement” approach in hippocampal neurons derived from mice of both sexes, we demonstrate a critical role for the postsynaptic density protein Norbin in regulating the ligand-induced internalization of Group I mGluRs. We show that Norbin associates with protein kinase A (PKA) through its N-terminus and anchors mGluR5 through its C-terminus, both of which are necessary for the ligand-mediated endocytosis of mGluR5, a member of the Group I mGluR family. A point mutation (A687G) at the C-terminus of Norbin inhibits the binding of Norbin to mGluR5 and blocks mGluR5 endocytosis. Finally, we demonstrate an important mechanism by which Norbin regulates mGluR-mediated AMPAR endocytosis in hippocampal neurons, a cellular correlate for mGluR-dependent synaptic plasticity. Norbin, through its PKA-binding regions, recruits PKA to AMPARs on activation of mGluRs; and deletion of the PKA-binding regions of Norbin inhibits mGluR-triggered AMPAR endocytosis. We further report that Norbin is important specifically for the mGluR-mediated AMPAR endocytosis, but not for NMDAR-dependent AMPAR endocytosis. Thus, this study unravels a novel role for Norbin in the internalization of mGluRs and mGluR-mediated AMPAR endocytosis that can have clinical relevance to the function of Group I mGluRs in pathologic processes.SIGNIFICANCE STATEMENTThe postsynaptic protein Norbin interacts with mGluR5, and both of them have been implicated in disorders, such as schizophrenia. However, the mechanistic basis underlying the regulation of mGluRs by Norbin remains elusive. We have identified Norbin as an essential mediator of ligand-mediated endocytosis of Group I mGluRs. Mechanistically, Norbin N-terminus associates with protein kinase-A (PKA) and C-terminus binds to mGluR5 to coordinate receptor internalization. A point mutation NorA687G inhibits endocytosis by disrupting this interaction. Additionally, Norbin is critical for the recruitment of PKA to AMPARs on activation of Group I mGluRs that assists in mGluR-mediated AMPAR endocytosis. Thus, Norbin has a dual function in the hippocampus: regulation of mGluR internalization and PKA-dependent modulation of mGluR-mediated AMPAR endocytosis, a prerequisite for mGluR-mediated synaptic plasticity.</jats:p

Fuzzy cross-entropy, mean, variance, skewness models for portfolio selection

AbstractIn this paper, fuzzy stock portfolio selection models that maximize mean and skewness as well as minimize portfolio variance and cross-entropy are proposed. Because returns are typically asymmetric, in addition to typical mean and variance considerations, third order moment skewness is also considered in generating a larger payoff. Cross-entropy is used to quantify the level of discrimination in a return for a given satisfactory return value. As returns are uncertain, stock returns are considered triangular fuzzy numbers. Stock price data from the Bombay Stock Exchange are used to illustrate the effectiveness of the proposed model. The solutions are done by genetic algorithms

Fuzzy mean–variance–skewness portfolio selection models by interval analysis

AbstractIn portfolio selection problem, the expected return, risk, liquidity etc. cannot be predicted precisely. The investor generally makes his portfolio decision according to his experience and his economic wisdom. So, deterministic portfolio selection is not a good choice for the investor. In most of the recent works on this problem, fuzzy set theory is widely used to model the problem in uncertain environments. This paper utilizes the concept of interval numbers in fuzzy set theory to extend the classical mean–variance (MV) portfolio selection model into mean–variance–skewness (MVS) model with consideration of transaction cost. In addition, some other criteria like short and long term returns, liquidity, dividends, number of assets in the portfolio and the maximum and minimum allowable capital invested in stocks of any selected company are considered. Three different models have been proposed by defining the future financial market optimistically, pessimistically and in the combined form to model the fuzzy MVS portfolio selection problem. In order to solve the models, fuzzy simulation (FS) and elitist genetic algorithm (EGA) are integrated to produce a more powerful and effective hybrid intelligence algorithm (HIA). Finally, our approaches are tested on a set of stock data from Bombay Stock Exchange (BSE)