Spin currents and magnetoresistance of graphene-based magnetic junctions

Using the tight-binding approximation and the nonequilibrium Green's function approach, we investigate the coherent spin-dependent transport in planar magnetic junctions consisting of two ferromagnetic (FM) electrodes separated by a graphene flake (GF) with zigzag or armchair interfaces. It is found that the electron conduction strongly depends on the geometry of contact between the GF and the FM electrodes. In the case of zigzag interfaces, the junction demonstrates a spin-valve effect with high magnetoresistance (MR) ratios and shows negative differential resistance features for a single spin channel at positive gate voltage. In the case of armchair interfaces, the current-voltage characteristics behave linearly at low bias voltages and hence, both spin channels are in on state with low MR ratios.Comment: 6 pages, 5 figure

A STUDY OF USING FINANCIAL AND NON-FINANCIAL CRITERIA IN EVALUATING PERFORMANCE: SOME EVIDENCE OF IRAN

The success of any organization is reflected upon by its performance which is in turn highlydependent upon its strategies. In this era of cut-throat competition, what an organization requires isnot just framing the right strategies, but also managing the same. The impact of the right strategieswill automatically be reflected in the results. This research includes analyzing balanced scorecard(BSC) is inclusively. BSC pays attention to institutions traditional criteria evaluation i.e. financialand non-financial criteria that are mostly guidance and controlling criteria. Therefore, the mainquestions of this research include: How much financial and non-financial criteria are used to evaluatethe efficiency? Do the efficiency evaluators who know well about balanced scorecard pay moreattention to non-financial criteria? The results of T-test, independence sample, multi variable singlevariance analysis test and Tokay test, the following show that.First the efficiency evaluators are mostly interested in using financial criteria rather than nonfinancialonce; and second using non-financial criteria, there was significant difference betweenthose evaluators who were familiar with BSC and the others

Novel High-Gain Narrowband Waveguide-Fed Filtenna using Genetic Algorithm

Filtenna is an antenna with filtering feature. There are many ways to design a filtenna. In this paper, a high-gain narrowband waveguide-fed aperture filtenna has been proposed and designed. A patterned plane, which is designed using genetic algorithm has been used at the open end of the waveguide fed, mounted on a conducting ground plane. To design the patterned pattern, magnetic field integral equation of the structure has been derived, so it has been solved using method of moments. The proposed filtenna has been simulated with HFSS that confirms the results obtained by method of moments. Finally, an unprinted dielectric as a superstrate has been used to enhance the gain of the filtenna. The filtenna bandwidth is 1.76% (160 MHz)  which has the gain of 15.91 dB at the central frequency of 9.45 GHz

Hinted Dictionaries: Efficient Functional Ordered Sets and Maps

This paper introduces hinted dictionaries for expressing efficient ordered sets and maps functionally. As opposed to the traditional ordered dictionaries with logarithmic operations, hinted dictionaries can achieve better performance by using cursor-like objects referred to as hints. Hinted dictionaries unify the interfaces of imperative ordered dictionaries (e.g., C++ maps) and functional ones (e.g., Adams\u27 sets). We show that such dictionaries can use sorted arrays, unbalanced trees, and balanced trees as their underlying representations. Throughout the paper, we use Scala to present the different components of hinted dictionaries. We also provide a C++ implementation to evaluate the effectiveness of hinted dictionaries. Hinted dictionaries provide superior performance for set-set operations in comparison with the standard library of C++. Also, they show a competitive performance in comparison with the SciPy library for sparse vector operations

Molecular dynamics simulation and machine learning study of biological processes

In this dissertation, I use computational techniques especially molecular dynamics (MD) and machine learning to study important biological processes. MD simulations can effectively be used to understand and investigate biologically relevant systems with lengths and timescales that are otherwise inaccessible to experimental techniques. These include but are not limited to thermodynamics and kinetics of protein folding, protein-ligand binding free energies, interaction of proteins with membranes, and designing new therapeutics for diseases with rational design strategies. The first chapter includes a detailed description of the computational methods including MD, Markov state modeling and deep learning. In the second chapter, we studied membrane active peptides using MD simulation and machine learning. Two cell penetrating peptides MPG and Hst5 were simulated in the presence of membrane. We showed that MPG enters the model membrane through its N-terminal hydrophobic residues while Hst5 remains attached to the phosphate layer. Formation of helical conformation for MPG helps its deeper insertion into membrane. Natural language processing (NLP) and deep generative modeling using a variational attention based variational autoencoder (VAE) was used to generate novel antimicrobial peptides. These in silico generated peptides have a high quality with similar physicochemical properties to real antimicrobial peptides. In the third chapter, we studied kinetics of protein folding using Markov state models and machine learning. We studied the kinetics of misfolding in β2-microglobulin using MSM analysis which gave us insights about the metastable states of β2m where the outer strands are unfolded and the hydrophobic core gets exposed to solvent and is highly amyloidogenic. In the next part of this chapter, we propose a machine learning model Gaussian mixture variational autoencoder (GMVAE) for simultaneous dimensionality reduction and clustering of MD simulations. The last part of this chapter is about a novel machine learning model GraphVAMPNet which uses graph neural networks and variational approach to markov processes for kinetic modeling of protein folding. In the last chapter, we study two membrane proteins, spike protein of SARS-COV-2 and EAG potassium channel using MD simulations. Binding free energy calculations using MMPBSA showed a higher binding affinity of receptor binding domain in SARS-COV-2 to its receptor ACE2 than SARS-COV which is one of the major reason for its higher infection rate. Hotspots of interaction were also identified at the interface. Glycans on the spike protein shield the spike from antibodies. Our MD simulation on the full length spike showed that glycan dynamics gives the spike protein an effective shield. However, breaches were found in the RBD at the open state for therapeutics using network analysis. In the last section, we study ligand binding to the PAS domain of EAG potassium channel and show that a residue Tyr71 blocks the binding pocket. Ligand binding inhibits the current through EAG channel

Innovation In Market Management By Utilizing Business Intelligence: Introducing Proposed Framework

AbstractNowadays, business organization needs to analyze market so that be able to stay stable in facing market variant changes and eventually to be able to handle market management. For this purpose, organization should update their business processes by utilizing modern technologies which this is called business intelligence (BI). In this paper while introducing market management processes, significant necessity of innovation and creativity in these processes for competing in current global trading is discussed. Furthermore, BI definitions from different authors’ point of view and BI principles and characteristics is addressed. Then the proposed framework is introduced with consideration to variant dimensions and functions of BI to furnish organization characteristics toward acquiring BI approach and derived benefits from it in business trend. Business area development, progressive and goal-based presence in international environment, and organization efficiency increase are some of few key functions that is argued in continue. Purpose of this paper is introducing practical framework to help organizations in direction of their goals toward BI, which it caused they acquire correct and well-timed understanding from market condition

Implementing Shannon Entropy, SWOT and Mathematical Programming for Supplier Selection and Order Allocation

Supplier selection is a multiple criteria decision making (MCDM) problem which is affected by several conflicting factors. In the business market of flaming competition in recent years, more attention has been paid to this problem. In this paper, a two-phased model is proposed for supplier selection and order allocation. At the first, suppliers are evaluated according to both qualitative and quantitative criteria resulting from SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis. SWOT is a useful technique in strategic management and is utilized to determine criteria and to deal with suppliers situation in competitive market. Defining the criteria, Shannon entropy is then used to calculate weight of criteria. Then the results are used as an input for integer linear programming (ILP) to allocate order to suppliers