Analysis of the experimental level scheme of 61Cu using computational technique

2015 ◽  
Author(s):  
Anuradha Gupta ◽  
Preeti Verma ◽  
Arun Bharti
Keyword(s):  
Level Scheme ◽  
Computational Technique ◽  
Experimental Level

scholarly journals Level studies of 73As via 73Ge(p, nγ)73As reaction and density of discrete levels in 73As

2009 ◽  
Vol 24 (2) ◽  
pp. 82-85 ◽  
Author(s):  
Aziz Behkami ◽  
Rohallah Razavi ◽  
Tayeb Kakavand
Keyword(s):  
Proton Beam ◽  
Excited States ◽  
Level Scheme ◽  
Level Density ◽  
Fermi Gas ◽  
Temperature Model ◽  
Nuclear Level Density ◽  
Nuclear Level ◽  
Level Densities ◽  
Experimental Level

The excited states of 73As have been investigated via the 73Ge(p, n?)73As reaction with the proton beam energies from 2.5-4.3 MeV. The parameters of the nuclear level density formula have been determined from the extensive and complete level scheme for 73As. The Bethe formula for the back-shifted Fermi gas model and the constant temperature model are compared with the experimental level densities.

scholarly journals The Decay of 147Nd

1974 ◽  
Vol 27 (6) ◽  
pp. 869 ◽  
Author(s):  
C Rangacharyulu ◽  
SN Chaturvedi ◽  
GK Mehta ◽  
N Nath
Keyword(s):  
High Resolution ◽  
Level Scheme ◽  
Conclusive Evidence ◽  
Coincidence Spectrometer ◽  
Experimental Level

An experimental level scheme has been determined for 147Pm from an investigation of the decay of 147Nd which employed a high resolution Ge(Li) detector and a sum-coincidence spectrometer with fast-slow coincidence. Conclusive evidence is presented for the existence of levels in 147Pm at 182,228�5,275,319�5 and 725 keY besides the six already established levels, while no evidence is found for another six levels suggested by some earlier workers. Thirty-two y-ray transitions and their cascade relations have been established, and relative intensities have been determined for most of the transitions.

scholarly journals Level studies of 93Mo via 93Nb(p, nγ)93Mo reaction and density of discrete levels in 93Mo

2011 ◽  
Vol 26 (1) ◽  
pp. 69-73 ◽  
Author(s):  
Rohallah Razavi ◽  
Tayeb Kakavand
Keyword(s):  
Proton Beam ◽  
Excited States ◽  
Level Scheme ◽  
Level Density ◽  
Fermi Gas ◽  
Temperature Model ◽  
Nuclear Level Density ◽  
Nuclear Level ◽  
Level Densities ◽  
Experimental Level

The excited states of 93Mo have been investigated via the 93Nb(P,n?)93Mo reaction with proton beam energies of 2.5-4.3 MeV. The parameters of the nuclear level density formula were determined from the extensive and complete level scheme of 93Mo. The Bethe formula for the back-shifted Fermi gas model and the constant temperature model are compared with experimental level densities.

Inelastic Electron-Matter Interactions

1978 ◽  
Vol 36 (3) ◽  
pp. 259-267
Author(s):  
J. Silcox
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Chemical Structure ◽  
Inelastic Electron ◽  
Primary Concern ◽  
Unique Probe ◽  
Introductory Paper ◽  
Elastic Processes ◽  
Experimental Level ◽  
Inelastic Processes

In this introductory paper, my primary concern will be in identifying and outlining the various types of inelastic processes resulting from the interaction of electrons with matter. Elastic processes are understood reasonably well at the present experimental level and can be regarded as giving information on spatial arrangements. We need not consider them here. Inelastic processes do contain information of considerable value which reflect the electronic and chemical structure of the sample. In combination with the spatial resolution of the electron microscope, a unique probe of materials is finally emerging (Hillier 1943, Watanabe 1955, Castaing and Henri 1962, Crewe 1966, Wittry, Ferrier and Cosslett 1969, Isaacson and Johnson 1975, Egerton, Rossouw and Whelan 1976, Kokubo and Iwatsuki 1976, Colliex, Cosslett, Leapman and Trebbia 1977). We first review some scattering terminology by way of background and to identify some of the more interesting and significant features of energy loss electrons and then go on to discuss examples of studies of the type of phenomena encountered. Finally we will comment on some of the experimental factors encountered.

Computational Technique for the Qualification of Focal Spot Size and Shape of Industrial Radioscopy Equipment Using Star Patterns

2020 ◽  
Vol 78 (4) ◽  
pp. 479-486
Author(s):  
Marcela Tatiana Fernandes Beserra ◽  
◽  
Ricardo Tadeu Lopes ◽  
Davi Ferreira de Oliveira ◽  
Claudio Carvalho Conti ◽  
...  
Keyword(s):  
Focal Spot ◽  
Spot Size ◽  
Computational Technique ◽  
Focal Spot Size ◽  
Size And Shape

A Novel Approach for Thermomechanical Analysis of Stationary Rolling Tires within an ALE–Kinematic Framework

2013 ◽  
Vol 41 (3) ◽  
pp. 174-195 ◽  
Author(s):  
Anuwat Suwannachit ◽  
Udo Nackenhorst
Keyword(s):  
Constitutive Model ◽  
Numerical Solutions ◽  
Numerical Technique ◽  
Thermomechanical Analysis ◽  
Rolling Contact ◽  
Computational Technique ◽  
Heat Equations ◽  
Arbitrary Lagrangian Eulerian ◽  
Material Particle ◽  
Operator Split

ABSTRACT A new computational technique for the thermomechanical analysis of tires in stationary rolling contact is suggested. Different from the existing approaches, the proposed method uses the constitutive description of tire rubber components, such as large deformations, viscous hysteresis, dynamic stiffening, internal heating, and temperature dependency. A thermoviscoelastic constitutive model, which incorporates all the mentioned effects and their numerical aspects, is presented. An isentropic operator-split algorithm, which ensures numerical stability, was chosen for solving the coupled mechanical and energy balance equations. For the stationary rolling-contact analysis, the constitutive model presented and the operator-split algorithm are embedded into the Arbitrary Lagrangian Eulerian (ALE)–relative kinematic framework. The flow of material particles and their inelastic history within the spatially fixed mesh is described by using the recently developed numerical technique based on the Time Discontinuous Galerkin (TDG) method. For the efficient numerical solutions, a three-phase, staggered scheme is introduced. First, the nonlinear, mechanical subproblem is solved using inelastic constitutive equations. Next, deformations are transferred to the subsequent thermal phase for the solution of the heat equations concerning the internal dissipation as a source term. In the third step, the history of each material particle, i.e., each internal variable, is transported through the fixed mesh corresponding to the convective velocities. Finally, some numerical tests with an inelastic rubber wheel and a car tire model are presented.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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