A Monte Carlo Sampling Method of Amino Acid Sequences Adaptable to Given Main-Chain Atoms in the Proteins

2006 ◽  
Vol 140 (4) ◽  
pp. 543-552
Author(s):  
Koji Ogata ◽  
Kenji Soejima ◽  
Junichi Higo
Keyword(s):  
Monte Carlo ◽  
Amino Acid ◽  
Sampling Method ◽  
Main Chain ◽  
Monte Carlo Sampling ◽  
Amino Acid Sequences

scholarly journals An integrated inverse adaptive neural fuzzy system with Monte-Carlo sampling method for operational risk management

2018 ◽  
Vol 98 ◽  
pp. 11-26 ◽  
Author(s):  
Alejandro Peña ◽  
Isis Bonet ◽  
Christian Lochmuller ◽  
Francisco Chiclana ◽  
Mario Góngora
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Fuzzy System ◽  
Sampling Method ◽  
Operational Risk ◽  
Monte Carlo Sampling ◽  
Operational Risk Management ◽  
Neural Fuzzy

Receptor–Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method

2020 ◽  
Vol 16 (10) ◽  
pp. 6645-6655
Author(s):  
Hao Liu ◽  
Jianpeng Deng ◽  
Zhou Luo ◽  
Yawei Lin ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Free Energies ◽  
Receptor Ligand ◽  
Binding Free Energies

Modeling and implementation of spin diode based on two dimensional materials using Monte Carlo sampling method

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Swapnali Makdey ◽  
Rajendra Patrikar ◽  
Mohammad Farukh Hashmi
Keyword(s):  
Magnetic Field ◽  
Monte Carlo ◽  
Density Functional ◽  
Sampling Method ◽  
Spin Current ◽  
Electrical Current ◽  
Monte Carlo Sampling ◽  
Equilibrium Density ◽  
Content Type ◽  
Spin Diode

Purpose A “spin-diode” is the spintronics equivalent of an electrical diode: applying an external magnetic field greater than the limit of spin-diode BT flips the spin-diode between an isolating state and a conducting state [1]. While conventional electrical diodes are two-terminal devices with electrical current between the two terminals modulated by an electrical field, these two-terminal magneto resistive devices can generally be referred to as “spin-diodes” in which a magnetic field modulates the electrical current between the two terminals. Design/methodology/approach Current modulation and rectification are an important subject of electronics as well as spintronics spin diode is two-terminal magnetoresistive devices in which change in resistance in response to an applied magnetic field; this magnetoresistance occurs due to a variety of phenomena and with varying magnitudes and directions. Findings In this paper, an efficient rectifying spin diode is introduced. The resulting spin diode is formed from graphene gallium and indium quantum dots and antimony-doped molybdenum disulfide. Converting an alternating bias voltage to direct current is the main achievement of this model device with an additional profit of rectified spin-current. The non-equilibrium density functional theory with a Monte Carlo sampling method is used to evaluate the flow of electrons and rectification ratio of the system. Originality/value The results indicate that spin diode displaying both spin-current and charge-current rectification should be possible and may find practical application in nanoscale devices that combine logic and memory functions.

Capacity Expansion Problem by Monte Carlo Sampling Method

2009 ◽  
Vol 13 (6) ◽  
pp. 697-703
Author(s):  
Takayuki Shiina ◽  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Sampling Method ◽  
Sampling Technique ◽  
Capacity Expansion ◽  
Monte Carlo Sampling ◽  
Previous Approach ◽  
Importance Sampling Technique ◽  
Stochastic Vector ◽  
Additive Form

We consider the stochastic programming problem with recourse in which the expectation of the recourse function requires a large number of function evaluations, and its application to the capacity expansion problem. We propose an algorithm which combines an L-shaped method and a Monte Carlo method. The importance sampling technique is applied to obtain variance reduction. In the previous approach, the recourse function is approximated as an additive form in which the function is separable in the components of the stochastic vector. In our approach, the approximate additive form of the recourse function is perturbed to define the new density function. Numerical results for the capacity expansion problem are presented.

scholarly journals Monte Carlo Sampling Method for a Class of Box-Constrained Stochastic Variational Inequality Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Pei-Yu Li
Keyword(s):  
Monte Carlo ◽  
Variational Inequality ◽  
Optimization Problem ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Merit Function ◽  
Variational Inequality Problems ◽  
Sufficient Condition ◽  
Existence Of A Solution ◽  
Stochastic Variational Inequality

This paper uses a merit function derived from the Fishcher–Burmeister function and formulates box-constrained stochastic variational inequality problems as an optimization problem that minimizes this merit function. A sufficient condition for the existence of a solution to the optimization problem is suggested. Finally, this paper proposes a Monte Carlo sampling method for solving the problem. Under some moderate conditions, comprehensive convergence analysis is included as well.

scholarly journals Fitting coefficients of differential systems with Monte Carlo methods

2015 ◽  
Vol Volume 20 - 2015 - Special... ◽  
Author(s):  
Christian Chan Shio ◽  
Francine Diener
Keyword(s):  
Monte Carlo ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Point Estimate ◽  
System Of Differential Equations ◽  
Rejection Sampling ◽  
Differential Systems ◽  
New Methods ◽  
Deterministic Methods ◽  
International Audience

International audience We consider the problem of estimating the coefficients in a system of differential equations when a trajectory of the system is known at a set of times. To do this, we use a simple Monte Carlo sampling method, known as the rejection sampling algorithm. Unlike deterministic methods, it does not provide a point estimate of the coefficients directly, but rather a collection of values that "fits" the known data well. An examination of the properties of the method allows us not only to better understand how to choose the different parameters when implementing the method, but also to introduce a more efficient method by using a new two-step approach which we call sequential rejection sampling. Several examples are presented to illustrate the performance of both the original and the new methods. On considère le problème d'estimer les coefficients d'un système d'équations différentielles quand une trajectoire du système est connue en un petit nombre d'instants. On utilise pour cela une méthode de Monte Carlo très simple, la méthode de rejet qui ne fournit pas directement une estimation ponctuelle des coefficients comme le font les méthodes déterministes mais plutôt un ensemble de valeurs de ces coefficients qui sont cohérentes avec les données. L'examen des propriétés de cette méthode permet de comprendre non seulement comment bien choisir les différents paramètres de la méthode lorsqu'on l'utilise mais aussi d'introduire une méthode plus efficace, en deux étapes, que nous appelons la méthode de rejet séquentielle. Plusieurs exemples illustrent les performances respectives de la méthode d'origine et de la nouvelle méthode.

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