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The critical properties of two-dimensional Ising magnetic lattice-gas model are studied by Monte-Carlo simulation in the canonical ensemble. The results are analyzed considering the modified Fisher scaling for systems with zero specific heat exponent, which applies when there are constrained variables such as the density in the canonical ensemble. The estimates of the exponents are obtained and compared to the exponents of the Ising universality class to which the model is expected to belong.

In Penna model, the genetic disease which influences the existence of individual has been described by bit-string. In fact, the individual will get extragenetic disease and die of that. In this paper we consider the extragenetic disease and discuss the influence of disease-resistant capability and cure on the existence of species.

We perform Monte–Carlo simulations of a three-dimensional spin system with a Hamiltonian which contains only four-spin interaction term. This system describes random surfaces with extrinsic curvature – gonihedric action. We study the anisotropic model when the coupling constants β_S for the space-like plaquettes and β_T for the transverse-like plaquettes are different. In the two limits β_S = 0 and β_T = 0 the system has been solved exactly and the main interest is to see what happens when we move away from these points towards the isotropic point, where we recover the original model. We find that the phase transition is of first order for β_T = β_S ≈ 0.25, while away from this point it becomes weaker and eventually turns to a crossover. The conclusion which can be drawn from this result is that the exact solution at the point β_S = 0 in terms of 2D-Ising model should be considered as a good zero-order approximation in the description of the system also at the isotropic point β_S = β_T and clearly confirms the earlier findings that at the isotropic point the original model shows a first-order phase transition.

This paper explains the hidden positive feedback in a two-stage fully differential amplifier through external feedback resistors and possible DC latch-up during the amplifier start-up. The biasing current selection among the cascade branches has been explained intuitively, with reference to previous literature. To avoid the latch-up problem, irrespective of the transistor bias currents, a novel hysteresis-based start-up circuit is proposed. An 87dB, 250MHz unity gain bandwidth amplifier has been developed in 65nm CMOS Technology and post-layout simulations demonstrate no start-up failures out of 1000 Monte-Carlo (6-Sigma) simulations. The circuit draws 126$\mu$A from a 1.2V supply and occupies the 2184$\mu$m² area.

Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the so-called Monte-Carlo Tree Search family of algorithms, which provide the possibility of storing and reusing previously calculated results to improve precision in the calculation of future outcomes. However, developers and researchers working in this area tend to have to carry out software developments from scratch to use their designs or improve designs previously created by other researchers. This makes it difficult to see improvements in current algorithms as it takes a lot of hard work. This work presents JGraphs, a toolset implemented in the Java programming language that will allow researchers to avoid having to reinvent the wheel when working with Monte-Carlo Tree Search. In addition, it will allow testing experiments carried out by others in a simple way, reusing previous knowledge.

We develop new Monte Carlo techniques based on stratifying the stock's hitting-times to the barrier for the pricing and Delta calculations of discretely-monitored barrier options using the Black-Scholes model. We include a new algorithm for sampling an Inverse Gaussian random variable such that the sampling is restricted to a subset of the sample space. We compare our new methods to existing Monte Carlo methods and find that they can substantially improve convergence speeds.

The fractal properties of the Lattice Limit Cycle model are explored when the process is realized on a 2-dimensional square lattice support via Monte Carlo Simulations. It is shown that the structure of the steady state presents inhomogeneous fluctuations in the form of domains of identical particles. The various domains compete with one another via their borders which have self-similar, fractal structure. The fractality is more prominent, (fractal dimensions d_f < 2), when the parameter values are near the critical point where the Hopf bifurcation occurs. As the distance from the Hopf bifurcation increases in the parameter space the system becomes more homogeneous and the fractal dimension tends to the value d_f = 2.

Stochastic finite element method is the main analysis method of complicated stochastic structure, and it is mainly applied to the Monte Carlo simulation method. Its sampling method is commonly called the Latin Hypercube sampling method. But for stochastic finite element analysis, and if level numbers for sampling are too many, sampling numbers will be difficult for most engineering purposes. In this paper, uniform sampling method is applied. Calculating examples with different factors and levels are compared with calculating examples in which Latin Hypercube sampling method is applied. Calculating examples show that when sampling numbers by uniform sampling technique is obviously fewer than sampling numbers by Latin Hypercube, mean values and standard deviations of displacements, stresses of node by the two sampling methods are almost completely identical. Uniform samplings for stochastic finite element analysis have good computing efficiency, and have certain applied perspectives.

Microfinance institutions (MFIs) play a crucial role in the emerging financial system as well as in the innovation of rural financial systems. MFIs significantly promote capital flow, alleviate financing difficulties for small and micro enterprises, and address poverty in underserved areas. However, the demands to address poverty through development and meet social goals expose MFIs to financial risks, particularly cash flow risk associated with capital repayment, which can hinder normal operations. Therefore, it is essential to systematically study the cash flow risk faced by MFIs to enhance their sustainable development capabilities. This research utilizes the bottom-up approach along with Monte Carlo simulation (MCS) to compute the value at risk (VaR), assessing the financial flow of listed microfinance firms in China. The analysis provides a straightforward and specific measure of cash flow uncertainty for management, investors and analysts of microfinance institutions. By comparing the VaR of corporate cash flow and evaluating the VaR of cash flow, the study identifies the existence of cash flow risks within the entire microfinance industry. The study provides policy recommendations to mitigate cash flow risk in microfinance institutions, focusing on business strategy and internal control, to improve the industry’s ability to manage risks and promote sustainable development.

The Geant4-DNA project proposes to develop an open-source simulation software based and fully included in the general-purpose Geant4 Monte-Carlo simulation toolkit. The main objective of this software is to simulate biological damages induced by ionizing radiations at the cellular and sub-cellular scale. This project was originally initiated by the European Space Agency for the prediction of the deleterious effects of radiations that may affect astronauts during future long duration space exploration missions. In this paper, the Geant4-DNA collaboration presents an overview of the whole on-going project, including its most recent developments that are available in the Geant4 toolkit since December 2009 (release 9.3), as well as an illustration example simulating the direct irradiation of a biological chromatin fiber. Expected extensions involving several research domains, such as particle physics, chemistry and cellular and molecular biology, within a fully interdisciplinary activity of the Geant4 collaboration are also discussed.

This paper proposes a (stochastic) Langevin-type formulation to modelize the continuous time evolution of the state of a biological reactor. We adapt the classical technique of asymptotic observer commonly used in the deterministic case, to design a Monte–Carlo procedure for the estimation of an unobserved reactant. We illustrate the relevance of this approach by numerical simulations.