scholarly journals Accurate and Scalable Techniques for the Complex/Pathway Membership Problem in Protein Networks

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Orhan Çamoğlu ◽  
Tolga Can ◽  
Ambuj K. Singh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Hierarchical Clustering ◽  
Network Flow ◽  
High Efficiency ◽  
Protein Network ◽  
Protein Networks ◽  
Membership Problem ◽  
Physical Interactions

A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly connected proteins. In this paper, we show that methods that are not affected by the degree (number of linkages) of a protein give more accurate predictions for certain complexes and pathways. We propose a network flow-based technique to compute the association probability of a pair of proteins. We extend the proposed technique using hierarchical clustering in order to scale well with the size of proteome. We also show that top-k queries are not suitable for a large number of cases, and threshold queries are more meaningful in these cases. Network flow technique with clustering is able to optimize meaningful threshold queries and answer them with high efficiency compared to a similar method that uses Monte Carlo simulation.

scholarly journals Monte Carlo Modeling and Design of Photon Energy Attenuation Layers for >10× Quantum Yield Enhancement in Si-Based Hard X-ray Detectors

Instruments ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 17
Author(s):  
Eldred Lee ◽  
Kaitlin M. Anagnost ◽  
Zhehui Wang ◽  
Michael R. James ◽  
Eric R. Fossum ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quantum Yield ◽  
Photon Energy ◽  
High Efficiency ◽  
High Energy ◽  
Yield Enhancement ◽  
X Ray ◽  
Simulation Results ◽  
Conversion Efficiencies

High-energy (>20 keV) X-ray photon detection at high quantum yield, high spatial resolution, and short response time has long been an important area of study in physics. Scintillation is a prevalent method but limited in various ways. Directly detecting high-energy X-ray photons has been a challenge to this day, mainly due to low photon-to-photoelectron conversion efficiencies. Commercially available state-of-the-art Si direct detection products such as the Si charge-coupled device (CCD) are inefficient for >10 keV photons. Here, we present Monte Carlo simulation results and analyses to introduce a highly effective yet simple high-energy X-ray detection concept with significantly enhanced photon-to-electron conversion efficiencies composed of two layers: a top high-Z photon energy attenuation layer (PAL) and a bottom Si detector. We use the principle of photon energy down conversion, where high-energy X-ray photon energies are attenuated down to ≤10 keV via inelastic scattering suitable for efficient photoelectric absorption by Si. Our Monte Carlo simulation results demonstrate that a 10–30× increase in quantum yield can be achieved using PbTe PAL on Si, potentially advancing high-resolution, high-efficiency X-ray detection using PAL-enhanced Si CMOS image sensors.

Monte Carlo Simulation of the Thermal Conductivity and Phonon Transport in Nanocomposites

2005 ◽  
Author(s):  
Ming-Shan Jeng ◽  
Ronggui Yang ◽  
David Song ◽  
Gang Chen
Keyword(s):  
Thermal Conductivity ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Silicon Germanium ◽  
High Efficiency ◽  
Three Dimensional ◽  
Simulation Method ◽  
Phonon Transport ◽  
Boltzmann Transport ◽  
Nanoparticle Composites

This paper presents a Monte Carlo simulation scheme to study the phonon transport and thermal conductivity of nanocomposites. Special attention has been paid to the implementation of periodic boundary condition in Monte Carlo simulation. The scheme is applied to study the thermal conductivity of silicon germanium (Si-Ge) nanocomposites, which are of great interest for high efficiency thermoelectric material development. The Monte Carlo simulation was first validated by successfully reproducing the results of (two dimensional) nanowire composites using the deterministic solution of the phonon Boltzmann transport equation and the experimental thermal conductivity of bulk germanium, and then the validated simulation method was used to study (three dimensional) nanoparticle composites, where Si nanoparticles are embedded in Ge host. The size effects of phonon transport in nanoparticle composites were studied and the results show that the thermal conductivity of nanoparticle composites can be lower than alloy value. It was found that randomly distributed nanopaticles in nanocomposites rendered the thermal conductivity values close to that of periodic aligned patterns.

scholarly journals Using Service Simulating Test in Designing of Tillage Combine

2017 ◽  
Vol 20 (4) ◽  
pp. 110-114
Author(s):  
Sergey Vladimirovich Oskin ◽  
Boris Fedorovich Tarasenko
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fuel Consumption ◽  
Soil Compaction ◽  
High Efficiency ◽  
Cost Savings ◽  
Optimal Number ◽  
Optimal Choice ◽  
Low Efficiency ◽  
Fuel Costs

Abstract Determining the optimal structure of the tillage combine for working in a particular company is a very difficult task due to many factors. While searching for the optimal choice, it is necessary to strive for having fewer combines in operation, reduce the fuel costs and compensate damages due to changes in agrotechnical terms and soil compaction during the combines’ operation. In this article it is proposed to apply the Monte Carlo simulation for solving this issue. As a result of the analysis of models, it was observed that all combines can be divided into separate efficiency groups and form certain tillage complexes. After the analysis of these complexes, it was proposed to replace the tillage tools, which led to further reduction in total costs. So the transition to non-mouldboard technology in both high-efficiency and low-efficiency combines will lead to cost savings by 45%, and the introduction of new tools will reduce the fuel consumption by 61-64%. For high-efficiency machine complexes, non-mouldboard technology allows the reduction of the optimal number of aggregates by 25-32%. At the same time, the introduction of new tillage combines will reduce the number of operating combines by 50-58% due to reduced resistance and the combination of technological operations.

The Maximum Entropy Estimation of Structural Reliability Based on Monte Carlo Simulation

2014 ◽  
Author(s):  
Wenbo Huang ◽  
Jiangang Mao ◽  
Zhiyong Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maximum Entropy ◽  
Failure Probability ◽  
Structural Reliability ◽  
High Efficiency ◽  
Numerical Examples ◽  
Entropy Estimation ◽  
Probabilistic Distribution ◽  
Maximum Entropy Estimation

Taking the advantage of the high efficiency of Monte Carlo simulation for events of high failure probability, it is adopted to estimate the probabilities of failure of the reduced safe margins of structures. By assuming that the low tail of the probabilistic distribution of the safe margin to follow a Weibull distribution, the failure probabilities simulated are taken as empirical data to extrapolate the Weibull parameters. Among the candidate Weibull distributions, the maximum entropy is used to identify the optimum one which is used to predict the truth probabilities of failure of structures. Two typical numerical examples are carried out to demonstrate the method developed.

Efficient Monte Carlo Simulation for Pricing Variance Derivatives under Multi-Factor Stochastic Volatility Models

2013 ◽  
Vol 411-414 ◽  
pp. 1089-1094
Author(s):  
Jun Mei Ma ◽  
Gui Ding Gu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stochastic Volatility ◽  
High Efficiency ◽  
Stochastic Volatility Models ◽  
Control Variate ◽  
Volatility Models ◽  
High Efficient ◽  
Efficient Control ◽  
Speed Up

This paper studied the pricing of variance swap derivatives under the multi-factor stochastic volatility models by Monte Carlo simulation. Control variate technique was well used to reduce the variance of the simulation effectively. How to choose the high efficient control variate was also contained. Then the numerical results show the high efficiency of the speed up method. The pricing structure in the paper is also applicable for the valuation of other types of variance swaps and other financial derivatives under multi-factor models.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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