scholarly journals How many Monte-Carlo simulations are needed to adequately process interval uncertainty: an explanation of the smart electric grid-related simulation results

2018 ◽  
Vol 5 (1) ◽  
pp. 1-5
Author(s):  
Afshin Gholamy ◽  
Vladik Kreinovich
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Interval Uncertainty ◽  
Electric Grid ◽  
Simulation Results

scholarly journals MULTILEVEL BLOCKING MONTE CARLO SIMULATIONS FOR QUANTUM DOTS

2001 ◽  
Vol 15 (10n11) ◽  
pp. 1416-1425 ◽  
Author(s):  
R. EGGER ◽  
C. H. MAK
Keyword(s):  
Quantum Dots ◽  
Monte Carlo ◽  
Low Temperature ◽  
Monte Carlo Simulations ◽  
Strongly Correlated Electrons ◽  
Correlated Electrons ◽  
Strongly Correlated ◽  
Path Integral Monte Carlo ◽  
Sign Problem ◽  
Simulation Results

This article provides an introduction to the ideas behind the multilevel blocking (MLB) approach to the fermion sign problem in path-integral Monte Carlo simulations, and also gives a detailed discussion of MLB results for quantum dots. MLB can turn the exponential severity of the sign problem into an algebraic one, thereby enabling numerically exact studies of otherwise inaccessible systems. Low-temperature simulation results for up to eight strongly correlated electrons in a parabolic 20 quantum dot are presented.

Analyzing Push Schemes for Social Networking Services in Wireless Networks by Using Monte Carlo Simulations

2013 ◽  
Vol 717 ◽  
pp. 760-765
Author(s):  
Kyung Koo Jun
Keyword(s):  
Monte Carlo ◽  
Wireless Networks ◽  
Monte Carlo Simulations ◽  
Social Networking ◽  
Everyday Life ◽  
Weighting Factor ◽  
Social Networking Services ◽  
Simulation Results ◽  
Push And Pull ◽  
Do So

Social networking services such as Twitter and Facebook have occupied a large portion of our everyday life and it is expected to do so for the time being. SNS systems use both push and pull schemes to deliver contents to users. Regarding the push schemes, there are push N based scheme and timer based scheme. In this paper, we analyze the performance of both schemes by using Monte Carlo simulations and understand their characteristics. We present the simulation results that show the performance under various conditions. Moreover, we find that a weighting factor should be given more emphasis because of its significant impact on the performance. Also, we claim that the former work that analyzed PNS and TPS used wrong assumption that leads to the misconception about PNS.

Use of Monte Carlo Modeling to Aid Interpretation and Quantification of the Low Energy-Loss Electron Yield at Low Primary Energies

2008 ◽  
Vol 14 (5) ◽  
pp. 439-450 ◽  
Author(s):  
Christopher Bonet ◽  
Andrew Pratt ◽  
Mohamed M. El-Gomati ◽  
Jim A.D. Matthew ◽  
Steven P. Tear
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
High Vacuum ◽  
Stopping Power ◽  
Low Loss ◽  
Yield Curves ◽  
Beam Sample ◽  
Detector Geometry ◽  
Simulation Results ◽  
Lle Data

AbstractExperimental low-loss electron (LLE) yields were measured as a function of loss energy for a range of elemental standards using a high-vacuum scanning electron microscope operating at 5 keV primary beam energy with losses from 0 to 1 keV. The resulting LLE yield curves were compared with Monte Carlo simulations of the LLE yield in the particular beam/sample/detector geometry employed in the experiment to investigate the possibility of modeling the LLE yield for a series of elements. Monte Carlo simulations were performed using both the Joy and Luo [Joy, D.C. & Luo, S., Scanning11(4), 176–180 (1989)] expression for the electron stopping power and recent tabulated values of Tanuma et al. [Tanuma, S. et al., Surf Interf Anal37(11), 978–988 (2005)] to assess the influence of the more recent stopping power data on the simulation results. Further simulations have been conducted to explore the influence of sample/detector geometry on the LLE signal in the case of layered samples consisting of a thin C overlayer on an elemental substrate. Experimental LLE data were collected from a range of elemental samples coated with a thin C overlayer, and comparisons with Monte Carlo simulations were used to establish the overlayer thickness.

Three Dimensional Monte Carlo Simulations with Real Grain Orientations for the Role of Sub-Boundaries and Precipitates in Abnormal Grain Growth

2013 ◽  
Vol 753 ◽  
pp. 367-372
Author(s):  
Tae Wook Na ◽  
Chang Soo Park ◽  
Hyung Seok Shim ◽  
Byeong Joo Lee ◽  
Chan Hee Han ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Solid State ◽  
Monte Carlo Simulations ◽  
Grain Growth ◽  
Three Dimensional ◽  
Abnormal Grain Growth ◽  
Grain Orientations ◽  
The Matrix ◽  
Simulation Results

Three-dimensional Monte Carlo simulations with real grain orientations are performed to study the role of precipitates and sub-boundaries in the abnormal grain growth. According to the simulation results, sub-boundaries in the abnormally growing grain and precipitates in the matrix grains are necessary for the abnormal grain growth. The simulation results can be best explained by the mechanism of sub-boundary enhanced solid state wetting. The simulated microstructure is very similar to that experimentally observed.

SIMULATION STUDIES OF THE IMPURE ATOMS DIFFUSION ON Cu(001) SURFACE

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2766-2770
Author(s):  
GUOCE ZHUANG
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Critical Point ◽  
Kinetic Monte Carlo ◽  
Diffusion Mechanism ◽  
Indium Atom ◽  
Simulation Studies ◽  
Kinetic Monte Carlo Simulations ◽  
Simulation Results ◽  
Close Packed Layer

Based on the vacancy-assisted diffusion mechanism, the impure atomic moving processes on the close-packed layer of a Cu (001) surface are studied by kinetic Monte Carlo simulations. Our simulation results show that the diffusivity of the Indium atom agrees quite well with the exact lattice solution. In addition, it is found that in the case of muti-impure atoms, there exist a critical point near the value of ∊=0, and the diffusivity of In atom depends on the number of impure atoms only when ∊ is positive.

scholarly journals Simulasi Monte Carlo untuk Memprediksi Persediaan Darah

2020 ◽  
Author(s):  
Rahmi Darnis ◽  
Gunadi Widi Nurcahyo ◽  
Yuhandri Yunus
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Blood Supply ◽  
Transport System ◽  
Real Data ◽  
Transportation System ◽  
Production Data ◽  
Accuracy Rate ◽  
Simulation Results

Blood is a special organ as a communication and transportation system whose job is to circulate nutrients and oxygen. As a very vital transport system and its very useful existence for many people, blood must be managed properly. Monte Carlo simulations can predict problems related to blood stock and demand for blood. The data that is processed to predict blood supply is blood production data in 2018 and 2019, Simulation results data for 2018 are compared with real data for 2019 and simulation results for 2019 are compared with real data for 2020. The results of the simulations that have been carried out have an accuracy rate of 96.21% for 2018 and 79.22% for 2019. Based on the results of the tests that have been done, it can provide information that can help in the management of forecasting future blood stocks.

scholarly journals The validity of the kinetic collection equation revisited – Part 2: Simulations for the hydrodynamic kernel

2010 ◽  
Vol 10 (3) ◽  
pp. 6219-6240
Author(s):  
L. Alfonso ◽  
G. B. Raga ◽  
D. Baumgardner
Keyword(s):  
Differential Equation ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Numerical Solution ◽  
Mass Distribution ◽  
Total Mass ◽  
Analytic Solution ◽  
Numerical Solutions ◽  
Integro Differential Equation ◽  
Simulation Results

Abstract. The kinetic collection equation (KCE) has been widely used to describe the evolution of the average droplet spectrum due to the collection process that leads to the development of precipitation in warm clouds. This deterministic, integro-differential equation only has analytic solution for very simple kernels. For more realistic kernels, the KCE needs to be integrated numerically. In this study, the validity time of the KCE for the hydrodynamic kernel is estimated by a direct comparison of Monte Carlo simulations with numerical solutions of the KCE. The simulation results show that when the largest droplet becomes separated from the smooth spectrum, the total mass calculated from the numerical solution of the KCE is not conserved and, thus, the KCE is no longer valid. This result confirms the fact that for realistic kernels appropriate for precipitation development within warm clouds, the KCE can only be applied to the continuous portion of the mass distribution.

On Guessing: An Alternative Adjusted Positive Learning Estimator and Comparing Probability Misspecification With Monte Carlo Simulations

2021 ◽  
pp. 014662162110139
Author(s):  
Ben O. Smith ◽  
Dustin R. White
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Test Score ◽  
Recursive Partitioning ◽  
True Positive ◽  
Learning Gain ◽  
Estimation Techniques ◽  
The Past ◽  
Post Test ◽  
Simulation Results

Practitioners in the sciences have used the “flow” of knowledge (post-test score minus pre-test score) to measure learning in the classroom for the past 50 years. Walstad and Wagner, and Smith and Wagner moved this practice forward by disaggregating the flow of knowledge and accounting for student guessing. These estimates are sensitive to misspecification of the probability of guessing correct. This work provides guidance to practitioners and researchers facing this problem. We introduce a transformed measure of true positive learning that under some knowable conditions performs better when students’ ability to guess correctly is misspecified and converges to Hake’s normalized learning gain estimator under certain conditions. We then use simulations to compare the accuracy of two estimation techniques under various violations of the assumptions of those techniques. Using recursive partitioning trees fitted to our simulation results, we provide the practitioner concrete guidance based on a set of yes/no questions.

scholarly journals Simulasi Monte Carlo dalam Mengidentifikasi Peningkatan Penjualan Tanaman Mawar (Studi Kasus di Toko Bunga 5 Bersaudara Kota Solok)

2020 ◽  
Author(s):  
Dian Cyntia Dewi ◽  
S Sumijan ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Monte Carlo Simulations ◽  
Random Numbers ◽  
Cumulative Frequency ◽  
Simulation Process ◽  
The Monte Carlo Method ◽  
Simulation Results

Roses are one of the most popular types of plants in the community. The sale of roses at the flower shop of 5 siblings is increasingly in demand. Identifying the increase in sales is important in analyzing sales progress. At the present time the seller can only see a manual increase in sales that are most in demand. This study aims to determine predictions of the increase in sales of rose flowers with a monte carlo simulation accurately and accurately. The data that will be processed in this study in the last 2 years, namely 2018 and 2019, rose plants obtained at the 5 Brothers Flower Shop in Solok City. There are several types of roses in the predicted sales level. Then the data will be converted into the probability distribution into cumulative frequency and followed by generating random numbers so that they can determine random numbers. Next, we will group the boundary intervals of the random numbers that have been obtained and continue with the simulation process so that the simulation results and percentage accuracy are obtained using the Monte Carlo method. The results of this study on data processing from 2019 to 2020 have an accuracy of 90%. So this research is very appropriate in identifying the increase in sales for the following year. The design of this system determines the amount of increased sales of goods using the monte carlo method in a flower shop of 5 siblings. Monte Carlo simulations can be used to identify specific sales increases. The results obtained are quite accurate using the Monte Carlo method.

Identifying influential spreaders based on diffusion K-truss decomposition

2018 ◽  
Vol 32 (22) ◽  
pp. 1850238 ◽  
Author(s):  
Li Yang ◽  
Yu-Rong Song ◽  
Guo-Ping Jiang ◽  
Ling-Ling Xia
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Network Structure ◽  
Decomposition Method ◽  
Spreading Behavior ◽  
The Core ◽  
Influential Nodes ◽  
Simulation Results ◽  
The Impact ◽  
Real Complex

Identifying the most influential spreaders is important in optimizing the network structure or disseminating information through networks. Recent study showed that the K-truss decomposition could filter out the nodes that performed a worse spreading behavior in the maximal K-shell subgraph. The spreaders belonging to the maximal K-truss subgraph show better performance compared to previously used importance criteria. However, the accuracy of the K-truss or the K-shell in determining node coreness is largely susceptible to core-like group. In this paper, we propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes. To validate the effectiveness of the proposed method, we compare it with five typical methods by carrying out Monte–Carlo simulations over six real complex networks. Simulation results demonstrate that the proposed method can effectively disintegrate the core-like group and accurately identify the influential nodes.

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