Statistical evidence for the Proto-Indo-European-Euskarian hypothesis

Diachronica ◽  
2021 ◽  
Author(s):  
Juliette Blevins ◽  
Richard Sproat
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Common Ancestor ◽  
Statistical Evidence ◽  
Language Models ◽  
Actual Data ◽  
Distant Relationship ◽  
Statistical Support ◽  
Basic Vocabulary ◽  
The Given

Abstract Based on a new reconstruction of Proto-Basque, and regular sound correspondences between this Proto-Basque and Proto-Indo-European as standardly reconstructed, Blevins (2018) argues that Proto-Basque and Proto-Indo-European have a common ancestor that pre-dates the two proto-languages. Part of this argument is based on proposed Proto-Indo-European/Proto-Basque cognate sets that include basic vocabulary items. In this study we offer statistical support for Blevins’ conclusions by using a Monte Carlo simulation that allows us to estimate the probability that the proposed lexical correspondences could have arisen by chance. The method makes use of phonotactic language models to generate possible words in a pair of languages, and then attempts to discover consistent correspondences between the words, producing a list of possible “cognates”. The method differs from some previous approaches by considering matches between all segments in the word pairs. By running such a simulation a large number of times, we can estimate the probability that two languages with the given phonotactics could have produced the number of cognate pairs observed in the actual data. The method is independently assessed by comparing wordlists from 100 pairs of languages, related and unrelated, where relations are known. Our conclusion is that the proposed correspondences are unlikely to have arisen by chance, supporting a distant relationship between Proto-Basque as reconstructed by Blevins (2018) and Proto-Indo-European.

MONTE CARLO SIMULATION OF SELF-ASSEMBLED AMPHIPHILIC DIBLOCK COPOLYMER IN SOLUTION

2003 ◽  
Vol 17 (01n02) ◽  
pp. 241-244 ◽  
Author(s):  
PINGCHUAN SUN ◽  
YUHUA YIN ◽  
BAOHUI LI ◽  
QINGHUA JIN ◽  
DATONG DING
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diblock Copolymer ◽  
Self Assembly ◽  
Diblock Copolymers ◽  
The Self ◽  
Amphiphilic Diblock Copolymer ◽  
The Core ◽  
Spherical Micelles ◽  
The Given

In this paper, Monte Carlo method is applied to simulate the process of the self-assembly of amphiphilic diblock copolymer with a series of block lengths of the insoluble and soluble blocks. Under the given simulation conditions, the diblock copolymers form spherical micelles in solution. The dependence of the core radii of spherical micelles on both block lengths is obtained and compared with experimental results of Eisenberg and coworkers.

Strategic management of organizational resources using predicting the organization's bankruptcy level: New approach using Monte Carlo Simulation

2020 ◽  
Vol 2 (2) ◽  
pp. 113-127
Author(s):  
Ali Mosayeb Moradi ◽  
◽  
Neda Ahmad Khan Beigi ◽  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Initial Data ◽  
Strategic Management ◽  
Simulated Data ◽  
Modified Model ◽  
New Approach ◽  
Genetic Method ◽  
Knowledge Based ◽  
The Given

Abstract Purpose: There are several methods to predict organization bankruptcy; each of them has different accuracy. Another considerable note in investigating organization bankruptcy is the data considered for the study. The goal of this study is to determine which model is the most accurate in predicting organization bankruptcy. Research methodology: In this study, the initial data were used to compare predicting Monte Carlo processes that simulate bankruptcy models to compare models and results more accurately. Simulated data coefficient modification of Mckee, CA-SCORE, Springate, Zmijewski, Shirata, and Altman methods were implemented in some healthy and bankrupt organizations. After that, the results of each modified model were considered to determine the predicting bankruptcy accuracy. Results: Using the final Mckee's method, predicting organization bankruptcy was done in an organization and the results show that the given organization is on a trend of bankruptcy in 2025. Limitations: This research was only described in knowledge-based organizations. Contribution: The Mckee genetic method is more accurate than other methods. Also, modifying coefficient and by using simulated data shows that CA-SCORE and Shirata methods are not able to predict the organization's bankruptcy by using simulated data.

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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