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Wyszukujesz frazę "Le, M.-Q" wg kryterium: Autor


Tytuł:
Molecular dynamics study of the fracture of single layer buckled silicon monosulfide and germanium selenide
Autorzy:
Le, M.-Q
Tematy:
2D materials
fracture
molecular dynamics simulation
mechanical properties
Pokaż więcej
Wydawca:
Instytut Podstawowych Problemów Techniki PAN
Powiązania:
https://bibliotekanauki.pl/articles/38629974.pdf  Link otwiera się w nowym oknie
Opis:
Molecular dynamics simulations were conducted with the Stillinger–Weber potential at room temperature to study the mechanical properties and find the mode-I critical stress intensity factor of buckled two-dimensional (2D) hexagonal silicon mono-sulfide (SiS) and germanium selenide (GeSe) sheets. Uniaxial tensile tests were simulated for pristine and pre-cracked sheets. 2D Young’s modulus of SiS and GeSe are estimated at 38.3 and 26.0 N/m, respectively. Their 2D fracture strength is about 3.1–3.5 N/m. By using the initial crack length with the corresponding fracture stress, their mode-I critical stress intensity factor is estimated in the range from 0.19 through 0.22 MPapm. These values differ within 5% from those obtained by the surface energy and are very small compared to the reported fracture toughness of single-crystalline monolayer graphene.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm
Autorzy:
Khuat, T. T.
Le, Q. C.
Nguyen, B. L.
Le, M. H.
Tematy:
Artificial Bee Colony algorithm
Artificial Neural Network
back-propagation algorithm
stock price forecasting
wavelet transform
Pokaż więcej
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:
https://bibliotekanauki.pl/articles/308651.pdf  Link otwiera się w nowym oknie
Opis:
Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Front-end electronics for the FAZIA experiment
Autorzy:
Kordyasz, A.
Gruyer, D.
Barlini, S.
Morelli, L.
Ordine, A.
Neindre, N. Le
Guerzoni, M.
Pastore, G.
Frankland, J. D.
Maurenzig, P.
Boiano, A.
Bruno, M.
Vient, E.
Merrer, Y.
Marchi, T.
Cinausero, M.
Brulin, G.
Duenas, J. A.
Spadacini, G.
Edelbruck, P.
Bini, M.
Valdré, S.
Maiolino, C.
Scarlini, E.
Rivet, M. F.
Borderie, B.
Tortone, G.
Salomon, F.
Préaumont, H. De
Fable, Q.
Rosato, E.
Verde, G.
Casini, G.
Meoli, A.
Fabris, D.
Chbihi, A.
Stefaninni, A.
Nannini, A.
Torre, R. La
Bonnet, E.
Piantelli, S.
Lopez, O.
Lombardo, I.
Wanlin, E.
Richard, A.
Pârlog, M.
Alba, R.
Mabiala, J.
Pasquali, G.
Santonocito, D.
Poggi, G.
Galichet, E.
Vigilante, M.
Bougault, R.
Francalanza, L.
Petcu, M.
Gramegna, F.
Olmi, A.
Kozik, Tomasz
Dell'Aquila, D.
Opis:
FAZIA is a multidetector specifically designed to optimize A and Z reaction product identification in heavy-ion collision experiments. This multidetector is modular and based on three-layer telescopes made of two silicon detectors followed by a thick (10 cm) CsI(Tl) scintillator read-out by a photodiode. Its electronics is fully digital. The goal to push at maximum identification capability while preserving excellent energy resolution, can be achieved by using pulse-shape analysis techniques and by making an intensive use of high-speed flash ADCs. This paper presents the front-end part of the electronics.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł

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