Pengaruh Celebrity Endorsers dan Pesan Iklan di Televisi terhadap Keputusan Pembelian pada Es Krim Walls Magnum Belgium Chocolate

This study aims to determine the effect of Celebrity endorsers and advertising messages on television on purchasing decisions on Magnum ice cream Walls Belgium Chocolate. This research was conducted in the city of Denpasar. The population in this study is that consumers who have been consuming ice cream products walls magnum Belgium Chocolate. Sampling of this research is purporsive sampling technique. Data were collected using a questionnaire. Respondents from the study of 100 respondents. Data analysis techniques used in this research is multiple linear regression analysis. These results indicate that (1) Variable Celebrity endorser positive and significant impact on purchasing decisions partially Walls ice cream Magnum Belgium Chocolate. (2) Variable Message television advertising in a positive and significant influence on purchasing decisions partially Walls ice cream Magnum Belgium Chocolate

On analog quantum algorithms for the mixing of Markov chains

The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical mixing time. In this article, we deal with analog quantum algorithms for mixing. First, we provide an analog quantum algorithm that given a Markov chain, allows us to sample from its stationary distribution in a time that scales as the sum of the square root of the classical mixing time and the square root of the classical hitting time. Our algorithm makes use of the framework of interpolated quantum walks and relies on Hamiltonian evolution in conjunction with von Neumann measurements. There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense. In this scenario, the quantum mixing time is defined as the time required to sample from a distribution that is close to this limiting distribution. Recently we provided an upper bound on the quantum mixing time for Erd\"os-Renyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we also extend and expand upon our findings therein. Namely, we provide an intuitive understanding of the state-of-the-art random matrix theory tools used to derive our results. In particular, for our analysis we require information about macroscopic, mesoscopic and microscopic statistics of eigenvalues of random matrices which we highlight here. Furthermore, we provide numerical simulations that corroborate our analytical findings and extend this notion of mixing from simple graphs to any ergodic, reversible, Markov chain.Comment: The section concerning time-averaged mixing (Sec VIII) has been updated: Now contains numerical plots and an intuitive discussion on the random matrix theory results used to derive the results of arXiv:2001.0630

Structure and energetics of the Si-SiO_2 interface

Silicon has long been synonymous with semiconductor technology. This unique role is due largely to the remarkable properties of the Si-SiO_2 interface, especially the (001)-oriented interface used in most devices. Although Si is crystalline and the oxide is amorphous, the interface is essentially perfect, with an extremely low density of dangling bonds or other electrically active defects. With the continual decrease of device size, the nanoscale structure of the silicon/oxide interface becomes more and more important. Yet despite its essential role, the atomic structure of this interface is still unclear. Using a novel Monte Carlo approach, we identify low-energy structures for the interface. The optimal structure found consists of Si-O-Si "bridges" ordered in a stripe pattern, with very low energy. This structure explains several puzzling experimental observations.Comment: LaTex file with 4 figures in GIF forma

Structure and oxidation kinetics of the Si(100)-SiO2 interface

We present first-principles calculations of the structural and electronic properties of Si(001)-SiO2 interfaces. We first arrive at reasonable structures for the c-Si/a-SiO2 interface via a Monte-Carlo simulated annealing applied to an empirical interatomic potential, and then relax these structures using first-principles calculations within the framework of density-functional theory. We find a transition region at the interface, having a thickness on the order of 20\AA, in which there is some oxygen deficiency and a corresponding presence of sub-oxide Si species (mostly Si^+2 and Si^+3). Distributions of bond lengths and bond angles, and the nature of the electronic states at the interface, are investigated and discussed. The behavior of atomic oxygen in a-SiO2 is also investigated. The peroxyl linkage configuration is found to be lower in energy than interstitial or threefold configurations. Based on these results, we suggest a possible mechanism for oxygen diffusion in a-SiO2 that may be relevant to the oxidation process.Comment: 7 pages, two-column style with 6 postscript figures embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#ng_sio

Differential Photoelectron Holography: A New Approach for Three-Dimensional Atomic Imaging

We propose differential holography as a method to overcome the long-standing forward-scattering problem in photoelectron holography and related techniques for the three-dimensional imaging of atoms. Atomic images reconstructed from experimental and theoretical Cu 3p holograms from Cu(001) demonstrate that this method suppresses strong forward-scattering effects so as to yield more accurate three-dimensional images of side- and back-scattering atoms.Comment: revtex, 4 pages, 2 figure

Origin of Hysteresis Observed During Fatigue of Ceramic-Matrix Composites

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66076/1/j.1151-2916.1990.tb05239.x.pd

An improved constraint satisfaction adaptive neural network for job-shop scheduling

Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601

Frequency Dependence of Fatigue Life and Internal Heating of a Fiber-Reinforced/Ceramic-Matrix Composite

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65943/1/j.1151-2916.1994.tb04587.x.pd