Shallow arch model. An asymptotic approach

1996 ◽  
Vol 43 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Andrzej Karwowski
Keyword(s):  
Arch Model ◽  
Shallow Arch ◽  
Asymptotic Approach

Analysis On the In-Plane 2:2:1 Internal Resonance of a Complex Cable-Stayed Bridge System Under External Harmonic Excitation

2021 ◽  
Author(s):  
Houjun Kang ◽  
Tieding Guo ◽  
Weidong Zhu
Keyword(s):  
Nonlinear Dynamic ◽  
Internal Resonance ◽  
Multiple Scales ◽  
Initial Conditions ◽  
Nonlinear Dynamic Analysis ◽  
Nonlinear Phenomena ◽  
Arch Model ◽  
Shallow Arch ◽  
Cable Stayed Bridge ◽  
Bridge System

Abstract Nonlinear dynamic analysis of a cable-stayed bridge has been a hot topic due to its structural flexibility. Based on integro-partial differential equations of a double-cable-stayed shallow-arch model, in-plane 2:2:1 internal resonance among three first in-plane modes of two cables and a shallow arch under external primary or subharmonic resonance is considered. Galerkin's method and the method of multiple scales are used to derive averaged equations of the cable-stayed bridge system. Nonlinear dynamic behaviours of the system are investigated via the numerical simulation. Results show rich nonlinear phenomena of the cable-stayed bridge system and some new phenomena are observed. Two identical cables that are symmetrically located above the shallow arch can have different dynamic behaviours even when initial conditions of the system are symmetrically given. Two cables with some differences between their parameters can exhibit either softening or hardening characteristics.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Exchange Rate Volatility in Emerging Markets:The Chilean Case

2007 ◽  
pp. 87-100
Author(s):  
Juan R. Castro
Keyword(s):  
Exchange Rate ◽  
Exchange Rate Regime ◽  
External Debt ◽  
Exchange Rate Volatility ◽  
Arch Model ◽  
Macroeconomic Shocks ◽  
Domestic Debt ◽  
Domestic Credit ◽  
The Face ◽  
The Exchange Rate

The document conducts an empirical investigation on the volatility of the Chilean exchange rate regime, using a model of Objective Zones. Through the use of the ARCH model, the document tests the volatility of the exchange rate in the presence of different levels of international reserves and other macroeconomic shocks. The results show that domestic credit, domestic debt and external debt have the greatest impact on the volatility of the variables studied, especially when compared with other fundamental variables. The variance of the exchange rate is heterosedastic but it is not persistent, which implies that the exchange rate is stable, probably when it oscillates between two bands. The volatility of the exchange rate fluctuates to a greater extent in the face of changes in internal and external debt, than with the other variables used.

scholarly journals Assessment of Dental Arch Reproduction Quality by Using Traditional and Digital Methods

2021 ◽  
Vol 11 (3) ◽  
pp. 1263
Author(s):  
Mateusz Wójcik ◽  
Dariusz Skaba ◽  
Małgorzata Skucha-Nowak ◽  
Marta Tanasiewicz ◽  
Rafał Wiench
Keyword(s):  
Standard Model ◽  
Significant Degree ◽  
Physical Models ◽  
Average Error ◽  
Dental Arch ◽  
Arch Model ◽  
Linear Dimensions ◽  
The Standard Model ◽  
Digital Methods

Background: There exists few scientific reports on the quality of digitally reproduced dental arches, even though digital devices have been used in dentistry for many years. This study assesses the accuracy of the standard dental arch model reproduction using both traditional and digital methods. Methods: The quality of the full upper dental arch standard model reproduction by physical models obtained through traditional and digital methods was compared: gypsum models (SGM) and models printed from data obtained using an intraoral scanner (TPM) (n = 20). All models were scanned with a reference scanner. Comparisons were made using Geomagic Control X program by measuring deviations of the models relative to the standard model and analyzing linear dimensions deviations. Results: The average error of reproduction accuracy of the standard model ranged from 0.0424 ± 0.0102 millimeters (mm) (SGM) to 0.1059 ± 0.0041 mm (TPM). In digital methods, all analyzed linear dimensions were shortened to a statistically significantly degree compared to traditional. The SGM method provided the smallest deviations to a significant degree of linear dimensions from the pattern, and TPM the largest. The intercanine dimension was reproduced with the lowest accuracy, and the intermolar the highest in each method. Conclusions: Traditional methods provided the highest reproduction trueness of the full dental arch and all analyzed linear dimensions. The intercanine dimension was reproduced with the lowest accuracy, and the intermolar the highest in each method, where digital methods shortened all analyzed linear dimensions.

A comparison of the Lee–Carter model and AR–ARCH model for forecasting mortality rates

2012 ◽  
Vol 50 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Rosella Giacometti ◽  
Marida Bertocchi ◽  
Svetlozar T. Rachev ◽  
Frank J. Fabozzi
Keyword(s):  
Mortality Rates ◽  
Arch Model ◽  
Carter Model
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