• Title/Summary/Keyword: Jordan model

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The Characteristic for Undrainded Shear Behavior of in Low-Plastic Silt and its Prediction (저소성 실트의 비배수 전단거동 특성과 예측)

  • Kim, Daeman
    • Journal of the Korean GEO-environmental Society
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    • v.9 no.6
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    • pp.61-70
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    • 2008
  • In this study, undrained triaxial (CU) tests were performed on low-plastic silt of Nakdong River in order to investigate the undrained shear behavior of low-plastic silt. In experimental results, the deviator stress showed the hardening behavior after reaching its yield stress like the tendency of common sand, and the pore water pressure was gradually decreased to critical state after the maximum value. In the effective stress paths, regardless of consolidation stress or overconsolidation ratios, both a critical state line (CSL) and a phase transformation line (PTL) exist in the effective stress path that is similar to the case of sand. The behavior of low-plastic silt was predicted by the Modified Cam-Clay (MCC) model, the Jordan and the Elman-jordan model that is artificial neural network model. According to predicted results, the overall undrained shear behavior of low-plastic silt could not be predicted with the MCC model, but the Jordan and Elman-Jordan model showed well-matched experiment results.

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A Study on the Settlement Prediction of Soft Ground Embankment Using Artificial Neural Network (인공신경망을 이용한 연약지반성토의 침하예측 연구)

  • Kim, Dong-Sik;Chae, Young-Su;Kim, Young-Su;Kim, Hyun-Dong
    • Journal of the Korean Geotechnical Society
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    • v.23 no.7
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    • pp.17-25
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    • 2007
  • Various geotechnical problems due to insufficient bearing capacity or excessive settlement are likely to occur when constructing roads or large complexes on soft ground. Accurate predictions of the magnitude of settlement and the consolidation time provide numerous options of ground improvement methods and, thus, enable to save time and expense of the whole project. Asaoka's method is probably the most frequently used one for settlement prediction and the empirical formulae such as Hyperbolic method and Hoshino's method are also often used. To find an elaborate method of predicting the embankment settlement, two recurrent type neural network models, such as Jordan model and Elman-Jordan model, are adopted. The data sets of settlement measured at several domestic sites are analyzed to obtain the most suitable model structures. It was shown from the comparison between predicted and measured settlements that Jordan model provides better predictions than Elman-Jordan model does and that the predictions using CPT results are more accurate than those using SPT results. It is believed that RNN using cone penetration test results can be a highly efficient tool in predicting settlements if enough field data can be obtained.

Prediction model for concrete carbonation depth using gene expression programming

  • Murad, Yasmin Z;Tarawneh, Bashar K;Ashteyat, Ahmed M
    • Computers and Concrete
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    • v.26 no.6
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    • pp.497-504
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    • 2020
  • Concrete can lose its alkalinity by concrete carbonation causing steel corrosion. Thus, the determination of the carbonation depth is necessary. An empirical model is proposed in this research to predict the carbonation depth of concrete using Gene expression programming (GEP). The GEP model was trained and validated using a large and reliable database collected from the literature. The model was developed using the six parameters that predominantly control the carbonation depth of concrete including carbon dioxide CO2 concentration, relative humidity, water-to-cement ratio, maximum aggregate size, aggregate to binder ratio and carbonation period. The model was statistically evaluated and then compared to the Jiang et al. model. A parametric study was finally performed to check the proposed GEP model's sensitivity to the selected input parameters.

Consumer Adoption of Self-Service Technologies: Integrating the Behavioral Perspective with the Technology Acceptance Model

  • ASHOUR, Mohammed L.;AL-QIREM, Raed M.
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.3
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    • pp.1361-1369
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    • 2021
  • Recent technological advancements have had a substantial impact on consumer buying behavior. This research aims to determine the factors affecting consumer behavior related to the adoption of self-service technologies (SSTs). The intended findings of this study are expected to contribute to understanding consumer behavior towards the adoption of SSTs taking into account the logic of two main theories in this regard: the Technology Acceptance Model (TAM) and the assumptions of the Behavioral Perspective Model (BPM). This research follows a triangulation approach. Consequently, a number of semi structured interviews were conducted with experts and executive directors from selected SSTs providers in Jordan. In addition, the convenience sampling technique was employed focusing on current (or) previous users of SSTs in the public and private sectors in Jordan using a self-administrative questionnaire (66% response rate). The results confirmed the influence (direct and indirect) of previous experience and personal initiatives and characteristics on consumer intention to use SSTs. In addition, the results indicated the important role of the mediator variables namely: perceived ease of use (EOU), perceived risk (PR), and perceived usefulness (PU) on consumer attitude towards SSTs which in turn will positively affect consumer intention to use SSTs.

The Improving Method of Characters Recognition Using New Recurrent Neural Network (새로운 순환신경망을 사용한 문자인식성능의 향상 방안)

  • 정낙우;김병기
    • Journal of the Korea Society of Computer and Information
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    • v.1 no.1
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    • pp.129-138
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    • 1996
  • In the result of Industrial development. largeness and highness of techniques. a large amount of Information Is being treated every year. Achive informationization. we must store in computer ,all informations written on paper for a long time and be able to utilize them In right time and place. There Is recurrent neural network as a model rousing the output value In learning neural network for characters recognition. But most of these methods are not so effectively applied to it. This study suggests a new type of recurrent neural network to classifyeffectively the static patterns such as off-line handwritten characters. This study shows that this new type Is better than those of before in recognizing the patterns. such as figures and handwritten characters, by using the new J-E (Jordan-Elman) neural network model in which enlarges and combines Jordan and Elman Model.

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The Fiscal Policy Instruments and the Economic Prosperity in Jordan

  • ALZYADAT, Jumah A.;AL-NSOUR, Iyad A.
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.1
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    • pp.113-122
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    • 2021
  • This study aims to investigate the effects of fiscal policy instruments on economic growth in Jordan using annual data from 1970 to 2019, by applying the VAR model (Vector Auto regression) and the Vector Error Correction Model (VECM). The study also examines the dynamic relationship among economic variables over time using the Granger casualty test, Impulse Response Function, and Variance Decomposition. The results show that not only the public expenditures have a positive effect on economic growth in Jordan, but also the tax revenues positively affect the economic growth in the short-run, and this is because of using the tax revenues to finance the government activities in Jordan. This effect becomes negative in the long run, and this is explained because the tax seems a source of distortions in the economy, The extreme taxes may cause huge distortions in the economy, and these distortions destroys the purchasing power, the aggregate demand, and supply. More governmental dependence on tax revenues is the main source of tax evasion and less efficiency. The effect of taxation will curb any prosperity in the economy. Therefore, the government should estimate the fair tax rates to generate sufficient revenues to finance the public expenditure required to enhance economic prosperity.

The Impact of Mergers on the Financial Performance of Jordanian Public Shareholding Companies

  • AYOUSH, Maha;RABAYAH, Hesham;JIBREEL, Thaer
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.10
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    • pp.751-759
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    • 2020
  • This study examines the impact of mergers on the financial performance of the Jordanian public shareholding companies. The study employs data collected for a sample of 10 Jordanian non-financial public firms that were engaged in legal horizontal merger deals between 2000 and 2013. The data was collected from the published annual financial reports of the merging companies and comparative companies for three years before the merger and three years after the merger. Event study methodology was applied to examine the data. Four measures of financial performance (FP) were used, which are return on assets (ROA), return on equity (ROE), earnings per share (EPS), and net profit margin (NPM). Two methods were used in the analysis - the change model and the intercept model using financial performance raw data and industry-adjusted data. The findings in general showed no significant impact of mergers on the financial performance of merging firms using the change model. However, by using the intercept model, significant impact of mergers on the financial performance was found on the sample of the study. The significant impact was found for mergers on the raw ROE of the merging firms, and on the ROA and NPM of the industry-adjusted firms.

Relationship Between Stock Price Indices of Abu Dhabi, Jordan, and USA - Evidence from the Panel Threshold Regression Model

  • Ho, Liang-Chun
    • The Journal of Industrial Distribution & Business
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    • v.4 no.2
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    • pp.13-19
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    • 2013
  • Purpose - The paper tested the relationship between the stock markets of the Middle East and the USA with the oil price and US dollar index as threshold variables. Research design, data, and methodology - The stock price indices of the USA, the Middle East (Abu Dhabi, Jordan), WTI spot crude oil price, and US dollar index were daily returns in the research period from May 21, 2001 to August 9, 2012. Following Hansen (1999), the panel threshold regression model was used. Results - With the US dollar index as the threshold variable, a negative relationship existed between the stock price indices of Jordan and the USA but no significant result was found between the stock price indices of Abu Dhabi and the USA. Conclusions - The USA is an economic power today:even if it has a closer relationship with the US stock market, the dynamic US economy can learn about subsequent developments and plan in advance. Conversely, if it has an estranged relationship with the US stock market, thinking in a different direction and different investment strategies will achieve good results.

Audit Quality and Stock Price Synchronicity: Evidence from Emerging Stock Markets

  • ALMAHARMEH, Mohammad I.;SHEHADEH, Ali A.;ISKANDRANI, Majd;SALEH, Mohammad H.
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.3
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    • pp.833-843
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    • 2021
  • This research examines the impact of audit quality on the extent to which firm-specific information is integrated with a firm's share price - which is determined inversely using stock price synchronicity. The study sample consists of non-financial companies listed on the Amman Stock Exchange i.e., the Jordanian Stock Market, from 2014-2018. After examining 810 firm-year observations from Jordanian industrial companies listed on the ASE, during the study period, we find that the companies using one of the BIG4 audit firms for auditing have less synchronous and more informative stock prices, suggesting high-quality audit improved governance and reduce information asymmetry between firms' insiders and investors which enhances the capitalization of firm's specific information into the stock price, thus less synchronous and more informative stock return. The findings remain consistent over 2 separate measurements of stock price synchronicity (Market and Industry model and Market Model) and show robustness for fixed effect tests. Our multivariate regression results are also robust after controlling for a number of features at the firm level with potential associations with stock price synchronicity. These include the firm size, leverage, return on assets (ROA), and market to book value (MBV).