• The Journal of Asian Finance, Economics and Business
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• v.5 no.2
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• pp.25-33
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• 2018

In the globalization and free trade era, the current account deficit problem is a common phenomenon experienced by most countries, both developing and developed countries. Also with managed floating regime of exchange rate, it becomes very important to analyze the dynamics of current account balance which determine the trade. The deficit condition has lasted for four years in Indonesia, as well the deficit value above the value of the surplus that has been experienced during the period 2005-2011. This study is firstly aim to examine the condition of the deficit which happens in the export and import, manufactured goods and oil and gas, whether related to the transaction of goods and services. We try to build a predicted model which near the actual. Then, the focuses examines an exchange rate volatility impact on current account deficit. The model used in this research is a simultaneous model of Indonesia current account deficit from 2005 to 2014. The simulation result indicated that depreciation increase surplus to current account deficit. The decrease of export manufactured goods (non oil and gas) higher than the increase of import. For the oil and gas sector, depreciation of the rupiah against the US dollar results in an increased burden of higher oil and gas imports due to import transactions.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.325-332
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• 2022

The aim of this research is to use a conceptual model to experimentally evaluate the mediating impact of government financial and training support on structural social capital and non-financial performance of family farms. Questionnaires were used to collect data from family farms in Guangxi, China, from August 25^th to September 8^th, 2021. There were 759 valid responses, accounting for 94.99 percent of the total number of respondents. The scales' reliability and validity, and the research's mediating effects and hypotheses, are tested using SPSS 22.0 and AMOS 26.0. The findings suggest that the impact of government financial assistance on family farms' non-financial performance cannot be substantiated. The intermediary chain connection of financial and training support, on the other hand, has a significant mediating effect between structural social capital and family farm non-financial performance. Direct financial assistance could be thought to encourage family farms to rely too much on funding, making them less competitive in market competition, innovation, and long-term operations. According to the conclusions of the study, government assistance to family farms could take a variety of forms, including providing diversified skills training programs in farming practices, managerial skills, and other areas.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.45-58
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• 1991

The importance of marketing mix variables, replacement demand, and competition in a new product growth model has been cited by many researchers. In this paper, these factors are integrated with an aim to model company sales of competing durables. Based on the most popular new product growth model, numerous extensions and incorporations of contributions from related research fields are tried. Model parameters are estimated by the Kalman filter. And, the proposed model is applied to the sales of four consumer durable goods. Empirical applications show the benefits, as well as the limitations of the proposed model.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.295-303
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• 2021

The aim of this study is to analyze the effects of oil prices on economic growth and exchange rate in Saudi Arabia during the period 1980-2020. For this purpose, the linear and nonlinear ARDL models are estimated. The linear ARDL model shows that the oil price and economic growth are cointegrated. Moreover, the two variables have a significant positive association in the long run. However, the oil price has no significant impact on the exchange rate. When estimating the nonlinear ARDL model, it has been shown that oil price is only cointegrated with economic growth but not with the exchange rate. The estimation of nonlinear effects using the nonlinear ARDL model shows that economic growth is affected by both positive and negative oil shocks in the long run. However, the impact of positive shocks is higher than those of negative shocks. Moreover, results show that the short-run effects of positive and negative oil shocks are not statistically significant. Regarding the exchange rate, our results show that the effects of positive and negative oil shocks are not statistically significant. Consequently, this study concludes that the oil price has an asymmetric effect on economic growth in Saudi Arabia, but not on the exchange rate.

• Structural Engineering and Mechanics
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• v.53 no.3
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• pp.589-605
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• 2015

The use of the rigid polyurethane foam (RPF) to strengthen buried structures against blast terror has great interests from engineering experts in structural retrofitting. The aim of this study is to use the RPF to strengthen the buried structures under blast load. The buried structure is considered to study the RPF as structural retrofitting. The Guowei model (Guowei et al. 2010) is considered as a case study. The finite element analysis (FEA) is also used to model the buried structure under shock wave. The buried structure performance is studied based on detonating different TNT explosive charges. There is a good agreement between the results obtained by both the Guowei model and the proposed numerical model. The RPF improves the buried structure performance under the blast wave propagation.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.185-199
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• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Advances in nano research
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• v.16 no.5
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• pp.521-529
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• 2024

The integration of nanoparticles into various industries has spurred interest in understanding their impact on logistics and transportation systems. In this study, we investigate the effect of nanoparticles on the economic aspects of railway logistics transport using a mathematical model. By incorporating factors such as transportation costs, time efficiency, and environmental considerations, we aim to assess the overall economic feasibility of integrating nanoparticles into railway logistics operations. Through mathematical modeling and analysis, we explore how the introduction of nanoparticles affects cost-benefit analyses, resource allocation, and decision-making processes within railway logistics. Our findings provide valuable insights into the economic implications of nanoparticle integration in railway transport, offering potential strategies for optimizing logistics operations and enhancing overall efficiency and sustainability.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.59-69
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• 2017

The best 'hub' to communicate with the citizen is using social media to marketing the business. However, there has several issued and the most common issue that face in critical is a capital issue. This issue is always highlight because most of automatic sentiment detection tool for Facebook or any other social media price is expensive and they lack of technical skills in order to control the tool. Therefore, in directly they have some obstacle to get faster product's feedback from customers. Thus, the personal online retailing need to struggle to stay in market because they need to compete with successful online company such as G-market. Sentiment analysis also known as opinion mining. Aim of this research is develop the tool that allow user to automatic detect the sentiment comment on social media account. RAD model methodology is chosen since its have several phases could produce more activities and output. Soppy tool will be develop using Microsoft Visual. In order to generate an accurate sentiment detection, the functionality testing will be use to find the effectiveness of this Soppy tool. This proposed automated Soppy Tool would be able to provide a platform to measure the impact of the customer sentiment over the postings on their social media site. The results and findings from the impact measurement could then be use as a recommendation in the developing or reviewing to enhance the capability and the profit to their personal online retailing company.

• Structural Engineering and Mechanics
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• v.37 no.2
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• pp.197-213
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• 2011

Concrete is a heterogeneous material exhibiting quasi-brittle behaviour. While homogenization of concrete is commonly accepted in general engineering applications, a detailed description of the material heterogeneity using a mesoscale model becomes desirable and even necessary for problems where drastic spatial and time variation of the stress and strain is involved, for example in the analysis of local damages under impact, shock or blast load. A mesoscale model can also assist in an investigation into the underlying mechanisms affecting the bulk material behaviour under various stress conditions. Extending from existing mesoscale model studies, where use is often made of specialized codes with limited capability in the material description and numerical solutions, this paper presents a mesoscale computational model developed under a general-purpose finite element environment. The aim is to facilitate the utilization of sophisticated material descriptions (e.g., pressure and rate dependency) and advanced numerical solvers to suit a broad range of applications, including high impulsive dynamic analysis. The whole procedure encompasses a module for the generation of concrete mesoscale structure; a process for the generation of the FE mesh, considering two alternative schemes for the interface transition zone (ITZ); and the nonlinear analysis of the mesoscale FE model with an explicit time integration approach. The development of the model and various associated computational considerations are discussed in this paper (Part 1). Further numerical studies using the mesoscale model for both quasi-static and dynamic loadings will be presented in the companion paper (Part 2).

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.99-108
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• 2020

Although the relationship between income and income inequality has previously been discussed, the present study applies a dynamic approach to analyze the specific relationship between forest household income and income inequality. For this analysis, a unit root test and a cointegration test were conducted to characterize the nature of income time-series data. After converting unstable time-series data into stable time-series data, a VAR model was estimated. Based on this model, an impulse-response was generated and variance-decomposition analysis was performed. These analyses showed that the effect of forest household income was relatively larger than that of the Gini coefficient, and that the impact of forest household income not only caused income to increase but also caused the Gini coefficient to decrease. In addition, the impact of the Gini coefficient had an impact on reducing forest household income and further increasing income inequality. We conclude that, with the aim of alleviating the inequality of forest household income, an income growth policy would be more effective than an income distribution policy.