• International Area Studies Review
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• v.13 no.2
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• pp.66-88
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• 2009

This paper studies optimal discretionary monetary policy in the presence of uncertainty in the housing sector. In particular, we allow two possible regimes regarding the evolution of housing price inflation and the effects of housing price inflation on the aggregate demand. Estimation results with the US data confirm the presence of two distinctive regimes, one 'normal' and the other more akin to the housing price 'bubble' state. The optimal policy is 'asymmetric' in that the optimal responses in the 'normal' regime require the central bank to lean against the wind to inflationary pressure from CPI and housing inflation, while the central bank is recommended to accommodate it in the other regime.

• Current Optics and Photonics
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• v.3 no.1
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• pp.66-71
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• 2019

Application of liquid crystal (LC) materials to a flexible device is challenging because the bending of LC displays easily causes change in thickness of the LC layer and orientation of LCs, resulting in deterioration in a displayed image quality. In this work, we demonstrate a prototype device combining a flexible polymer substrate and an optically isotropic LC-polymer composite in which the device consists of interdigitated in-plane switching electrodes deposited on a flexible colorless polyimide substrate and the composite consisting of nano-sized LC droplets in a polymer matrix. The device can keep good electro-optic characteristics even when it is in a bending state because the LC orientation is not disturbed in both voltage-off and -on states. The proposed device shows a high potential to be applicable for future flexible LC devices.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.134-140
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• 2021

The development of technology speeds up the process of obtaining information and its analysis to track the level of corruption in different countries and develop countermeasures. This study examines the role of information and analytical support of anti-corruption policy as a tool for government accountability and analysis, evaluation, combating corruption in Eastern Europe. The purpose of the article is to identify the components of the information-analytical system that help reduce the level of corruption. The research methodology is based on a qualitative content analysis of the functioning of information and analytical systems of Ukraine used by anti-corruption bodies. A quantitative analysis of the CPI score was conducted, according to Transparency International, to identify the effectiveness of anti-corruption policies in developing countries. The results show similar trends in countries developing on the effect of the use of information and analytical systems in the implementation of anti-corruption policies, strategies and measures. The strategy to combat corruption mainly involves increasing the independence and powers of anti-corruption bodies. Therefore, the development of information and analytical support is aimed at automating the processes of pre-trial investigations and criminal proceedings, information protection. As a tool for accountability, information and analytical systems may be ineffective due to the abuse of power by higher anti-corruption bodies due to political pressure from elite structures. Restrictions on political will are a major problem for the effectiveness of anti-corruption policies.

• Molecules and Cells
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• v.45 no.7
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• pp.454-464
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• 2022

DJ-1 is one of the causative genes of early-onset familial Parkinson's disease (PD). As a result, DJ-1 influences the pathogenesis of sporadic PD. DJ-1 has various physiological functions that converge to control the levels of intracellular reactive oxygen species (ROS). Based on genetic analyses that sought to investigate novel antioxidant DJ-1 downstream genes, pyruvate dehydrogenase (PDH) kinase (PDK) was demonstrated to increase survival rates and decrease dopaminergic (DA) neuron loss in DJ-1 mutant flies under oxidative stress. PDK phosphorylates and inhibits the PDH complex (PDC), subsequently downregulating glucose metabolism in the mitochondria, which is a major source of intracellular ROS. A loss-of-function mutation in PDK was not found to have a significant effect on fly development and reproduction, but severely ameliorated oxidative stress resistance. Thus, PDK plays a critical role in the protection against oxidative stress. Loss of PDH phosphatase (PDP), which dephosphorylates and activates PDH, was also shown to protect DJ-1 mutants from oxidative stress, ultimately supporting our findings. Further genetic analyses suggested that DJ-1 controls PDK expression through hypoxia-inducible factor 1 (HIF-1), a transcriptional regulator of the adaptive response to hypoxia and oxidative stress. Furthermore, CPI-613, an inhibitor of PDH, protected DJ-1 null flies from oxidative stress, suggesting that the genetic and pharmacological inhibition of PDH may be a novel treatment strategy for PD associated with DJ-1 dysfunction.

• Journal of Korea Trade
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• v.25 no.1
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• pp.34-46
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• 2021

Purpose - This paper aims to analyze the impact of the terms of trade, export price, and import price on the Korean economy (that is, real GDP, CPI, money market rate, and real effective exchange rate), and vice versa in the simple vector autoregression. Design/methodology - We impose two assumptions, i.e., diagonality and bloc exogeneity, to correctly identify the impact of a factor to the others in the structural equation. With two contemporaneous assumptions in the structural VAR, this paper investigates the impacts of the terms of trade on the Korean economy and vice versa. Findings - Impulse responses to the shocks in the terms of trade and Korean economy show that 1) an impact of the terms of trade on the economy is different in export prices and in import prices. A higher export price is beneficial to the economy while a higher import price hurts the economy, and 2) an increase in real effective exchange rate and in interest rate constrains domestic production and lowers consumer prices. Originality/value - Unlike the conventional perception that a depreciation of a currency would promote exports and domestic production at the price of inflation, our result shows the opposite, and 3) real GDP and consumer prices are positively correlated. That is, an increase in real GDP does not only cause inflation, but an increase in consumer prices also promote domestic production. Yet, the only difference is that export prices and import prices end up higher with an increase in real GDP, but lower with inflation.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.385-391
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• 2022

In this study, a multivariate time series analysis was conducted to identify various variables that impact ocean freight rates in addition to supply and demand factors. First, we used the ClarkSea Index, Clarksons Average Bulker Earnings, and Clarksons Average Tanker Earnings provided by the Shipping Intelligence as substitute variables for the dependent variable, ocean freight. The following ndependent variables were selected: World Seaborne Trade, World Fleet, Brent Crude Oil Price, World GDP Growth Rate, Industrial Production (IP OECD) Growth Rate, Interest Rate (US$ LIBOR 6 Months), and Inflation (CP I OECD) through previous studies. The time series data comprise annual data (1992-2020), and a regression analysis was conducted. Results of the regression analysis show that the World Seaborne Trade and Brent Crude Oil P rice impacted the ClarkSea Index. Only the World Seaborne Dry Bulk Trade impacted the Clarksons Average Bulker Earnings, World Seaborne Oil Trade, Brent Crude Oil Price, IP, and CP I on the Clarksons Average Tanker Earnings.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.447-456
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• 2023

Even after 2000, oil prices rose enough to be comparable to the past, but the impact on economic variables was relatively stable. Therefore, this study tries to empirically examine that the response of the Korean economy to oil prices has changed since the 1998 financial crisis, when there was a structural change in the Korean economy. Through empirical analysis, it was tested that the influence of oil prices and producer prices on consumer prices had changed in the period before and after 1998, and that the influence of producer prices on the value-added ratio by industry sector also changed. This means that the transfer of the increase in production cost to consumer prices has been alleviated, and the impact on added value has also been alleviated. Various studies should be conducted to understand the causes of the empirical results, such as changes in the relationship between producer prices and consumer prices, factors in the industrial sector due to rising oil prices, and changes in products.

• Journal of dental hygiene science
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• v.17 no.1
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• pp.38-45
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• 2017

We evaluated the association between sleep duration, dental caries, and periodontitis by using representative nationwide data. We examined 8,356 subjects aged ${\geq}19$ years who participated in the sixth Korea National Health and Nutrition Examination Survey (2013~2014). Sleep duration were grouped into ${\leq}5$, 6, 7, 8, and ${\geq}9$ hours. Presence of dental caries was defined as caries in ${\geq}1$ permanent tooth on dental examination. Periodontal status was assessed by using the community periodontal index (CPI), and a CPI code of ${\geq}3$ was defined as periodontitis. A chi-square test and multiple logistic regression analysis were used to determine statistical significance. Model 1 was adjusted for age and sex, model 2 for household income, educational level, and marital status plus model 1, and model 3 for smoking status, alcohol consumption, blood pressure level, fasting blood glucose level, total cholesterol level, and body mass index plus model 2. The prevalence of dental caries according to sleep duration showed a U-shaped curve of 33.4%, 29.4%, 28.4%, 29.4%, and 31.8% with ${\leq}5$, 6, 7, 8, and ${\geq}9$ hours of sleep, respectively. In the fully adjusted model 3, the risk of developing dental caries was significantly higher with ${\leq}5$ than with 7 hours of sleep (odds ratio, 1.23; 95% confidence interval, 1.06~1.43). The prevalence of periodontitis according to sleep duration showed a U-shaped curve of 34.4%, 28.6%, 28.1%, 31.3%, and 32.5%, respectively. The risk of periodontitis was significantly higher with ${\geq}9$ than with 7 hours of sleep in models 1 and 2, whereas the significant association disappeared in model 3. In a nationally representative sample, sleep duration was significantly associated with dental caries formation and weakly associated with periodontitis. Adequate sleep is required to prevent oral diseases such as dental caries and periodontitis.