• 국제지역연구
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• 제13권2호
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• pp.66-88
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• 2009

우리는 경제 내에 불확실성이 있을 경우의 최적 통화정책 준칙을 살펴본다. 특히 총수요에 대한 주택가격의 효과 뿐 만 아니라 주택가격 상승률에 관한 두 가지 가능한 영역을 허용한다. 두 가지 상태에 대한 불확실성이 Markov 상태 변환으로 모형화 된다. 미국 자료에 대한 예비 추정 결과는 두 개의 다른 상태 즉 정상 상태와 주택가격 버블 상태 영역의 존재를 확인한다. 다음으로, 본 연구에서는 주택시장에 두 개의 상태가 존재할 경우 중앙은행의 최적통화준칙을 살펴본다. '통상적' 상태 하에서는 인플레이션 압력에 반대로 대응해야 하는 반면, 버블 상태에서는 인플레이션 압력을 수용해야 한다는 면에서 중앙은행의 최적통화정책은 비대칭적이다. 또한, 미래 상태에 대한 불확실성이 있을 경우 더욱 보수적으로 통화정책을 운용해야 한다는 결과를 도출한다.

• Current Optics and Photonics
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• 제3권1호
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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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• 제21권3호
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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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• 제45권7호
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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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• 제25권1호
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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.

• 한국항해항만학회지
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• 제46권4호
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• pp.385-391
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• 2022

본 연구에서는 수요 및 공급요인 외 해상운임에 영향을 주는 다양한 변수들을 발굴하고자 다변량 시계열분석을 수행해 보았다. 우선 종속변수에는 해상운임을 대용할 변수로 Shipping Intelligence에서 제공하고 있는 종합운임지수(ClarkSea Index), 벌크선운임(Clarksons Average Bulker Earnings), 탱커선운임(Clarksons Average Tanker Earnings) 등을 활용하였다. 선행연구를 통해 해상운임에 영향을 미칠 것으로 예상되는 세계 해상물동량(World Seaborne Trade), 세계 선복량(World Fleet), 유가(Brent Crude Oil Price), 세계 GDP성장률(GDP World), OECD 산업생산성장률(Industrial Production OECD), 금리(US$ LIBOR 6 Months), OECD 인플레이션(CPI OECD) 등을 독립변수로 설정하여 회귀분석을 수행해 보았다. 데이터는 시계열자료로 1992년부터 2020년까지의 연데이터로 구성하였다. 분석결과 종합운임지수에는 해상물동량과 유가가, 벌크선운임에는 해상물동량만이, 탱커선운임에는 해상물동량, 유가, 산업생산성장률, 인플레이션 등이 통계적으로 유의미한 영향을 미치는 것으로 나타났다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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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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• 제23권8호
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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.

• 자원환경지질
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• 제56권4호
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• pp.447-456
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• 2023

2000년 이후에도 유가상승은 과거에 비견될 수 있을 만큼 상승하였으나 경제성장, 소비 등 경제변수들에 미치는 영향은 상대적으로 안정적인 모습을 보였다. 이에 본 연구는 우리 경제에 구조변화가 있었던 1998년 외환위기 시점을 기준으로 유가에 대한 우리 경제의 반응이 변화하였음을 실증적으로 보이고자 한다. 실증분석을 통해 1998년을 기준으로 전후 기간에 대해 유가 및 생산자물가가 소비자물가에 미치는 영향이 변화하였음을 확인하였고, 이어 산업부문별 부가가치율에 생산자물가가 미치는 영향도 변화하였음을 확인하였다. 이는 생산비용 상승의 소비자 가격에 전가가 완화되었으며 부가가치에도 영향이 완화되었음을 의미한다. 실증분석 결과의 원인에 대해서는 생산자물가와 소비자물가 간의 관계변화 및 그 원인, 유가상승에 따른 산업부문의 요소투입 및 생산품 변화 등 다양한 접근의 연구가 수행되어야 할 것이다.

• 치위생과학회지
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• 제17권1호
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• pp.38-45
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• 2017

본 연구는 수면시간에 따른 치아우식증과 치주질환의 관련성을 평가하였다. 2013~2014년 국민건강영양조사의 원시자료를 이용하였으며, 최종 8,356명을 대상으로 분석하였다. 수면시간에 따른 치아우식증 유병률은 U자형 곡선 모양이었으며, 수면시간에 따라 치아우식증은 유의한 차이가 있었다(p=0.020). 특히, 수면시간 7시간 그룹의 치아우식증 유병률이 28.4%로 가장 낮은 반면, 수면시간 5시간 이하 그룹의 유병률은 33.4%, 수면시간 9시간 이상 그룹의 유병률은 31.8%로 높았다. 모형 1과 모형 2 및 모든 변수를 보정한 모형 3의 로지스틱 회귀분석 결과, 수면시간 7시간 기준으로 수면시간 5시간 이하의 OR이 유의하게 높았다(모형 3: OR, 1.23; 95% CI, 1.06~1.43). 한편, 수면시간에 따른 치주질환 유병률은 U자형 곡선 모양이었으며, 수면시간에 따라 치주질환은 유의한 차이가 있었다(p<0.001). 수면시간 7시간 그룹의 치주질환 유병률이 28.1%로 가장 낮은 반면, 수면시간 5시간 이하 그룹의 유병률은 34.4%, 수면시간 9시간 이상 그룹의 유병률은 32.5%로 높았다. 로지스틱 회귀 분석 결과, 수면시간 7시간을 기준으로 수면시간 9시간 이상의 OR이 모형 1 (OR, 1.25; 95% CI, 1.00~1.56)과 모형 2 (OR, 1.27; 95% CI, 1.01~1.59)에서 유의하게 높았지만, 모든 변수를 보정한 모형 3에서는 수면시간과 치주질환의 관련성은 더 이상 유의하지 않았다. 이상의 연구 결과를 통해서 수면시간이 치아우식증 및 치주질환과 유의한 관련이 있음을 확인하였으며, 치아우식증과 치주질환의 위험을 줄이기 위해 적정시간의 수면이 필요할 것으로 생각된다.