Discriminatory vs Uniform Price Auction: Auction Revenue

We compare auction revenues from discriminatory auctions and uniform price auctions in the case of the Korean treasury bonds auction market. For this purpose, we employ detailed bidder level data for each of 16 discriminatory auctions recently carried out in Korea. We first theoretically recover unobserved individual bidding functions under counter-factual uniform price auctions from the observed bidding functions under the actual discriminatory auctions, and then empirically estimate revenue differences. To test significance of the auction revenue differences, we use Bootstrap re-sampling methods where uncertainty in the cut-off yield spreads and uncertainty in the bidders are addressed individually as well as simultaneously. Our results indicate that uniform price auction increases the auction revenue relative to the discriminatory auction in most of the 16 cases, justifying the Korean government’s decision to switch to the uniform price auction mechanism in August 2000Treasury bonds auction, discriminatory auction, uniform price auction, hazard rate, Bootstrap re-sampling, yield spread, bidding function, bid shading

A Longitudinal Study of Seventh-day Adventist Adolescents Through Young Adulthood Concerning Retention in or Disaffiliation From the Church

Problem. Why do some Seventh-day Adventist youth leave the church in North America? The proportion of the youth who disaffiliate themselves from the church is considered to be a problem of serious concern for parents, teachers, other religious educators, and the church itself. It was the purpose of this study to discover the relationships that may exist between youth retention in the church and other selected variables. Method. The Ten-Year Youth Study of Youth Retention in the Seventh-day Adventist Church in North America furnished data for statistical analyses. Out of the 578 questions of the Ten-Year Youth Study, relevant items for this study were sorted out, and some of them were grouped together for scales development. SPSS factor analysis and reliability analysis programs were utilized in formulating the scales. Then, these scales and other selected individual items were put into statistical analysis such as Pearson correlation and a stepwise logistic regression analysis. Results. Approximately 55% of the members who were baptized at the age of 15 or 16 were active in attending worship regularly after 10 years. The stepwise logistic regression result selected seven primary predictors that seem to influence youth retention the most as measured by worship attendance. The positive influential predictors were, in descending order, Teacher encouraged thinking, Giving tithe regularly, Involvement in the church, and Agreement with distinctive Adventist doctrines. And the negative influential predictors were, in descending order, Teacher emphasized rules and regulations, Anti-traditional Adventist behavior, and Mother\u27s indifference and rejection. Conclusions. Youth retention in the church is a combined result of psycho-social and cognitive experiences a person had at home, school, and church during childhood through adolescence. Parents\u27 modeling with warm and caring attitudes, teachers\u27 grace oriented attitudes, teachers\u27 encouragement of thinking, congregational leaders\u27 affectionate and supportive attitudes are significantly correlated with the youth retention in the church. Also, youth\u27s agreement with distinctive Seventh-day Adventist doctrines, agreement with church standards, involvement in church activities, and paying of tithes significantly correlated with youth retention in the church as measured by worship attendance

Do Interesting Things Increase Behavioral Intentions? A Test of the Appraisal Structure of Interest and Relationship between Interest and Behavioral Intention: Applications in the Hospitality Industry

The emotion of interest has significant implications for human behavior. However, prior research in interest is limited to the domain of psychology. This study applies the appraisal theory of interest to test the inducers of interest, and the relationship between interest and behavioral intentions. An experiment with hypothetical scenarios in a restaurant setting is to be completed. Stimuli appraised as new and complex and with information about them are proposed to cause interest, and interest is expected to increase behavioral intention. Implications for hospitality managers are briefly discussed

RNAi Therapeutic Potentials and Prospects in CNS Disease

Over the past 20 years, RNA interference (RNAi) technology has provided a new regulatory paradigm in biology. This technique can efficiently suppress target genes of interest in mammalian cells. Small non-coding RNAs play important roles in gene regulation, including both in post-transcriptional and in translational regulation. For in vivo experiments, continuous development has resulted in successful new ways of designing, identifying, and delivering small interfering RNAs (siRNAs). Proof-of-principle studies in vivo have clearly demonstrated that both viral and non-viral delivery methods can provide selective and potent target gene suppression without any clear toxic effects. There are also the persistent problems with off-target effects (OTEs), competition with cellular RNAi components, and effective delivery in vivo. Although recent researches and trials from a large number of animal model studies have confirmed that most OTEs are not dangerous, other important issues need to be addressed before RNAi-based drugs are ready for clinical use. Currently, RNAi may be harnessed as a new therapeutic modality for brain diseases. Finally, there are already several RNAi-based human clinical trials in progress. It is hoped that this technology will have also effective applications in human central nervous system (CNS)-related disease

Cerebrovascular Atherosclerosis: Cognitive Dysfunction Progress and Autophagic Regression

As the aging of society, metabolic disorders have become a major concern and a major cause for cardio- and neurovascular diseases such as atherosclerosis, stroke, and even cognitive decline. This chapter shows the progressive plaque formation mechanisms and regression under autophagic flow in both experimental and clinical side. Atherosclerotic plaque formation is not irrevocable. Clinical and experimental reports accept that atherosclerosis can regress after statin treatment. This chapter focuses on autophagic roles in atherosclerotic plaque formation, progression, and regression. Another focus is on the relationship between atherosclerosis and an increased risk of cognitive decline and further conversion from mild cognitive impairment (MCI) to dementia. There has been broad and strong support on the relationship between atherosclerotic severity and cognitive function. Ultrasound findings such as intima-media thickness (IMT) and plaque numbers could potentially be useful in identifying individuals with a higher risk of progression from cognitive decline according to morphological criteria. This also suggests the possibility as a predictive indicator of MCI and dementia by considering the presence of atherosclerotic changes. Focusing on therapeutics, this chapter provides mechanisms for regressing atherosclerotic plaques. Autophagy suggests therapeutic possibilities for atherosclerosis and it consequently paves the way for preventing cognitive impairment

U.S. state clean energy policy and impacts on innovative technology adoption and employment: Analyzing impacts of energy-based economic development

Nations, states, cities, and towns are increasingly concerned about resilient and sustainable development against climate change. Energy-based economic development (EBED) has become a growing field of practice and research in the United States as well as across the world. EBED, a term recently coined by Carley et al. (2011; 2014), reflects the emerging convergence of two disciplines: energy planning and economic development. EBED captures the integration of policy-driven transformations of energy systems for low-emission and efficient energy generation and regional concerns for economic competitiveness and resilience. Meanwhile, Hurricanes Sandy and Maria, and other catastrophic blackouts have strengthened the demand to secure energy systems during weather-related or human-induced disruptions. Distributed generation (DG) systems have received renewed interests because of the growing demand for resilient power supplies, low- or zero-carbon energy generation, economic and regulatory environment changes, and advances in DG technology efficiency with declining life-cycle costs (U.S. Department of Energy, 2017). From amidst various technology options for DG, this research focuses on a combined heat and power (CHP) system that is a mature and innovative DG technology promising efficient production of energy on site. However, the CHP deployment is challenged by financial, regulatory, and workforce barriers. To fill the gap between private and public interests, federal, state, and local policymakers have implemented incentive-based and/or regulatory policies, which aim to promote EBED. This research began from recognizing the lack of theoretical approaches and empirical analyses in current EBED strategies and thus raised the question: How do clean energy policies affect clean energy use and related job creation? I assume that consumers are more likely to adopt CHP technologies when the state government provides a number of clean energy policy instruments. To test this hypothesis, this research examines two relationships—1) state governments’ activities on clean energy policy entrepreneurship and firms’ adoption of CHP technology, and 2) state governments’ activities on clean energy policy entrepreneurship and the growth of relevant employment opportunities. I developed an empirical method to address the influence of state clean energy policies on technology adoption. I first identified types of state policy instruments, and then scored states by the intensity of policy implementations. Using a framework of types of environmental policy instruments defined by Goulder and Parry (2008), I characterized the intensity of state clean energy policies by selective criteria, including the first year of policy enactment and the range of eligible CHP technologies. Second, I investigated regional differentiations of CHP generation by state and by year. The data of new CHP installations were collected in two forms: number of new CHP units and new installed capacity per GDP (kilowatt/million dollars). Third, I found correlationships in two relationships; the first group examined the aggregated impacts of state clean energy policy on CHP technology adoption, while the second group examined the policy impacts on CHP technology adoption by nine different types of policy tools. Random-effects (RE) regression models were employed to analyze panel data by controlling for all time-invariant differences, such as geographic location, political system, etc. To control for non-policy conditions, time-varying variables were added to the models to explain energy market conditions (electricity generation by fuel and fuel prices) and economic characteristics (personal income per capita and CO2 emission per capita). A panel data set for the 50 states and Washington, D.C. within a time period from 1980 to 2014 was created for the RE regression analyses. Last, to strengthen the findings from the RE models, I employed multiple-case studies by selecting four sample states—California as a state having high intensity of clean energy policy entrepreneurship and a high number of new CHP projects, Texas as a state having low policy entrepreneurship but a high number of new CHP projects, Ohio as a state having high policy entrepreneurship but a low number of new CHP projects, and Wyoming as a state having low policy entrepreneurship and a low number of CHP projects. The multiple-case study is conducted by four areas—(1) economic base study by using socio-economic archival and statistical data, (2) industry cluster analysis by using location quotient (LQ) and employment data, (3) energy market analysis by using EIA’s state profiles and energy estimates, and (4) CHP supportive policies and legislations by exploring media, formal policy reports, state governments’ documentations, and other website resources created by interest groups and associated stakeholders. The multi-case study of four selected states confirms distinct approaches to CHP policy development and implementation, resulting in different degrees of CHP technology adoption. I extend the existing literature by developing a theoretical framework to converge two fields—economic development planning and energy planning. Within this framework, I demonstrate how EBED is embedded in reality, how firms act along with clean energy policies, and how energy efficiency and clean energy could be a source of economic development.Ph.D

Manner Assimilation in Korean

This study is to show that compared with rule-based analyses, a constraint-based analysis in Optimality Theory presents a better account of manner assimilation in Korean and that Sonority Contact Law operating 10 the intersyllabic consonants plays a key role in explaining as well as in describing the phenomenon. The account using feature changing rules does not explain why nasalization, not denasalization, occurs, while another account in the framework of feature geometry and underspecification cannot provide an explanation as to why the manner assimilation occurs from right to left, not to left to right. Both accounts show another weakness of lack in generality by treating obstruent nasalization, I l/-nasalization, and /n/-Iateralization as three separate processes. On the other hand, the present analysis in Optimality Theory overcomes the problems of directionality and generality of the rule-based analyses. Manner assimilation is a result of the constraint ranking in which, interwoven with the faithfulness constraints, the sonority-based markedness constraints*SR and*SP operate to observe Sonority Contact Law. According to the law, a coda must not be less sonorous than the following onset and thus manner assimilation applies from right to left. By using the same constraint hierarchy the three separate processes can be described as one process of repairing the inappropriate situation of sonority difference between a coda and the following onset

Vowel Length Change In LuGanda

This paper reviews the analysis of compensatory lengthening in LuGanda by Clements (1986) and reanalyses it in the framework of OT. First, the defects of Clements' analysis in CV-phonology are pointed out: inconsistent use of C and V slots in representing nasals and consonants, syllabification problems with word-initial nasal clusters and geminate consonants, and an extrinsic ordering of many rules in resolving vowel hiatus. These problems are shown to be eliminated by the interaction of faithfulness and markendness constraints. The first problem does not occur in our OT-based analysis at all. The second one is solved by the constraint ranking Mᴀx-C》 Mᴀx-μ, Coᴍᴩ. Vowel hiatus contexts are also resolved by the constraint ranking. Especially, the directionality of vowel deletion is decided by constraints such as a contiguity constraint I-Coᴎᴛ[X, Rooᴛ] and a positional faithfulness constraint Mᴀx-Wi and by their position in the ranking. The former constraint is responsible for preserving the contiguity of a root and its immediately preceding segment, while the latter is for keeping word-initial segments, which are salient compared with their word-medial or word-final counterparts. Violability and strict domination of OT constraints are shown to be important in explaining the change in vowel length in LuGanda

Data Analysis for Solar Energy Generation in a University Microgrid

This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1% even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6%. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning