• Asia pacific journal of information systems
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• 제28권2호
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• pp.114-132
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• 2018

Quantified-self technologies (QSTs) provide functions for users to collect, track, and monitor personal data for self-reflection and acquisition of self-knowledge. Although QSTs require prolonged use to reap the attendant benefits, many users stop using their devices or tracking within weeks or months. To address this issue, this study seeks to determine ways to sustain the use of QSTs. Combining motivational affordance theory with technology continuance theory, this study develops a theoretical model that accounts for an individual's continued intention to use a QST. Within the proposed model, unique QST affordances were identified as antecedents of individual motivation in relation to technology continuance, and their different roles in stimulating hedonic, utilitarian, and eudaimonic motivations were examined. The model was tested using data collected from 180 QST users. Results demonstrate that although utilitarian and eudaimonic motivations are complementary forces in determining continuance intention, hedonic motivation loses its predictive power in favor of eudaimonic motivation. Tracking, visualizing, and sharing affordances play different roles in elevating user motivations. The sharing affordance does not influence utilitarian and eudaimonic motivations, but it positively influences hedonic motivation. This research contributes to the literature on technology continuance by shifting scholarly attention from hedonic-utilitarian duality to eudaimonic motivation, characterized by meaning, self-growth, and pursuit of excellence.

• 한국식품과학회지
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• 제53권5호
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• pp.657-668
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• 2021

본 연구는 일반우유와 무지방우유에서 장출혈성 대장균과 캠필로박터 제주니의 행동예측모델을 개발하고, 미생물학적 안전관리를 위한 기준의 적절성 평가를 위해 정량적 위해성평가를 수행하였다. 시중 마트에서 유통 판매되고 있는 일반우유(n=195)에서 장출혈성 대장균과 캠필로박터 제주니의 오염실태를 모니터링한 결과 모든 제품에서 장출혈성 대장균과 캠필로박터 제주니는 검출되지 않아 초기 오염도는 각각 -3.94 log CFU/mL로 동일하게 추정되었다. 장출혈성 대장균은 7℃ 이상의 온도에서 성장하였고, 캠필로박터 제주니는 4-25℃ 온도의 우유에서 사멸하였다. 우유에서 1차 모델에서 얻은 parameter를 사용하여 장 출혈성 대장균은 2차 성장모델을 캠필로박터 제주니는 2차 사멸예측모델을 개발하였다. 일반우유의 섭취패턴은 식품의약품안전처(2015) 연구에서 수행한 "50대 주요 축산식품의 섭취량 및 섭취패턴조사" 결과를 바탕으로 @RISK 프로그램을 활용하여 하루에 일반우유의 1회 섭취를 통하여 장출혈성 대장균과 캠필로박터 제주니에 의한 식중독 발생 확률을 추정하였다. 추정 결과 1일 1회 일반우유 섭취로 장출혈성 대장균과 캠필로박터 제주니로 인한 평균 식중독 발생 확률은 각각 5.70×10^-5, 9.86×10^-9 것으로 확인되었다. 본 연구에서 정량적 위해평가를 통해 일반우유에서 장출혈성 대장균과 캠필로박터 제주니의 위해수준을 산출한 결과 일반우유에서 장출혈성 대장균의 식중독 발생 가능성이 상대적으로 높으므로 우선관리 대상임을 알 수 있었고, 우유제조업체에서 교차오염 방지, 살균온도/시간 관리, 유통온도, 가정에서 온도 관리 등이 매우 중요할 것으로 사료된다.

• Animal Bioscience
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• 제36권7호
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• pp.1003-1009
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• 2023

Objective: The objective was to compare (pedigree-based) best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods for genomic evaluation of growth traits in a Mexican Braunvieh cattle population. Methods: Birth (BW), weaning (WW), and yearling weight (YW) data of a Mexican Braunvieh cattle population were analyzed with BLUP, GBLUP, and ssGBLUP methods. These methods are differentiated by the additive genetic relationship matrix included in the model and the animals under evaluation. The predictive ability of the model was evaluated using random partitions of the data in training and testing sets, consistently predicting about 20% of genotyped animals on all occasions. For each partition, the Pearson correlation coefficient between adjusted phenotypes for fixed effects and non-genetic random effects and the estimated breeding values (EBV) were computed. Results: The random contemporary group (CG) effect explained about 50%, 45%, and 35% of the phenotypic variance in BW, WW, and YW, respectively. For the three methods, the CG effect explained the highest proportion of the phenotypic variances (except for YW-GBLUP). The heritability estimate obtained with GBLUP was the lowest for BW, while the highest heritability was obtained with BLUP. For WW, the highest heritability estimate was obtained with BLUP, the estimates obtained with GBLUP and ssGBLUP were similar. For YW, the heritability estimates obtained with GBLUP and BLUP were similar, and the lowest heritability was obtained with ssGBLUP. Pearson correlation coefficients between adjusted phenotypes for non-genetic effects and EBVs were the highest for BLUP, followed by ssBLUP and GBLUP. Conclusion: The successful implementation of genetic evaluations that include genotyped and non-genotyped animals in our study indicate a promising method for use in genetic improvement programs of Braunvieh cattle. Our findings showed that simultaneous evaluation of genotyped and non-genotyped animals improved prediction accuracy for growth traits even with a limited number of genotyped animals.

• 응용통계연구
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• 제37권2호
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• pp.119-138
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• 2024

본 연구는 노이즈 필터링과 차원축소 등의 방법을 이용하여 텍스트 지표의 정상화에 대해 검토하고 실증 분석을 통해 동 지표의 활용가능성을 제고할 수 있는 후처리 과정을 탐색하고자 하였다. 실증분석에 대한 예측 목표 변수로 월별 선행지수 순환 변동치, BSI 전산업 매출실적, BSI 전산업 매출전망 그리고 분기별 실질 GDP SA전기비와 실질 GDP 원계열 전년동기비를 상정하고 계량경제학에서 널리 활용되는 Hodrick and Prescott 필터와 비모수 차원축소 방법론인 충분차원축소를 비정형 텍스트 데이터와 결합하여 분석하였다. 분석 결과 월별과 분기별 변수 모두에서 자료의 수가 많은 경우 텍스트 지표의 노이즈 필터링이 예측 정확도를 높이고, 차원 축소를 적용함에 따라 보다 높은 예측력을 확보할 수 있음을 확인하였다. 분석 결과가 시사하는 바는 텍스트 지표의 활용도 제고를 위해서는 노이즈 필터링과 차원 축소 등의 후처리 과정이 중요하며 이를 통해 경기 예측의 정도를 높일 수 있다는 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권3호
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• pp.309-330
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• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• ETRI Journal
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• 제45권4호
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• pp.713-723
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• 2023

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

• Biomolecules & Therapeutics
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• 제31권6호
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• pp.661-673
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• 2023

Treatment of colorectal cancer (CRC) has always been challenged by the development of resistance. We investigated the antiproliferative activity of licochalcone H (LCH), a regioisomer of licochalcone C derived from the root of Glycyrrhiza inflata, in oxaliplatin (Ox)-sensitive and -resistant CRC cells. LCH significantly inhibited cell viability and colony growth in both Ox-sensitive and Ox-resistant CRC cells. We found that LCH decreased epidermal growth factor receptor (EGFR) and AKT kinase activities and related activating signaling proteins including pEGFR and pAKT. A computational docking model indicated that LCH may interact with EGFR, AKT1, and AKT2 at the ATP-binding sites. LCH induced ROS generation and increased the expression of the ER stress markers. LCH treatment of CRC cells induced depolarization of MMP. Multi-caspase activity was induced by LCH treatment and confirmed by Z-VAD-FMK treatment. LCH increased the number of sub-G1 cells and arrested the cell cycle at the G1 phase. Taken together LCH inhibits the growth of Ox-sensitive and Ox-resistant CRC cells by targeting EGFR and AKT, and inducing ROS generation and ER stress-mediated apoptosis. Therefore, LCH could be a potential therapeutic agent for improving not only Ox-sensitive but also Ox-resistant CRC treatment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2627-2629
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• 2003

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN). The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized networks, and to be much more flexible and preferable neural network than the conventional SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented with using nonlinear system data.

• International Journal of Advanced Culture Technology
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• 제12권2호
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• pp.167-179
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• 2024

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

• 한국군사과학기술학회지
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• 제27권1호
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• pp.80-93
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• 2024

This study attempted to analyze the factors that influence the participation of beneficiary companies in the government's defense industry promotion support project. To this end, experimental data were analyzed by constructing a prediction model consisting of highly important variables in beneficiary company decisions among various company information using the decision tree model, one of the data mining techniques. In addition, various rules were derived to determine the beneficiary companies of the government's support project using the analysis results expressed as decision trees. Three policy measures were presented based on the important rules that repeatedly appear in different predictive models to increase the effect of the government's industrial development. Using the analysis methods presented in this study and the determinants of the beneficiary companies of the government support project will help create a sustainable future defense industry growth environment.