• Food Science and Biotechnology
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• 제16권2호
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• pp.238-242
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• 2007

Square root models were developed for predicting the kinetics of growth of Listeria monocytogenes in sesame leaves as a function of temperature (4, 10, or $25^{\circ}C$). At these storage temperatures, the primary growth curves fit well ($R^2=0.898$ to 0.980) to a Gompertz equation to obtain lag time (LT) and specific growth rate (SGR). The square root models for natural logarithm transformations of the LT and SGR as a function of temperature were obtained by SAS's regression analysis. As storage temperature ($4-25^{\circ}C$) decreased, LT increased and SGR decreased, respectively. Square root models were identified as appropriate secondary models for LT and SGR on the basis of most statistical indices such as coefficient determination ($R^2=0.961$ for LT, 0.988 for SGR), mean square error (MSE=0.l97 for LT, 0.005 for SGR), and accuracy factor ($A_f=1.356$ for LT, 1.251 for SGR) although the model for LT was partially not appropriate as a secondary model due to the high value of bias factor ($B_f=1.572$). In general, our secondary model supported predictions of the effects of temperature on both LT and SGR for L. monocytogenes in sesame leaves.

• 한국식품위생안전성학회지
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• 제24권3호
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• pp.232-237
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• 2009

본 연구는 오염된 양상치를 알카리전해수로 세척한 처리구와 비처리구에 오염된 E. coli O157 : H7균이 다양한 온도 (4, 10, 15, 20, 25, 30, $35^{\circ}C$)에 저장할 경우 이균의 specific growth rate (SCR) 과 lag time (LT) 생육변수에 미치는 영향을 조사하기 위한 모델을 개발하기 위하여 수행되었다. E. coli O157 : H7의 specific growth rate (SGR) 과 lag time (LT)를 결정하기 위해 생육도를 Gompertz 식을 사용하여 fitting한 결과, $R^2$값이 0.994로 나타났다. 실험값으로부터 얻은 SGR과 LT는 저장온도에 의존하는 것으로 나타났으며 $4^{\circ}C$에서 $35^{\circ}C$까지 온도가 증가할수록 성장 속도가 증가하는 것으로 나타났다. AIEW 처리구 또는 비처리구의 양상치 에서 E. coli O157 : H7의 성장 kinetics에 대한 저장 온도의 효과를 평가하기 위해 SRG에 대한 두개의 모델을 개발하였다. 유도된 2개의 모델 검증은 $R^2$, $R^2_{Adj}$ (adjusted determination coefficient) 및 MSE (mean square error)를 적용하였으며, 그 결과 $R^2$, $R^2_{Adj}$가 1 (>0.93)에 근접하였으며, 알카리 전해수 처리구 및 비처리구 양상치 모델의 MSE는 각각 0.031, 0.025로 나타났다. 따라서, 본연구에서 개발된 모델의 생육변수는 실험 치에서 얻은 E. coli O157 : H7의 생육변수 결과와 매우 유사한 것으로 나타났다.

• 한국농림기상학회지
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• 제24권4호
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• pp.256-266
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• 2022

일사량은 작물모형의 구동에 필수적인 요소지만, 일사량의 직접관측은 다른 기상자료들과 다르게 많은 인적, 물적 자원이 필요하다. 직접 일사량을 측정하는 대신 다른 기상자료를 통해 일사량을 추정하는 여러 방식이 존재하고 그중 대표적인 방법이 일조시간을 통해 일사량을 추정하는 Angstrom-Prescott 모델이다. Frere and Popov(1979)에 의해 전세계의 기후를 세 분류로 나누어 일조시간을 일사량으로 변환하는 AP 계수(AP_Frere)가 제시되었고, 국내 18개 종관기상관측소에서 30년간 관측한 일단위 일사량과 일조량 관측자료를 통해 AP계수를 경험적으로 도출한 계수(AP_Choi)가 Choi et al.(2010)에 의해 제시되었다. 본 연구에서는 2012년부터 2021년까지 일사량 관측값(S_Obs)과 AP_Frere와 AP_Choi를 통해 도출한 일사량(S_Frere, S_Choi)을 NRMSE와 t검정을 통해 분석하였고, 이를 DSSAT 작물모형에 입력모수로 사용하여 벼 품종 오대, 화성 및 추청에 대한 생육모의를 하였다. 일사량 추정 결과 일사량의 추정값과 측정값 사이에는 12%에서 22%사이의 오차가 존재하였고, 이를 3월부터 9월 사이의 생육기간에 한정하여 누적 일사량을 계산하면 오차가 줄었다. 18개의 지역중 관찰값과 생육기간의 누적 일사량은 S_Frere의 경우에 10개의 지역에서 S_Choi 보다 S_Obs와 가까웠고, 일일 일사량의 오차율을 통해 분석하였을때 S_Frere가 12개 지역에서 더 가까웠다.

• 융합신호처리학회논문지
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• 제18권2호
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• pp.55-61
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• 2017

전파천문분야에서 관측데이터의 저장을 위해 대용량 HDD를 RAID로 연결한 디스크 팩을 활용하고 있다. VLBI 관측의 경우 관측속도가 빨라지고 광대역으로 확장되면서 많은 양의 관측데이터를 저장해야 한다. HDD는 사용회수가 많아질수록 고장이 많이 발생하고 있으며, 이것을 찾아서 복구하는데 많은 시간이 소요된다. 또한 고장난 HDD를 계속 사용할 경우 관측데이터의 손실이 발생한다. 그리고 새 HDD를 구입하여 많은 비용도 필요하게 된다. 본 연구에서는 FirmOS를 이용하여 SATA HDD의 무결성 검사 시스템을 개발하였다. FirmOS는 일반 서버보드와 CPU를 갖는 시스템에서 특정목적에만 동작하도록 개발한 OS이다. 개발한 시스템은 FirmOS 기반에서 SATA HDD의 물리적인 영역에 특정 패턴의 데이터를 쓰고 읽는 과정을 수행한다. 그리고 HDD 제어기의 메모리 영역에서 HDD로부터 읽어들인 저장된 패턴 데이터와 비교를 수행하는 방식으로 HDD의 무결성 검사를 확인하는 방법을 채용하였다. 개발한 시스템을 활용하여 VLBI 관측에서 활용하고 있는 디스크 팩의 고장여부를 쉽게 확인할 수 있었으며, 관측효율을 향상시킬 수 있는데 많은 도움이 되고 있다. 본 논문에서는 개발한 SATA HDD 무결성 확인 시스템의 설계, 구성, 시험 등에 대해 자세히 기술한다.

• Imaging Science in Dentistry
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• 제53권3호
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• pp.221-227
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• 2023

Purpose: This study was performed to develop a linear regression model using the pulp-to-tooth volume ratio (PTVR) ratio of the maxillary canine, assessed through cone-beam computed tomography (CBCT) images, to predict chronological age (CA) in Indonesian adults. Materials and Methods: A sample of 99 maxillary canines was collected from patients between 20 and 49.99 years old. These samples were obtained from CBCT scans taken at the Universitas Padjadjaran Dental Hospital in Indonesia between 2018 and 2022. Pulp volume (PV) and tooth volume (TV) were measured using ITK-SNAP, while PTVR was calculated from the PV/TV ratio. Using RStudio, a linear regression was performed to predict CA using PTVR. Additionally, correlation and observer agreement were assessed. Results: The PTVR method demonstrated excellent reproducibility, and a significant correlation was found between the PTVR of the maxillary canine and CA(r= -0.74, P<0.01). The linear regression analysis showed an R² of 0.58, a root mean square error of 5.85, and a mean absolute error of 4.31. Conclusion: Linear regression using the PTVR can be effectively applied to predict CA in Indonesian adults between 20 and 49.99 years of age. As models of this type can be population-specific, recalibration for each population is encouraged. Additionally, future research should explore the use of other teeth, such as molars.

• 대한한의학회지
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• 제45권1호
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• pp.1-16
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• 2024

Introduction: The importance of clinical skills training in traditional Korean medicine education is increasingly emphasized. Since the clinical skills tests are high-stakes tests that determine success in national licensing exams, it is essential to develop reliable multifaceted analysis methods for clinical skills tests in actual education settings. In this study, we applied the multifaceted validity evaluation methods to the evaluation results of the cardiopulmonary resuscitation module to confirm the applicability and effectiveness of the methods. Methods: In this study, we used internal consistency, factor analysis, generalizability theory G-study and D-study, ANOVA, Kendall's tau, descriptive statistics, and other statistical methods to analyze the multidimensional validity of a cardiopulmonary resuscitation test in clinical education settings over the past three years. Results: The factor analysis and internal consistency analysis showed that the evaluation rubric had an unstable structure and low concordance. The G-study showed that the error of the clinical skills assessment was large due to the evaluator and unexpected errors. The D-study showed that the variance error of the evaluator should be significantly reduced to validate the evaluation. The ANOVA and Kendall's tau confirmed that evaluator heterogeneity was a problem. Discussion and Conclusion: Clinical skills tests should be continuously evaluated and managed for validity in two steps of pre-production and actual implementation. This study has presented specific methods for analyzing the validity of clinical skills training and testing in actual education settings. This study would contribute to the foundation for competency-based evidence-based education in practical clinical training.

• Food Science and Biotechnology
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• 제17권3호
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• pp.642-650
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• 2008

A mathematical model was developed for predicting the growth rate of Enterobacter sakazakii in tryptic soy broth medium as a function of the combined effects of temperature (5, 10, 20, 30, and $40^{\circ}C$), pH (4, 5, 6, 7, 8, 9, and 10), and the NaCl concentration (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10%). With all experimental variables, the primary models showed a good fit ($R^2=0.8965$ to 0.9994) to a modified Gompertz equation to obtain growth rates. The secondary model was 'In specific growth $rate=-0.38116+(0.01281^*Temp)+(0.07993^*pH)+(0.00618^*NaCl)+(-0.00018^*Temp^2)+(-0.00551^*pH^2)+(-0.00093^*NaCl^2)+(0.00013^*Temp*pH)+(-0.00038^*Temp*NaCl)+(-0.00023^*pH^*NaCl)$'. This model is thought to be appropriate for predicting growth rates on the basis of a correlation coefficient (r) 0.9579, a coefficient of determination ($R^2$) 0.91, a mean square error 0.026, a bias factor 1.03, and an accuracy factor 1.13. Our secondary model provided reliable predictions of growth rates for E. sakazakii in broth with the combined effects of temperature, NaCl concentration, and pH.

• BMB Reports
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• 제39권3호
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• pp.329-334
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• 2006

RNAi (RNA interference) has become a popular means of knocking down a specific gene in vivo. The most common approach involves the use of chemically synthesized short interfering RNAs (siRNAs), which are relatively easy and fast to use, but which are costly and have only transient effects. These limitations can be overcome by using short hairpin RNA (shRNA) expression vectors. However, current methods of generating shRNA expression vectors require either the synthesis of long (50-70 nt) costly oligonucleotides or multi-step processes. To overcome this drawback, we have developed a one-step short-oligonucleotides-based method with preparation costs of only 15% of those of the conventional methods used to obtain essentially the same DNA fragment encoding shRNA. Sequences containing 19 bases homologous to target genes were synthesized as 17- and 31-nt DNA oligonucleotides and used to construct shRNA expression vectors. Using these plasmids, we were able to effectively silence target genes. Because our method relies on the onestep ligation of short oligonucleotides, it is simple, less error-prone, and economical.

• 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.