Pine Wilt Disease (PWD) is a disease causing mass deaths of pine trees in South Korea, and the dead trees serve as breeding grounds for insect vectors responsible for spreading the disease to other host trees. Because the PWD requires early monitoring to minimize its damage on domestic forestry, this study aims to develop a species distribution model for predicting the potential distribution of PWD by using artificial neural network (ANN) with time-series data. Among the architectures, the Convolutional Neural Network exhibited the highest performance, achieving a validation accuracy of 0.854 and a cross-entropy loss of 0.401, and the InceptionTime model emerged as the second-best performer. This study identified the best-performing ANN architecture for a spatiotemporal evaluation of PWD occurrence, emphasizing the importance for determining hyperparameters with ecological characteristics and data types to apply deep learning into SDMs.

Chemical environments of near-field (Engineered barrier and surrounded host rock) can influence performance of a deep geological repository. The chemical environments of near-field change as time evolves eventually reaching a steady state. During the construction of a deep geological repository, O2 will be introduced to the deep geological repository. The O2 can cause corrosion of Cu canisters, and it is important predicting remaining O2 concentration in the near-field. The remaining O2 concentration in the near field can be governed by the following two reactions: oxidation of Cu(I) from oxidation of Cu and oxidation of pyrite in bentonite and backfill materials. These oxidation reactions (Cu(I) and pyrite oxidation) can influence the performance of the deep geological repository in two ways; the first way is consuming oxidizing agents (O2) and the second way is the changing pH in the near-field and ultimately influencing on the mass transport rate of radionuclides from spent nuclear fuel (failure of canisters) to out of the engineered barrier. Hence, it is very important to know the evolution of chemical environments of near-field by the oxidation of pyrite and Cu. However, the oxidation kinetics of pyrite and Cu are different in the order of 1E7 which means the overall kinetics cannot be fully considered in the deep geological repository. Therefore, it is important to develop a simplified Cu and pyrite oxidation kinetics model based on deep geological repository conditions. Herein, eight oxidation reactions for the chemical species Cu(I) were considered to extract a simplified kinetic equation. Also, a simplified kinetics equation was used for pyrite oxidation. For future analysis, simplified chemical reactions should be combined with a Multiphysics Cu corrosion model to predict the overall lifetime of Cu canisters.

본 연구에서는 투과 유량 모델을 개발하기 위하여, 시간, 막 전후의 압력 차, 회전 속도, 막의 기공 크기, 동점도, 농도 및 공급 유체의 밀도 등 7개의 입력 변수에 기반한 두 종류(ANN 및 SVM) 인공지능 기법을 이용하였다. 시행착오법과 실험데이터와 예측 데이터 간의 결정 계수(R2) 와 평균절대상대편차(AARD)를 포함한 두 가지 통계 변수를 통해 최적의 모델 을 선정하였다. 최종적으로 얻어진 결과에서 최적화된 ANN 모델이 R2 = 0.999 및 AARD% = 2.245인 투과 플럭스 예측 정 확도를 보여서, R2 = 0.996 및 AARD% = 4.09의 정확도를 보인 SVM 모델에 비해 더 정확함을 알 수 있었다. 또한, ANN 모델은 SVM 방식에 비해 투과 유속을 예측하는 능력도 더 높은 것으로 나타났다.

The management of algal bloom is essential for the proper management of water supply systems and to maintain the safety of drinking water. Chlorophyll-a(Chl-a) is a commonly used indicator to represent the algal concentration. In recent years, advanced machine learning models have been increasingly used to predict Chl-a in freshwater systems. Machine learning models show good performance in various fields, while the process of model development requires considerable labor and time by experts. Automated machine learning(auto ML) is an emerging field of machine learning study. Auto ML is used to develop machine learning models while minimizing the time and labor required in the model development process. This study developed an auto ML to predict Chl-a using auto sklearn, one of most widely used open source auto ML algorithms. The model performance was compared with other two popular ensemble machine learning models, random forest(RF) and XGBoost(XGB). The model performance was evaluated using three indices, root mean squared error, root mean squared error-observation standard deviation ratio(RSR) and Nash-Sutcliffe coefficient of efficiency. The RSR of auto ML, RF, and XGB were 0.659, 0.684 and 0.638, respectively. The results shows that auto ML outperforms RF, and XGB shows better prediction performance than auto ML, while the differences between model performances were not significant. Shapley value analysis, an explainable machine learning algorithm, was used to provide quantitative interpretation about the model prediction of auto ML developed in this study. The results of this study present the possible applicability of auto ML for the prediction of water quality.

The recent earthquake in Korea caused a lot of damage to reinforced concrete (RC) columns with non-seismic details. The nonlinear analysis enables predicting the hysteresis behavior of RC columns under earthquakes, but the analytical model used for the columns must be accurate and practical. This paper studied the nonlinear analysis models built into a commercial structural analysis program for the existing RC columns. The load-displacement relationships, maximum strength, initial stiffness, and energy dissipation predicted by the three analysis models were compared and analyzed. The results were similar to those tested in the order of the fiber, Pivot, and Takeda models, whereas the fiber model took the most time to build. For columns subjected to axial load, the Pivot model could predict the behavior at a similar level to that of the fiber model. Based on the above, it is expected that the Pivot model can be applied most practically for existing RC columns.

The sorption/adsorption behavior of radionuclides, usually occurring at the solid-water interface, is considered to be one of the primary reactions that can hinder the migration of radiotoxic elements contained in the spent nuclear fuel. In general, various physicochemical properties such as surface area, cation exchange capacity, type of radionuclides, solid-to-liquid ratio, aqueous concentration, etc. are known to provide a significant influence on the sorption/adsorption characteristics of target radionuclides onto the mineral surfaces. Therefore, the distribution coefficient, Kd, inherently shows a conditiondependent behavior according to those highly complicated chemical reactions at the solid-water interfaces. Even though a comprehensive understanding of the sorption behavior of radionuclides is significantly required for reliable safety assessment modeling, the number of the chemical thermodynamic model that can precisely predict the sorption/adsorption behavior of radionuclides is very limited. The machine-learning based approaches such as random forest, artificial neural networks, etc. provide an alternative way to understand and estimate complicated chemical reactions under arbitrarily given conditions. In this respect, the objective of this study is to predict the sorption characteristics of various radionuclides onto major bentonite minerals, as backfill materials for the HLW repository, in terms of the distribution coefficient by using a machine-learning based computational approach. As a background dataset, the sorption database previously established by the JAEA was employed for random forest machine learning calculation. Moreover, the hyperparameters such as the number of decision trees, the number of variables to divide each node, and random seed numbers were controlled to assess the coefficient of determination, R2, and the final calculation result. The result obtained in this study indicates that the distribution coefficients of various radionuclides onto bentonite minerals can be reliably predicted by using the machine learning model and sorption database.

The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In ‘offset printing’ mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called ‘spot color’ ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through ‘Delta E’ provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

교정시설의 과밀화 수용, 개별교정처우, 출소 후 재범예방 등을 위해서 수형자 분류 지표에 대한 논의는 지난 10여 년간 꾸준히 논의되어 온 주제이다. 현 교정본부에서는 교정재범예측지표를 활용하여 수형자의 가석방심사를 비롯하여 개벌적인 교정처우와 프로그램참여 등을 수행하고 있다. 수형자 분류는 수용시설에서 범죄성에 대한 억제와 재사회화를 위한 프로그램 설계 및 운영하는데 있어서 가장 중요한 도입단계라 볼 수 있다. 수형자의 재범억제와 성공적인 재사회화를 위해서 수형자 분류와 관련된 지표를 활용하고자 한다면, 이 지표에 대한 과학적 근거를 바탕으로 한 효과성 및 타당성 등의 평가가 뒷받침 되어야 한다. 우선 이 연구에서는 수형자 분류 지표에 대한 효과성을 검증하기 앞서 탐색적 연구를 수행하고자 하였다. 현재 국내 수형자 재범위험성 평가 도구를 파악하고, 내용 및 각 요인들을 살펴보았다. 다음으로 국내외 수형자 또는 범죄자의 위험성 등을 분류할 수 있는 평가도구를 자세히 살펴보았다. 마지막으로 개별처우, 수형자 분류 등의 중요성이 강조되어야 하는지를 뒷받침 할 수 있는 국내외 선행연 구를 검토하였다. 이 연구를 통해 현재 국내에서 수행되고 있는 수형자 분류와 관련된 지표를 살펴보고, 분산된 관련 자료들을 정리하여 추후 실증연구를 진행할 수 있는 근거자료로 활용하고자 하였다.

The high level of lithium storage in synthetic porous carbons has necessitated the development of accurate models for estimating the specific capacity of carbon-based lithium-ion battery (LIB) anodes. To date, various models have been developed to estimate the storage capacity of lithium in carbonaceous materials. However, these models are complex and do not take into account the effect of porosity in their estimations. In this paper, a novel model is proposed to predict the specific capacity of porous carbon LIB anodes. For this purpose, a new factor is introduced, which is called normalized surface area. Considering this factor, the contribution of surface lithium storage can be added to the lithium stored in the bulk to have a better prediction. The novel model proposed in this study is able to estimate the lithium storage capacity of LIB anodes based on the porosity of porous carbons for the first time. Benefiting porosity value (specific surface area) makes the predictions quick, facile, and sensible for the scientists and experts designing LIBs using porous carbon anodes. The predicted capacities were compared with that of the literature reported by experimental works. The remarkable consistency of the measured and predicted capacities of the LIB anodes also confirms the validity of the approach and its reliability for further predictions.

Do happy applicants achieve more? Although it is well established that happiness predicts desirable work-related outcomes, previous findings were primarily obtained in social settings. In this study, we extended the scope of the "happiness premium" effect to the artificial intelligence (AI) context. Specifically, we examined whether an applicant's happiness signal captured using an AI system effectively predicts his/her objective performance. Data from 3,609 job applicants showed that verbally expressed happiness (frequency of positive words) during an AI interview predicts cognitive task scores, and this tendency was more pronounced among women than men. However, facially expressed happiness (frequency of smiling) recorded using AI could not predict the performance. Thus, when AI is involved in a hiring process, verbal rather than the facial cues of happiness provide a more valid marker for applicants' hiring chances.

The first stage of the SMART-Navigation project was completed in 2020, and a new project that aims to develop smart AtoN (aids to navigation) has begun. If the location information of ships is continuously collected, processed and accumulated, it will be possible to identify the behavioral characteristics of each ship and to combine the characteristics of active ships at some point to provide various forms of predictive information. In this paper, we describe how current and traditional fishing areas are identified based on the location information of fishing boats and then introduce a new method for predicting fishing areas in advance using historical information of currently active fishing boats.

본 연구에서는 3차원 네트워크 폴리아크릴산나트륨 겔의 가교환경을 변화시켜 기계적 강도 및 팽윤거동을 제어하고 그 물성을 평가하는 연구를 진행하였다. 일반적으로 겔 용액의 가교도가 증가함에 따라 3차원 네트워크 겔의 팽윤비는 감소하고 겔의 기계적 강도는 증가한다. 본 연구에서는 3차원 네트워크 겔 상의 가교개수밀도를 산출하여, 겔화 과정에서 가교환경에 의존하는 중합효율 및 가교효율을 확인하였다. 그 결과, 겔 용액에서 단량체와 가교제의 중량비가 동일하더라도 가교환경이 달라지면 실제 제조된 겔 내부의 가교개수밀도가 3.6배 이상 달라질 수 있음을 확인하였다. 본 연구에서 시도한 가교개수밀도 기반 겔 평가 방법을 활용하면 효과적인 VOCs 흡수제로써 3차원 네트워크 겔을 최적화 할 수 있으리라 기대된다.

Human resource management has always been the most important part of any organization (corporate and government-owned). Until whenever improvement in human resource management is always the background of every problem that occurs in the organization. This study aims to examine the relationship of procedural justice and organizational commitment to OCB satisfaction and job satisfaction in the Public Works Office of Kutai Kartanegara Regency. Sampling involved the entire population, i.e. 109 informants. They are employees who have goods and services certificates. Hypothesis test carried out with the SEM-PLS model in two stages (outer model and inner model). After that, the survey data was used SMART PLS 3.0. Based on empirical findings, we find that procedural justice has a positive and significant effect on OCB, while organizational commitment does not. Procedural justice, organizational commitment, and OCB have had a positive and significant effect on job satisfaction. The novelty of the study lies in the originality value that describes the conditions in a government agency with different benchmarks (variables and indicators) from previous studies, so it is very interesting and varied.