Rhaphidophoridae (Orthoptera: Ensifera), commonly known as cave crickets, are a wingless family and considered the most ancient lineage within Tettigoniidea. However, previous molecular phylogenetic studies and morphological hypotheses have shown inconsistencies. Although their fossils have been found in Baltic amber, their systematic placement remains unrevealed. This study reconstructed a comprehensive phylogeny integrating both extant and fossil lineages. Initially, we revealed relationships within extant lineages through molecular phylogenetics including all extant subfamilies for the first time. Subsequently, using a cladistic approach based on morphology, we confirmed the systematic position of fossil taxa †Protroglophilinae with a report of a new species. Integrating molecular and morphological phylogeney by total evidence tip-dating, we present the comprehensive phylogeny of Rhaphidophoridae considering both extant and fossil groups.

원자력발전소 지진 확률론적 안전성 평가인 PSA(Probabilistic Safety Assessment)는 오랜 기간에 걸쳐 확고히 구축되어 왔다. 반면 에 다양한 공정 기반의 산업시설물의 경우 화재, 폭발, 확산(유출) 재난에 대해 주로 연구되어 왔으며, 지진에 대해서는 상대적으로 연 구가 미미하였다. 하지만, 플랜트 설계 당시와 달리 해당 부지가 지진 영향권에 들어갈 경우 지진 PSA 수행은 필수적이다. 지진 PSA 를 수행하기 위해서는 확률론적 지진 재해도 해석(Probabilistic Seismic Hazard Analysis), 사건수목 해석(Event Tree Analysis), 고장수 목 해석(Fault Tree Analysis), 취약도 곡선 등을 필요로 한다. 원자력 발전소의 경우 노심 손상 방지라는 최우선 목표에 따라 많은 사고 시나리오 분석을 통해 사건수목이 구축되었지만, 산업시설물의 경우 공정의 다양성과 최우선 손상 방지 핵심설비의 부재로 인해 일 반적인 사건수목 구축이 어렵다. 따라서, 본 연구에서는 산업시설물 지진 PSA를 수행하기 위해 고장수목을 바탕으로 확률론적 시각 도구인 베이지안 네트워크(Bayesian Network, BN)로 변환하여 리스크를 평가하는 방법을 제안한다. 제안된 방법을 이용하여 임의로 생성된 가스플랜트 Plot Plan에 대해 최종 BN을 구축하고, 다양한 사건 경우에 대한 효용성있는 의사결정과정을 보임으로써 그 우수 성을 확인하였다.

Concerns about North Korea’s 7th nuclear test have been rising recently, and it is a significant threat to the situation around the Korean Peninsula. Amidst these threats, the Korean government also shows a strong will for denuclearizing the Korean Peninsula, referring to the “Audacious Initiative.” For denuclearization negotiations with North Korea, it is essential first to understand North Korea’s nuclear capabilities. However, since access to information is complicated and contains many uncertainties, many studies have been conducted to estimate it. Among them, Von Hippel surveyed to estimate the total amount of uranium ore based on information on uranium mining, which is relatively widely known throughout North Korea’s nuclear fuel cycle, and the amounts of HEU and Pu suggested by many experts. KINAC has conducted a study on a methodology that can narrow the estimation range and improve reliability through the Bayesian Network based on Von Hippel’s research results. However, in this study, the probability distribution is assumed to be the simplest form of uniform distribution, and the estimation formula for the amount of Pu produced compared to the amount of uranium loaded in the core is used as it is, which is an error in Von Hippel’s study. Improvement is needed. This study proposes a more reliable BN model by supplementing this and attempts to estimate the amount of uranium ore that North Korea produces or possesses. Of course, the data used as the basic structure of the model is insufficient, and the estimation formula is straightforward, so it is somewhat unreliable to trust the estimate for uranium ore. However, it is expected to be a suitable methodology that can narrow the scope of North Korea’s nuclear material production estimate or compensate for the uncertainty of the nuclear material production estimation model being developed at KINAC.

We report the discovery of four quasars with M1450 ≳ −25.0 mag at z ∼ 5 and supermassive black hole mass measurement for one of the quasars. They were selected as promising high-redshift quasar candidates via deep learning and Bayesian information criterion, which are expected to be effective in discriminating quasars from the late-type stars and high-redshift galaxies. The candidates were observed by the Double Spectrograph on the Palomar 200-inch Hale Telescope. They show clear Lyα breaks at about 7000–8000 ˚A, indicating they are quasars at 4.7 < z < 5.6. For HSC J233107-001014, we measure the mass of its supermassive black hole (SMBH) using its Civ λ1549 emission line. The SMBH mass and Eddington ratio of the quasar are found to be ∼108 M⊙ and ∼0.6, respectively. This suggests that this quasar possibly harbors a fast growing SMBH near the Eddington limit despite its faintness (LBol < 1046 erg s−1). Our 100% quasar identification rate supports high efficiency of our deep learning and Bayesian information criterion selection method, which can be applied to future surveys to increase high-redshift quasar sample.

A method of quantitatively analyzing radioactivity of uranium waste in the In-situ measurement using Bayesian inference was proposed. When applying the traditional efficiency calibration method, which uses standard sources or Monte Carlo simulation, the radioactivity error is large depending on the degree of spread of the radioactive contamination especially in large sample such as a 200 L drum. In addition, the existing method has a limitation in that it is difficult to reflect the uncertainty according to the location of the source. In this preliminary study, to overcome the limitations of the existing method, a Bayesian statistical-based radioactivity quantitative analysis model was proposed that can increase the accuracy of analysis even in situations where radioactive contamination of uranium waste is non-uniformly distributed. As a result of evaluating the simulated waste with the proposed Bayesian method, the accuracy was improved more than about 6 times compared to the classical efficiency calibration method.

A GoldSim Total System Performance Assessment has been developed and utilized for assessment of the various conceptual HLW repositories for spent nuclear fuels during last a few decades. Even though, almost all required parameter values associated with the repository system are frequently assumed or sometimes overestimated, they are still far from being highly reliable. Uncertainties nested in nuclide transport modeling around the repository are mainly dominated by these parametric uncertainties aside from intrinsic model uncertainty. Reliable estimate of the parameter values commonly expressed as probability density functions (PDFs) always require a large amount of measured data. Such input distributions are used as input to the probabilistic assessment program through Monte Carlo simulation to quantitatively provide possible uncertainty of the results. However, in most cases, especially in the safety assessment of the repository which is typically related with both long-time span and wide modeling domain, inefficient observed data from the field measurements are common, making conventional probabilistic calculations rather even uncertain. Since Bayesian approach is known to be especially powerful and efficient in the case of lacking of available data measured, such short data could be compensated by coupling with a priori belief, reducing uncertainty. By allowing the a priori knowledge for incorporating insufficient observed data, which include expert’ elicitation, their beliefs and judgment regarding the parameters as well as recent site-specific measurements, based on the Bayes’ theorem, the older parameter distributions, “prior” distribution can be updated to a rather newer and reliable “posterior” distribution. Newer distributions are not necessarily expressed as PDFs for probabilistic calculation. These updates could be done even iteratively as many times as data values are sequentially available, which calls sequential Bayesian updating, making belief of posterior distributions become much higher by reducing parametric uncertainty. To show a possible way to enhance the belief as well as to reduce the uncertainty involved in parameter for the Bayesian scheme, nuclide travel length in the far-field area of a hypothetical deep borehole spent fuel Repository was investigated. The algorithm and module that have been developed and implemented in GSTSPA through current study was shown to work well for all assumed prior, three sequential posterior distributions and likelihoods.

Bayesian statistics, which is an approach to analyzing data based on Bayes’ theorem, is currently widely used in all fields. However, it has been applied very limitedly to studies related to nuclear nonproliferation. Therefore, this paper provides a knowledge base and directions for using various Bayesian techniques in nuclear non-proliferation. First, the concepts and advantages of the Bayesian approach are summarized and the basic solving methods of Bayesian inference are explained. The Bayesian approach enables more precise posterior estimation using the prior probability and the likelihood functions. To solve Bayes’ theorem, it is necessary to use the conjugate prior distribution, which is analytically solvable, or to use a numerical approach with computing power. Next, for several Bayesian statistics methods, the purpose of use and the mathematical derivation process are described. Bayesian linear regression analysis aims for obtaining a function that outputs the closest value to data of variables and results. Factor analysis is mainly used to derive a smaller number of unobserved latent variables that can represent observed variables. The logit and probit model are nonlinear regression models for when the outcome is binary. The hierarchical model is to analyze by introducing hyper-parameters in an integrated manner when there are several groups of similar data. The Bayesian approach of these methods is generally based on the numerical solution of the Bayesian inference of the multivariate normal distribution. Finally, the previous researches that each introduced method have been applied to nuclear non-proliferation are investigated, and research topics that can be applied in the future are suggested. Bayesian statistics have been mainly used for precise estimation of the amount, location, and radioactivity spectrum of nuclear materials using detectors. Using Bayesian approach, it will be possible to perform various analyzes. For example, the change of activeness of nuclear program can be estimated by Bayesian inferences on the frequency and scale of nuclear tests. And it can be tried predicting the production of plutonium according to the core configuration and burnup using the Bayesian linear regression. Also, by introducing the Bayesian approach to factor analysis or logit analysis of nuclear development motives or nuclear proliferation probability, it can be expected to improve precision. With the development of computer technology, the use of Bayesian statistics increases rapidly. Based on the theory and applied topics summarized in this paper, it is expected that Bayesian statistics will be more actively used for nuclear non-proliferation in the future.

Efforts for nuclear non-proliferation have continued since the development of nuclear weapons and the conclusion of the NPT Treaty. Nuclear proliferation requires materials, facilities, and human resources to make nuclear weapons, and it takes a medium to long-term time. There are many restrictions in the current system to obtain nuclear materials and facilities, so it is often done through illegal means, black markets, or confidential transactions. Methods have been developed to evaluate the nuclear non-proliferation regime to strengthen the non-proliferation and solve the problems. The IAEA and the United States DOE initiated the proliferation resistance evaluation in 1980. The DOE conducted the assessment in three main evaluation categories: materials, technical characteristics of facilities, and institutional barriers. In another nuclear non-proliferation evaluation study, some researchers evaluated three main types: current capacity, political situation, and international situation. Detailed indicators include economic capacity, industrial capacity, nuclear capacity, leader’s intentions, political structure, competitive relations, alliances, and international norms. Most of these evaluations are based on the situation at the time of assessment at the national level. Historical examples of nuclear proliferation are rare, and verification is also challenging. The Bayesian probability is widely used when the data is small, experiments are impossible, and the causal relationship is unclear. A Bayesian network is a combination of Bayesian probability and graphics. It is used throughout the industry because it can easily derive results according to causal relationships and weights of various variables, evaluate the risk for decision-making, and obtain changed results through data updates. In particular, to evaluate the proliferation of nuclear weapons, Freeman developed the Freeman network in 2008 and the Freeman-Mella network in 2014. Freeman explained in detail only the process of deriving variables, correlations, and probabilities of factors related to factors such as motivation, intention, and resources. It isn’t easy to view as an objective result value because it does not describe the academic background for path selection, motivation list, intention, and resource variable selection. However, the research was meaningful because he first used the Bayesian network for nuclear proliferation. Although some studies have been done at the macro level, there is no case of applying it in export controls, which is the beginning of the actual spread. Also, there is no quantitative value for factors for risk assessment. There is little data, and verification of causality is difficult, so if the Bayesian network is applied to export control and applied to actual implementation, it will help make decisions such as export license or export denial.

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

This study explored the usefulness and implications of the Bayesian hyperparameter optimization in developing species distribution models (SDMs). A variety of machine learning (ML) algorithms, namely, support vector machine (SVM), random forest (RF), boosted regression tree (BRT), XGBoost (XGB), and Multilayer perceptron (MLP) were used for predicting the occurrence of four benthic macroinvertebrate species. The Bayesian optimization method successfully tuned model hyperparameters, with all ML models resulting an area under the curve (AUC) > 0.7. Also, hyperparameter search ranges that generally clustered around the optimal values suggest the efficiency of the Bayesian optimization in finding optimal sets of hyperparameters. Tree based ensemble algorithms (BRT, RF, and XGB) tended to show higher performances than SVM and MLP. Important hyperparameters and optimal values differed by species and ML model, indicating the necessity of hyperparameter tuning for improving individual model performances. The optimization results demonstrate that for all macroinvertebrate species SVM and RF required fewer numbers of trials until obtaining optimal hyperparameter sets, leading to reduced computational cost compared to other ML algorithms. The results of this study suggest that the Bayesian optimization is an efficient method for hyperparameter optimization of machine learning algorithms.

안전한 구조부재의 위치파악, 정밀한 시공을 위하여 인양, 안착시스템의 위치추정은 매우 중요한 요소이다. 크레인 인양시스템, 침매터널 이동시스템은 운용 중에 돌풍, 파랑과 같은 외란에 노출되어 불필요한 진동이 유발되고, 인양 길이 등이 시간에 따라 변하는 시변 시스템으로 구조부재나 질량체의 설계위치이동에 방해 요인이 되고 있다. 본 연구에서는 베이지안 필터가 가지는 비가우시안 잡음, 시스템 비선형성에 대한 확장성을 이용하여 정교하게 위치를 추정하는 기법을 다룬다. 이를 위하여 베이지안 프러세스로부터 필터의 유도과정을 살펴보고 유도된 확장 칼만필터를 이용하여 시변 진자시스템의 위치 추정 성능을 평가하여 보았다. 시변 진자시스 템에 대한 수치 시뮬레이션결과 외란에 의한 진동, 시스템의 비선형성, 시변성에 대해 안정적으로 위치를 추정하는 것을 알 수 있었다.