Graphene-derived materials are an excellent electrode for electrochemical detection of heavy metals. In this study, a MnO2/ graphene supported on Ni foam electrode was prepared via ultrasonic impregnation and electrochemical deposition. The resulting electrode was used to detect Pb(II) in the aquatic environment. The graphene and MnO2 deposited on the Ni foam not only improved active surface area, but also promoted the electron transfer. The electrochemical performance towards Pb(II) was evaluated by cyclic voltammetry (CV) and square wave anodic stripping voltammetry (SWASV). The prepared electrode exhibited lower limit of detection (LOD, 0.2 μM (S/N = 3)) and good sensitivity (59.9 μAμM−1) for Pb(II) detection. Moreover, the prepared electrodes showed good stability and reproducibility. This excellent performance can be attributed to the strong adhesion force between graphene and MnO2, which provides compact structures for the enhancement of the mechanical stability. Thus, these combined results provide some technical considerations and scientific insights for the detection of heavy metal ions using composite electrodes.

In this study, we evaluate artificial neural network (ANN) models that estimate the positions of gamma-ray sources from plastic scintillating fiber (PSF)-based radiation detection systems using different filtering ratios. The PSF-based radiation detection system consists of a single-stranded PSF, two photomultiplier tubes (PMTs) that transform the scintillation signals into electric signals, amplifiers, and a data acquisition system (DAQ). The source used to evaluate the system is Cs-137, with a photopeak of 662 keV and a dose rate of about 5 μSv/h. We construct ANN models with the same structure but different training data. For the training data, we selected a measurement time of 1 minute to secure a sufficient number of data points. Conversely, we chose a measurement time of 10 seconds for extracting time-difference data from the primary data, followed by filtering. During the filtering process, we identified the peak heights of the gaussian-fitted curves obtained from the histogram of the time-difference data, and extracted the data located above the height which is equal to the peak height multiplied by a predetermined percentage. We used percentage values of 0, 20, 40, and 60 for the filtering. The results indicate that the filtering has an effect on the position estimation error, which we define as the absolute value of the difference between the estimated source position and the actual source position. The estimation of the ANN model trained with raw data for the training data shows a total average error of 1.391 m, while the ANN model trained with 20%-filtered data for the training data shows a total average error of 0.263 m. Similarly, the 40%-filtered data result shows a total average error of 0.119 m, and the 60%-filtered data result shows a total average error of 0.0452 m. From the perspective of the total average error, it is clear that the more data are filtered, the more accurate the result is. Further study will be conducted to optimize the filtering ratio for the system and measuring time by evaluating stabilization time for position estimation of the source.

Recently, the spent fuel pools withdrawn from nuclear power plants in Korea have been saturated. Therefore, specific regulations on the management of spent fuel pools, such as transportation and intermediate storage are needed. The burnup history is directly related to the management of spent nuclear fuel. This is because the decision to handle nuclear fuel may vary depending on the initial concentration of nuclear fuel, the degree to which nuclear fuel is irradiated and radioisotope nuclides are decayed, and the cooling state in the spent nuclear fuel storage tank. The purpose of this study is to determine the burnup of fuel based on the value obtained by scanning the surface of spent nuclear fuel through a neutron detector. Conversely, a database of neutron signals that scan bundles of spent nuclear fuel with an instrument with an already identified combustion history needs to be completed. First of all, the correlation between burnup history and nuclides was identified in previous studies. By setting the burnup history as the input value in the ORIGEN-ARP code, it was possible to identify the radioactive isotopes remaining in the bundle of nuclear fuel. Neutrons can finally be measured based on the amount of nuclide inventory that constitutes spent nuclear fuel. Through MCNP, the neutron detector was simulated and signals were measured to confirm how it correlates with the previously acquired burnup history database. In addition, the M (sub-critical multiplication) value, which is essential for neutron measurement, was checked to confirm the degree to which additional neutrons were generated in spent nuclear fuel in a subcritical state. The target nuclear fuel assembly was CE16×16, WH14×14, and WH17×17, which confirmed the correlation (1) between burnup, enrichment, and cooling time with the previous research topic, TNSI (Total neutron source intensity). 􀜤􀜷􁈺􀜩􀜹􀝀/􀜯􀜶􀜷􁈻 = 0.83􁈺􀜵􀯇􁈻􀬴.􀬶􀬷􀬼 ∙ 􁈺􀜫􀜧􁈻􀬴.􀬸􀬺􀬶􀬻 ∙ 􀝁􀬴.􀬴􀬴􀬼􀬷∙􀯧 􁈺1􁈻 A neutron signal will be obtained from the case according to each burnup history constituting this database. In particular, PAR=SF, a function that calculates the production amount of the fission product, was used. To confirm the computational logic of SF, it was confirmed whether a reasonable calculation was made by calculating with a nuclide spectrum.

With the introduction and implementation of the National Research and Development Innovation Act in 2021, researchers are required to have a greater understanding of research ethics and to comply more strictly. The range of misconduct in research and the standards for sanctions have been expanded with the introduction of the National Research and Development Innovation Act. In addition, researchperforming institutions and specialized agencies have been obligated to establish their own research management systems and standards according to the changed criteria. The Korea Institute of Nuclear Nonproliferation and Control (KINAC), a nuclear regulatory authority that is conducting national R&D in related fields, has sought to strengthen research ethics by revising related regulations, introducing a plagiarism detection system, and expanding related education in accordance with these policies. In this study, we analyzed the effectiveness of the plagiarism detection system as a basic quality control measure for research results and a tool for enhancing research ethics, which was introduced. KINAC did not simply introduce a plagiarism detection program but established institutional improvements and other regulatory measures to support it, with the aim of more effectively managing research results. To analyze the effectiveness of this system, we calculated the plagiarism rate by sampling 30 papers each year for the three years before the introduction of the plagiarism detection system. When comparing the plagiarism rates before and after the introduction of the plagiarism detection system, no exceptional cases of high plagiarism rates were found in papers published after the introduction of the system. Although most of the papers before the introduction of the system showed a satisfactory plagiarism rate, some cases showed high plagiarism rates. We analyzed the cause of such cases in detail. Some exceptional cases were also found to be included in the range of misconduct regulated by the National Research and Development Innovation Act. As no such cases were found after the introduction of the system, we could infer that the system is effectively functioning as a tool for basic quality control and enhancing research ethics. In the future, we plan to expand the sample qualitatively and quantitatively by including other forms of outcomes published by the institution, not just papers, and conduct a more detailed analysis. Based on the results, we will develop various improvement plans for enhancing the quality and research ethics of the institution’s research results.

양식장 부표 등과 같은 해상의 소형 장애물을 탐지하고 거리와 방위를 시각화시켜 주는 해상물체탐지시스템은 선체운동으로 인한 오차를 보정하기 위해 3축 짐벌이 장착되어 있지만, 파도 등에 의한 카메라와 해상물체의 상하운동으로 발생하는 거리오차를 보정 하지 못하는 한계가 있다. 이에 본 연구에서는 외부환경에 따른 수면의 움직임으로 발생하는 해상물체탐지시스템의 거리오차를 분석하 고, 이를 평균필터와 이동평균필터로 보정하고자 한다. 가우시안 표준정규분포를 따르는 난수를 이미지 좌표에 가감하여 불규칙파에 의 한 부표의 상승 또는 하강을 재현하였다. 이미지 좌표의 변화에 따른 계산거리, 평균필터와 이동평균필터를 통한 예측거리 그리고 레이저 거리측정기에 의한 실측거리를 비교하였다. phase 1,2에서 불규칙파에 의한 이미지 좌표의 변화로 오차율이 최대 98.5%로 증가하였지만, 이동평균필터를 사용함으로써 오차율은 16.3%로 감소하였다. 오차보정 능력은 평균필터가 더 좋았지만 거리변화에 반응하지 못하는 한계 가 있었다. 따라서 해상물체탐지시스템 거리오차 보정을 위해 이동평균필터를 사용함으로써 실시간 거리변화에 반응하고 오차율을 크게 개선할 수 있을 것으로 판단된다.

Novel ionic liquid-functionalized carbon quantum dots (IL-CDs) were prepared by hydrothermal method, and characterized with FT-IR, TEM and XPS. The IL-CDs exhibited narrower particle size distribution with more uniform dispersion and the surface potential changes from negative to positive due to the function of IL. IL-CDs could be quenched (“turned off”) after adding ascorbic acid (AA), and as an “on–off”, fluorescent probe could be established for direct analysis AA. The linear range of AA was 0.34–30.00 μg/mL and the LOD was 0.11 μg/mL. The method was successfully applied to the determination of AA in real samples with satisfactory results.

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.