In the nuclear fuel cycle (NFC) facilities, the failure of Heating Ventilation and Air Conditioning (HVAC) system starts with minor component failures and can escalate to affecting the entire system, ultimately resulting in radiological consequences to workers. In the field of air-conditioning and refrigerating engineering, the fault detection and diagnosis (FDD) of HVAC systems have been studied since faults occurring in improper routine operations and poor preventive maintenance of HVAC systems result in excessive energy consumption. This paper aims to provide a systematic review of existing FDD methods for HVAC systems therefore explore its potential application in nuclear field. For this goal, typical faults and FDD methods are investigated. The commonly occurring faults of HVAC are identified through various literature including publications from International Energy Agency (IEA) and American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). However, most literature does not explicitly addresses anomalies related to pressure, even though in nuclear facilities, abnormal pressure condition need to be carefully managed, particularly for maintaining radiological contamination differently within each zone. To build simulation model for FDD, the whole-building energy system modeling is needed because HVAC systems are major contributors to the whole building’s energy and thermal comfort, keeping the desired environment for occupants and other purposes. The whole-building energy modeling can be grouped into three categories: physics-based modeling (i.e., white-box models), hybrid modeling (i.e., grey-box models), and data-driven modeling (i.e., black-box models). To create a white-box FDD model, specialized tools such as EnergyPlus for modeling can be used. The EnergyPlus is open source program developed by US-DOE, and features heat balance calculation, enabling the dynamic simulation in transient state by heat balance calculation. The physics based modeling has the advantage of explaining clear cause-and-effect relationships between inputs and outputs based on heat and mass transfer equations, while creating accurate models requires time and effort. Creating a black-box FDD model requires a sufficient quantity and diverse types of operational data for machine learning. Since operation data for HVAC systems in existing nuclear cycle facilities are not fully available, so efforts to establish a monitoring system enabling the collection, storage, and management of sensor data indicating the status of HVAC systems and buildings should be prioritized. Once operational data are available, well-known machine learning methods such as linear regression, support vector machines, random forests, artificial neural networks, and recurrent neural networks (RNNs) can be used to classify and diagnose failures. The challenge with black-box models is the lack of access to failure data from operating facilities. To address this, one can consider developing black-box models using reference failure data provided by IEA or ASHRAE. Given the unavailability of operation data from the operating NFC facilities, there is a need for a short to medium-term plan for the development of a physics-based FDD model. Additionally, the development of a monitoring system to gather useful operation data is essential, which could serve both as a means to validate the physics-based model and as a potential foundation for building data-driven model in the long term.

As remote sensing measures, satellite imagery has played an essential role in verifying nuclear activities for decades. Starting with the first artificial satellite, Sputnik 1, in 1957, thousands of satellites are currently missioning in space. Since the 2000s, the level of detail in pixels of an image (spatial resolution) has been significantly improving, thereby identifying objects less than one meter, even tens of centimetres. The more things are identifiable, the wider regions become targets for observation. With the increasing number of satellites, computer vision technology is required to explore the applicability of algorithm-based automation. This paper aims to investigate the R&D publications worldwide from the 1990s to the present, which have tried to apply algorithms to verify any clandestine nuclear activities or detect anomalies at the site. The versatile open-source publications, including the IAEA, ESARDA, US-DOE national laboratories, and universities, are extensively reviewed from the perspective of nuclear nonproliferation (or counter-proliferation). Thus, target objects for applications are essentially located in nuclearrelated sites, and the source type of satellite sensors focuses on electro-optical images with high spatial resolution. The research trend over time by groups is discussed with limitations at the time in order to contemplate the role of algorithms in the field and to present recommendations on a way forward.

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.

The safe, efficient and cost-effective decommissioning and dismantling of radioactive facilities requires the accurate characterization of the radionuclide activities and dose rate environment. And it is critical across many nuclear industries to identify and locate sources of radiation accurately and quickly. One of the more challenging aspects of dealing with radiation is that you cannot see it directly, which can result in potential exposure when working in those environments. Generally, semiconductor detectors have better energy resolution than scintillation detectors, but the maximum achievable count rates are limited by long pulse signals. Whereas some high pure germanium detectors have been developed to operate at high count rates, and these HPGe detectors could obtain gamma-ray spectra at high count rates exceeding 1 Mcps. However, HPGe detectors require cooling devices to reduce the leak currents, which becomes disadvantageous when developing portable radiation detectors. Furthermore, chemicalcompound semiconductor detectors made of cadmium telluride and cadmium zinc telluride are popular, because they have good energy resolution and are available at room temperature. However, CdTe and CZT detectors develop irradiation-induced defects under intense gamma-ray fields. In this Review, we start with the fundamentals of gamma rays detection and review the recent developments in scintillators gamma-ray detectors. The key factors affecting the detector performance are summarized. We also give an outlook on the field, with emphasis on the challenges to be overcome.

Electromagnetic interference (EMI) shielding is an important issue in modern daily life due to the increasing prevalence of electronic devices and their compact design. This study estimated EMI-shielding effect (EMI-SE) of small (8–14×17 mm) Hanji (Korean traditional paper) doped with carbon nanotubes (CNTs) and compared to Hanji without CNT using 2H (92.1 MHz) and 23Na (158.7 MHz) nuclear magnetic resonance (NMR) peak area data obtained from 1 M NaCl in D2O samples in capillary tubes that were wrapped in the Hanji samples. The simpler method of using the variation of reflected power and tuning frequency by inserting the sample into an NMR coil was also tested at 242.9, 158.7, and 92.1 MHz. Overall, EMI shielding was relatively more effective at the higher frequencies. Our results validated that NMR methods to be useful to evaluate EMI-SE, particularly for small, flexible shielding materials, and demonstrated that EMI shielding by absorption is dominant in Hanji mixed with CNT.