Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

Various types of radioactive liquid and solid wastes are generated during the operation and decommissioning of nuclear power plants. To remove radionuclides Co-60, Cs-137 etc. from a liquid waste, the ion-exchange process based on organic resins has been commonly used for the operation of nuclear facilities. Due to the considerations for the final disposal of process endproduct, other treatment methods such as adsorption, precipitation using some inorganic materials have been suggested to prepare for large amounts of waste during decommissioning. This study evaluated sintering characteristics for radioactive precipitates generated during the liquid waste treatment process. The volume reduction efficiency and compressive strength of sintered pellets were the major parameters for the evaluation. Major components of a simulated precipitate were some coagulated (oxy) hydroxides containing light elements, such as Si, Al, Mg, Ca, and zeolite particles. Green pellets compressed to around 100 MPa were heated at a range of 750~850°C to synthesize sintered pellets. It was observed that the volume reduction percentages were higher than 50% in the appropriate sintering conditions. The volume reduction was caused by the reduction of void space between particles, which is an evidence of partial glassification and ceramization of the precipitates. This result can also be attributed to conversion reactions of zeolite particles into other minerals. The compressive strength ranged from 6 to 19 MPa. These results also showed a significant correlation with the volume reduction of sintered body. Although our lab-scale experiments showed many benefits of sintering for the precipitates, optimized conditions are needed for large-scale practical applications. Evaluation of sintering characteristics as a function of pellet size and further testing will be conducted in the future.

Over the years, in the field of safety assessment of geological disposal system, system-level models have been widely employed, primarily due to considerations of computational efficiency and convenience. However, system-level models have their limitations when it comes to phenomenologically simulating the complex processes occurring within disposal systems, particularly when attempting to account for the coupled processes in the near-field. Therefore, this study investigates a machine learning-based methodology for incorporating phenomenological insights into system-level safety assessment models without compromising computational efficiency. The machine learning application targeted the calculation of waste degradation rates and the estimation of radionuclide flux around the deposition holes. To develop machine learning models for both degradation rates and radionuclide flux, key influencing factors or input parameters need to be identified. Subsequently, process models capable of computing degradation rates and radionuclide flux will be established. To facilitate the generation of machine learning data encompassing a wide range of input parameter combinations, Latin-hypercube sampling will be applied. Based on the predefined scenarios and input parameters, the machine learning models will generate time-series data for the degradation rates and radionuclide flux. The time-series data can subsequently be applied to the system-level safety assessment model as a time table format. The methodology presented in this study is expected to contribute to the enhancement of system-level safety assessment models when applied.

The natural barrier system surrounding the geological repository for high-level radioactive waste plays a crucial role in preventing or delaying the leakage of radionuclides. Therefore, the natural barrier should ensure low permeability to prevent groundwater flow into the engineered barrier system throughout the repository’s lifetime. Crystalline rock, considered as the host rock for the geological repository in Korea, exhibits low intact rock permeability, but the crystalline rock often contains the multiple discontinuities due to its high brittleness that can allow the unexpected fluid flow. Therefore, the long-term hydraulic behavior of the discontinuity should be characterized while considering additional thermal, mechanical, and chemical effects. In comparison to thermal, hydraulic, and mechanical processes, the chemical processes on the discontinuities progress relatively slowly, resulting in limited researches to include these chemical processes. This research introduces mechanisms the involving coupled thermal-hydraulic-mechanicalchemical processes focusing on the rough fracture surfaces and asperities. The chemically-induced changes in mechanical and hydraulic properties are described based on pressure solution and precipitation concepts. A comprehensive review of laboratory tests, field tests, and numerical simulations is conducted related to the chemically-induced coupled processes in fractured rock. Laboratory tests, in particular, concentrate on microscopic changes in fracture asperities induced by pressure solution to analyze chemically-induced aperture changes. The TOUGHREACT, an integral finite difference method program for thermal-hydraulic-chemical simulations, is generally employed to model the chemical response of pressure solution and precipitation on fracture surfaces. The TOUGHREACT includes a module to describe effective porosity and permeability changes based on the modified cubic law, so the real-time change of the fracture permeability can be reflected during the flow simulation. Considering the coupled thermal-hydraulic-mechanicalchemical processes of discontinuity, it becomes evident that the chemical processes under repository conditions (long-term, high temperature, and high pressure) can disturb the hydraulic performance of the natural barrier, so further research is required to characterize the chemically-induced coupled processes for assessing the long-term performance of the natural barrier system.

Spodoptera species (S. exigua and S. litura) are important pests of several crops and vegetables in Korea. We investigated development processes of Spodoptera species under constant temperatures (20, 25 and 30 oC) regimes and relative humidity (RH) (30-35, 50-55, 70-75, and 90-95%) conditions. We collected eggs of Spodoptera species by releasing them into a rectangular box inner walls covered with a sheet of white paper. Temperature and RH significantly impacted on oviposition, immature survival, adult emergence and longevity of Spodoptera species. Maximum number of eggs, shorter developmental time, higher adult emergence with longer longevity were reported in 70-75% at 30 oC. Minimal eggs and larval survival were recorded in 30-35% and 90-95% RH, respectively. This results suggest that temperature and RH had individual apparent effect on the developmental processes of Spodoptera species instead interactive effect. Therefore, there is chance to cause a significant damage to field crops and vegetables in 70-75% at 30 oC.

Involving issues of environmental, consumer safety and health, and/or social concerns, a morally controversial action refers to an activity that does not have a single standard of ethics for all people, making it challenging to draw a clear line between what is ethical versus unethical. Despite its ambiguity, any firm can be involved in it, especially when the firm operates in the global marketplace where a variety of social, health, and environmental consequences surrounding a given action are questioned by consumers. If consumers come to know of a familiar brand’s morally controversial behavior, it can create approach-avoidance conflicts in their minds; they may see the brand’s action as mismatched with their own moral standards (“avoidance” manifested through a measurable construct––moral incongruence), while being tempted to justify the action (“approach” manifested through moral rationalization).

With a rapid expansion in electric vehicles, a huge amount of the spent Li-ion batteries (LIBs) could be discharged in near future. And thus, the proper handling of the spent LIBs is essential to sustainable development in the industry of electrical vehicles. Among various approaches such as pyrometallurgy, hydrometallurgy, and direct recycling, the hydrometallurgical manner has gained interest in recycling the spent LIBs due to its high effectiveness in recycling raw materials (e.g., lithium, nickel, cobalt, and manganese). However, the hydrometallurgical process not only requires the use of large amounts of acids and water resources but also produces toxic gases and wastewater leading to environmental and economic problems, considering potential economic and environmental problems. Thus, this review aims to provide an overview of conventional and state-of-the-art hydrometallurgical processes to recover valuable metals from spent LIBs. First, we briefly introduce the basic principle and materials of LIBs. Then, we briefly introduce the operations and pros-and cons- of hydrometallurgical processes. Finally, this review proposes future research directions in hydrometallurgy, and its potential opportunities in the fundamental and practical challenges regarding its deployment going forward.