시멘트 콘크리트 포장의 양생 공정에서는 피막양생제를 살포하는 것이 가장 일반적이며 양생포와 같은 덮개를 콘크리트 포장 위에 덮어 온도와 습도를 유지하는 방법으로 콘크리트 포장의 강도를 발현시키기도 한다. 콘크리트 포장의 미끄럼 저항 및 배수, 주행안전 성을 향상시키기 위해서는 양생 공정 이전에 표면 타이닝 공정을 수행하는 것이 일반적이지만 양생 이후에 그루빙을 실시하기도 한다. 본 연구에서는 콘크리트 포장 품질에 지대한 영향을 주는 양생 작업과 표면 그루빙 작업의 일원화 방법 개발을 위한 기초 연구로써 3D 스케치 프로그램과 3D 프린터를 이용하여 타원형, 삼각형, 사각형 모양의 홈으로 그루빙을 형성하면서 동시에 양생포로 사용이 가 능한 그루빙 양생 플레이트를 설계하여 제작하였다. 그루빙 양생 플레이트의 적용성을 분석하기 위해 콘크리트 공시체를 제작하여 실 내 실험을 수행하였으며 양생 플레이트의 그루빙 홈 형상에 따른 콘크리트 포장 표면 그루빙 형성 상태를 분석하였다.

The condensation phenomenon can affect the product in terms of function and aesthetics, so it is a complaint of many users from the past, and continuous research has been conducted to solve it. A portable instrument panel is installed inside combat vehicles such as tanks and armored vehicles. Due to the nature of the combat vehicle operated in the special situation of battle, the internal heat generation of the instrument panel has increased significantly, which is presumed to be the cause of condensation inside the instrument panel. In this paper, a study on the development of subsequent processes was conducted to reduce the condensation phenomenon of the instrument panel for combat vehicles. In order to reduce the condensation phenomenon, the experiment was carried out by setting baking time and stabilization time as major factors. This paper is considered to be a reference research data for all systems in which similar assemblies are used as well as instrument panels for combat vehicles.

There are two primary sludge drying methods such as the direct heating microwave method and the indirect heating steam one. In this study, the drying treatment facility at sewage treatment plant A applied both of these drying methods. The research aimed to investigate the optimal operation approach for the drying facility, considering the input sludge and the moisture content data after the drying process. Moisture content and removal rate data were executed at the research facility from January 2016 to December 2018. First, the microwave, a direct heating drying method, performed intensive drying only on the outer surface of the sludge by directly applying heat to the sludge using far infrared rays, so effective sludge drying was not achieved. On the other hand, the steam method of the indirect heating method used steam from a gas boiler to maximize the utilization of the heat transfer area and reduce energy of the dryer, resulting in an effective sludge drying efficiency. The sludge moisture content brought into the sludge drying facility was about 80%, but the moisture content of the sludge that went through the drying facility was less than 10% of the design standard. Therefore, the steam method of the indirect heating method is more effective than the microwave method of the previous direct heating method and is more effective for maintenance It has proven that it is an efficient method of operating construction facilities.

Lycium barbarum extract has a high potential to be developed as a health functional food due to the various health-promoting effects of Lycium barbarum. This study analyzed changes in nutritional and functional components depending on the extraction solvent (purified water and a mixture of purified water and alcohol) and the condition of the sample. The nutritional components (carbohydrates, protein, fat, ash), organic acids, amino acids, total phenolic compounds, and total flavonoids of the extract produced during the extraction process were analyzed. The nutritional composition and functional substances of the extracts showed some differences depending on the type of solvent and the condition of the sample. The amounts of crude protein (7.61%), crude fat (1.63%), carbohydrate (90.22%), and ash (0.54%) of dried Lycium barbarum extract using purified water as a solvent were similar to those of the powder sample extract. The highest content of citric acid was 4.31 mg/mL, similar to the case of acetic acid, when the powder sample used a mixture of purified water and alcohol as a solvent. The highest amino acid content was 357.39 mg/mL when the powder sample was mixed with purified water and alcohol as a solvent. The total amount of phenolic compounds was 686.16 g/L when the powder sample was extracted with a mixture of purified water and alcohol as a solvent. The highest total flavonoid content was 111.32 g/L when the powder sample was extracted with a mixture of purified water and alcohol as a solvent.

Graphene quantum dots (GQDs) are zero-dimensional carbonous materials with exceptional physical and chemical properties such as a tuneable band gap, good conductivity, quantum confinement, and edge effect. The introduction of GQDs in various layers of solar cells (SCs) such as hole transport layer (HTL), electron transport materials (ETM), cathode interlayer (CIL), photoanode materials (PAM), counter electrode (CE), and transparent conducting electrode (TCE) could improve the solar energy (SE) harvesting, separation and transportation of electrons and hole, thus ultimately enhance the overall performance and stability of SCs. The incorporation of GQDs in various layers such as HTL, ETM, CIL, PAM, CE, and TCE achieved photo conversion efficiencies (PCEs) of 18.63, 21.1, 12.81, 9.41, 8.1, and 3.66%, respectively. Furthermore, GQDs improved stabilities such as resistance to degradation for HTL (up to 77%), ETM (80%), resistance to UV light for ETM (94%), resistance to temperature in ETM (90%), and bending stabilities after 1000 cycles for HTL (88%) and for TCE (90%). There are reviews focused on the utilization of different carbon-structured materials such as graphene, carbon nanotubes (CNT), fullerenes, and carbon dots in SCs applications. More specifically, the utilization of GQDs for SCs is limited and yet to be explored in greater detail. This review mainly focuses on the recent advancement of various techniques of production of GQDs synthesis, utilization of GQDs in various layers like HTL, ETM, CIL, PAM, CE, and TCE for the enhancement of PCE, and the stability of SCs. As a result, we believe that an exclusive study on GQDs-sensitized solar cells (GQDSSCs) could provide an in-depth analysis of the recent progress, achievements, and challenges.

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.