Hydraulic conductivity is a critical design parameter for buffers in high-level radioactive waste repositories. Most employed prediction models for hydraulic conductivity are limited to various types of bentonites, the main material of the buffer, and the associated temperature conditions. This study proposes the utilization of a novel integrated prediction model. The model is derived through theoretical and regression analyses and is applied to all types of compacted bentonites when the relationship between hydraulic conductivity and dry density for each compacted bentonite is known. The proposed model incorporates parameters such as permeability ratio, dynamic viscosity, and temperature coefficient to enable accurate prediction of hydraulic conductivity with temperature. Based on the results obtained, the values are in good agreement with the measured values for the selected bentonites, demonstrating the effectiveness of the proposed model. These results contribute to the analysis of the hydraulic behavior of the buffer with temperature during periods of high-level radioactive waste deposition.

This study numerically compares optimum solutions generated by element- and node-wise topology optimization designs for free vibration structures, where element-and node-wise denote the use of element and nodal densities as design parameters, respectively. For static problems optimal solution comparisons of the two types for topology optimization designs have already been introduced by the author and many other researchers, and the static structural design is very common. In dynamic topology optimization problems the objective is in general related to maximum Eigenfrequency optimization subject to a given material limit since structures with a high fundamental frequency tend to be reasonable stiff for static loads. Numerical applications topologically maximizing the first natural Eigenfrequency verify the difference of solutions between element-and node-wise topology optimum designs.

This study aimed to determine the optimal planting density for sesame cultivation in a double cropping system after harvesting onion and garlic in the Muan region. It compared the growth, yield, disease susceptibility, and labor hours for two varieties of sesame (Landrace and Geonbaek sesame) at different planting densities. Plant height in the Landrace sesame showed no significant variation with different planting densities, whereas Geonbaek sesame increased in height with higher planting densities. Both cultivars increased the number of viable branches at lower densities. The stem diameter was thicker in Landrace sesame with decreasing planting density, while in the Geonbaek sesame, there was no significant difference in stem diameter regardless of planting density. The number of capsules per plant for the Landrace sesame increased with decreasing density, whereas Geonbaek sesame showed no significant difference. Yield for both cultivars was higher at greater densities. However, the late direct sowing time and shorter cultivation period significantly reduced the yield of sesame cultivated in a double cropping system compared to that of sesame grown in a single cropping system, due to the late direct sowing time and shorter cultivation period. There was no significant difference in disease occurrence based on planting density. Furthermore, although labor hours did not vary with planting density when comparing the labor hours required for cultivating the Landrace sesame and Geonbaek sesame, the latter needed less labor time. This study reveals the optimum planting distance and density for sesame cultivation as a double cropping after harvesting onion and garlic, providing invaluable data for establishing sesame double cropping cultivation techniques.

In this study, we estimated the distribution density of giant jellyfish in coastal waters of Korea in 2023 and compared the occurrence of giant jellyfish over four years. In May, the giant jellyfish were mainly distributed in the waters of the Yangtze River outflow, and in July, they were highly distributed in the west of Jeju Island and near the south coast of Korea. In addition, when comparing the distribution densities of giant jellyfish in Korea over four years, both acoustic and sighting surveys showed that the highest density of jellyfish occurred in 2021.

This study was conducted to analyze the effects of stand density on fire fuel (FF) changes in a Chamaecyparis obtusa forest. The study site was located in Mt. Munsu in Jeollabuk-do and consisted of a control, 30% thinning treatment (LT), and 50% thinning treatment (HT). Three-year-old seedlings were planted at a density of 3,000 trees ha-1 in 1976, and thinning was carried out in 2000. FF production was measured every 2 months by installing 3 circular litter traps 1.2 m above the ground. Litter bags containing 5 g of each leaf and branch were made and buried in the organic layer to investigate the FF decomposition rate. The decay constant was calculated after 18 months. FF accumulation was measured by collecting dry-weight organic matter from each plot using a square frame (0.09 m2) in September 2018. The FF production in LT and HT was significantly lower than that of the control (P<0.001). The leaf decay constant for HT was significantly lower than that of the control (P<0.05). The FF accumulation in HT was significantly lower than that of the control (P<0.01), but LT was not significantly different from the control. The results of this study showed that thinning decreased FF production.

도농복합시는 1995년 지방자치제가 본격 시행되면서 주민 편의를 증진하고, 도시와 농촌 지역의 균형발전을 목적으로 설치되었다. 본 연구는 우리나라 전체 54개 도농복합시를 대상으로 도시 스프롤 측면에서 도시공간구조를 분석하고자 하였다. 이를 위해 밀도 기반의 도시 스프롤 측정 지수를 적용하여 2000년, 2010년, 2020년 도농복합시의 도시 스프롤을 인구밀도 및 고용밀도의 측면에서 탐색하였다. 분석 결과, 도농복합시의 인구 스프롤은 전반적으로 증가한 반면, 고용 스프롤은 감소하는 경향을 보였다. 그리고 도시 규모별 도농복합시의 도시 스프롤은 차별화되어 진행되었음을 확인하였다.

Concrete is the primary building material for nuclear facilities, making it one of the most common forms of radioactive waste generated when decommissioning a nuclear facility. Of the total waste generated at the Connecticut Yankee and Maine Yankee nuclear power plants in the United States, concrete waste accounts for 83.5% of the total for Connecticut Yankee and 52% for Maine Yankee. In order to dispose of the low- to medium-level radioactive concrete waste generated during the decommissioning of nuclear power plants, it is necessary to analyze the radioactivity concentration of gamma nuclides such as Co-58, Co-60, Cs-137, and Ce-144. Gamma-ray spectroscopy is commonly used method to measure the radioactivity concentration of gamma nuclides in the radioactive waste; however, due to the nature of gamma detectors, gamma rays from sequentially decaying nuclides such as Co-60 or Y-88 are subject to True Coincidence Summing (TCS). TCS reduces the Full Energy Peak Efficiency (FEPE) of specific gamma ray and it can cause underestimation of radioactivity concentration. Therefor the TCS effect must be compensated for in order to accurately assess the radioactivity of the sample. In addition, samples with high density and large volume will experience a certain level of self-shielding effect of gamma rays, so this must also be compensated for. The Radioactive Waste Chemical Analysis Center at the Korea Atomic Energy Research Institute performs nuclide analysis for the final disposal of low- and intermediate-level concrete waste. Since a large number of samples must be analyzed within the facility, the analytical method must simultaneously satisfy accuracy and speed. In this study, we report on the results of evaluating the accuracy of the radioactivity concentration correction by applying an efficiency transfer method that appears to satisfy these requirements to concrete standard reference material.

This paper proposes the optimal molecular weight for a petroleum-based binder pitch to enhance the density and strength of the prepared graphite block. The effect of the molecular weight on the binder properties, which was quantified using solvent fractionation, was considered based on the evaluation of the coking and viscosity characteristics. The affinity of the pitch to coke influenced the carbonization yield of the block, and the proportion of closed pores was reduced via the use of a highaffinity binder pitch. In addition, the viscosity was found to influence the uniformity of the coke and pitch dispersions, and numerous open pores were formed in the graphite block under high-viscosity conditions. In terms of the molecular weight, a reduction in the content of the insoluble 1-methyl-2-pyrrolidone (NMP) fraction, which was the heaviest fraction present in the pitch, was found to reduce the affinity of the binder to coke while increasing its viscosity. Therefore, the density and strength of the prepared graphite block were reduced upon increasing the insoluble NMP content of the binder pitch. Consequently, it was necessary to control the content of this fraction within < 13.81 wt% to obtain high-density and high-strength graphite blocks.

A causality exists between insect density and plant health, where plant health is affected by both the plant’s potential and environmental factors. In other words, causality is possible between insect density and environmental factors, allowing for the analysis of insect density based on these environmental factors. Machine learning enables studying insect density alongside environmental factors, providing insights into the causality between insects, the environment, and plant health. Machine learning is a methodology that involves the design of models by learning patterns from input data. This study aims to predict F. occidentalis density by sampling environmental factors and applying them to machine learning models.

Since the importance of integrated pest management to minimize environmental damage and maximize pest control effectiveness has emerged, efforts to put it into practice have continued. To implement IPM, it is necessary to estimate the economic injury level to determine the control method by identifying pests and weeds that damage the quantity and quality of crops in the field, investigating the occurrence level, and calculating the ratio of cost and effectiveness. Also, damage to host plants caused by increased density of insect pests appears to change plant’s health that key factor for managing crops. Therefore, understanding the relationship between the density of pests and the damage to the host plants is necessary. This study aims to analyze the causal relationship between the density of insect pests and damage to the host plants for estimating the economic injury level of insect pests on the host plants and investigating the possibility of pest control decision-making using plant health status.

The objective of this study was to achieve biological control of green mold disease in Pyogo mushrooms using antagonistic microorganisms. Bacillus subtilis BSM320 cells inhibited mycelial growth by 48–60% against three Trichodermaisolates including T. hazianumisolated from the substrates of Lentinula edodes, showing their antifungal activity.The bacteria were cultured to a high density of 4.2 x 109±113.7 cfu/mlin aqueous extract of composted spent mushroom substrates of L. edodes containing 1% glucose and showed a higher growth rate than that observed when using the commercial medium, Luria-Bertani broth. The bacterial culture showed a 75% protective effect without damaging the mushroom fruiting bodies. These results suggest that B. subtilis BSM320culture is suitable for biological control of green mold disease during mushroom cultivation.

Metal additive manufacturing (AM) has transformed conventional manufacturing processes by offering unprecedented opportunities for design innovation, reduced lead times, and cost-effective production. Aluminum alloy, a material used in metal 3D printing, is a representative lightweight structural material known for its high specific strength and corrosion resistance. Consequently, there is an increasing demand for 3D printed aluminum alloy components across industries, including aerospace, transportation, and consumer goods. To meet this demand, research on alloys and process conditions that satisfy the specific requirement of each industry is necessary. However, 3D printing processes exhibit different behaviors of alloy elements owing to rapid thermal dynamics, making it challenging to predict the microstructure and properties. In this study, we gathered published data on the relationship between alloy composition, processing conditions, and properties. Furthermore, we conducted a sensitivity analysis on the effects of the process variables on the density and hardness of aluminum alloys used in additive manufacturing.

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of highquality Al alloy components for various applications.