With the development of information technology, online food delivery services have significantly changed people’s food consumption patterns. The outbreak of COVID-19 has further facilitated the development due to restaurant restrictions and social distancing measures. Due to the emergence of online food delivery services, scholars have paid increasing attention to this popular topic, and a large number of studies have examined which factors are related to consumers’ intention to use online food delivery services. However, previous studies showed inconsistent findings, and relationships between variables lack a comprehensive understanding. Therefore, this study aimed to develop a research model and examine the factors influencing consumers’ intention to use online food delivery services. By reviewing and analyzing 52 studies with 53 independent samples (N = 22,937), the meta-analytic structural equation modeling approach was carried out to assess the research model. The findings indicate that two attributes of online food delivery services (i.e., convenience and price-saving orientation) play different roles in the research model. Specifically, convenience significantly impacted perceived ease of use but had no direct impact on perceived usefulness. Price-saving orientation significantly influenced both perceived ease of use and perceived usefulness. Moreover, relationships between perceived ease of use, perceived usefulness, and perceived trust were significant, and these factors ultimately significantly led to the intention to use food delivery services. The findings contribute to theoretical advancement in the extant literature on food delivery services and help firms develop to provide better food delivery services for consumers’ continuous usage.

In this study, numerical modeling on the gas flow and off-gases in the low temperature carbonization furnace for carbon fiber was analyzed. The furnace was designed for testing carbonization process of carbon fibers made from various precursors. Nitrogen gas was used as a working gas and it was treated as an incompressible ideal gas. Three-dimensional computational fluid dynamics for steady state turbulent flow was used to analyze flow pattern and temperature field in the furnace. The off-gas mass fraction and cumulative emission gas of species were incorporated into the CFD analyses by using the user defined function(UDF). As a results, during the carbonization process, the emission of CO2 was the dominant among the off-gases, and tow moving made the flow in the furnace be uniform.

Social welfare facilities are used by a wide range of local residents, including vulnerable populations such as the elderly, children, and people with disabilities. During emergencies like fires, confusion can arise as these individuals try to evacuate. Evacuation simulation results have shown that utilizing evacuation systems based on specific evacuation scenarios can significantly decrease the time required for evacuation compared to general evacuation procedures. By anticipating potential fires based on changes in social and facility environments, appropriate evacuation scenarios can be developed and applied to evacuation systems, thus contributing to the safety and security of individuals during emergencies. In conclusion, for social welfare facilities that serve a large number of people, it is necessary to expand the focus on performance-based design depending on the size of the facility, and to continuously develop and train for appropriate evacuation scenarios that align with changing facility environments.

In this study, superior carbon nanotubes (CNT) were chemically modified with itaconic acid (IA) and a polyaniline (PANI) composite was formed and used to remove methylene blue (MB) dye from an aqueous solution. The capacity of CNTs modified with IA (IA/CNT) and composited with PANI (PANI/CNT) to remove MB dye from an aqueous solution was compared and investigated. The effects of parameters such as pH (3–10), adsorbent dose (0.8–8 g/L), initial dye concentration (10–100 mg/L), and temperature (25–55 °C) on MB adsorption were investigated. IA/CNT and PANI/CNT adsorbents were characterized by analyzes such as Fourier Transform Infrared Spectroscopy (FT-IR), Field Emission Scanning Electron Microscopy (FE-SEM), transmission electron microscope (TEM), and Brunauer, Emmett and Teller (BET). It was determined that the isotherm data fit the Langmuir isotherm model. The maximum adsorption capacity (qmax) of PANI/CNT and IA/CNT calculated according to this model (at 25 °C) was 12.78 and 32.78 mg g− 1, respectively. Thermodynamic analysis results showed that the adsorption was exothermic, feasible, and spontaneous. It can be said that the possible mechanism of MB on PANI/CNT and IA/CNT adsorbents occurs with the participation of π–π interaction, electrostatic attraction and hydrogen bonding.

The rational evaluation of carbon-based conductive ink performance is critical to both industrial production and applications. Herein, a model to evaluate writing performance of conductive ink by line resistance was proposed by investigating possible relations among different parameters and establishing relevant model to estimate ink writing performance. Bulk conductive inks were prepared and characterized to provide samples for model. To improve the precision of model, the impact of external factors including writing speed and angle was studied. Nonlinear regression and back propagation artificial neural network were employed to estimate line resistance, and cross check validation was conducted to prove robustness and precision of model. Most importantly, the investigation will open up a new path for the exploration of other carbon-based handwritten electronic devices.

Solubility and species distributions of radionuclides in domestic groundwater conditions are required for the safety assessment of deep underground disposal system of spent nuclear fuel (SNF). Minor actinides including Am contribute significant extents to the long-term radiotoxicity of SNF. In this study, the solubility of Am was evaluated in synthetic groundwater (Syn-DB3), which were simulated for the groundwater of the DB3 site in the KAERI Underground Research Tunnel (KURT). Geochemical modeling was performed based on the ThermoChimie_11a (2022) thermochemical database from Andra to estimate the solubility and species distributions of Am in the Syn-DB3 condition. Dissolved Am concentrations in the Syn-DB3 were experimentally measured under oversaturation conditions. Am(III) stock solution in perchlorate media was sequentially diluted in Syn-DB3 to prepare 8 μM Am(III) in Syn-DB3. The pH of the solutions was adjusted to be in the range of 6.4–10.5. A portion of the samples was transferred to quartz cells for UV-Vis absorption and time-resolved laser fluorescence spectroscopy studies and the rest were stored in centrifuge tubes. The absorption spectra of the samples were monitored over 70 days and the results suggest that Am colloidal particles were formed initially in all the samples and precipitated rapidly within two days. Over the experimental period of 236 days, small volume (10 μL) of the samples in the centrifuge tubes were periodically withdrawn after centrifugation (18000 rpm, 1 hr) for the liquid scintillation counting to measure the concentrations of Am dissolved in Syn-DB3. In the end of the experiments, pH of the samples was checked again and the final dissolved Am concentrations were determined after ultrafiltration (10 kDa) to exclude the contribution of colloidal particles. In the pH range of 8-9, which is relevant to the KURT-DB3 groundwater condition, the measured dissolved Am(III) concentrations were converged to around 10-8 M. These values are higher than the solubility of AmCO3OH:0.5H2O(s), but lower than that of AmCO3OH(am). There was no indication of transformation of the amorphous phase to the crystalline phase in our observation time window.

The engineered barrier system (EBS) is an indispensable element of a deep geological repository (DGR) designed to prevent the discharge of radioactive materials into the environment. The buffer material is a vital component of the EBS by creating a physical and chemical barrier that prevents the migration of radioactive materials. In the disposal environment, gases can be generated from the corrosion of the canister. When the gas generation rate exceeds the diffusion rate, the buffer material’s performance can deteriorate by the physical damage induced by the increase in pore pressure. Therefore, understanding the EBS’s behavior under gas generation conditions is crucial to guarantee the longterm safety and performance of the DGR. Lab-scale and field-scale experiments have been conducted to examine the stability of the buffer material concerning gas generation and movement by the previous researchers. To evaluate long-term stability for more than 100,000 years, it is essential to assess stability using a numerical model verified by these experiments. This study investigated the effect of interfacial characteristics on the numerical modeling accuracy of experimental simulation while verifying a numerical model through field-scale experimental results. The findings of this study are expected to furnish fundamental data for establishing numerical analysis guidelines for the longterm stability assessment of disposal systems.

With the increasing demand for a repository to safely dispose of high-level radioactive waste (HLW), it is imperative to conduct a safety assessment for HLW disposal facilities for ensuring the permanent isolation of radionuclides. For this purpose, the Korea Atomic Energy Research Institute (KAERI) is currently developing the Adaptive Process-based total system performance assessment framework for a geological disposal system (APro). A far-field module, which specifically focuses on fluid flow and radionuclide transport in the host rock, is one of several modules comprising APro. In Korea, crystalline rock is considered the host rock for deep geological disposal facilities due to its high thermal conductivity and extremely low permeability. However, the presence of complex fracture system in crystalline rock poses a significant challenge for managing fluid flow and nuclide transport. To address this challenge, KAERI is participating in DECOVALEX-2023 Task F1, which seeks to compare and verify modeling results using various levels of performance assessment models developed by each country for reference disposal systems. Through the benchmark problems suggested by DECOVALEX-2023 Task F1, KAERI adopts the Discrete Fracture-Matrix (DFM) as the primary fracture modeling approach. In this study, the transport processes of reactive tracers in fractured rock, modeled with DFM, are simulated. Specifically, three different tracers (conservative, decaying, adsorbing) are introduced through the fracture under identical injecting conditions. Thereafter, the breakthrough curves of each tracer are compared to observe the impact of reactive tracers on nuclide transport. The results of this study will contribute to a better understanding of nuclide behavior in subsurface fractured rock under various conditions.

The safe disposal of high-level radioactive waste (HLW), including the discharged spent nuclear fuel (SNF) and contaminated by-products produced from relevant chemical treatments, has become a serious pending problem for numerous countries that operate the nuclear power plants. The deep geological disposal (DGD) has thus far been considered the most proven and viable solution for isolation of the HLW and preventing any significant release of radionuclides into the biosphere. The DGD system consists of the multiple engineered and natural barrier components. Among them, the montmorillonite-based buffer and tunnel backfills are designed to perform the two major geochemical functions: 1) preventing the ingress of groundwater and any chemicals that compromise the safety of waste canister and 2) retarding the migration of released radionuclides by providing sufficient chemisorption sites. Therefore, it is essential to investigate the sorption mechanism of radionuclides onto montmorillonite and develop a thermodynamic reaction model in advance in order to accurately predict the long-term performance of engineered barriers and to reduce the uncertainties in the safety assessment of a deep geological repository (DGR) ultimately; thus far, sorption of chemical species onto mineral adsorbents has been widely described based on the concept of sorption-desorption distribution coefficient (Kd), the value of which is intrinsically conditional, and active scientific efforts have been made to develop robust thermodynamic sorption models which offer the potential to improve confidence in demonstration of radionuclide migration under a wide range of geochemical conditions. The natural montmorillonites are generally classified into Na-type or Ca-type according to its exchangeable cation, and the Ca-montmorillonite containing clays are being considered as candidate materials for the engineered barriers of DGR in several countries; they generally have advantages of higher thermal conductivity and lower price than the Na-montmorillonite based clays, but their sorption capacities are still comparable. In this framework, we aimed to investigate the chemical interactions of Ca-montmorillonite with selenite [Se(IV)], which is a major oxyanionic species in terms of HLW disposal, and develop a reliable thermodynamic sorption model (TSM). The present work summarizes the characterization of Ca-montmorillonite separated from the newly adopted reference bentonite (Bentonil-WRK) by means of XRD, BET, FTIR, CEC measurement, and acid-base titration. Further, its sorption behaviors with aqueous selenite species under aqueous conditions of S/L = 5 g/L, I = 0.01-0.1 m CaCl2, pH = 4.5-8.5, pCO2 = 10-3.5 atm, and T = 25°C were examined, and the resulting thermodynamic data are discussed as well.

나노복합재료는 다기능성과 고성능을 가지는 혁신적인 복합재료이다. 나노 스케일 필러의 혼입함으로써 복합재료의 전기적, 역학 적 및 열적 특성이 크게 향상될 수 있기 때문에 나노 스케일 필러를 이용한 나노복합재료의 특성화에 관한 다양한 연구가 광범위하게 수행되어 왔다. 특히, 탄소계 나노 필러(탄소나노튜브, 카본블랙, 그래핀 나노판 등)를 활용하여 전기/역학적 특성을 향상시킨 나노복 합소재 개발에 관한 연구들이 복합재료 분야에서 큰 관심을 받고있다. 본 논문은 실제 응용에 필수적인 나노복합재료의 전기/역학적 특성을 문헌조사를 통해 고찰하는 것을 목표로 한다. 또한, 나노복합재료의 전기/역학적 특성 예측을 위한 최신 멀티스케일 모델링 연 구들에 대해서 검토하고, 멀티스케일 모델링에 대한 과제와 향후 발전 가능성에 대해서 논의한다.

As part of the safety case development for generic disposal sites in Korea, it is necessary to develop generic assessment models using various geosphere–biosphere interfaces (GBIs) and potentially exposed groups (PEGs) that reflect the natural environmental characteristics and the lifestyles of people in Korea. In this study, a unique modeling strategy was developed to systematically construct and select Korean generic biosphere assessment models. The strategy includes three process steps (combination, screening, and experts’ scoring) for the biosphere system conditions. First, various conditions, such as climate, topography, GBIs, and PEGs, were combined in the biosphere system. Second, the combined calculation cases were configured into interrelation matrices to screen out some calculation cases that were highly unlikely or less significant in terms of the exposure dose. Finally, the selected calculation cases were prioritized based on expert judgment by scoring the knowledge, probability, and importance. The results of this study can be implemented in the development of biosphere assessment models for Korean generic sites. It is believed that this systematic methodology for selecting the candidate calculation cases can contribute to increasing the confidence of future site-specific biosphere assessment models.

This study monitored temperature using electronic sensors and developed a prediction model for compost maturity. The experiment used swine manure in a mechanical composting facility equipped with a screw-type agitator, and the composting process was conducted for 60 d during the summer season in South Korea. Four electronic temperature sensors were installed on the inner wall between the compost piles on Days 7, 14, 21, and 28 for daily temperature monitoring. Compost samples were collected daily for 60 d, and compost maturity was analyzed using the Solvita method. Multiple comparisons, correlations, and modeling were performed using the stat package in R software. The average compost pile temperatures was 39.1±3.9, 36.4±4.3, 31.3±4.5, and 35.4±8.1 on days 7, 14, 21, and 28, respectively, after composting. The average compost maturity according to the composting date was 3.61±0.60, 4.13±0.59, 4.26±0.47, and 4.32 ±0.56 on days 7, 14, 21, and 28, respectively. A significant negative correlation was observed between the compost composting periods (seven, 14, 21, and 28 d) and the temperature of all compost piles (p<0.05), where the correlation coefficients were -0.329, -0.382, -0.507, and -0.634, respectively. A significant positive correlation was observed between the compost composting periods (seven, 14, 21, and 28 d) and the maturity of the compost (p<0.05), where the correlation coefficients were 0.410, 0.550, 0.727, and 0.840, respectively. The model for predicting the maturation of the 14 d average compost pile according to the compost composting period and the average temperature for 14 d was y=0.026 x d – 0.021 x mt.x_14 d (mean temperature for 14 d) + 4.336 (R2=0.7612, p<0.001). This study can be considered a basic reference for predicting compost maturity by the proposed model using electronic temperature sensors.

This study aims to analyze research trends regarding outdoor wear. For this purpose, the data-collection period was limited to January 2002–October 2022, and the collection consisted of titles of papers, academic names, abstracts, and publication years from the Research Information Sharing Service (RISS). Frequency analysis was conducted on 227 papers in total to check academic journals and annual trends, and LDA topic-modeling analysis was conducted using 20,964 tokens. Data pre-processing was performed prior to topic-modeling analysis; after that, topic-modeling analysis, core topic derivation, and visualization were performed using a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: experiential marketing and lifestyle, property and evaluation of outdoor wear, design and patterns of outdoor wear, outdoor-wear purchase behavior, color, designs and materials of outdoor wear, promotional strategies for outdoor wear, purchase intention and satisfaction depending on the brand image of outdoor wear, differences in outdoor wear preferences by consumer group. The results of topic-modeling analysis revealed that the topic, which includes a study on the design and material of outdoor wear and the pattern of jackets related to the overall shape, was the highest at 30.9% of the total topics. The next highest topic was also the design and color of outdoor wear, indicating that design-related research was the main research topic in outdoor wear research. It is hoped that analyzing outdoor wear research will help comprehend the research conducted thus far and reveal future directions.

The forms and demands of language learning are changing in the pandemic era. Learners no longer rely solely on a formal language curriculum. Instead, they are using various informal language learning (ILL) channels. Although informal language learning has been in the spotlight and is growing, more research on ILL is needed. In this study, ILL taking place through an online community was analyzed. Articles from the Korean learning community (r/Korean in Reddit) were collected, and the topic modeling technique, Latent Dirichlet Allocation, was conducted on the collected data. As a result, seven major topics were selected. The most common topics in all posts were issues faced by beginner learners, followed by vocabulary and sentence meanings, interest in Chinese characters and applications of Korean language skills, culture and daily life, translation, online learning materials, and Korean phonics. Through this, the interests of ILL learners and the characteristics of learners could be identified. Due to the nature of ILL, in which a formal curriculum does not exist, it was found that questions about general strategies for learning and questions that could not be solved in formal language education were most prominent. In addition, the characteristics of ILL learners who actively sought learning content and materials were also found.

본 연구에서는 투과 유량 모델을 개발하기 위하여, 시간, 막 전후의 압력 차, 회전 속도, 막의 기공 크기, 동점도, 농도 및 공급 유체의 밀도 등 7개의 입력 변수에 기반한 두 종류(ANN 및 SVM) 인공지능 기법을 이용하였다. 시행착오법과 실험데이터와 예측 데이터 간의 결정 계수(R2) 와 평균절대상대편차(AARD)를 포함한 두 가지 통계 변수를 통해 최적의 모델 을 선정하였다. 최종적으로 얻어진 결과에서 최적화된 ANN 모델이 R2 = 0.999 및 AARD% = 2.245인 투과 플럭스 예측 정 확도를 보여서, R2 = 0.996 및 AARD% = 4.09의 정확도를 보인 SVM 모델에 비해 더 정확함을 알 수 있었다. 또한, ANN 모델은 SVM 방식에 비해 투과 유속을 예측하는 능력도 더 높은 것으로 나타났다.