Animal experiments have demonstrated the effectiveness of fermented rice germ and soybean extracts in lowering blood alcohol concentration. A compound primarily derived from fermented soybean extract constitutes the principal component of DA-5521, an experimental dietary substance examined in this study. We investigated the possible hangover-alleviating effects of DA-5521 in individuals aged 19 to 50 who had previously experienced hangovers. Moreover, we enrolled 22 participants who met the eligibility criteria and conducted a double-blind, randomized, placebo-controlled crossover trial. Six hours after alcohol consumption, the test group exhibited a statistically significant reduction in blood acetaldehyde concentration compared with the placebo group. Further, our results displayed significantly lower levels in the DA- 5521 group at 0.50 and 0.75 h post-ingestion and substantially lower peak breath alcohol concentration compared to the placebo group. These findings confirm that consumption of DA-5521 can significantly ameliorate hangover symptoms by diminishing blood acetaldehyde concentration and reduce breath alcohol concentration.

In order to attract students to the radio universe, we have constructed a three-element radio interferometer in the National Youth Space Center, Goheung, Korea. It consists of three 1.8 m off-axis parabola antennas with driving systems, sideband separation receivers operating in 12 GHz, a narrow band digitizer, and correlation software. We have used as many commercial products as possible to reduce development costs. The maximum separation of 20 m gives an angular resolution of ∼4′, and the shortest baseline of 3.8 m prevents a serious missing flux. Fringes are detected for several radio sources, including the sun and Cas A. After a rough relative calibration, we have derived visibilities for the sun, whose amplitudes are decreasing for longer baselines. We have made a solar image using the visibility amplitudes and closure phases, referring to the 17 GHz image by Nobeyama Radioheliograph. Developing a flexible real-time correlator seems most crucial if this kind of the system is to be used for more rigorous scientific studies.

Background: Kinesio taping is being applied to improve ankle dorsiflexion in stroke patients. Currently, the elasticity of kinesio taping is applied in various ways. Objectives: To investigated the effect of tibialis anterior kinesio taping elasticity level on gait speed in stroke patients. Design: A randomized cross-over pilot study. Methods: A total of 12 study subjects were allowed to experience three conditions within a single group. The three conditions are strong elastic taping condition, weak elastic taping condition, and non-elastic taping condition. Study subjects were randomly assigned to each condition sequentially. For the evaluation, gait variables (cadence, gait speed, stride length) were measured 24 hours after applying the taping appropriate for each condition. Results: The strong elastic taping condition significantly increased gait variables compared to the weak elastic taping and non-elastic taping conditions (P<.05). Weak elastic taping significantly increased gait variables compared to non-elastic taping (P<.05). Conclusion: As tibialis anterior kinesio taping elasticity increased, gait variables significantly improved in stroke patients.

A carbon matrix for high-capacity Li/Na/K-alloy-based anode materials is required because it can effectively accommodate the variation in the volume of Li/Na/K-alloy-based anode materials during cycling. Herein, a nanostructured porous polyhedral carbon (PPC) was synthesized via a simple two-step method consisting of carbonization and selective acid etching, and their electrochemical Li/Na/K-ion storage performance was investigated. The highly uniform PPC, with an average particle size of 800 nm, possesses a porous structure and large specific surface area of 258.82 cm2 g– 1. As anodes for Li/Na/K-ion batteries (LIBs/NIBs/KIBs), the PPC matrix exhibited large initial reversible capacity, fast rate capability (LIB: ~ 320 mAh g– 1 at 3C; NIB: ~ 140 mAh g– 1 at 2C; KIB: ~ 110 mAh g– 1 at 2C), better cyclic performance (LIB: ~ 550 mAh g– 1; NIB: ~ 210 mAh g– 1; KIB: ~ 190 mAh g– 1 at 0.2C over 100 cycles), high ionic diffusivity, and excellent structural robustness upon cycling, which demonstrates that the PPC matrix can be highly used as a carbon matrix for high-capacity alloy-based anode materials for LIBs/NIBs/KIBs.

The physicochemical similarities of hydrogen isotopes have made their separation a challenging task. Conventional methods such as cryogenic distillation, Girdler sulfide process, chromatography, and thermal cycling absorption have low separation factors and are energy-intensive. To overcome these limitations, research has focused on kinetic quantum sieving (KQS) and chemical affinity quantum sieving (CAQS) effects for selective separation of hydrogen isotopes. Porous materials such as metal-organic frameworks (MOF), covalent organic frameworks (COF), zeolites, carbon, and organic cages have been studied for hydrogen separation. In this study, we focus the enhancement for CAQS to provide the cations due to the chemical affinity between hydrogen isotope and unsaturated sites by cations in zeolite beads. Cation exchanged zeolite beads was synthesized with cobalt, copper, nickel, iron and silver in zeolite 4A beads. Synthesized cation exchanged zeolite was analyzed for the surface area and pore size in N2 and adsorption behaviors of hydrogen isotopes (D2/H2) for various cation exchanged zeolite beads using BET at 77 K. The study predicts the D2/H2 adsorption selectivity based on the results obtained with BET. These hydrogen isotope adsorption results will provide a foundation for future processes for tritium separation.

The seven-year research project entitled “Development of workflow for integrated 3D geological site descriptive modeling” is being carried out from 2023. This research is funded by Ministry of Trade, Industry, and Energy (MOTIE). Progress of the research is discussed here. The integrated 3D geological SDM (site descriptive model; GSDM hereafter) consists of three part; 1) three dimensional representation of geologic elements, 2) database for material properties and modeling results from SDMs of other disciplines (e.g., rock mechanics), and 3) a visualization tool for geology, material properties and modeling results. The GSDM is comparable to the GDSMs of SKB and POSIVA in its representation of geology by volume of geologic elements. However, our GSDM is different in that extra information of material properties and an extra tool for visualization is included in the GDSM. The rationale for incorporating material properties and a visualization tool into the GSDM is to expedite the development of the GSDM and SDMs of other disciplines by allowing single institution to integrate database and visualization with the GSDM. SKUA-GOCAD is used for representation of geologic surfaces for ductile and brittle shear zones, and also for surfaces for delineation of volumes of rock units. We have adopted SKUAGOCAD because the program offers powerful functions of interpolation including borehole data and geophysical prospecting. So far, we have tested the program for five different geologies, including sedimentary, high-grade metamorphic, and intrusive igneous geology. The test results are promising. Incorporation of data and modeling results for the SDMs of other disciplines is at conceptual stage. The working conceptual model involves the following steps, 1) to provide the modeler of other disciplines with surface information representing geologic elements, 2) the modeler returns not only material properties but the results of numerical analysis, and 3) incorporation of material properties and modeling results into database. Since the numerical codes in other disciplines adopt different types of formats for 3D geology, we plan to adopt the widely used FEM format prepared by Gmsh. The visualization tool will also adopt Gmsh for graphical representation of 3D geology as well as database for material properties and modeling results. When the working model of GSDM becomes available, rapid and significant progress is expected in the SDMs of other disciplines and related areas, for example, geotechnical investigation for deep geological repository.

본 연구는 벼 화분에 잔류한 네오니코티노이드계 약제가 꿀벌 봉군 내로 유입하여 만성적으로 피해를 주는지 에 대해 실험적으로 검증하고자 한다. 벼 꽃 개화기에 맞춰 논 인근의 세 지역에 각 6개 봉군을 설치하였다. 3개의 지역 중 두 지역은 벼 꽃 개화기 항공 방제 수행지역이고, 1개 지역은 미수행 지역이다. 지역마다 봉군 3개에는 채분기를 설치하여 벌통 내 화분 유입을 차단한 그룹과 미설치 그룹 간 봉세와 꿀벌 면역 및 수명 관련 유전자 발현량을 비교하였다. 약제 방제가 수행된 지역에서 채분기를 설치한 봉군의 봉세는 미설치 봉군보다 상대적으 로 강한 것을 확인하였다. 또한, 약제 처리 지역에서 채분기 설치 봉군에서 채분기를 설치한 봉군의 봉세는 미설치 봉군보다 상대적으로 강한 것을 확인하였다. 또한, 약제 처리 지역에서 채분기 설치 봉군에서 채집된 꿀벌의 면역 및 수명 관련 유전자 발현량이 미설치 그룹과 차이가 있는 것을 확인하였다.

생활권 수목이란 주민들이 생활하는 지역에 식재된 수목으로, 가로수, 학교수목, 아파트 수목 등 종류가 다양 하며 도시경관과 생활환경 개선의 이점을 제공한다. 그러므로 생활권 수목의 유지와 관리에 있어서도 인근 주민 에 혐오감이나 피해가 없도록 방제를 수행하는 것이 매우 중요하다. 일반적인 경엽살포의 경우, 약액 비산에 의해 주민들의 활동 제한, 안전사고 및 재산상 피해가 발생할 수 있으므로 사용이 제한적이다. 이러한 문제를 해결하기 위해, 나무에 약제를 직접 주입하는 나무주사(trunk injection)가 보편적인 생활권 수목 방제법으로 주목 받고 있으며, 이는 주입물질의 손실이 없고, 외부환경에 크게 영향 받지 않는다는 장점이 있다. 하지만 생활권 수목 전반에 대한 나무주사 처리방법이 명확하지 않아 약효가 일부 가지에서만 나타나는 등 사후 검증 및 관리가 미흡한 실정이다. 본 연구는 생활권 수목 병해충 방제 나무주사 제품의 효과제고를 위한 기존 처리방법 개선 및 최적의 약제처리 방법을 검증하였다. 느티나무외줄면충(Colopha moriokaensis)을 대상으로 시험을 진행하였 으며, Acetamiprid을 시험약제로 사용하였다. 느티나무에 대하여 약제처리 위치, 주입량, 천공수, 주사시기에 따른 약효 및 약해를 확인하였으며, 이를 바탕으로 최적의 약제처리 방법을 제시하였다.

Blockchain is an immutable ledger that records transactions and tracks assets using a common communication protocol. It stores a copy of the blockchain and implements a consensus function to verify transactions. Blockchain is applied to industries beyond finance, such as retail, to maintain security and transparency. Consumers with knowledge of blockchain technology are likely to be affected when evaluating products with blockchain embedded, impacting their product evaluation. The study investigates the impact of blockchain technology on consumers' product evaluation and how knowledge of blockchain and product quality moderate its effects.

This paper examines how consumers evaluate digital products with multiple Non-fungible Tokens (NFTs), which are blockchain-enabled cryptographic assets that represent proof-of-ownership for digital objects. The study predicts that people prefer fractioned NFTs (unique fractions of a digital object) versus duplicated NFTs (identical digital objects with distinct numbers) due to their preference for uniqueness. The study also examines the moderating role of product attributes, predicting that preferences for fractioned NFTs will be reduced when the product provides unique aspects, such as a serial number.

In this article, we address this shortcoming by exploring the concept of AI-based sustainable service—an offering that embeds artificial intelligence in ways that meet the needs of current consumers by contributing to socio-economic equalities and conserving the natural environment.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.