The focus of this study was on the preparation of a clinoptilolite-based adsorbent, utilizing natural zeolite, to adsorb and remove ammonia (NH3) emitted from various environmental facilities, and to evaluate its performance. To create an adsorbent suitable for humid environments, hydrophobicity was introduced through HCl acid treatment. The impact of acid concentration and treatment time was analyzed to optimize the preparation conditions. As a result, the adsorbent treated with 0.5 M HCl for 2 hours demonstrated the highest NH3 adsorption performance. These findings suggest that the developed adsorbent could serve as an effective solution for controlling NH3 emissions in humid environments, contributing to the mitigation of environmental pollution and odor issues.

This study explores the introduction and applicability of the Species Protection Index (SPI) as a tool to evaluate the effectiveness of biodiversity conservation. Specifically, the SPI was recalculated for amphibians in Korea and compared with the internationally provided SPI results. The pilot evaluation of SPI for amphibians showed an increase from 41.52, based on international data, to 44.25, indicating that SPI calculations using domestic data can reflect conservation status more accurately than international SPI results. The findings suggest that SPI can serve as an important scientific basis for formulating national biodiversity conservation policies and managing protected areas, contributing to the development of more effective conservation strategies.

This review examines the importance of measuring practical enteric methane emissions from ruminants, considering their significant impact on global warming. Global warming is significantly driven by an increase in greenhouse gases, with rising methane (CH4) emissions from ruminants accelerating global warming recently. To successfully mitigate CH4 emissions and establish effective strategies, it is essential to apply reliable measurement techniques. This will allow for an accurate assessment of on-farm CH4 emissions. The priority should be to gather CH4 emission data that reflects the actual state of CH4 emissions from ruminants. The review provides an overview of the methods used to measure CH4 emissions from ruminants by compiling existing researches. It introduces the concepts, principles, and limitations of these methods to facilitate comparisons between existing approaches. This review discusses methods for measuring enteric CH4 emissions from ruminants at the farm level, including the tracer technique, laser methane detector, GreenFeed, and sniffer system. These methods are highlighted as potential tools to accumulate substantial data on on-farm CH4 emission from domestic animals with provides examples of international cases. Among these, this review introduces the Sniffer method, a CH4 emission measurement techniques that are suitable for on-farm use under domestic conditions, and emphasizes the necessity of its application. In addition, by presenting international cases where predictive models were developed based on on-farm CH4 measurement techniques, it is projected that if predictive models for CH4 emissions are developed by accumulating data at the farm level, it can contribute to sustainable livestock industry in various promising ways.

A multi-criteria decision-making(MCDM) method allows the decision makers to systematically evaluate the alternatives based on a predefined set of decision criteria. The most commonly used MCDM methods include Analytic Hierarchy Process(AHP), Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS), Weighed Aggregated Sum Product Assessment(WASPAS), Preference Selection Index(PSI), etc. In MCDM Problems, it is common that performance ratings for different criteria are measured by different units. Normalization is thus used to convert performance ratings into commensurable data. There are many normalization techniques that can be used for MCDM problems. Much effort has been made for comparative studies on the suitability of normalization techniques used in MCDM methods. However, most studies present normalization methods suitable for specific MCDM problems based on specific data samples. Therefore, this study proposes the most suitable normalization method for each MCDM method under consideration using extensive data samples. A wide range of MCDM problems with various measurement scales are generated by simulation for comparative study. The experimental results show that vector normalization method is best suited for all MCDM methods considered in this study.

본 연구에서는 source face와 target face를 합성해서 게임 캐릭터의 얼굴을 효과적으로 생성 하기 위한 방법으로 target face의 특징을 제거한 nullface를 생성한 다음 source face의 얼굴 의 특징을 적용해서 새로운 얼굴을 합성하는 기법을 제안한다. 이를 위해서 본 연구는 2 가지 모듈을 이용해서 수행된다. 첫 번째 모듈은 nullface를 생성하며, 두번째 모듈은 nullface에 source face의 특징을 적용해서 새로운 얼굴을 합성한다. 이 두 과정 모두 diffusion model에 기반한 방법론을 적용함으로써 효과적인 얼굴 합성 기법을 구현한다.

변화와 농촌 인구감소에 따른 농업 경쟁력 확보 방법으로 ICT 기술을 활용한 스마트팜 기술의 개발과 보급이 증가하고 있으며, 도시 농업에 대한 관심 및 도시 생활과 농촌 생활을 공유하 고자 하는 수요도 증가하고 있다. 소규모 모듈형 스마트팜은 가상과 현실을 통합하여 사람들 의 이러한 요구를 쉽게 충족시켜줄 수 있는 방법을 제공할 수 있다. 본 논문에서는 확장현실 (XR)과 스마트팜 시스템을 통합하여 스마트 농업에 대한 몰입 경험을 제공할 수 있는 기능성 게임 구현 방법을 제안한다. 제안된 방법은 스마트팜 제어 시스템과의 REST 방식의 연동 인 터페이스를 구현하여 데이터를 실시간으로 동기화하고 WebGL을 이용하여 3D 환경 및 사용자 인터페이스를 구현하여 사용자에게 작물 재배 과정에 대한 몰입 경험을 제공한다. 이러한 웹 기반 개발 방법은 다양한 환경에서 쉬운 접근성을 확보하고 개발의 효율성을 높일 수 있다.

This study aimed to develop a comprehensive validation methodology for an Infra-guidance system, which is an infrastructure-based service aimed at enhancing the safety of autonomous driving. The proposed method includes quantitative techniques for validating both the Infra-guidance algorithm module and the guidance message module using each optimal indicator. In addition, a promising method is suggested to validate the entire system by applying a multicriteria decision methodology. The relative weight for the algorithm module was higher than relative weight for the message module. Moreover, the relative weight of the latency for the message module was slightly higher than weight of the packet error rate. The proposed methodology is applicable for validating the performance of infrastructure-based services for enhancing connected autonomous driving based on the comprehensive quantification of various factors and indicators.

The purpose of this study was to identify and evaluate hazardous road sections based on roadside friction. Using GIS mapping and clustering techniques, this study analyzed traffic accidents and roadside friction data based on latitude and longitude coordinates. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied, with parameters of MinPts = 5 and eps = 0.0001, determined through a K-nearest neighbor analysis. The data were separated based on traffic flow direction (uphill/ downhill), and clustering was performed separately in each direction to identify specific hazard zones. The DBSCAN clustering results revealed 18 clusters in traffic accident data and 44 clusters in roadside friction data. Traffic accident clusters include various types of accidents (e.g., vehicle-to-vehicle and vehicle-to-pedestrian accidents), identifying locations as high-accident zones. The clustering results from the roadside friction data highlighted areas with crosswalks, absence of curbs, and roadside parking zones as major risk sections. Future research should analyze the operational design domain (ODD) of autonomous vehicles on hazardous road sections and explore the integration of multiple data sources to establish a comprehensive safety management system for accident prevention in autonomous driving environments. Additionally, road hazard sections are categorized into stages (e.g., hazardous, cautious, and safe) to enhance the precision in assessing road conditions. This categorization, combined with a detailed analysis of ODD, serves as a foundation for future research aimed at improving the safety of autonomous driving environments.

This study proposes a method to evaluate the publicity of real-time, demand-responsive, autonomous public-transportation systems. By analyzing real-time data collected based on publicity evaluation indicators suggested in previous research studies, this study seeks to establish a system that objectively assesses the publicity of public transportation. Thus, the introduction of autonomous public transportation systems is expected to contribute to solving problems in underserved transportation areas and enable more sophisticated public transportation operations. We reviewed evaluation indicators proposed in previous studies. Based on this review, publicity evaluation indicators were derived and specific criteria were selected to assess systematically the publicity of autonomous public transportation. An AHP analysis was conducted to assess the relative importance of each indicator by analyzing the importance of the selected indicators. Additionally, to score the indicators, minimum and maximum target values were established, and a method for assigning scores to each indicator was examined. The most important factor in the publicity evaluation of autonomous demand-responsive transport (DRT) was the “success rate of allocation to weak public transportation service areas,” with a significance level p of 0.204. This was analyzed as a key evaluation criterion because of the importance of service provision in areas with low-public-transportation accessibility. Subsequently, “Accessing distance to a virtual station” (p = 0.145) was evaluated as an important factor representing the convenience of the service. “Waiting time after allocation” (p = 0.134) also appeared as an important evaluation factor, as reducing waiting time considerably affected service quality. Conversely, “compliance rate of velocity” yielded the lowest significance (p = 0.017), as speed compliance was typically guaranteed owing to autonomous driving technology. This study proposed a specific evaluation method based on publicity indicators to provide a strategic direction for improving services and enhancing the publicity of autonomous DRT systems. These results can serve as a foundational resource for improving transportation services in underserved areas and for enhancing the overall quality of public transportation services. However, the study’s limitation was its inability to use real-time autonomous public transportation data, relying instead on I-MoD data from Incheon. This limitation constrained the ability to establish universal benchmarks because data from various municipalities were not included. Future research should collect and analyze data from diverse regions to establish more reliable evaluation indicators.

This paper explores a convergent approach that combines advanced informatics and computational science to develop road-paving materials. It also analyzes research trends that apply artificial-intelligence technologies to propose research directions for developing new materials and optimizing them for road pavements. This paper reviews various research trends in material design and development, including studies on materials and substances, quantitative structure–activity/property relationship (QSAR/QSPR) research, molecular data, and descriptors, and their applications in the fields of biomedicine, composite materials, and road-construction materials. Data representation is crucial for applying deep learning to construction-material data. Moreover, selecting significant variables for training is important, and the importance of these variables can be evaluated using Pearson’s correlation coefficients or ensemble techniques. In selecting training data and applying appropriate prediction models, the author intends to conduct future research on property prediction and apply string-based representations and generative adversarial networks (GANs). The convergence of artificial intelligence and computational science has enabled transformative changes in the field of material development, contributing significantly to enhancing the performance of road-paving materials. The future impacts of discovering new materials and optimizing research outcomes are highly anticipated.

The fishing industry is globally recognized as a high-risk sector with substantial safety challenges. This study analyzes Norway's fishing vessel safety management legislation and risk management practices. The goal is to derive insights that can enhance South Korea's safety management for fishing vessels. Norway has established a culture of safety through regular inspections, mandatory safety training, the implementation of safety management systems, and active involvement of fishermen in risk assessments. These measures have significantly reduced maritime accidents. This study suggests incorporating effective elements of Norway’s safety management into the South Korean context, emphasizing a participatory approach that actively involves fishers.

Recently, high-rise residential buildings in Korea have adopted slender shear walls with irregular section shapes, such as T-shape, H-shape, and C-shape. In the seismic design of the slender shear walls, the transverse reinforcement for lateral confinement should be provided in the boundary elements to increase deformation capacity and subsequent ductility. However, in practice, the irregularity of the shear walls is not adequately considered, and the lateral confinement region is calculated for the rectangular wall segments. This study investigated the proper design method for lateral confinement regions using finite element analysis. The lateral confinement region was considered in analysis for two cases: 1) as a typical rectangular wall segment and 2) as an irregular wall. When the irregularity of the walls was considered, the compression zone depth was increased because the vertical reinforcement in the flange was addressed. The effect of lateral confinement design methods on the structural performance of the walls was directly compared under various design parameters, including the length of the flange, concrete compressive strength, vertical rebar layout, axial load ratio, and loading direction. According to the results of the parametric analysis, the peak strength and deformation capacity could be significantly increased when the lateral confinement region was calculated based on irregularly shaped walls, regardless of the design parameters. In addition, the effective compression zone was located within the lateral confinement region. Thus, it is recommended that the lateral confinement region of T-shaped walls is calculated by addressing the irregularity of the walls.

This study investigated the properties of starch isolated from Betaone barley (high β-glucan content of 11.32%), waxy-type Hwanggeumchal barley, and non-waxy type Hyeyang barley using water or 0.2% NaOH. Starch isolated with NaOH contained 4.79% damaged starch, 0.17% protein, 0.01% β-glucan, and 0.20% crude fat, while starch isolated with water contained 2.95%, 1.91%, 0.10%, and 0.31%, respectively. The starch granule were oval in shape, with particle sizes ordered as follows: Betaone (4.9 μm), Hwanggeumchal (8.9 μm), and Hyeyang (13.6 μm). Thermal properties revealed, that gelatinization onset, peak, and end temperatures increased with particle size. NaOH isolation was found to be more effective than water isolation in removing proteins, but resulted in higher levels of damaged starch and partially disrupted the crystalline structure.

Purpose: This mixed methods study aimed to investigate the anxiety and nursing satisfaction levels and experiences among users of a general health checkup center. Methods: A total of 152 participants completed a questionnaire to assess their pre-checkup anxiety and post-checkup nursing satisfaction levels. Additionally, 11 participants were individually interviewed to determine their pre-checkup anxiety and post-checkup nursing satisfaction experiences. Survey data were analyzed using SPSS software, while qualitative data were analyzed using content analysis. Results: The mean anxiety scores were 2.80±2.24 on the Visual Analog Scale for Anxiety and 44.06±9.55 on the State-Trait Anxiety Inventory (STAI) scale. Education level was a significant factor influencing the STAI scores. Participants with a college education or higher had significantly lower STAI scores(p<.005), indicating the association between higher education levels and lower STAI scores. The mean nursing satisfaction score was 36.49±8.84, with male participants reporting higher nursing satisfaction levels. The pre-health checkup anxiety experience included three themes: “contrasting expectations about checkup results,” “various emotions felt during the checkup process,” and “physical and mental reactions.” The health checkup nursing satisfaction experience included four themes: “satisfaction with nurses’ support and care,” “comfort during the checkup process,” “dissatisfaction due to nurses’ habitual responses,” and “expectations for nurses’ emotional support and communication.” Conclusion: Providing comprehensive nursing information is essential to reduce user anxiety and improve nursing satisfaction. Moreover, integrating advanced technologies, such as virtual reality, augmented reality, and metaversing, into information delivery can enhance educational effectiveness and better address the experiences and needs of checkup users.

이 논문은 마틴 루터와 존 웨슬리의 신학방법론들이 어떻게 사회 변혁에 대한 그들의 태도에 영향을 끼쳤는지에 특별히 집중해서 연구한다. 연구자는 신학적 사고를 형성하는 신학방법론의 중요성을 역설하며 루터와 웨슬리를 분석하기 위해 후스토 L. 곤잘레스와 토레 마이스타드의 체계들을 사용한다. 루터의 신학은 창조와 구원을 강하게 분리하며 도덕적 법적 범주들을 우선시한다. 농민전쟁과 같은 사건들이 진행되는 동안의 그의 정치적 보수성에서 보이듯이 이 이원론은 루터의 사회 개혁 동참을 제한했 다. 대조적으로 웨슬리의 신학은 성화와 연대 구원을 강조하면서 보다 더 목회적이고 사회적이다. 원숙한 웨슬리의 구조적 사회 개혁으로의 전환 은, 특별히 1770년대에 입법 수단들을 통해 구조적 가난을 다룬 것은 그의 점증하는 사회적 관심사를 반영한다. 이 논문은 어떻게 신학방법론들 이 사회 정의와 개혁에 관한 루터와 웨슬리의 차이점들을 만들었는지를 강조한다. 이 논문은 궁극적으로 루터의 이원론적 접근보다 원숙한 웨슬리 의 사회적 구조적 변혁을 포함하는 신학적 발전이 현대 신학과 선교를 위한 더 가치 있는 통찰들을 제공한다고 주장한다.