As the transportation paradigm shifts from vehicle-oriented to pedestrian-oriented, active research has been conducted on road designs that consider the safety of pedestrians, cyclists, and personal mobility users. This study aims to respond to this change by developing installation warrant factors and improving the minimum size design standards for triangular islands. This study involved reviewing domestic and international laws and guidelines, analyzing the current installation status of triangular islands, examining case studies of improvements, and assessing policy changes. Based on the findings, important insights were derived, and improvement plans to enhance the safety of pedestrians, vulnerable users, and other road users were proposed. This study identified several issues and confirmed that policies in both domestic and international contexts are shifting towards minimizing or removing the triangular islands. Based on these findings, this study developed 24 factors for installation warrants to determine the installation of triangular islands, such as the design speed and peak-hour volume for pedestrians. In addition, the proposed improvements suggest increasing the minimum size design standards from 9m2 to 22m2 to ensure the safety of users. The factors of installation warrants and improved minimum size design standards proposed in this study are expected to help shift the operation of triangular islands from a vehicle-oriented to a pedestrian-oriented approach.

This paper aims to quantify the retrofit effect of the Bolt Prefabricated Concrete-Filled Tube reinforcement method on non-seismic school reinforced concrete building through static cyclic loading experiments. To achieve the objective, two-story specimens including a non-retrofitted frame(NRF) and a Bolt Prefabricated Concrete-Filled Tube Reinforcement Frame(BCRF) were tested under static cyclic loading, and the lateral resistant capacities were compared in terms of maximum strength, initial stiffness, effective stiffness, and total energy dissipation. In addition, the load-displacement curves were compared to the story drift limit specified in Seismic Performance Evaluation and Retrofit Manual for School Facilities to investigate if the retrofitted frame was satisfied in target performance(life safety). Experimental results showed that BCRF successfully met the target performance, with a 200% increase in maximum strength and a 300% increase in energy dissipation capacity. Additionally, both initial stiffness and effective stiffness improved by more than 30% compared to NRF. Furthermore, BCRF exhibited an effect that delayed the occurrence of bond failure.

This study presents a novel methodology for analyzing disease relationships from a network perspective using Large Language Model (LLM) embeddings. We constructed a disease network based on 4,489 diseases from the International Classification of Diseases (ICD-11) using OpenAI’s text-embedding-3-small model. Network analysis revealed that diseases exhibit small-world characteristics with a high clustering coefficient (0.435) and form 16 major communities. Notably, mental health-related diseases showed high centrality in the network, and a clear inverse relationship was observed between community size and internal density. The embedding-based relationship analysis revealed meaningful patterns of disease relationships, suggesting the potential of this methodology as a novel tool for studying disease associations. Results suggest that mental health conditions play a more central role in disease relationships than previously recognized, and disease communities show distinct organizational patterns. This approach shows promise as a valuable tool for exploring large-scale disease relationships and generating new research hypotheses.

생성형 인공지능의 급속한 발전은 사회 전반에 광범위한 영향을 미치며, 일상생활을 포함한 다양한 분야 에 활용되고 있다. 본 연구에서는 인공지능 기술의 발전 동향을 대규모 언어모델(Large Language Models, LLM)을 중심으로 살펴보고 생성형 인공지능 기반 솔루션이 정치 및 공공 부문의 효율성과 서비스 최적화 에 기여하고 있음을 확인하였다. 본 연구는 미국, 싱가포르, 인도 등의 사례분석을 통해 인공지능 도구가 선거의 확장성과 시민과의 상호작용 개선에 역할 할 수 있다는 것을 주장한다. 동시에, 대규모 언어모델의 실사용 과정에서 제기되는 편향성, 허위정보 확산, 규제 공백 등의 쟁점들을 고찰할 필요가 있음을 지적한 다. 요컨대, 생성형 인공지능은 민주주의 발전과 공공서비스 증진에 대한 가능성을 제공하지만, 기술의 지속 가능하고 적실한 활용을 위해 투명성, 공정성과 책임성을 고려한 사용이 요구된다.

대규모 하천의 수량(river storage) 변동으로 인해 발생하는 지각 변형을 정량적으로 평가하기 위해, GNSS (Global Navigation Satellite System) 기반의 지각 변위 자료와 GRACE (Gravity Recovery and Climate Experiment) 인공위성 중력 자료, 그리고 WaterGAP 수리 모형 산출 자료를 종합적으로 분석하였다. 우리는 아마존 강 유역에 대해 수로에 집중되어 분포하는 하천 수량 변동을 선 형태의 하중으로 모형화하고, 이로부터 유발되는 지각의 탄성 변형을 계산해 GNSS 관측치와 비교하였다. 이를 통해, 이 지역에서 발생하는 계절적 지각 변위 중 하천 수량 변동에 기인하 는 성분을 선 하중 모형으로 성공적으로 설명할 수 있음을 확인하였다. 이러한 결과는 원격 탐사 자료를 활용해 대규 모 하천의 수량 변동을 추정할 수 있을 뿐 아니라, 이를 토대로 GRACE가 관측하는 육상 물 저장량(terrestrial water storage, TWS)에서 토양 수분이나 지하수 변동 등의 개별 요소를 분리 및 검증할 수 있는 방법론을 제시한다. 나아가, 본 연구에서 제안된 접근법은 기후 변화로 인한 수문학적 재해 예측과 수자원 관리 등 다양한 분야에서 더욱 정교한 해석과 활용을 가능하게 할 것으로 기대된다.

본 연구에서는 전지구 해양 예측 모델 결과를 동아시아 지역 해양 모델인 ROMS의 초기 및 경계 조건에 적용 한 역학적 규모 축소 모의 실험을 수행하였다. 우선 ROMS 모델의 성능을 AMOR3D, EN4, 정선 관측 자료, 인공위성 영상 및 기존에 발표된 MOHID 모델과 비교하여 검증하였다. 전반적으로 봄과 가을에는 관측 자료와 잘 일치하였으나, 해양 성층화가 강화되는 여름에는 모델 성능이 저하되는 것을 확인하였다. 또한, 동해와 남해보다 황해에서 더 우수한 성능을 보였으며, MOHID 모델보다 아표층 모의 성능이 개선되었다. RCP 4.5 시나리오를 적용하여 2015년부터 2030 년까지 예측한 CM2.1 전지구 해양 모델의 결과를 사용한 역학적 규모 축소 모의를 수행한 결과, 한반도 남서 연안의 저수온 영역, 황해난류의 경로 및 쿠로시오 해류의 사행 등 실제 해양의 다양한 현상이 잘 재현되었다. 또한, 지역 모델은 저해상도 전구 모델보다 평균 수온의 경년 변동 폭이 커지는 것을 확인하였다. 본 연구를 통해 ROMS를 이용한 역학적 규모 축소 결과의 신뢰성이 확인되었으나, 향후 동해 및 남해와 같은 특정 지역의 ROMS 모델의 모의 성능 개 선과 2030년 이후의 장기 시뮬레이션 연구가 추가로 필요할 것으로 보인다.

본 연구에서는 대류권부터 중층대기까지 전체 대기의 기체상 화학과정을 전지구 규모에서 수치 모의하도록 고 안된 두 가지 대기화학과정(Strattrop와 CRI) 각각을 영국 지구시스템모형(UKESM)에 연동시켜 CMIP6 과거기후 모의 를 수행하였다. 두 대기과학과정에 따른 모의 결과를 재분석자료와 비교하여 기체상 대기화학과정에 따른 전지구시스템 모형의 모의 특성의 변화를 살펴보았다. 단순화된 화학과정인 Strattrop를 기본 장착한 UKESM-Strattrop과 오존 화학과 정을 강화한 CRI 대기화학과정을 연동시킨 UKESM-CRI의 수치 모의는 1981-2010년 약 30년 기간 CMIP6 과거기후 모의이며, 모형의 가동은 CentOS-8 기반 리눅스 클러스터에서 수행되었다. 이 두 모의 실험 결과의 분석은 마지막 10년(2001-2010) 결과만을 이용하였다. 두 모델이 모의한 대류권 지상 기온과 강수량은 전지구 공간 분포와 월별 시계열 의 변동에서 기존에 보고된 결과와 유사하게 나타났고, 대기화학과정에 의한 특징적인 변화는 크게 두드러지지 않았다. 하지만 모델 모의 전지구 평균 기온의 선형 증가율의 경우, UKESM-Strattrop은 ERA5 재분석자료와 비슷한 선형 시간 변화 경향을 보였으나 UKESM-CRI는 더 크게 증가하도록 모의하였다. 에어로졸 광학 두께(AOD)의 공간 분포는 두 모델 모두 사막 지역을 제외하고 MERRA-2 재분석자료와 유사했다. 기체상 화학과정이 강화된 UKESM-CRI는 예상했 던 바와 같이 UKESM-Strattrop 보다 오존전량에서 MSR 재분석자료와 더 유사한 결과를 보였으며, 성층권 오존분포에 서는 MERRA-2 재분석자료에 더 가까운 결과를 보였다. 특히, 적도 성층권에서 나타나는 준격년진동(QBO) 현상과 QBO와 연관된 적도 성층권 오존 농도의 증가와 감소 현상의 모의는 UKESM-CRI가 UKESM-Strattrop 보다 더욱 잘 일치하였다. 이를 통해 보다 정교한 전지구규모 대기화학과정의 도입은 대기 조성 물질의 수치 모의 성능을 향상시키며, 더 나아가 중층대기 Brewer-Dobson 순환(BDC)의 모의에 도움이 됨을 확인할 수 있다.

This study developed and tested a pilot-scale biowindow for simultaneous removal of odor and methane from landfills. The test was conducted in a sanitary landfill site during the summer season (July and August). The average temperature inside the biowindow was 5°C higher than the average air temperature, rising to 37–48oC when the outdoor temperature was very hot. The complex odor removal rate (based on the dilution-to-threshold value) in the biowindow during the summer was 91.3- 98.8% (with an average of 96.2±4.2%). The average concentration of hydrogen sulfide was 3,024.9±805.8 ppb, and its concentration was found to be the highest among 22 odorous compounds. The removal efficiencies of hydrogen sulfide and methyl mercaptan were 89.1% and 83.2%, respectively. The removal of dimethyl sulfide was 17.7%, and no ammonia removal was observed. Additionally, the removal efficiencies of toluene and xylene were 85.2% and 72.5%, respectively. Although the initial methane removal was low (24.9%), the methane removal performance improved to 53.7–75.6% after the 11th day of operation. These results demonstrate that the odor and methane removal performance of the pilot-scale biowindow was relatively stable even when the internal temperature of the biowindow rose above 40oC in the summer. Since the main microorganisms responsible for decomposing odor and methane are replaced by thermotolerant or thermophilic microorganisms, and high community diversity is maintained, odor and methane in the biowindow could be stably removed even under high-temperature conditions.

The objective of this study is to identify the priority of elements for effective implementation of MES in small and medium-sized manufacturing enterprises trying to develop into smart factories. For this purpose, the Delphi method and the Analytic Hierarchy Process(AHP) mothod are applied. As a result of the study, the cooperation of the members in the supply chain is the most important factor for small and medium-sized enterprises in order to survive in the global competitive environment. Therefore, the enterprises need to make various efforts to create synergies through the technical strength of suppliers and the cooperation in the process of introducing and operating MES.

PURPOSES : This study analyzes the accident damage scale of hazardous material transportation vehicles not monitored in real time by the Hazardous Material Transportation Safety (HMTS) management center. METHODS : To simulate hazardous-material transportation vehicle accidents, a preliminary analysis of transportation vehicle registration status was conducted. Simulation analyses were conducted for hazardous substance and flammable gas transportation vehicles with a high proportion of small- and medium-sized vehicles. To perform a spill accident damage-scale simulation of hazardous-substance transportation vehicles, the fluid analysis software ANSYS Fluent was used. Additionally, to analyze explosion accidents in combustible gas transportation vehicles, the risk assessment software Phast and Aloha were utilized. RESULT : Simulation analysis of hazardous material transportation vehicles revealed varying damage scales based on vehicle capacity. Simulation analysis of spillage accidents showed that the first arrival time at the side gutter was similar for various vehicle capacities. However, the results of the cumulative pollution analysis based on vehicle capacity exhibited some differences. In addition, the simulation analysis of the explosion overpressure and radiant heat intensity of the combustible gas transportation vehicle showed that the difference in the danger radius owing to the difference in vehicle capacity was insignificant. CONCLUSIONS : The simulation analysis of hazardous-material transportation vehicles indicated that accidents involving small- and medium-sized transportation vehicles could result in substantial damage to humans and ecosystems. For safety management of these small and medium-sized hazardous material transportation vehicles, it is expected that damage can be minimized with the help of rapid accident response through real-time vehicle control operated by the existing HMTS management center.

This study introduces and experimentally validates a novel approach that combines Instruction fine-tuning and Low-Rank Adaptation (LoRA) fine-tuning to optimize the performance of Large Language Models (LLMs). These models have become revolutionary tools in natural language processing, showing remarkable performance across diverse application areas. However, optimizing their performance for specific domains necessitates fine-tuning of the base models (FMs), which is often limited by challenges such as data complexity and resource costs. The proposed approach aims to overcome these limitations by enhancing the performance of LLMs, particularly in the analysis precision and efficiency of national Research and Development (R&D) data. The study provides theoretical foundations and technical implementations of Instruction fine-tuning and LoRA fine-tuning. Through rigorous experimental validation, it is demonstrated that the proposed method significantly improves the precision and efficiency of data analysis, outperforming traditional fine-tuning methods. This enhancement is not only beneficial for national R&D data but also suggests potential applicability in various other data-centric domains, such as medical data analysis, financial forecasting, and educational assessments. The findings highlight the method's broad utility and significant contribution to advancing data analysis techniques in specialized knowledge domains, offering new possibilities for leveraging LLMs in complex and resource- intensive tasks. This research underscores the transformative potential of combining Instruction fine-tuning with LoRA fine-tuning to achieve superior performance in diverse applications, paving the way for more efficient and effective utilization of LLMs in both academic and industrial settings.

There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune’s source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.

The purpose of this study intends to development of a lap scale 1-ton standard combustion chamber. The manufactured standard combustion chamber analyzes pilot combustion tests and emission standard data of MGO fuel oil. The actual capacity of the standard combustion chamber is about 900L, total weight of 265kg. As a result of the pilot combustion test, the O2 was about 8.01% and the CO2 was about 9.34%. In the case of NOx, it was about 33.50 ppm, and SOx (SO2) was about 0.76ppm. The combustion efficiency was about 72.41%, the exhaust gas temperature was 366.7℃, and the combustion chamber internal temperature was about 448.0℃.

본 연구는 2020년 10월 중 충청남도내 단체 급식소에서 발생한 대규모 집단 식중독 원인에 대하여 분석하였다. 전 체 급식원 135명 중 21명(15.6%)에서 음식을 섭취한 후 1 시간 이내에 주로 매스꺼움과 구토 증상을 보였다. 유증 상자 21명 중 11명과, 조리종사자 1명, 조리기구 2건과 보 존식 8건에서 B. cereus가 검출됨에 따라 B. cereus에 의 한 집단 식중독으로 판단하였다. 분리된 21개의 균주를 PFGE 분석한 결과, 19개의 균주가 동일한 것으로 판단되 었고, 이들 균주가 가지고 있는 독소 유전자는 CER, nheA, entFM이었다. 실험결과, CER을 포함하고, 증상 발현 시간 이 1시간 이내로 매우 짧아 B. cereus의 구토형 식중독으 로 판단하였다. 집단식중독 원인으로 안전하지 않은 급식 환경과 제대로 관리되지 않은 음식에 의한 것이라 조사되 었다. 이러한 결과는 단체급식에서의 급식환경과 제공되 는 음식이 철저하게 관리되어야 한다는 것을 보여준다. 이 와 더불어 보존식에서 원인 병원체를 찾아내는 것은 식중 독의 원인을 추정하는데 매우 중요하므로, 집단급식소에 서 규정에 맞는 보존식 용기를 이용하여 이를 적정온도에 잘 보관해야한다. 또한 정밀한 식중독 역학조사를 기반으 로 사례를 분석하고 결과를 전파함으로써 유사한 식중독 이 재발하지 않도록 해야 한다.

Recently, ESG management has become a global trend, receiving increasing attention from stakeholders such as consumers, investors, and governments, as regulations related to ESG disclosure and supply chain due diligence have been strengthened since the United Nations Principles of Responsible Investment (UN PRI) was announced in 2006. ESG is an acronym for the environment (E), social (S), and governance (G) and is accepted as a key factor for the continuous survival and growth of a company. As a result, there are over 600 ESG management evaluation indicators operated domestically and internationally, and numerous global initiatives have emerged. Korea’s Ministry of Trade, Industry and Energy also announced “K-ESG Guidelines (December 2011)” and “K-ESG Guidelines for Supply Chain Response (December 22)” to help SMEs introduce ESG management and respond to supply chain due diligence. However, small-scale manufacturing companies with poor financial, human resources, and technological capabilities face significant challenges in introducing ESG management. Accordingly, this study aims to examine the current status of ESG management adoption in small-scale manufacturing companies with less than 150 people in Korea and propose activation plan ESG management based on the diagnostic requirements of the “Supply Chain Response K-ESG Guidelines.”