Non-seismic-designed reinforced concrete (RC) pier walls often include lap splices in potential plastic hinge regions. This study develops an analytical model to capture the post-yield load–deformation response of lap-spliced RC pier walls subjected to earthquake loading. The parameters of the model were calibrated using experimental results, and linear regression was conducted to propose predictive equations for these parameters. The accuracy of the model was validated by comparing it to the load–deformation responses of specimens not included in the calibration database. Subsequently, the developed model was applied to probabilistic bridge models supported by RC pier walls. A multi-parameter seismic demand model was constructed, taking into account geometric, material, and structural uncertainties, using Lasso regression. This model achieved R² values of 0.73 for in-plane loading and 0.75 for out-of-plane loading. The improvements in performance metrics and the results of the sensitivity analysis emphasize the need to develop a multi-parameter seismic demand model to ensure more reliable seismic demand predictions.

With a view towards reducing traffic accidents on roadways, various methods have been considered to predict accidents. In this study, we analyze traffic accident frequency models that employ fixed- and random-parameter negative binomial approaches. Random parameters enable the inclusion of unobserved heterogeneity in traffic accident data, which current popular methods with fixed parameters such as Poisson or negative binomial models cannot consider in terms of time variation or segment-specific effects. A continuous, unbalanced panel of accident histories for 208 four-way signalized intersections on national highways in Seoul was used to estimate a traffic accident occurrence model that considered traffic volumes and various geometric characteristics at intersections. The results revealed that the left-turn exclusive lanes and traffic volumes on minor roads had random parameters that affected the likelihood of accident frequencies differently; the other variables were found to significantly affect traffic safety at the intersections on the national highways as fixed parameters. Based on these results, it can be concluded that the same traffic safety facilities have different effects on traffic accidents on major and minor roads. The insights from this study suggest the need for a broader analysis of integrated guidelines for facilities that impact intersection accident propensities.

Wearable thermoelectric devices offer a transformative approach to energy harvesting, providing sustainable solutions for powering next-generation technologies. In pursuit of efficient, flexible, biocompatible, and cost-effective thermoelectric materials, zinc oxide (ZnO) has emerged as a distinctive candidate due to its unique combination of favorable properties. This study explores the growth and optimization of ZnO nanorods on conductive carbon fabric (CF) using a simple microwave-assisted solvothermal technique. This method circumvents traditional complex processes that typically involve high temperatures or lengthy growth times, offering advantages such as rapid, uniform, and controllable volumetric heating. By systematically varying growth parameters, including microwave power and reaction time, we established conditions that promote the vertical alignment of ZnO nanorods, essential for enhancing thermoelectric performance. Structural and morphological analyses highlight the pivotal influence of the seed layer and growth parameters in achieving dense, uniform growth of ZnO nanorods. Interestingly, at higher microwave power levels, a transformation from nanorod structures to sheetlike morphologies was observed, likely due to Ostwald ripening, where larger particles grow at the expense of smaller ones. The optimized growth conditions for achieving superior growth and thermoelectric performance were identified as 15 min of growth at 100 W microwave power. Under these conditions, ZnO nanorods exhibited enhanced crystallinity and a higher growth rate, contributing to an improved thermoelectric power factor of 777 nW/mK2 at 373 K. This work underscores the importance of precise parameter control in tailoring ZnO nanostructures for wearable thermoelectric applications and demonstrates the potential of scalable, low-cost methods to achieve high-performance energy-harvesting materials.

목적 : 본 연구는 안경 처방에 필요한 매개변수를 분석하여 정확하고 편안한 안경 처방을 위한 객관적 자료를 제시하는 것이다. 방법 : 20세 이상 성인 41명(남자: 28명, 여자: 13명)을 대상으로 사진, 트르뷰 아이, 안경자 및 스마트 센터링 장치 계측법을 이용하여 단안 동공 거리, 동공간거리, 광학중심점높이, 경사각, 안면각 및 정점간거리를 계측하고, 사진 계측법을 표준으로 정확도(오차)를 비교하였다. 또한, 오차 보정 식을 통하여 임상에서 적용하기 위한 보정 값 을 산출하였다. 결과 : 4가지 장비의 계측법을 비교했을 때 광학중심점높이와 경사각, 안면각 및 정점간거리는 유의한 차이가 있었다(p<0.050). 사진 계측법을 표준으로 한 보정 값에서 트르뷰 아이는 단안 동공 거리, 동공간거리, 광학중심점 높이, 경사각, 안면각 및 정점간거리의 상관계수가 높고 통계적으로 유의하였다(p<0.050). 안경자는 단안 동공 거 리, 동공간거리, 광학중심점높이 및 정점간거리는 상관계수가 높고 통계적으로 유의하였다(p<0.050). 스마트 센터 링 장치는 안면각에서 통계적으로 유의하였다(p<0.050). 보정 값에 대한 95% 신뢰구간은 단안 동공 거리에서 스마 트 센터링 장치가 좁은 범위와 적은 편차를 보였고, 다른 모든 매개변수는 트르뷰 아이에서 좁게 나타났고, 평균값 의 편차도 가장 적었다. 결론 : 4가지 측정 장비를 이용하여 안경 처방에 필요한 매개변수를 측정한 결과 측정 방법에 따라 차이가 있었 고, 임상에서 사진 측정값을 대신하여 적용할 수 있는 보정 값을 제시하였다.

본 연구는 강내탄도 해석 코드 IBHVG2(Interior Ballistics of High Velocity Guns, version 2)를 이용하여 40mm L/70 포신을 가진 무 기체계에서 발사체의 출구 속도와 약실 압력을 해석하고, 시험 결과와 비교를 수행하였다. 집중 매개변수 모델(Lumped Parameter Model)을 기반으로 한 Chambrage 모델을 적용하여 탄환 속도와 약실 압력 해석을 수행하였고 시험은 총 10회를 수행하였다. 해석 인 자로 들어간 추진제의 반응률 계수는 폐쇄 폭발 실험(Closed Bomb Test)를 통해 추정하였다. 해석과 시험을 비교한 결과 탄환의 출구 속도는 약 6% 정도 오차를, 약실 최대 압력은 8% 정도의 오차를 보였다. 결론적으로 IBHVG2 모델의 유효성과 예측 정확도를 확인하 였으며, 개선의 여지가 있음을 확인하였다.

본 연구에서는 전하 이동 특성을 가지는 분자[쿠마린(C)-DNP]의 흡수 스펙트럼을 정확하게 예측하기 위해 장거 리 보정 밀도 범함수 이론 (long-range corrected density functional theory, LC-DFT)인 LC-BLYP의 범위 분리 매개변수 (μ)를 여러 가지 피팅 방법을 이용하여 최적화하였다. 기체 상태의 Koopmans 이론을 기반으로 최적화된 μ값은 실험적 흡수 피크에 비해 청색 이동(blue-shift)되는 경향성을, 반대로 용매 환경에서 최적화된 μ값은 과도하게 적색 이동 (red-shift)이 되는 경향성을 보였다. 반면, 실험적 데이터에 맞춰 조정된 μ값은 흡수 스펙트럼의 피크 위치와 세기를 가 장 고정확도로 재현하였으며, 특히 C-DNP와 C-OH 분자에서 나타나는 최대 흡수 피크 에너지의 차이를 잘 예측하였 다. C-DNP의 HOMO와 LUMO 전자 분포는 모든 μ값에서 일정한 모양(shape)을 가지고 있었으며, HOMO에서 LUMO 의 전이는 C에서 DNP로의 분자 내 전하 이동(Intramolecular Charge Transfer, ICT)임을 보였다.

This paper chronicles the evolution of load-sharing parameter estimation methodologies, with a particular focus on the significant contributions made by Kim and Kvam (2004) and Park (2012). Kim and Kvam's pioneering work underscored the inherent challenges in deriving closed-form solutions for load-share parameters, which necessitated the use of sophisticated numerical optimization techniques. Park's research, on the other hand, provided groundbreaking closed-form solutions and extended the theoretical framework to accommodate more general distributions of component lifetimes. This was achieved by incorporating EM-type methods for maximum likelihood estimation, which represented a significant advancement in the field. Unlike previous efforts, this paper zeroes in on the specific characteristics and advantages of closed-form solutions for load-share parameters within reliability systems. Much like the basic Economic Order Quantity (EOQ) model enhances the understanding of real-life inventory systems dynamics, our analysis aims to thoroughly explore the conditions under which these closed-form solutions are valid. We investigate their stability, robustness, and applicability to various types of systems. Through this comprehensive study, we aspire to provide a deep understanding of the practical implications and potential benefits of these solutions. Building on previous advancements, our research further examines the robustness of these solutions in diverse reliability contexts, aiming to shed light on their practical relevance and utility in real-world applications.

Mathematically modeling photosynthesis helps to interpret gas exchange in a plant and estimate the photosynthetic rate as affected by environmental factors. Notably, the photosynthetic rate varies among leaf vertical positions within a single plant. The objective of this study was to measure the distinct photosynthetic rate of lily (Lilium Oriental Hybrid ‘Casa Blanca’) at the upper, medium, and basal leaf positions. Subsequently, the FvCB (Farquhar-von Caemmerer-Berry) photosynthesis model was employed to determine the parameters of the model and compared it with a rectangular hyperbola photosynthesis model. The photosynthetic rates were measured at different intracellular CO2 concentrations () and photosynthetic photon flux density (PPFD) levels. SPAD values significantly decreased with lowered leaf position. The photosynthetic rates at the medium and basal leaves were lower compared with the upper leaves. FvCB model parameters,  and   , showed no significant difference between the medium and basal leaves. Estimated photosynthetic rates from derived parameters by the FvCB model demonstrated over 0.86 of R2 compared with measured data. The rectangular hyperbola model tended to overestimate or underestimate photosynthetic rates at high  with high PPFD levels or low  with high PPFD levels, respectively, at each leaf position. These results indicated that the parameters of the FvCB model with different leaf positions can be used to estimate the photosynthetic rate of lily.

Liquefied hydrogen is attracting attention as an energy source of the future due to its hydrogen storage rate and low risk. However, the disadvantage is that the unit price is high due to technical difficulties in production, transportation, and storage. This study was conducted to improve the design accuracy and development period of needle valves, which are important parts with a wide technical application range among liquefied hydrogen equipment. Since the needle valve must discharge an appropriate flow rate of the liquefied fluid, it is important to determine the needle valve design parameters suitable for the target flow rate. Computational Fluid Dynamics and Artificial Neural Network technology used to determine the design variables of fluid flow were applied to improve the setting and analysis time of the parameter. In addition, procedures and methods for applying the design parameter of needle valves to Convolutional Neural Networks were presented. The procedure and appropriate conditions for selecting parameters and functional conditions of the Convolutional Neural Network were presented, and the accuracy of predicting the flow coefficient according to the design parameter was secured 95%. It is judged that this method can be applied to other structures and machines.

신뢰성 있는 토양의 이산요소모델을 개발하기 위해서는 토양의 특성을 고려하여 매개변수를 교정해야 한다. 본 연구에서는 이산요소모델을 구성하는 각 매개변수가 토양 입자의 거동에 미치는 영향을 분석하였고, 분석된 결과를 이용하여 토양의 이산요소모델을 개발하였다. 민감도 분석의 대상이 되는 매개변수는 전단 계수, 마찰 계수, 표면 에너지 등으로 선정하였으며, 교정의 기준이 되는 토양의 특성은 가비중, 안식각, 점착력 및 내부마찰각으로 선정하였다. 또한, 토성이 서로 다른 해안가, 논 및 밭을 구성하는 토양을 대상으로 연구를 수행하여 다양한 토성에 대한 적용성을 확인하였다. 결과적으로 본 연구에서 수행한 민감도 분석 결과를 이용하여 각 토양의 거동을 모사할 수 있는 이산요소모델을 교정하였으며, 시험 결과와의 비교를 통해 교정된 이산요소모델을 검증하였다.

The study used the whole-life carbon assessment method to conduct a thorough carbon-neutral evaluation of a standard steel structure. To further assess carbon emissions, 11 design-changed models were evaluated, with changes made to the span between beams and columns. The results of the carbon emission assessment showed savings of approximately 13.1% by implementing the stage of the beyond life cycle. Additionally, the evaluation of carbon emissions through design changes revealed a difference of up to 42.2%. These findings confirmed that recycling and structural design changes can significantly reduce carbon emissions by up to 48.6%, making it an effective means of achieving carbon neutrality. It is therefore necessary to apply the stage of beyond life cycle and structural change to reduce carbon emissions.