Ischemic stroke causes severe neuronal damage. Chlorogenic acid is a phenolic substance present in fruits and coffee. It also exerts neuroprotective effects against various brain injuries. The 14-3-3 family protein perform a variety of functions including metabolism, signal transduction, cell differentiation, and apoptosis. The purpose of this study is to investigate whether chlorogenic acid regulates the expression of 14-3-3 protein in stroke animal models. Ischemic stroke was induced by middle cerebral artery occlusion (MCAO) surgery. Phosphate buffered saline (PBS) or chlorogenic acid (30 mg/kg) were intraperitoneally injected to adult male rats 2 h before MCAO surgery. Adhesive-removal test was performed 24 h after MCAO surgery and cerebral cortical tissues were collected for further study. MCAO damage caused severe neurological impairment and chlorogenic acid treatment ameliorated this disorder. Our proteomic approach showed a decrease in 14-3-3 expression in MCAO animals with PBS. The decrease in 14-3-3 expression alleviated in MCAO animal with chlorogenic acid. We confirmed changes in various 14-3-3 protein isoforms, including beta/alpha, zeta/delta, gamma, epsilon, eta, and tau through reverse transcription-PCR. These results explained that chlorogenic acid regulates the expression of 14-3-3 protein in MCAO-induced cerebral ischemia. 14-3-3 is considered to be an important protein for cell survival through binding to pro-apoptotic proteins. The maintenance of 14-3-3 levels is an important event in neuroprotection against ischemic injury. Therefore, we can demonstrate that the 14-3-3 protein contributes to the neuroprotective effect of chlorogenic acid in stroke animal models.

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

목적 : 본 연구의 목적은 Coronavirys Disease(COVID-19) 팬데믹을 거친 고령자의 사회적 건강의 수준의 변화궤적에 대한 잠재계층을 분류하고 잠재 계층별 특성을 분석함에 있다. 또한 이러한 사회적 건강에 영향을 미치는 예측요인을 파악하여 고령자의 사회적 건강을 증진을 위한 기초자료를 마련하고자 한다. 연구방법 : 국내 고령자의 사회적 친밀도에 따른 사회적 건강 유형을 파악하기 위해서 한국복지패널의 3 년차 종단자료를 토대로 분석하였으며, 연구대상자는 세 시점 모두를 응답한 2845명의 고령자를 대상으 로 하였다. 대상자중심접근인 성장혼합모형(Growth Mixture Model; GMM)을 적용하여 변화궤적에 따 른 잠재계층을 분석 하였고, 도출된 각 잠재유형별 특성을 파악하기 위해 χ2 분석, 분산분석을 실시하 였으며, 계층 간 차이를 유발하는 요인을 파악하기 위해 다항로지스틱 회귀분석을 실시하였다. 결과 : GMM 적용결과, 사회적 건강의 변화궤적에 대한 잠재계층은 최종 4개의 집단으로 저수준 감소-증 가 집단, 중수준 유지-증가 집단, 고수준-감소 집단, 고수준 유지’집단으로 분류되었다. 또한 사회적 건강 수준에 따라 여가만족도에서 차이가 나타나는 것으로 드러났으며, 그 외에도 연령 차이가 존재하였 다. 잠재계층분류에 영향을 미치는 영향변인을 검증한 결과, 특히 여성일수록, 종교를 가지고 있을수록, 여가만족도와 전반적 만족도가 모두 높을수록 고수준 유지 집단에 속할 확률이 높은 것으로 나타났다. 결론 : 국내 고령자의 사회적 건강은 시간이 지남에 따라 감소하는 궤적을 보이는 것으로 나타났다. 변화 궤적에 따라 4개의 집단으로 구분 지을 수 있으며, 각 잠재 유형별 연령과 여가 만족도 부분에서 집단별 차이가 드러났다.

With the recent advent of Metaverse, the character industry that reflects the characteristics of users' faces is drawing attention. there is a hassle that users have to select face components such as eyes, nose, and mouth one by one. In this paper, we propose a diffusion-based model that automatically generates characters from content human photographs. Our model generates user artistic characters by reflecting content information such as face angle, direction, and shape of a content human photo. In particular, our model automatically analyzes detailed information such as glasses and whiskers from content photo images and reflects them in artistic characters generated. Our network generates the final character through a three-step: diffusion process, UNet, and denoising processes. We use image encoders and CLIP encoders for the connection between style and input data. In the diffusion process, a collection of noise vectors is gradually added to a style vector to enable lossless learning of the detailed styles. All input values except for the style images are vectorized with CLIP encoders and then learned with noise style vectors in the UNet. Subsequently, noise is removed from the vectors through the UNet to obtain the artistic character image. We demonstrate our performance by comparing the results of other models with our results. Our method reflects content information without loss and generates natural high-definition characters.

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure’s safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

물질주의는 다양한 사회적 사건에 의해서 촉발될 수 있다. 본 연구에서는 물질주의를 정체성과 관련된 목표 달성 이라고 개념화하는 이론을 기반으로 사회적 배제가 자기 정체성과 관련된 욕구를 불러일으키며 이는 물질주의 수준 의 상승으로 이어질 것이라고 가정하였다. 구체적으로, 사회적 배제가 소속의 욕구와 통제의 욕구를 상승시켜 물질 주의를 상승시킬 것이라고 예상하였다. 또, 권력감의 접근 경향성을 기반으로 권력감이 높은 사람은 낮은 사람에 비해서 사회적 배제를 경험할 때 소속의 욕구와 통제의 욕구가 더 커질 것으로 예상하였다. 20 ̴30대 한국 여성 202 명을 대상으로 실험 연구를 진행한 결과, 사회적 배제는 통제 욕구의 상승을 통해서 물질주의를 상승시켰다. 또, 권 력감이 높은 사람의 경우, 권력감이 낮은 사람들보다 사회적 배제가 통제 욕구에 미치는 영향이 더 강해졌으며, 사회 적 배제가 통제 욕구를 통해 물질주의로 이어지는 경로는 권력이 높은 사람들에게만 유의했다. 사회적 배제는 소속 의 욕구를 상승시켰으나 소속의 욕구와 물질주의의 관계는 유의하지 않았고, 소속의 욕구의 매개 효과도 지지되지 않았다. 해당 결과를 기반으로 사회적 배제와 물질주의의 관계 및 연구의 한계에 대해 논의하였다.

In order to solve the rapidly increasing domestic delivery volume and various problems in the recent metropolitan area, domestic researchers are conducting research on the development of “Urban Logistics System Using Underground Space” using existing urban railway facilities in the city. Safety analysis and scenario analysis should be performed for the safe system design of the new concept logistics system, but the scenario analysis techniques performed in previous studies so far do not have standards and are defined differently depending on the domain, subject, or purpose. In addition, it is necessary to improve the difficulty of clearly defining the control structure and the omission of UCA in the existing STPA safety analysis. In this study, an improved scenario table is proposed for the AGV horizontal transport device, which is a key equipment of an urban logistics system using underground space, and a process model is proposed by linking systematic STPA safety analysis and scenario analysis, and UCA and Control Structure Guidelines are provided to create a safety analysis.

As the first step of risk management, risk identification is inevitable to understand the degree of work safety. However, the safety requirements can be divided in necessary factors and additional factors. Thus, we propose a safety requirements assessment model using Kano model derived from Herzberg’s two-factor theory, classifying safety requirements into ideal elements and must-be elements. The Kano model is usually applied to evaluate customer satisfaction divided into three major requirements in the fields of product development and marketing: attractive, must-be, and one-dimensional requirements. Among them, attractive requirement and must-be requirement are matched with ideal element and must-be element for safety requirement classification, respectively. The ideal element is defined as preventive safety elements to make systems more safe and the must-be element is referred to as fatal elements to be essentially eliminated in systems. Also, coefficients of safety measurement and safety prevention are developed to classify different class of safety requirements. The positioning map is finally visualized in terms of both coefficients to compare the different features. Consequently, the proposed model enables safety managers to make a decision between safety measurement and prevention.

This study proposes a methodology for assessing seismic liquefaction hazard by implementing high-resolution three-dimensional (3D) ground models with high-density/high-precision site investigation data acquired in an area of interest, which would be linked to geotechnical numerical analysis tools. It is possible to estimate the vulnerability of earthquake-induced geotechnical phenomena (ground motion amplification, liquefaction, landslide, etc.) and their triggering complex disasters across an area for urban development with several stages of high-density datasets. In this study, the spatial-ground models for city development were built with a 3D high-precision grid of 5 m x 5 m x 1 m by applying geostatistic methods. Finally, after comparing each prediction error, the geotechnical model from the Gaussian sequential simulation is selected to assess earthquake-induced geotechnical hazards. In particular, with seven independent input earthquake motions, liquefaction analysis with finite element analyses and hazard mappings with LPI and LSN are performed reliably based on the spatial geotechnical models in the study area. Furthermore, various phenomena and parameters, including settlement in the city planning area, are assessed in terms of geotechnical vulnerability also based on the high-resolution spatial-ground modeling. This case study on the high-precision 3D ground model-based zonations in the area of interest verifies the usefulness in assessing spatially earthquake-induced hazards and geotechnical vulnerability and their decision-making support.

In this paper, a dynamic centrifuge model test was conducted on a 24.8-meter-deep excavation consisting of a 20 m sand layer and 4.8 m bedrock, classified as S3 by Korean seismic design code KDS 17 10 00. A braced excavation wall supports the hole. From the results, the mechanism of seismically induced earth pressure was investigated, and their distribution and loading points were analyzed. During earthquake loadings, active seismic earth pressure decreases from the at-rest earth pressure since the backfill laterally expands at the movement of the wall toward the active direction. Yet, the passive seismic earth pressure increases from the at-rest earth pressure since the backfill pushes to the wall and laterally compresses at it, moving toward a passive direction and returning to the initial position. The seismic earth pressure distribution shows a half-diamond distribution in the dense sand and a uniform distribution in loose sand. The loading point of dynamic thrust corresponding with seismic earth pressure is at the center of the soil backfill. The dynamic thrust increased differently depending on the backfill's relative density and input motion type. Still, in general, the dynamic thrust increased rapidly when the maximum horizontal displacement of the wall exceeded 0.05 H%.