염화동 에칭 공정에서 발생한 철염 폐수를 전구체로 활용하여 마그네타이트(Fe3O4)를 합성하고, 이를 인산염 흡착 및 회수에 적용하였다. 합성 조건 최적화를 위하여 Box–Behnken Design를 적용한 반응표면분석법(Response Surface Methodology, RSM)을 활용하여 회귀모델을 구축하였다. 모델을 통해서 도출한 최적 합성 조건은 Fe3+/Fe2+ 비율 1.7, NaOH 농도 0.7 N, 숙성 시간 86.3분으로 확인되었다. 해당 조건에서 합성된 마그네타이트는 10.9 mg-P g-1 마그네타이트의 인산염 흡착 용량을 나타내었다. 기기 분석 결과, 최적화된 마그네타이트는 고순도 결정 구조와 초상자성 특성을 나타냈으며, 비표면적과 반응성이 향상된 것이 확인되었다. 또한 연속 회분식 반응조(Sequencing Batch Reactor, SBR)에 적용한 결과, 5회 반복 흡착–탈착 동안 평균 인산염 회수율은 46.6%로 나타났다. 유입 인산염 농도가 200 mg-P L-1의 고농도 조건에서도 회수율이 안정적으로 유지되어, 마그네타이트가 인산염 흡착제로서의 활용 가능성과 안정성을 입증하였다.

Existing reinforced concrete building structures have seismically-deficient details on columns and beam–column joints; therefore, accurate modeling of structural behavior is required for reliable seismic performance assessment. This study aims to investigate the differences in dynamic responses resulting from modeling variations through developing four distinct numerical models. Separate models were established to simulate flexural and shear failures of columns and beam–column joints. Using these component-level models, a structural analysis model of the target building was constructed, and nonlinear time-history analyses were performed to evaluate seismic performance. Based on the simulated dynamic behavior of the target building, soft-story mechanisms were identified, and it was identified and confirmed that column behavior plays a dominant role in governing the overall structural response.

This study proposes empirical formulas for predicting the nonlinear behavior of GIR beam-to-column connections in timber structures to evaluate their structural performance. A database comprising 59 experimental results of GIR connections was collected, and the normality of data distribution was verified. Statistical analysis were conducted to investigate the correlations between input and output parameters. Based on input parameters with high correlation, derived variables were formulated and utilized in a multiple regression analysis to develop empirical formulas for moment capacity and rotation. The R-squared values of the proposed formulas exceeded 0.9, and the predicted initial stiffness and strength closely matched those of experimental results not used in the regression analysis. So the suggested empirical formulas exhibit excellent predictive performance for the nonlinear behavior of GIR beam-to-column connections in timber structures.

Seismically deficient reinforced concrete(RC) structures experience reduced structural capacity and lateral resistance due to the increased axial loads resulting from green retrofitting and vertical extensions. To ensure structural safety, traditional performance assessment methods are commonly employed. However, the complexity of these evaluations can act as a barrier to the application of green retrofitting and vertical extensions. This study proposes a methodology for rapidly calculating the allowable axial force range of RC buildings by leveraging simplified structural details and seismic wave information. The methodology includes three machine-learning-based models: (1) predicting column failure modes, (2) assessing seismic performance under current conditions, and (3) evaluating seismic performance under amplified mass conditions. A machine learning model was specifically developed to predict the seismic performance of an RC moment frame building using structural details, gravity loads, failure modes, and seismic wave data as input variables, with dynamic response-based seismic performance evaluations as output data. Classifiers developed using various machine learning methodologies were compared, and two optimal ensemble models were selected to effectively predict seismic performance for both current and increased mass scenarios.

Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

This study investigated the correlation between compound malodor and total hydrocarbons (THC) to evaluate the potential use of THC as a predictor of compound malodor. A total of 87 samples were analyzed from five target facilities: two petrochemical manufacturing facilities (A, B), a wastewater treatment facility (C), a recycled plastic injection molding facility (D), and a surfactant manufacturing facility (E). The correlation coefficients of compound malodor and THC for each facility were as follows: A: 0.6698, B: 0.8068, C: –0.2767, D: 0.2071, and E: 0.7695. The correlation coefficient for all facilities was 0.5634, indicating a weak correlation. The coefficients of determination for the regression analysis to predict the compound malodor for facilities A, B, and E were 0.4093, 0.6316, and 0.5695, respectively, which validated the results of the correlation analysis. These values improved to 0.8394, 0.6941, and 0.7476 in the multiple regression analysis with the VOC analysis results added as independent variables. Therefore, it is expected that THC measurement that considers the characteristics of the facility can be used to establish a systematic odor management plan.

본 연구에서는 baird paker agar (BPA)와 MC-media pad SA (MMPSA)의 황색포도상구균 검출효율과 정성 및 정 량 정확도를 비교하여 황색포도상구균 건조필름의 사용가 능성을 평가하였다. 본 실험에 사용된 재료는 마카롱(30건), 편육(30건) 및 김밥(30건), 총 90건을 실험 재료로 사용하 였다. 유통식품 90건의 황색포도상구균 검출효율 실험결과, BPA와 MMPSA의 검출효율은 48건(53.3%)으로 동일 하게 검출되었다. 또한 정량 정확도는 BPA와 MMPSA에 서 각각 2.0±0.8, 2.0±0.7 log CFU/g로 유의적인 차이가 나 타나지 않았다. 황색포도상구균의 정성 정확도 실험결과, BPA의 경우 97.7%, MMPSA의 경우 96.4%로 산출되어 유 의적인 차이가 나타나지 않았다. 황색포도상구균의 검출효 율, 정성과 정량 정확도 실험결과, BPA와 MMPSA 두 배지 에서 유의적인 차이가 나타나지 않아 황색포도상구균 정성 과 정량실험 시 MMPSA도 사용이 가능할 것으로 판단된다.

The world population is rapidly increasing; over the past 40 years, it has risen by 3.3 billion, reaching 7.8 billion people. Globally, Solanaceae plants contribute more than 6.5 million tons of food to the world’s diet, with potatoes being the fourth most produced crop, cultivated in over 300 countries. However, caution is necessary when consuming plants from the Solanaceae family, as they contain toxic substances known as alkaloids. While alkaloids can offer beneficial effects, such as antioxidants, anti-inflammatory compounds, and anti-cancer properties, tolerance to these compounds varies among individuals, meaning even very small amounts can have fatal effects.Among the major crops in the Solanaceae family, potatoes contain solanine and chaconine; tomatoes contain tomatine; and eggplants contain solasonine and solamargine. The concentration of these substances varies depending on the part of the plant, its developmental stage, and its variety. Additionally, levels can increase significantly due to environmental stress. The environment profoundly impacts the synthesis of secondary metabolites related to survival and defense. Research has confirmed that environmental conditions-such as high-temperature cultivation, low-temperature storage, drought and rainfall, strong light, weak light (shade conditions), and excessive fertilizer application-can increase alkaloid synthesis. Therefore, this study reviews research on alkaloids in Solanaceae crops.

Due to seismically deficient details, existing reinforced concrete structures have low lateral resistance capacities. Since these building structures suffer an increase in axial loads to the main structural element due to the green retrofit (e.g., energy equipment/device, roof garden) for CO2 reduction and vertical extension, building capacities are reduced. This paper proposes a machine-learning-based methodology for allowable ranges of axial loading ratio to reinforced concrete columns using simple structural details. The methodology consists of a two-step procedure: (1) a machine-learning-based failure detection model and (2) column damage limits proposed by previous researchers. To demonstrate this proposed method, the existing building structure built in the 1990s was selected, and the allowable range for the target structure was computed for exterior and interior columns.