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.

Given the hazards posed by black ice, it is crucial to investigate the conditions that contribute to its formation. Two ensemble machinelearning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were employed to forecast the occurrence of black ice using atmospheric data. Additionally, explainable artificial intelligence techniques, including Feature Importance (FI) and partial dependence Plot (PDP), were utilized to identify atmospheric conditions that significantly increase the likelihood of black ice formation. The machinelearning algorithms achieved a forecasting accuracy of 90%, demonstrating reliable performance. FI analysis revealed distinct key predictors between the algorithms: relative humidity was the most critical for RF, whereas wind speed was paramount for XGBoost. The PDP analysis identified the specific atmospheric conditions under which black ice was likely to form. This study provides detailed insights into the atmospheric precursors of frost/fog-induced black ice formation. These findings enable road managers to implement proactive winter road maintenance strategies, such as optimizing anti-icing patrol routes and displaying warnings on various message signs, thereby enhancing road safety.

본 연구는 성장 단계별 돼지의 평균 사료 섭취량을 추정하고, 각 매개변수 간의 상관분석을 통해 변수를 선별한 후, 기계학습 기반 회귀분석을 통해 돼지의 사료 섭취량(FI)을 예측하는 모델을 만들고자 한다. 본 실험은 2023년 9월 14일부터 2023년 12월 15일까지 93일 동안 진행하였다. 사료는 09:00와 17:00 하루에 2회 제공하였으며, 제공된 사료의 양은 돼지의 평균 체중의 5%를 지급하였다. 돼지의 몸무게(PBW)는 매일 09:00에 이동식 돈형기를 사용하여 측정하였다. 축산환경관리시스템(LEMS) 센서를 이용하여, 돈사 내 온도(RT), 상대습도(RH), NH3를 5분 간격으로 수집하였다. 성장 단계를 3단계로 나누었으며, 각 GS1, GS2 및 GS3으로 명명하였다. 각 성장 단계별 평균 사료 섭취량과 표준편차를 구하여, 유의미성과 성장 단계별 사료 섭취의 경향을 분석하였다. 각 모델의 성능평가( , RMSE, MAPE) 시 8:2의 비율로 데이터를 분할하여, 정확도 검증을 수행하였다. 연구 결과 성장 단계별 돼지의 사료 섭취량에 유의미한 차이(p < 0.05)가 있음과 돼지가 성장할수록 일정한 양의 사료를 섭취하는 것을 확인하였다. 또한 각 변수의 상관분석 시 FI와 PBW에서 강한 상관관계가 나타났으며(R > 0.94), 각 모델의 성능평가 결과 RFR 모델이 가장 높은 정확성(  = 0.959, RMSE = 195.9, MAPE = 5.739)을 보였다.

Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.

작물 증발산량은 잠재 증발산량에서 작물계수를 곱하여 작 물의 요수량을 산출할 수 있어 수자원 관리에 널리 사용되는　 방법이다. 특히 유엔식량농업기구(FAO)가 관개 및 배수 논 문 NO.56에서 발표한 Penman-Monteith　방정식(FAO 56-PM) 은 잠재 증발산량을 추정하는 표준방법으로, 평균온도, 최대 온도, 최소온도, 상대습도, 풍속 및 일사량의 6가지 기상 데이 터가 필요하다. 그러나　농경지 인근에 설치된 기상센서는 설 치 및 유지보수 비용이 높아 결측, 이상치와 같은 데이터 신뢰 성 문제를 야기하여 정확한 증발산량 계산을 복잡하게 만든 다. 본 연구에서는 인근 기상청의 데이터를 사용하여 필요한 6가지 기상 변수를 예측함으로써 기상 센서 없이 작물 증발산량을 추정할 수 있는지 조사하였다. 우리는 기상청의 API를 통해 수집할 수 있는 22개의 기상 변수를 입력 데이터로 활용 했다. 9개의 회귀 모델을 학습한 후 성능에 따라 상위 3개를 선 택하고 하이퍼파라미터 튜닝을 적용하여 최적의 모델을 식별 했다. 가장 좋은 성능을 보인 모델은 Extreme Gradient Boosting Regression(XGBR)이었으며 평균온도, 최대온도, 최소온도, 상대습도, 풍속 및 일사량에서 결정계수(R2)가 각 0.98, 0.99, 0.99, 0.91, 0.72, 0.86로 높은 결과를 얻을 수 있었다. 이러한 결과는 XGBR 모델이 작물 기상 데이터를 사용하여 작물 증 발산 모델에 필요한 입력 값을 정확하게 예측할 수 있어 값비 싼 기상 센서가 필요 없음을 시사한다. 이 접근 방식은 센서 설 치 및 유지보수가 어려운 지역에서 특히 유용할 수 있으며, 직 접적인 센서 데이터 없이도 표준 증발산 모델의 사용을 가능 하게 한다.

본 연구는 표현 형질 생육 데이터인 엽장, 엽 수와 기상 데이 터인 생육도일을 활용하여 여러 기계 학습을 통해 마늘의 생 체중을 예측하는 모델을 개발하고자 하였다. 검증 데이터에 서 random forest 모델의 결정계수가 0.924, 평균제곱근오차 (g)는 13.583 그리고 평균절대오차는 8.885로 가장 우수하였 다. 평가 데이터에서는 Catboost 모델이 결정계수가 0.928, 평균제곱근오차(g)는 13.486 그리고 평균절대오차는 9.181 로 가장 우수하였다. 그러나 Catboost, Random forest 그리고 LightGBM 모델을 0.5, 0.3 그리고 0.2 가중치를 두어 학습한 Weighted ensemble 모델이 마늘 생체중 예측의 검증 및 평가 에 있어서 검증 데이터의 결정계수가 0.922, 평균제곱근오차 (g)가 13.752 그리고 평균절대오차는 8.877이었으며 평가 데 이터에서는 결정계수가 0.923, 평균제곱근오차(g)가 13.992 그리고 평균절대오차가 9.437로 두 번째로 우수한 결과를 나 타내었다. 이러한 결과들을 종합적으로 미루어 보았을 때, Weighted ensemble 모델이 모델의 안정성 측면에서 최적의 모델이라고 판단하였다. 따라서 농가들이 표현 형질과 기상 데이터만으로도 기계학습 기법을 통하여 마늘의 생체중 예측 을 통해 작형 모니터링이 가능할 것으로 보이며 추가적으로 다년도 데이터 취득과 검증을 통하여 성능을 고도화가 가능할 것으로 판단된다.

Background: Automated classification systems using Artificial Intelligence (AI) and Machine Learning (ML) can enhance accuracy and efficiency in diagnosing pet skin diseases within veterinary medicine. Objectives: This study created a system that classifies pet skin diseases by evaluating multiple ML models to determine which method is most effective. Design: Comparative experimental study. Methods: Pet skin disease images were obtained from AIHub. Models, including Multi-Layer Perceptron (MLP), Boosted Stacking Ensemble (BSE), H2O AutoML, Random Forest, and Tree-based Pipeline Optimization Tool (TPOT), were trained and their accuracy assessed. Results: The TPOT achieved the highest accuracy (94.50 percent), due to automated pipeline optimization and ensemble learning. H2O AutoML also performed well at 94.25 percent, illustrating the effectiveness of automated selection for intricate imaging tasks. Other models scored lower. Conclusion: These findings highlight the potential of AI-driven solutions for faster and more precise pet skin disease diagnoses. Future investigations should incorporate broader disease varieties, multimodal data, and clinical validations to solidify the practicality of these approaches in veterinary medicine.

This study investigates the effect of machine translation (MT) use on the writing performance of Korean EFL students, focusing on complexity, accuracy, and fluency (CAF). Six participants completed a series of writing tasks in which they first translated their L1 writing into L2 manually and then used MT to revise their L2 drafts. This process was repeated across ten different writing topics. Participants’ drafts were analyzed using CAF measures to assess MT’s impact on their writing performance and observe changes over tasks. The results show that MT significantly improved accuracy and fluency. However, gains in syntactic and lexical complexity were less evident. While group-level analysis showed consistent progress, individual trajectories varied widely, indicating diverse patterns of development. Overall, the findings suggest that MT enhances writing accuracy and fluency among Korean EFL students, although its impact on syntactic and lexical complexity is limited. These results indicate that MT can serve as a valuable tool for self-directed learning, helping students independently improve their writing accuracy and fluency and develop essential self-editing skills. This study highlights the potential of MT as a supplementary tool to support EFL students’ writing development, along with traditional instruction.

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.

Bearing-shaft systems are essential components in various automated manufacturing processes, primarily designed for the efficient rotation of a main shaft by a motor. Accurate fault detection is critical for operating manufacturing processes, yet challenges remain in sensor selection and optimization regarding types, locations, and positioning. Sound signals present a viable solution for fault detection, as microphones can capture mechanical sounds from remote locations and have been traditionally employed for monitoring machine health. However, recordings in real industrial environments always contain non-negligible ambient noise, which hampers effective fault detection. Utilizing a high-performance microphone for noise cancellation can be cost-prohibitive and impractical in actual manufacturing sites, therefore to address these challenges, we proposed a convolution neural network-based methodology for fault detection that analyzes the mechanical sounds generated from the bearing-shaft system in the form of Log-mel spectrograms. To mitigate the impact of environmental noise in recordings made with commercial microphones, we also developed a denoising autoencoder that operates without requiring any expert knowledge of the system. The proposed DAE-CNN model demonstrates high performance in fault detection regardless of whether environmental noise is included(98.1%) or not(100%). It indicates that the proposed methodology effectively preserves significant signal features while overcoming the negative influence of ambient noise present in the collected datasets in both fault detection and fault type classification.

This study analyzes the impact of ESG (Environmental, Social, and Governance) activities on Corporate Financial Performance(CFP) using machine learning techniques. To address the linear limitations of traditional multiple regression analysis, the study employs AutoML (Automated Machine Learning) to capture the nonlinear relationships between ESG activities and CFP. The dataset consists of 635 companies listed on KOSPI and KOSDAQ from 2013 to 2021, with Tobin's Q used as the dependent variable representing CFP. The results show that machine learning models outperformed traditional regression models in predicting firm value. In particular, the Extreme Gradient Boosting (XGBoost) model exhibited the best predictive performance. Among ESG activities, the Social (S) indicator had a positive effect on CFP, suggesting that corporate social responsibility enhances corporate reputation and trust, leading to long-term positive outcomes. In contrast, the Environmental (E) and Governance (G) indicators had negative effects in the short term, likely due to factors such as the initial costs associated with environmental investments or governance improvements. Using the SHAP (Shapley Additive exPlanations) technique to evaluate the importance of each variable, it was found that Return on Assets (ROA), firm size (SIZE), and foreign ownership (FOR) were key factors influencing CFP. ROA and foreign ownership had positive effects on firm value, while major shareholder ownership (MASR) showed a negative impact. This study differentiates itself from previous research by analyzing the nonlinear effects of ESG activities on CFP and presents a more accurate and interpretable prediction model by incorporating machine learning and XAI (Explainable AI) techniques.

Neural machine translators (NMTs), such as Google Translate, may assist second language (L2) readers with general comprehension. However, previous empirical studies show mix ed r esults r egarding their e ffectiveness. In this study, 145 Korean English learners from a girls’ high school were asked to solve three types of reading comprehension problems (grammar judgment, inferring meaning from context, inferring main idea) under three reading conditions (no aid, MT, glossary). Overall, when using MT, reading comprehension scores were higher than in either the no aid or glossary conditions individually. However, none of the reading aid conditions improved grammar judgment. Only mid-proficiency learners benefited from MT in both inferring meaning from context and inferring main idea tasks. The results suggest that the glossary may have interrupted the flow of the reading process. With the widespread availability of MT as an online reference tool, L2 teachers should consider incorporating MT as a legitimate reading aid for different proficiency levels and reading purposes.

In the development of a digital multi-process welding machine, we aimed to analyze the heat dissipation effects resulting from changes in the transformer's shape. Two installation configurations for the transformer, vertical and horizontal, were proposed. Thermal-flow analysis was conducted for the welding machine, taking into account variations in spacing between each proposed configuration. The results indicated that the shape and spacing of the components did not significantly alter the airflow around the reactor coil, which is the main heat-generating component of the machine. When comparing the heat dissipation effects across models with different transformer spacings, it was observed that models with narrower spacing exhibited improved heat dissipation, while the vertical configuration demonstrated a slightly higher heat dissipation effect overall. Transient analysis revealed the irregularities in internal flow and the resulting scattered temperature distribution over time within the welding machine.

This study examines career trajectories among women with career breaks, using data from the 2019 National Survey of Women on Career Breaks (n=1,138). The data underwent preprocessing, including outlier detection, feature scaling, and class imbalance correction with SMOTEENN. Three machine learning models were evaluated, with the Random Forest model achieving the best performance. Key predictors included flexible leave policies, social insurance, remote work options, and job security. The findings highlight the importance of supportive organizational policies in retaining female employees. Future research should explore longitudinal impacts and additional variables like organizational culture.

This study aims to propose new grading standards that can be applied to AI-based automatic sorting machines, reflecting current distribution and consumption trends. The current domestic grading standards for onions in South Korea are based on the “Agricultural and Fishery Products Quality Control Act”. They classify onions based on criteria such as uniformity, shape, color, and the presence of foreign matter. Onion grading standards are divided into four categories based on bulb diameter and weight. However, in the actual domestic market, onions are distributed according to a five-grade classification based on bulb diameter. Therefore, this study classified onions into eight grades, reflecting current distribution and consumption trends in the domestic market. These grades are applicable to AI-based automatic sorting machines. Marketable onions were classified into A1 (extra large) to A5 (extra small) based on the diameter of a single bulb. Onions used for non-marketable purposes (processing) were classified as grade B. Additionally, grade C and grade D were designated for processing and disposal, respectively. By establishing quality grading classifications that align with current distribution and consumption market trends as well as the operational characteristics of AI-based automatic sorting machines, we can expect improvements in work efficiency and reductions in distribution costs. Following this study, it will be necessary to establish comprehensive quality grading standards that include both external criteria (such as bulb weight and size) and internal criteria (such as detection of internal decay and disease occurrence).

This study aimed to improve the accuracy of road pavement design by comparing and analyzing various statistical and machine-learning techniques for predicting asphalt layer thickness, focusing on regional roads in Pakistan. The explanatory variables selected for this study included the annual average daily traffic (AADT), subbase thickness, and subgrade California bearing ratio (CBR) values from six cities in Pakistan. The statistical prediction models used were multiple linear regression (MLR), support vector regression (SVR), random forest, and XGBoost. The performance of each model was evaluated using the mean absolute percentage error (MAPE) and root-mean-square error (RMSE). The analysis results indicated that the AADT was the most influential variable affecting the asphalt layer thickness. Among the models, the MLR demonstrated the best predictive performance. While XGBoost had a relatively strong performance among the machine-learning techniques, the traditional statistical model, MLR, still outperformed it in certain regions. This study emphasized the need for customized pavement designs that reflect the traffic and environmental conditions specific to regional roads in Pakistan. This finding suggests that future research should incorporate additional variables and data for a more in-depth analysis.