This study investigates the seismic performance of beam-column connections using Thin-Walled Steel Composite (TSC) beams and Prestressed Reinforced Concrete (PSRC) columns. TSC beams are constructed from U-shaped thin steel plates that are filled with concrete, allowing for composite action with slabs through the use of shear connectors. They are widely applied in industrial buildings due to excellent strength, stiffness, and constructability. However, slender web plates are prone to local buckling, which may compromise their performance during seismic events. To mitigate this issue, internal supports have been introduced to enhance web stability and concrete confinement. Cyclic loading tests on three specimens—with and without internal supports—demonstrated that the supports increased moment capacity, improved energy dissipation, and effectively reduced buckling. Even slender sections demonstrated performance comparable to that of compact sections. All specimens reached peak strength at a 2.44% rotation angle, with damage localized near the supports. A practical connection detail was also proposed, taking into account constructability and structural reliability. The results provide valuable guidance for the seismic design of composite systems in large-scale structures.

This study proposes a methodology for predicting properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, this study investigates the variations in molecular dynamics (MD) results with molecular structural complexity and assesses the independence and correlation of variables that influence density. In this study, MD simulations were performed on hydrocarbon-based and individual asphalt binder molecules. The effects of various temperatures, molecular conditions, and structural features on the density were analyzed. MD-related variables influencing the calculated density were evaluated and compared with experimentally measured densities. The MD-calculated densities were used as target variables in a subsequent study, in which a machine learning model was applied to perform quantitative structure–property relationship analysis.The MD-calculated densities showed a strong correlation with experimental measurements, achieving a coefficient of determination of R2 > 0.95. Potential energy exhibited a tendency to cluster into 4–6 groups depending on the molecular structure. In addition, increasing molecular weight and decreasing temperature led to higher density and viscosity. Torsional energy and other individual energy components were identified as significant factors influencing both potential energy and density. This study provided foundational data for the property prediction of asphalt binders by quantitatively analyzing the relationship between the molecular structure and properties using MD simulations. Key features that could be used in the construction of polymer structure databases and AI-based material design were also proposed. In particular, the integration of MD-based simulation and machine learning was confirmed to be a practical alternative for predicting the properties of complex polymer composite systems.

The use of aluminum-based hybrid metal matrix composite (HMMC) materials, especially in engine components like pistons, is intended to improve wear resistance and overall performance. Crucial tribological indicators, such as wear and friction coefficients, underscore the significance of these materials. However, present aluminum alloys have limited wear because of clustered reinforced particles and relatively high coefficients of thermal expansion (CTE), resulting in inadequate anti-seizure properties during dry sliding conditions. This research introduces a novel “Hybrid Metal Matrix Composite of Al7068 Reinforced with Fly Ash-SiC-Al2O3”. Al7068 is employed for its superior strength-to-weight ratio and specific modulus, which is ideal for components exposed to cyclic loads and varying temperatures. The integration of fly Ash (FA), silicon carbide (SiC), and alumina (Al2O3) as reinforcements enhances wear resistance, diminishes particle clustering, improves stiffness, mitigates CTE discrepancies, and fortifies the composite against strain and corrosion, thereby enhancing its overall performance. The Stir-casting method was used with optimized reinforcement percentages (10 % total), and comprehensive evaluations through wear tests and mechanical property analyses determined the composite's optimal composition. The proposed HMMC variant with the most suitable reinforcement percentage exhibited enhanced engine piston functionality, reduced wear, low deformation of 0.20 mm, and a comparatively higher ultimate tensile strength of 190 megapascals (Mpa).

Background: Loss of sagittal balance can lead to excessive thoracolumbar (TL) kyphosis, which is a postural impairment characterized by an increase in kyphotic curvature in these two regions of the spine. Excessive TL kyphosis has been shown to adversely affects quality of life and activities of daily living (ADL). Objectives: This study aimed to investigate immediate spinal motion and resultant postural changes after the application of single thoracic extension mobilization. This was compared to the application of two extension mobilizations, one of which was applied to the lumbar region in the second group of patients with excessive TL kyphosis. Design: Quasi-experimental study. Methods: A total of 53 participants (71.6 years, 20 male/33 female) were recruited. All participants had greater than 40° of TL kyphosis, as measured with a single gravity-dependent inclinometer positioned over the T1 spinous process. One group received thoracic extension mobilization only, whereas the other group received both thoracic and lumbar extension mobilization. Results: Both groups demonstrated an improvement (decrease) in the thoracolumbar kyphosis angle. The group that received thoracic mobilization alone demonstrated a 6.46° change (P<0.0001), while the group that received both mobilizations demonstrated a change of 11.96° (P<0.0001). Combined mobilization applied to both the thoracic and lumbar regions resulted in a significantly greater change (reduction) in TL kyphosis (5.50°, P<0.0001). Conclusion: The results demonstrate that the addition of a second mobilization to the lumbar region results in greater active TL extension and reduced TL kyphosis. Clinicians treating patients with excessive kyphotic curvature should be mindful of the contribution of the lumbar region to loss of sagittal balance. The addition of this simple manual mobilization to the lumbar region appears to yield better short-term improvements in patients with overly kyphotic spinal posture.

High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al30Cu17.5Fe17.5Cr17.5Mn17.5 alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

The purpose of this study is to measure and analyze the number of chest compressions, chest compressions, depth of chest compressions, ventilation, duration of interruption, and accuracy in college students when eyewitness cardiac arrest occurs. The results of the experiment are as follows.(1) The result of the difference in the number of chest compressions was that A was a 20-year-old woman with an average of 114 chest compressions. E was a 22-year-old man with 96 chest compressions, and J was a 24-year-old woman with 109 chest compressions. (2) The result of the difference in chest compressibility depth was that A was a woman in her 20s with an average chest compression depth of 5.0 to 5.2 cm, E was a man in his 20s with an average chest compression depth of 5.0 to 5.4 cm, and J was a woman in her 20s with an average chest compression depth of 5.1 to 5.5 cm. (3) Ventilation was performed for A, E, and J. (4) CPR discontinuation time (second) was performed for a 20-year-old woman for 0 seconds, E was for a 22-year-old man for 5 seconds, and J was a 24-year-old woman for 5 seconds or less. (5) CPR accuracy was found to be 95.2% for a 20-year-old woman, E was found to be a 22-year-old man for 79.6% for a 22-year-old man, and J was found to be 86% for a 24-year-old woman on average. In order to properly cope with an emergency situation in which cardiac arrest occurs, it is confirmed that CPR practice should be sufficiently performed in advance to improve the accuracy of cardiac pressure, and CPR should be performed when cardiac arrest patients occur by maintaining skills through steady retraining.

Our study experimentally evaluates the structural characteristics of a Cone-Shaped Friction Isolator (CFI) as part of research on sliding bearings. With its relatively simple configuration and effective restoring mechanism, the CFI has significant practical implications for structural engineering. We designed the shape and components of the CFI, and its operation and restoring mechanisms were theoretically reviewed. A prototype of the CFI was developed, and structural characteristic experiments were conducted, focusing on design parameters such as the cone’s inclination angle, the friction coefficient of the contact surface, the magnitude of the vertical load applied to the isolator, and the horizontal loading frequency. The experimental results provide valuable insights into the structural characteristics of devices in terms of critical shear force and restoring shear force.

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.