Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete

Concrete-filled steel tube (CFST) columns are used in the construction industry because of their high strength, ductility, stiffness, and fire resistance. This paper developed machine learning techniques for inferring the axial strength in short CFST columns infilled with various strength concrete. Additive Random Forests (ARF) and Artificial Neural Networks (ANNs) models were developed and tested using large experimental data. These data-driven models enable us to infer the axial strength in CFST columns based on the diameter, the tube thickness, the steel yield stress, concrete strength, column length, and diameter/tube thickness. The analytical results showed that the ARF obtained high accuracy with the 6.39% in mean absolute percentage error (MAPE) and 211.31 kN in mean absolute error (MAE). The ARF outperformed significantly the ANNs with an improvement rate at 84.1% in MAPE and 65.4% in MAE. In comparison with the design codes such as EC4 and AISC, the ARF improved the predictive accuracy with 36.9% in MAPE and 22.3% in MAE. The comparison results confirmed that the ARF was the most effective machine learning model among the investigated approaches. As a contribution, this study proposed a machine learning model for accurately inferring the axial strength in short CFST columns

Two-Phase Defect Detection Using Clustering and Classification Methods

Autonomous fault management of network and distributed systems is a challenging research problem and attracts many research activities. Solving this problem heavily depends on expertise knowledge and supporting tools for monitoring and detecting defects automatically. Recent research activities have focused on machine learning techniques that scrutinize system output data for mining abnormal events and detecting defects. This paper proposes a two-phase defect detection for network and distributed systems using log messages clustering and classification. The approach takes advantage of K-means clustering method to obtain abnormal messages and random forest method to detect the relationship of the abnormal messages and the existing defects. Several experiments have evaluated the performance of this approach using the log message data of Hadoop Distributed File System (HDFS) and the bug report data of Bug Tracking System (BTS). Evaluation results have disclosed some remarks with lessons learned

STIRLING ENGINE: FROM DESIGN TO APPLICATION INTO PRACTICE AND EDUCATION

Stirling motor is a type of outside ignition heat motor that can utilize various fuel sources from customary structures (coal, oil, kindling, rice husk, and so forth) to sustainable power sources (sun-oriented energy), climate, squander heat usage, and so forth). The article centers around introducing the fundamental highlights of the improvement history, activity qualities, and plan techniques for certain sorts of Stirling motors, in this way offering useful appropriateness as well as a college preparing for understudies. The understudy studying Thermal Engineering in our nation today.  

THE SHELF-LIFE OF BLACK TIGER SHRIMP (PENAEUS MONODON) TREATED WITH THE DIFFERENT CONDITIONS

This study examines the chemical and microbiological changes and the sensory attributes of black tiger shrimp (Penaeus monodon) when stored at the temperature of 0 ºC. Sodium propionate and sodium lactate were used to treat shrimps before storage. Vacuum packaging was also carefully investigated. Throughout storage, the quality indicates including TVB-N, TMA-N, histamine, quality index (QI) and total viable count (TVC) were used to evaluate the quality changes of shrimp. The results show that though with different rates, the increase in quality indicators caused the decrease in the quality of shrimp during storage. The quality of shrimps treated with salts of organic acids or those packed in the vacuum bags was significantly higher than that of the control. In particular, the shelf-life of the sample packed in the vacuum packages was twelve days, which was four days longer than that of the control

Somatic embryogenesis and plant regeneration from leaf callus with genetic stability validation using SCoT markers in Paramignya trimera, a medicinal plant native to Vietnam

Xao tam phan (Paramignya trimera (Oliv.) Guillaum) is a medicinal plant native to Vietnam, that is renowned for its therapeutic properties, particularly for the treatment of various ailments, including cancer. This study investigated in vitro propagation of P. trimera through somatic embryogenesis and shoot organogenesis using leaf callus. Various culture media, plant growth regulators, malt extract, and carbon sources were evaluated to optimize callus induction and somatic embryo formation from leaf explants. DNA barcoding confirmed 96.96% to 100% homology with P. trimera specimens from Khanh Hoa province, Vietnam. The highest callus formation rate, reaching 100%, was observed in one-year-old explants cultured in Woody Plant Medium (WPM) supplemented with 2.0 mg L-1 naphthaleneacetic acid (NAA) and 0.2 mg L-1 benzylaminopurine (BAP) in the dark for over six weeks. In WPM supplemented with 30 g L-1 sucrose, 4.0 mg L-1 BAP, and 500 mg L-1 malt extract, globular stage embryos developed into embryoids and shoots and buds clumped at 10 and 18 weeks, respectively. Shoot organogenesis was observed in WPM supplemented with 30 g L-1 sucrose and 0.07 mg L-1 thidiazuron (TDZ) after 18 weeks of culture. Genetic fidelity assessments using 12 SCoT markers indicated that in vitro plantlets were homologous to the mother plant. This study provides a viable method for the conservation and sustainable cultivation of Xao tam phan, ensuring a stable supply of this valuable medicinal resource