Prediction of high-strength concrete: high-order response surface methodology modeling approach

In the concrete industry, compressive strength is the most essential mechanical property. Therefore, insufficient compressive strength may lead to dangerous failure and, thus, becomes very difficult to repair. Consequently, early, and precise prediction of concrete strength is a major issue facing researchers and concrete designers. In this study, high-order response surface methodology (HORSM) is used to develop a prediction model to accurately predict the compressive strength of high-strength concrete (HSC). Different polynomial degrees order ranging from 2 to 5 is used in this model. The HORSM, with five-order polynomial degree, model outperforms several artificial intelligence (AI) modeling approaches which are carried out widely in the prediction of HSC compression strength. Besides, support vector machine (SVM) model was developed in this study and compared with the HORSM. The HORSM models outperformed the SVM models according to different statistical measures. Additionally, HORSM models managed to perfectly predict the HSC compressive strength in less than one second to accomplish the learning processes. While, other AI models including SVM much longer time. Lastly, the use of HORSM for the first time in the concrete technology field provided much accurate prediction results and it has great potential in the field of concrete technology.

     

On the use of textual feature extraction techniques to support the automated detection of refactoring documentation

Innovations in Systems and Software Engineering - Refactoring is the art of improving the internal structure of a program without altering its external behavior, and it is an important task when it...     

Application of CFD for simulation of a baffled tubular membrane

Computational fluid dynamics (CFD) investigation of a tubular membrane channel containing a set of baffles was conducted for predicting turbulent flow. Simulation was performed using an array of baffles oriented either in the flow or in the reverse direction. A range of local parameters such as stream function, velocity, static pressure, wall shear stress, turbulent kinetic energy, and turbulent dissipation energy on the membrane surface was computed using CFD code FLUENT. The simulation results indicate that the presence of baffle can improve the local shear stress on the membrane surface and produces eddy activities which enhance the filtration performance. The observed flux enhancement can be attributed to the intense fluctuations of wall velocity and shear stress which can disrupt the growth of boundary layer on the membrane surface. The experimental evaluation was performed through cross flow microfiltration of titanium dioxide suspension which showed an acceptable agreement with the CFD predictions.     

Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022. The tweets were labeled according to sentiment (positive or negative) and sector (public or private), and thereby the Hospital Patient Experiences in Saudi Arabia (HoPE-SA) dataset was produced. A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors. The authors trained five models to distinguish sentiments in tweets automatically with the following schemes: a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture, using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers (BERT), Multi-dialect Arabic BERT (MARBERT), and multilingual BERT (mBERT), as well as a pre-trained word2vec model with a support vector machine classifier. This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT. The deep-learning fine-tuned MARBERT-based model—the authors’ best-performing model—achieved accuracy, micro-F1, and macro-F1 scores of 98.71%, 98.73%, and 98.63%, respectively.