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Coupling CFD with 1D Model for the Prediction of Performance of a Hemodialysis Module with Undulated Fibers
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Universality and scaling relations for turbulent/non-turbulent interfaces in free shear flows
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Control-Relevant Identification of the Unstable Inertial Systems
Get PDFNowadays, with the rise of computing power, control-relevant identification methods have gained attention in various industrial applications, incorporating the requirements for control design into the process of system identification. Mathematical identification of stable linear dynamical systems is a widely studied problem in the literature, and it is prevalently performed in open-loop structures that may lead to high-order models suitable for control system design with imposed control objectives. However, in the case of unstable systems identification can become a challenge task, and usually is performed using closed-loop identification techniques. This paper presents the control-relevant identification approach for two kinds of unstable processes. The contribution focuses on establishing a well-fitted identified model by using a strategy that involves collecting data from the closed-loop system’s operation with a proportional controller when the system achieves an underdamped step response. In addition, a proportional-integral-derivative (PID) controller for each process was synthesized using maximal stability degree method. Concerning identification, the simulation results were compared with those of the genetic algorithm and offered better model estimation than the genetic algorithm. On the other hand, it is also demonstrated that the designed control algorithm offered a high degree of stability to the system and is more reliable in stabilizing the behavior of the unstable system than the genetic algorithm and the parametric optimization method
Binary and Multi-Classification Models for Breast Cancer Diagnosis Using Automated Deep Learning and Mammography Images with Different Augmentation Cases
Get PDFMammography is a very efficient medical imaging procedure that is used to detect and diagnose breast cancer. However, the use of mammography for the early detection and identification of cancer is very complicated and represents a considerable workload for radiologists. Machine learning (ML) can help address these challenges by providing accurate, automated diagnosis, but traditional ML methods are complex and resourceintensive. Google AutoML Vision offers a simplified approach, enabling healthcare professionals with minimal programming skills to develop effective diagnostic models. The aim of this study was to evaluate the ability of automated deep learning using mammography images using Google AutoML with different augmentation cases. In this work, two models were created: one for binary classification and another for multiclassification. The binary classification model includes two scenarios: noncancerous and malignant, while the multi-classification approach includes three scenarios: normal, benign and malignant. The average accuracy of the two classifications was evaluated and compared. The average accuracy of the binary and multi-classification models was 77.98% and 79.29%, respectively. These results suggest that Google AutoML can simplify the use of ML models in the clinical setting and provide a reliable diagnostic tool that can reduce the workload of radiologists. This study shows that AutoML has the potential to streamline diagnostic workflows in healthcare and make machine learning more accessible and effective in medical practise.OPEN ACCESS Received: 01/08/2024 Accepted: 29/11/202
The Influence of Bilingualism in Altering the Cortical Executive Functions of Adolescents
No full text195 countries, 7,000+ officially known languages, thousands of hidden dialects. When we think of languages, words that come to mind are often related to communication. Especially with knowing multiple languages, we think of amplifying relations with interpersonal communication. Yet, a problem exists with the lack of knowledge about bilingualism’s advantage besides these perceptions in globalized societies. This issue impacts monolinguals because of decreasing language education neglecting bilingualism’s increase of cognitive skills. Limited research on the benefits of bilingualism among adolescents exacerbates this problem, as existing studies often focus on infants and older adults, considering these age groups depict clear benefits without confounding variables. However, adolescents have the greatest adaptability for language acquisition, asserting suitability in measuring bilingualism before brain development hinders learning abilities in adulthood (Smith, 2018). The purpose of this study is to underscore the beneficial advantage bilingualism—specifically among adolescents—provides unconsciously daily, ultimately aiding to promote language education.
 
