Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate

This study compares different methods to predict the simultaneous effects of conductive and radiative heat transfer in a Polymethylmethacrylate (PMMA) sample. PMMA is a kind of polymer utilized in various sensors and actuator devices. One-dimensional combined heat transfer is considered in numerical analysis. Computer implementation was obtained for the numerical solution of governing equation with the implicit finite difference method in the case of discretization. Kirchhoff transformation was used to get data from a non-linear equation of conductive heat transfer by considering monochromatic radiation intensity and temperature conditions applied to the PMMA sample boundaries. For Deep Neural Network (DNN) method, the novel Long Short Term Memory (LSTM) method was introduced to find accurate results in the least processing time than the numerical method. A recent study derived the combined heat transfers and their temperature profiles for the PMMA sample. Furthermore, the transient temperature profile is validated by another study. A comparison proves a perfect agreement. It shows the temperature gradient in the primary positions that makes a spectral amount of conductive heat transfer from a PMMA sample. It is more straightforward when they are compared with the novel DNN method. Results demonstrate that this artificial intelligence method is accurate and fast in predicting problems. By analyzing the results from the numerical solution it can be understood that the conductive and radiative heat flux is similar in the case of gradient behavior, but it is also twice in its amount approximately. Hence, total heat flux has a constant value in an approximated steady state condition. In addition to analyzing their composition, ROC curve and confusion matrix were implemented to evaluate the algorithm performance.Comment: 15 pages, 11 figure

Effect of resistance training with and without vitamin D calcium chitosan nanoparticle supplements on apoptosis markers in ovariectomized rats: An experimental study

Background: Hormone therapy is one of the most effective treatments for menopausal disorders, but it may increase the risk of breast cancer, coronary heart disease, and pulmonary embolism. Objective: The present study investigated the effect of resistance training with and without vitamin D calcium (Ca++) chitosan nanoparticles on apoptosis markers in ovariectomized rats. Materials and Methods: 42 female Wistar rats were divided into 7 groups (n = 6/each). One group was assigned as the healthy controls to show the induction of menopause. The other 6 groups comprised ovariectomized (OVX) animals including: 1) vitamin D + calcium + chitosan + resistance training, 2) saline + estrogen + resistance training, 3) saline + resistance training, 4) vitamin D + calcium + chitosan, 5) saline + estrogen, and 6) OVX + control. 48 hr after the last intervention, the hippocampus tissue was extracted to measure the BCL-2-associated X (BAX), B-cell lymphoma 2 (BCL-2), and caspase-3 gene expression as well as the percentage of dead cells. Results: OVX rats demonstrated increased BAX gene expression, ratio of BAX gene expression to BCL-2, caspase-3 gene expression, and percentage of dead cells of hippocampal tissue, but decreased BCL-2 gene expression. Resistance training and vitamin D Ca++ chitosan nanoparticle supplements seemed to reverse these changes. Conclusion: The combination of resistance training and vitamin D Ca++ chitosan nanoparticle supplements may be considered a non-pharmacological treatment for OVX-induced apoptosis. Key words: Apoptosis, BCL-2-associated X protein, Caspase-3, Estrogen replacement therapy, Hormone replacement therapy

Proven pulmonary aspergillosis in a COVID-19 patient: A case report

Background and Purpose: Coronavirus disease 2019 (COVID-19) has become a significant clinical challenge in healthcare settings all over the world. Critically ill COVID-19 patients with acute respiratory distress syndrome may be at increased risk of co-infection with pulmonary aspergillosis. This study aimed to describe a clinical case of proven pulmonary aspergillosis caused by Aspergillus tubingensis in a 59-year-old man with a history of hospitalization due to COVID-19 infection.Case report: The Covid-19 infection was confirmed by positive nasopharyngeal polymerase chain reaction. He had a cavitary lesion measured 20 mm in diameter with intracavitary soft tissue density in the left lung in the first chest computerized tomography scan. After 25 days, he showed two cavitary lesions in both lungs which raised suspicion of fungal infection; hence, the patient underwent a trans-thoracic biopsy of the cavitary lesion. The direct examination and culture of the biopsy material revealed Aspergillus species. To confirm the Aspergillus species identification, the beta-tubulin region was sequenced. The patient was treated with oral voriconazole.Conclusion: This report underlined the importance of early diagnosis and management of invasive fungal infections in severe COVID-19 patient

The Effect of Drag Force on The Body Frequencies and The Power Spectrum of a Bladeless Wind Turbine

The new types of bladeless wind turbines and generating electricity using them is one of the most interesting topics for engineering nowadays. Electricity generation using the structural vibration due the resonance phenomenon is the concept behind a vortex bladeless turbine. The present study numerically investigates the effects of the drag force on the body frequency of an oscillating bladeless wind turbine. A 2-D numerical simulation was performed for a cylinder with a semi-circular cross-section in cross-flow in two different cases. The research was conducted for both uncontrolled and controlled oscillating cylinders. The controlling process was performed using a pair of ring magnets as a spring with a variable coefficient. The flow field, vibration, vortex shedding and structural frequencies, and the resonance phenomenon are studied in this research. Finally, the controlled and uncontrolled frequencies of the cylinder are explored, and the power spectrum for various velocities is analyzed in two different states, namely with and without a tuning system. From the results, it can be concluded that the usage of the controlling system in these turbines can greatly regulate the oscillations and increase the frequency value by limiting the vibration amplitude. According to this principle, it can be inferred that increasing the frequency of fluctuations greatly increases the production capacity of these turbines.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System

The combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for these sites. For this purpose, this research presents the feasibility of installing a combined hybrid site in the desired coastal location by predicting the net produced power due to the environmental parameters. For combining these two systems, an optimized array includes ten turbines and ten wave energy converters. The mathematical equations of the net force on the two introduced systems and the produced power of the wind turbines are proposed. The turbines’ maximum forces are 4 kN, and for the wave energy converters are 6 kN, respectively. Furthermore, the comparison is conducted in order to find the optimum system. The comparison shows that the most effective system of desired environmental condition is introduced. A number of machine learning and deep learning methods are used to predict key parameters after collecting the dataset. Moreover, a comparative analysis is conducted to find a suitable model. The models’ performance has been well studied through generating the confusion matrix and the receiver operating characteristic (ROC) curve of the hybrid site. The deep learning model outperformed other models, with an approximate accuracy of 0.96

Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System

The combination of an offshore wind turbine and a wave energy converter on an integrated platform is an economical solution for the electrical power demand in coastal countries. Due to the expensive installation cost, a prediction should be used to investigate whether the location is suitable for these sites. For this purpose, this research presents the feasibility of installing a combined hybrid site in the desired coastal location by predicting the net produced power due to the environmental parameters. For combining these two systems, an optimized array includes ten turbines and ten wave energy converters. The mathematical equations of the net force on the two introduced systems and the produced power of the wind turbines are proposed. The turbines’ maximum forces are 4 kN, and for the wave energy converters are 6 kN, respectively. Furthermore, the comparison is conducted in order to find the optimum system. The comparison shows that the most effective system of desired environmental condition is introduced. A number of machine learning and deep learning methods are used to predict key parameters after collecting the dataset. Moreover, a comparative analysis is conducted to find a suitable model. The models’ performance has been well studied through generating the confusion matrix and the receiver operating characteristic (ROC) curve of the hybrid site. The deep learning model outperformed other models, with an approximate accuracy of 0.96

Endocarditis with Aeromonas salmonicida

Aeromonas salmonicida (A. salmonicida) is a facultative Gram-negative bacillus, inhabiting in water. It is a common source of furunculosis and septicemia in fish. Report on the human infection with this organism is rare. A male farmer referred with weakness and intermittent fever. He had cardiac valves’ regurgitation due to fever with rheumatic heart disease. He had a history of swimming in well water. Transthoracic echocardiography (TTE) revealed a mobile mass of 1.3 × 0.9 cm attached to the mitral valve chordae, suggestive of a vegetation. Aeromonas salmonicida was isolated from the blood. After cardiac surgery and taking ceftriaxone for 4 weeks, he was discharged in good general condition. Five previous case reports of human infection with this organism were found. The patient was the sixth human case, and the first endocarditis, reported with this organism. A. salmonicida is a rare agent for human infection. Contact with water is a risk factor for this type of infection. It seems that the use of modern diagnostic methods has been effective in identifying the microorganism

Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning

Accurate simulation of tumor growth during chemotherapy has significant potential to alleviate the risk of unknown side effects and optimize clinical trials. In this study, a 3D simulation model encompassing angiogenesis and tumor growth was developed to identify the vascular endothelial growth factor (VEGF) concentration and visualize the formation of a microvascular network. Accordingly, three anti-angiogenic drugs (Bevacizumab, Ranibizumab, and Brolucizumab) at different concentrations were evaluated in terms of their efficacy. Moreover, comprehensive mechanisms of tumor cell proliferation and endothelial cell angiogenesis are proposed to provide accurate predictions for optimizing drug treatments. The evaluation of simulation output data can extract additional features such as tumor volume, tumor cell number, and the length of new vessels using machine learning (ML) techniques. These were investigated to examine the different stages of tumor growth and the efficacy of different drugs. The results indicate that brolucizuman has the best efficacy by decreasing the length of sprouting new vessels by up to 16%. The optimal concentration was obtained at 10 mol m with an effectiveness percentage of 42% at 20 days post-treatment. Furthermore, by performing comparative analysis, the best ML method (matching the performance of the reference simulations) was identified as reinforcement learning with a 3.3% mean absolute error (MAE) and an average accuracy of 94.3%. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.