Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.

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Abstract. Tribological properties of Ethylene-Propylene-Diene-rubber (EPDM) containing electron modified Polytetrafluoroethylene (PTFE) have been investiagted with the help of pin on disk tribometer without lubrication for a testing time of 2 hrs in atmospheric conditions at a sliding speed and applied normal load of 0.05 m·s -1 and FN = 1 N, respectively. Radiation-induced chemical changes in electron modified PTFE powders were analyzed using Electron Spin Resonance (ESR) and Fourier Transform Infrared (FTIR) specroscopy to characterize the effects of compatibility and chemical coupling of modified PTFE powders with EPDM on mechanical, friction and wear properties. The composites showed different friction and wear behaviour due to unique morphology, dispersion behaviour and radiation functionalization of PTFE powders. In general, EPDM reinforced with electron modified PTFE powder demonstrated improvement both in mechanical and tribological properties. However, the enhanced compatibility of PTFE powder resulting from the specific chemical coupling of PTFE powder with EPDM has been found crucial for mechanical, friction and wear properties. Vol.3, No.1 (2009) [39][40][41][42][43][44][45][46][47][48] Available online at www.expresspolymlett.com DOI: 10.3144/expresspolymlett.2009.7 ene (PTFE) with its remarkably low friction coefficient has also gained interest for use in tribological applications [9][10][11][12]. In rubbers, PTFE was initially used as a reinforcing additive in Silicone and Fluorosilicone rubbers [13][14][15] and afterwards in Styrene-butadiene-rubber, Acrylonitrile-butadienerubber and Butyl rubber [16]. New PTFE-based rubber compounds and compounding procedures have been introduced in improving mechanical properties of both low-strength (ethylene propylene, silicone) and high-strength (nitrile) rubbers for O-rings, sealing and valves etc. [17]. However, PTFE especially in rubbers have not been achieved with any commercially significant success. This is mainly due to the intractability of PTFE in providing homogeneous formulation because of its poor wetting and dispersion characteristic. This problem results from the unique properties of PTFE, most probably its highly hydrophobic surface which resists wetting. There is indeed a strong motivation to investigate new techniques and procedures for the use of PTFE powder in rubber compound as solid lubricant for tribological applications. More recently, chemically coupled PTFE-polyamide [18] and PTFE-rubber [19] compounds based on the modification of PTFE powder by high energy electrons has opened a new way in producing materials for tribological applications. Radiation functionalization produces PTFE micropowders containing persistent trapped-radicals radicals and functional groups on the surface of PTFE powder can be easily compounded into elastomers such as EPDM rubber. A detailed characterization related to the mechanical, friction and wear properties of PTFE-based EPDM compounds have been presented by the authors in [20,21]. In previous attemp...

Abstract. Low-temperature reactive mixing of controlled electron beam modified Polytetrafluoroethylene (PTFE) nanopowder with Ethylene-Propylene-Diene-Monomer (EPDM) rubber produced PTFE coupled EPDM rubber compounds with desired physical properties. The radiation-induced chemical alterations in PTFE nanopowder, determined by electron spin resonance (ESR) and Fourier transform infrared (FTIR) spectroscopy, showed increasing concentration of radicals and carboxylic groups (-COOH) with increasing irradiation dose. The morphological variations of the PTFE nanopowder including its decreasing mean agglomerate size with the absorbed dose was investigated by particle size and scanning electron microscopy (SEM) analysis. With increasing absorbed dose the wettability of the modified PTFE nanopowder determined by contact angle method increased in accordance with the (-COOH) concentration. Transmission electron microscopy (TEM) showed that modified PTFE nanopowder is obviously enwrapped by EPDM. This leads to a characteristic compatible interphase around the modified PTFE. Crystallization studies by differential scanning calorimetry (DSC) also revealed the existence of a compatible interphase in the modified PTFE coupled EPDM. Vol.2, No.4 (2008) [284][285][286][287][288][289][290][291][292][293] Available online at www.expresspolymlett.com DOI: 10.3144/expresspolymlett.2008.34 powder was specially utilized in NBR to expand its utility as wear-resistant material for sealing applications [25]. PTFE micropowders produced by emulsion polymerization are low-molecular weight fine coagulated powder commonly used as an additive in variety of applications [26][27]. In the previous study, PTFE coupled EPDM compounds were produced by reactive mixing of pre-modified PTFE nanopowder with EPDM [28]. In the present work the influence of dose-controlled agglomerate size, structural morphology and interfacial compatibility of PTFE nanopowder on the physical properties of the resulting modified PTFE-EPDM blends are presented. These investigations are of extreme importance especially in the development of new rubber compounds which require optimization of both the physical and tribological properties [29][30]. It has been shown that the desired physical properties can be achieved simply by controlled modification of PTFE nanopowder. Keywords: mechanical properties, PTFE nanopowder, EPDM, electron beam irradiation, compatibility eXPRESS Polymer Letters Materials and experimental MaterialsBoth EPDM (Buna EP G 6850) with ethylidene norbornene (ENB) content 7.7 wt%; ethylene content 51 wt%; Mooney viscosity, ML (1+4) at 125°C, 60; ash content 0.2 wt%; specific gravity, 0.86; and peroxide (Perkadox 14-40 MB GR) were supplied from Lanxess Deutschland GmbH, Germany while coagent (R-20S/Saret 634C) was used from Sartomer, USA. Algoflon L100X an emulsion grade received from Solvay Solexis S.p.A, Italy is an agglomerated white PTFE nanopowder with the bulk density and surface area of 0.25-0.44 g·cm -3 and 26 g·m -2 , respectively. Modification of...

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.

This study deals with the preparation and characterization of novel thermoplastic polyurethaneurea (TPUU) and carboxylated acrylonitrile butadiene rubber (XNBR) blends. Blends of different compositions were prepared in tetrahydrofuran using a solution technique, following an ultra-sonication. The chemical reaction between the two inherently immiscible blend phases was determined with the help of Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy and 1 H-nuclear magnetic resonance ( 1 H-NMR) spectroscopy. The identification of the new peaks in the FTIR-ATR spectra corroborates the existence of chemical reaction between the carboxylic functional group of XNBR and the amide group of the TPUU. In addition, an increase in the network crosslink density of the blend investigated using 1 H-NMR spectroscopy further supports the occurrence of the chemical reaction between the XNBR and the TPUU. The scanning and transmission electron micrographs of the blend morphology show a uniform dispersion of the minor TPUU phase in the XNBR. Furthermore, the existence of a single glass transition peak also confirms the enhancement in the interfacial miscibility. Additionally, the incorporation of 5 wt % of organomodified montmorillonite nanoclay improves the mechanical properties to a considerable extent in comparison with the unfilled blend elastomeric material.