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141.
142.
Ahsan Nazir Haojie Yu Li Wang Muhammad Haroon Raja Summe Ullah Shah Fahad Kaleem-ur-Rahman Naveed Tarig Elshaarani Amin Khan Muhammad Usman 《Journal of Materials Science》2018,53(12):8699-8719
With the development in the modern technologies such as telecommunication instruments and scientific electronic devices, large amount of the electromagnetic radiations are produced, which lead to harmful effect on the highly sensitive electronic devices as well as on the health of human beings. To minimize the effect of electromagnetic radiations produced by different technologies, more efficient shielding materials are required which must be cost-effective, lightweight and good corrosion resistive. In this review, we focused on the shielding materials based on composites of carbon nanotubes and graphene. The typical surface modification of carbon nanotubes and graphene to optimize their interactions with polymers matrix has also summarized. It was found that the composites based on these carbon fillers were more efficient for electromagnetic interference shielding due to their unique properties (i.e., superior electrical, mechanical and thermal) together with lightweight, easy processing. Hence, the carbon nanotubes and graphene-based composites are excellent shielding materials against the electromagnetic radiations. 相似文献
143.
Tracing the trajectory of scientific fields has been recognized by informaticians, nonetheless, little effort has been dedicated to understanding the evolution of the fast-moving research field of transport, quantitatively and qualitatively. This paper identifies intellectual turning points and emerging trends in the area of transport. Using bibliometric methods, co-keyword networks, journal co-citation networks, highly cited categories, and country and institute networks are detected, visualized and discussed. To conduct this analysis, all publications (35,712) in 23 top journals in the field of transport are extracted from the Institute for Scientific Information (Web of Science). The output of this article could be a valuable source for academics and practitioners working in the field of transport planning and those who work in the areas having a strong relationship with transport issues including mathematicians, economics, operation research, management and geography. 相似文献
144.
Hybrid composites of boron carbide (B4C) and Al62.5Cu25Fe12.5 quasicrystals (QCs) were prepared by ball milling and pressureless sintering in aluminium matrix to investigate their individual and hybrid effects on microstructural and mechanical properties. Hybrid composite contained B4C and QCs in 3?wt-% each, making a total of 6?wt-%. For reference, specimens of pure aluminium and two composites containing 6?wt-%B4C and 6?wt-% QCs were prepared. Microstructural characterisation was performed using optical, scanning electron microscopy and X-ray diffraction, while evaluation of mechanical properties was carried out by hardness and compression tests. Uniform dispersion of reinforcements in composites was observed along with significant increase in the mechanical properties. The composite containing 6?wt-% QCs demonstrated the highest hardness, while the hybrid composite showed better compressive properties. 相似文献
145.
Wearable Devices: Wearable Microfluidic Diaphragm Pressure Sensor for Health and Tactile Touch Monitoring (Adv. Mater. 39/2017)
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146.
Abdu Gumaei Mabrook Al-Rakhami Mohamad Mahmoud Al Rahhal Fahad Raddah H. Albogamy Eslam Al Maghayreh Hussain AlSalman 《计算机、材料和连续体(英文)》2021,66(1):315-329
The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30,
2020, this disease had infected more than 6 million people globally, with hundreds
of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases
so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.
This study uses gradient boosting regression (GBR) to build a trained model to predict
the daily total confirmed cases of COVID-19. The GBR method can minimize the loss
function of the training process and create a single strong learner from weak learners.
Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of
the GBR method. The results reveal that the GBR model achieves 0.00686 root mean
square error, the lowest among several comparative models. 相似文献
147.
Rehman Ullah Khan Woei Sheng Wong Insaf Ullah Fahad Algarni Muhammad Inam Ul Haq Mohamad Hardyman bin Barawi Muhammad Asghar Khan 《计算机、材料和连续体(英文)》2022,71(2):2755-2772
The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAM-ResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research. 相似文献
148.
Bassam A. Y. Alqaralleh Fahad Aldhaban Anas Abukaraki Esam A. AlQaralleh 《计算机、材料和连续体(英文)》2022,73(1):87-101
Diabetic Retinopathy (DR) has become a widespread illness among diabetics across the globe. Retinal fundus images are generally used by physicians to detect and classify the stages of DR. Since manual examination of DR images is a time-consuming process with the risks of biased results, automated tools using Artificial Intelligence (AI) to diagnose the disease have become essential. In this view, the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification (ODL-FDRDC) technique. The intention of the proposed ODL-FDRDC technique is to identify DR and categorize its different grades using retinal fundus images. In addition, ODL-FDRDC technique involves region growing segmentation technique to determine the infected regions. Moreover, the fusion of two DL models namely, CapsNet and MobileNet is used for feature extraction. Further, the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm (COA). Gated Recurrent Unit (GRU) is also utilized to identify DR. The experimental results of the analysis, accomplished by ODL-FDRDC technique against benchmark DR dataset, established the supremacy of the technique over existing methodologies under different measures. 相似文献
149.
Zeyad Ghaleb Al-Mekhlafi Ebrahim Mohammed Senan Taha H. Rassem Badiea Abdulkarem Mohammed Nasrin M. Makbol Adwan Alownie Alanazi Tariq S. Almurayziq Fuad A. Ghaleb 《计算机、材料和连续体(英文)》2022,72(1):775-796
Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively. 相似文献
150.
Taha T. A. Ahmed Emad M. El-Tantawy Asmaa I. Azab A. A. 《Journal of Materials Science: Materials in Electronics》2022,33(9):6368-6379
Journal of Materials Science: Materials in Electronics - In this work, the compatibility of zinc oxide for spintronic applications motivated the development of single-phase Fe/ZnO nanostructures by... 相似文献