A novel method, in situ polymerization, was used for the preparation of nylon 6/silica nanocomposites, and the mechanical properties of the nanocomposites were examined. The results showed that the tensile strength, elongation at break, and impact strength of silica-modified nanocomposites exhibited a tendency of up and down with the silica content increasing, while those of silica-unmodified nanocomposites decreased gradually. It also exhibited that the mechanical properties of silica-modified nanocomposites have maximum values only when 5% silica particles were filled. Based on the relationship between impact strength of the nanocomposites and the matrix ligament thickness t, a new criterion was proposed to explain the unique mechanical properties of nylon 6/silica nanocomposites. The nylon 6/silica nanocomposites can be toughened only when the matrix ligament thickness is less than t c and greater than t a , where t a is the matrix ligament thickness when silica particles begin to aggregate, and t c is the critical matrix ligament thickness when silica particles begin to toughen the nylon 6 matrix. The matrix ligament thickness, t, is not independent, which related with the volume fraction of the inorganic component because the diameter of inorganic particles remains constant during processing. According to the observation of Electron Scanning Microscope (SEM), the process of dispersion to aggregation of silica particles in the nylon 6 matrix with increasing of the silica content was observed, and this result strongly supported our proposal.
This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results
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