Effect of layer printing delay on mechanical properties and dimensional accuracy of 3D printed porous prototypes in bone tissue engineering, Ceramics International, http://dx. AbstractRecent advancements in computational design and additive manufacturing have enabled the fabrication of 3D prototypes with controlled architecture resembling the natural bone. Powder-based three-dimensional printing (3DP) is a versatile method for production of synthetic scaffolds using sequential layering process. The quality of 3D printed products by this method is controlled by the optimal build parameters. In this study, Calcium Sulphate based powders were used for porous scaffolds fabrication. The X-direction printed scaffolds with a pore size of 0.8 mm and a layer thickness of 0.1125 mm were subjected to the depowdering step. The effects of four layer printing delays of 50, 100, 300 and 500 ms on the physical and mechanical properties of printed scaffolds were investigated. The compressive strength, toughness and tangent modulus of samples printed with a delay of 300 ms were observed to be higher than other samples. Furthermore, the results of SEM and µCT analyses showed that samples printed with a delay of 300 ms have higher dimensional accuracy and are significantly closer to CAD software based designs with predefined 0.8 mm macro pore and 0.6 mm strut size.

An efficient single-layer dynamic semisupervised feedforward neural network clustering method with one epoch training, data dimensionality reduction, and controlling noise data abilities is discussed to overcome the problems of high training time, low accuracy, and high memory complexity of clustering. Dynamically after the entrance of each new online input datum, the code book of nonrandom weights and other important information about online data as essentially important information are updated and stored in the memory. Consequently, the exclusive threshold of the data is calculated based on the essentially important information, and the data is clustered. Then, the network of clusters is updated. After learning, the model assigns a class label to the unlabeled data by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and density of each cluster are updated. The accuracy of the proposed model is measured through the number of clusters, the quantity of correctly classified nodes, and F-measure. Briefly, in order to predict the survival time, the F-measure is 100% of the Iris, Musk2, Arcene, and Yeast data sets and 99.96% of the Spambase data set from the University of California at Irvine Machine Learning Repository; and the superior F-measure results in between 98.14% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center. We show that the proposed method is applicable in different areas, such as the prediction of the hydrate formation temperature with high accuracy.

We developed an efficient semisupervised feedforward neural network clustering model with one epoch training and data dimensionality reduction ability to solve the problems of low training speed, accuracy, and high memory complexity of clustering. During training, a codebook of nonrandom weights is learned through input data directly. A standard weight vector is extracted from the codebook, and the exclusive threshold of each input instance is calculated based on the standard weight vector. The input instances are clustered based on their exclusive thresholds. The model assigns a class label to each input instance through the training set. The class label of each unlabeled input instance is predicted by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and the density of each cluster are updated. The accuracy of the proposed model was measured through the number of clusters and the quantity of correctly classified nodes, which was 99.85%, 100%, and 99.91% of the Breast Cancer, Iris, and Spam data sets from the University of California at Irvine Machine Learning Repository, respectively, and the superiorFmeasure results between 98.29% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center to predict the survival time.

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