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BackgroundBioactivity refers to the ability of a material to interact with living organisms or biological systems in a way that elicits a specific response. In the context of materials science and medicine, bioactivity is particularly important because it can determine the suitability of material for various applications. ObjectiveTo evaluate and compare different commercially available calcium silicate-based materials regarding: 1. Morphological and elemental analysis at the dentin/material interface. 2. Calcium and silicon release and uptake by adjacent root canal dentine by evaluating the calcium and silicon incorporation depth in adjacent root canal dentin. Materials and MethodsThis study examined four calcium silicate-based cements: Biodentine, MTA Angelus, BioAggregate, and MTA Plus. One hundred extracted human teeth with intact apices and no cavities were selected. Root sections measuring 3 mm in length were created at the mid-root level using low-speed diamond discs. Bioactivity was evaluated at 1, 7, 30, and 90 days, respectively. ResultsThe principal composition of the interfacial dentine layer and incorporation of calcium and silicon into dentine was measured at 1, 7, 30, and 90 days. Statistical analysis was performed by multiple comparisons using post hoc Tukey HSD. ConclusionAll the materials have shown bioactivity, i.e. release of calcium, silicon, and their uptake in the adjacent dentin in the presence of phosphate-buffered saline.

Software-defined networking (SDN) is an approach to network management allowing to enhance the performance of the network and making it more flexible. The centralized architecture of SDN makes it vulnerable to cyberattacks, especially distributed denial of service (DDoS) attacks. Existing research investigates the detection of DDoS attacks separately on the control plane and data plane. However, there is a need for efficient and accurate detection of these attacks using features obtained from both control and data planes. Therefore, we present a mechanism for identifying DDoS attacks using entropy, multiple feature selection mechanisms, and deep learning. Initially, we use entropy on the control plane to detect anomalous activity and identify suspicious switches. Next, we capture traffic on the suspicious switches to detect DDoS attacks. To detect these attacks, we utilize multi-layer perceptron (MLP) deep learning models, convolutional neural network (CNN), and the long short-term memory (LSTM) approach. An InSDN dataset is used to train the model and test data are generated using Mininet emulation and the Ryu controller. The results reveal that LSTM outperforms MLP and CNN, achieving an accuracy of 99.83%. [ABSTRACT FROM AUTHOR]

Google earth with high-resolution imagery basically takes months to process new images before online updates. It is a time consuming and slow process especially for post-disaster application. The objective of this research is to develop a fast and effective method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial images from Massachusetts’s road and building open datasets, Saitama district datasets are used as input images. Semantic segmentation is then applied to input images. Semantic segmentation is a pixel-wise classification of images by implementing deep neural network technique. Deep neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as road, buildings etc., but also partially robust to incomplete and poorly registered target maps. Then, aerial images which contain semantic information are stored as database in 5D world map is set as ground truth images. This system is developed to visualise multimedia data in 5 dimensions; 3 dimensions as spatial dimensions, 1 dimension as temporal dimension, and 1 dimension as degenerated dimensions of semantic and colour combination dimension. Next, ground truth images chosen from database in 5D world map and a new aerial image with same spatial information but different time series are compared via difference extraction method. The map will only update where local changes had occurred. Hence, map updating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only update changed region.