Transparent Nanocrystalline Glass-Ceramic System for Organic Pollutants Degradation

Because of the superior photocatalytic activities of nanocrystalline TiO₂ and ZnO under UV irradiation, they were embedded into the glass system (SiO₂, TiO₂, ZnO, B₂O₃, Na₂O, K₂O, P₂O₅, Li₂O and BaO) to provide easy separation from the aqueous system. Different contents of TiO₂ and ZnO have been investigated. Conversion to glass-ceramic materials was carried out by heat treatment at 450 °C, which is the onset of the nucleation peak according to the differential thermal analysis (DTA) result, for different times. This heat treatment regime preserves the transparency of the prepared materials in the visible region and good absorption in the UV region. The high content of TiO₂ or ZnO caused an improvement of microhardness of the prepared materials, though the presence of the two oxides with the same ratio decreased the microhardness values. Photocatalytic activity of the prepared glass-ceramic materials was investigated according to their efficiency for the degradation of humic acid (HA), the major precursor of disinfection by-products (DBPs), from water. All samples were proved to be photoactive with different extents. Four hours heat treatment at 450 °C appears to be the best conditions for the development of TiO₂ and ZnO crystals leading to better photocatalytic activity.     

Application of nanostructured titanium dioxide pigments in paper coating: a comparison between prepared and commercially available ones

Nano-TiO₂ pigments in pure crystallographic anatase and rutile phases have been successfully prepared by hydrothermal at 120°C and hydrolysis methods, respectively. The laboratory-prepared pigments were characterized parallel to two commercial pigments of the same crystal structure. All pigments were applied in paper coating mixtures, and their influence on coated paper properties was systematically investigated. X-ray diffraction investigation showed that the laboratory-prepared pigments using the hydrothermal method at 120°C were pure anatase, whereas hydrolysis method produced pure rutile phase pigment. The application of the prepared nanopigments and the corresponding commercial TiO₂ phases in paper coating revealed that clay/rutile nano-TiO₂ pigments in paper coating mixture decreased coated paper roughness more than blending clay with anatase nano-TiO₂ pigments. Commercial nano-TiO₂ pigments increased porosity of coated paper at both the 30% and 50% addition of nano-TiO₂ pigments to clay, while laboratory-prepared nano-TiO₂ pigments highly decreased it at 30% addition of nano-TiO₂ to clay, compared to clay only. Blending of clay/nano-TiO₂ pigments improved both brightness and opacity of the coated paper where commercial pigments are more effective. Burst, tensile strength, stretching, and TEA were improved in the case of all pigments. The 50% addition of the prepared and commercial nanopigments in conjunction with clay improved the mechanical coated paper properties more than 30% addition (except the cases of stretching and TEA of the commercial pigments). The coated paper samples were offset printed. It was found that blending of clay/nano-TiO₂ pigments improved print density. Commercial nano-TiO₂ pigments improved print gloss more than the laboratory-prepared ones. This result was found consistent with the results of coated paper roughness.     

A statistical framework for few-shot action recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition remains crucial. This work addresses the challenge of classifying short video clips, using a domain-specific feature design approach, capable of performing significantly well using as little as one training example per action. The method is based on Gunner Farneback’s dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate representations of the human movements/actions by clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each test video to the trained action with the highest similarity (a.k.a lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets using One-Shot and K-Shot learning approaches, and the results reveal the discriminative nature of our proposed methodology in comparison with state-of-the-art techniques.

     

Approximate Solutions of Initial Value Problems for Ordinary Differential Equations Using Radial Basis Function Networks

We present a numerical approach for the approximate solutions of first order initial value problems (IVP) by using unsupervised radial basis function networks. The proposed unsupervised method is able to solve IVPs with high accuracy. In order to demonstrate the efficiency of the proposed approach, we also compare its solutions with the solutions obtained by a previously proposed neural network method for representative examples.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Online short text clustering using infinite extensions of discrete mixture models

Short text clustering is one of the fundamental tasks in natural language processing. Different from traditional documents, short texts are ambiguous and sparse due to their short form and the lack of recurrence in word usage from one text to another, making it very challenging to apply conventional machine learning algorithms directly. In this article, we propose two novel approaches for short texts clustering: collapsed Gibbs sampling infinite generalized Dirichlet multinomial mixture model infinite GSGDMM) and collapsed Gibbs sampling infinite Beta-Liouville multinomial mixture model (infinite GSBLMM). We adopt two flexible and practical priors to the multinomial distribution where in the first one the generalized Dirichlet distribution is integrated, while the second one is based on the Beta-Liouville distribution. We evaluate the proposed approaches on two famous benchmark datasets, namely, Google News and Tweet. The experimental results demonstrate the effectiveness of our models compared to basic approaches that use Dirichlet priors. We further propose to improve the performance of our methods with an online clustering procedure. We also evaluate the performance of our methods for the outlier detection task, in which we achieve accurate results.     

A new fast motion estimation algorithm using fast mode decision for high-efficiency video coding standard

High-efficiency video coding is the latest standardization effort of the International Organization for Standardization and the International Telecommunication Union. This new standard adopts an exhaustive algorithm of decision based on a recursive quad-tree structured coding unit, prediction unit, and transform unit. Consequently, an important coding efficiency may be achieved. However, a significant computational complexity is resulted. To speed up the encoding process, efficient algorithms based on fast mode decision and optimized motion estimation were adopted in this paper. The aim was to reduce the complexity of the motion estimation algorithm by modifying its search pattern. Then, it was combined with a new fast mode decision algorithm to further improve the coding efficiency. Experimental results show a significant speedup in terms of encoding time and bit-rate saving with tolerable quality degradation. In fact, the proposed algorithm permits a main reduction that can reach up to 75 % in encoding time. This improvement is accompanied with an average PSNR loss of 0.12 dB and a decrease by 0.5 % in terms of bit-rate.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.