Tailoring the reaction mixture composition for preparing zeolite coatings on aluminum supports in alkaline environments

Various alkaline reaction mixture compositions were tested for determining their suitability to be used in the direct crystallization of zeolites on aluminum supports. No organic materials, such as templates, complexing agents, and corrosion inhibitors were included in the reaction mixtures. It was observed that aluminum supports remained quite stable in alkaline synthesis mixtures when suitable reaction mixture compositions with relatively high silica content were used. These favorable compositions might be recognized from their sodium silicate/(sodium hydroxide?+?sodium aluminate) mass ratios, as depicted in this study. Coatings were prepared on aluminum by using some of these compositions. It was determined that aluminum hydroxide phases commonly forming on the support could be eliminated to a great extent and consequently, crystalline coatings of zeolites P and Y were obtained on aluminum surfaces. The coatings exhibited trivial mass loss in the mechanical stability tests applied.     

Fatigue Performance of Friction-Stir-Welded Al-Mg-Sc Alloy

Fatigue behavior of a friction-stir-welded Al-Mg-Sc alloy was examined in cast and hot-rolled conditions. In both cases, the joints failed in the base material region and therefore the joint efficiency was 100 pct. The specimens machined entirely from the stir zone demonstrated fatigue strength superior to that of the base material in both preprocessed tempers. It was shown that the excellent fatigue performance of friction-stir joints was attributable to the ultra-fine-grained microstructure, the low dislocation density evolved in the stir zone, and the preservation of Al₃Sc coherent dispersoids during welding. The formation of such structure hinders the initiation and growth of fatigue microcracks that provides superior fatigue performance of friction-stir welds.     

Stacked auto-encoder for classification of polarimetric SAR images based on scattering energy

This paper proposes a new algorithm, for polarimetric synthetic aperture radar (PolSAR) classification, based on a stacked auto-encoder and scattering energy. Previous approaches to PolSAR classification predominantly consider only the single pixel of distribution of the polarimetric data and scattering characteristics, and ignore other kinds of image features like the relationship of the local pixels. Besides, because of the complexities of PolSAR data, it is difficult to compute the derivatives that are needed for back-propagation in deep-learning classifiers. To overcome these difficulties, we propose a new approach that combines the scattering power and stacks sparse auto-encoder (Scattering SSAE) for PolSAR classification. Firstly, orientation compensation is used to compensate the polarization orientation angle, reducing the impact of polarimetric angle noise. Secondly, Freeman-Durden decomposition is adopted to extract three basic scattering powers: surface, double bounce and volume. Each PolSAR image pixel is transformed into these scattering powers, yielding a new kind of feature from the PolSAR data. Finally, using the three kinds of scattering power as inputs, we combine local spatial information using a patch-based approach, and use a deep learning architecture to achieve classification. We compare our method against several other state-of-the-art methods using ground-truthed test-data, and show that the Scattering SSAE method achieves higher accuracy than other methods on most categories.     

Empirical evaluation methods for multiobjective reinforcement learning algorithms

While a number of algorithms for multiobjective reinforcement learning have been proposed, and a small number of applications developed, there has been very little rigorous empirical evaluation of the performance and limitations of these algorithms. This paper proposes standard methods for such empirical evaluation, to act as a foundation for future comparative studies. Two classes of multiobjective reinforcement learning algorithms are identified, and appropriate evaluation metrics and methodologies are proposed for each class. A suite of benchmark problems with known Pareto fronts is described, and future extensions and implementations of this benchmark suite are discussed. The utility of the proposed evaluation methods are demonstrated via an empirical comparison of two example learning algorithms.     

Prevalence of hepatitis B and C viruses in healthy Indonesian blood donors

Blood samples were collected from 7572 healthy volunteer blood donors from 21 of the 27 Indonesian provinces, and tested for antibodies to hepatitis C virus (anti-HCV) using the new second-generation enzyme immunosorbent assay, and also tested for hepatitis B surface antigen (HBsAg). We detected anti-HCV in 2.1% of the blood donors. No statistically significant difference was found between males and females or between locations, but there was a statistically significant increasing likelihood of anti-HCV prevalence with increasing age. HBsAg was found in 8.8% of the 3839 tested donors. There was no statistically significant difference between sexes or age groups, but there was a statistically significant higher prevalence in the islands of Sulawesi and eastern Indonesia. Only 7 individuals, from 5 locations, were both anti-HCV and HBsAg positive. Based on responses to a questionnaire, a history of surgery, blood transfusion, intravenous medication, and acupuncture were identified as risk factors for the presence of anti-HCV. No such risk factor was identified for HBsAg prevalence. The combined data suggest separate modes of transmission for the 2 viruses, and indicate the need for continued surveillance for these agents in Indonesian blood banks.     

A computational strategy for damage‐tolerant design of hollow shafts under mixed‐mode loading condition

Three‐dimensional numerical analyses, using the finite element method (FEM), have been adopted to simulate fatigue crack propagation in a hollow cylindrical specimen, under pure axial or combined axial‐torsion loading conditions. Specimens, made of Al alloys B95AT and D16T, have been experimentally tested under pure axial load and combined in‐phase constant amplitude axial and torsional loadings. The stress intensity factors (SIFs) have been calculated, according to the J‐integral approach, along the front of a part through crack, initiated in correspondence of the outer surface of a hollow cylindrical specimen. The crack path is evaluated by using the maximum energy release rate (MERR) criterion, whereas the Paris law is used to calculate crack growth rates. A numerical and experimental comparison of the results is presented, showing a good agreement in terms of crack growth rates and paths.     

Involvement of Chaperone Sigma1R in the Anxiolytic Effect of Fabomotizole

Sigma-1 receptor (chaperone Sigma1R) is an intracellular protein with chaperone functions, which is expressed in various organs, including the brain. Sigma1R participates in the regulation of physiological mechanisms of anxiety (Su, T. P. et al., 2016) and reactions to emotional stress (Hayashi, T., 2015). In 2006, fabomotizole (ethoxy-2-[2-(morpholino)-ethylthio]benzimidazole dihydrochloride) was registered in Russia as an anxiolytic (Seredenin S. and Voronin M., 2009). The molecular targets of fabomotizole are Sigma1R, NRH: quinone reductase 2 (NQO2), and monoamine oxidase A (MAO-A) (Seredenin S. and Voronin M., 2009). The current study aimed to clarify the dependence of fabomotizole anxiolytic action on its interaction with Sigma1R and perform a docking analysis of fabomotizole interaction with Sigma1R. An elevated plus maze (EPM) test revealed that the anxiolytic-like effect of fabomotizole (2.5 mg/kg i.p.) administered to male BALB/c mice 30 min prior EPM exposition was blocked by Sigma1R antagonists BD-1047 (1.0 mg/kg i.p.) and NE-100 (1.0 mg/kg i.p.) pretreatment. Results of initial in silico study showed that fabomotizole locates in the active center of Sigma1R, reproducing the interactions with the site’s amino acids common for established Sigma1R ligands, with the ΔG_bind value closer to that of agonist (+)-pentazocine in the 6DK1 binding site.     

Hierarchical Structure of Protein Sequence

Most non-communicable diseases are associated with dysfunction of proteins or protein complexes. The relationship between sequence and structure has been analyzed for a long time, and the analysis of the sequences organization in domains and motifs remains an actual research area. Here, we propose a mathematical method for revealing the hierarchical organization of protein sequences. The method is based on the pentapeptide as a unit of protein sequences. Employing the frequency of occurrence of pentapeptides in sequences of natural proteins and a special mathematical approach, this method revealed a hierarchical structure in the protein sequence. The method was applied to 24,647 non-homologous protein sequences with sizes ranging from 50 to 400 residues from the NRDB90 database. Statistical analysis of the branching points of the graphs revealed 11 characteristic values of y (the width of the inscribed function), showing the relationship of these multiple fragments of the sequences. Several examples illustrate how fragments of the protein spatial structure correspond to the elements of the hierarchical structure of the protein sequence. This methodology provides a promising basis for a mathematically-based classification of the elements of the spatial organization of proteins. Elements of the hierarchical structure of different levels of the hierarchy can be used to solve biotechnological and medical problems.     

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.