Concomitant Crystallization of Copper(II) Sachharinato Complexes with 2-methylpyrazine as a Monomer and an One-dimensional Polymer: Syntheses,Crystal Structures,Spectroscopic and Thermal Properties

The reaction of [Cu(sac)₂(H₂O)₄] · 2H₂O with 2-methylpyrazine (mpyz) leads two complexes, concomitant crystallization of a mononuclear complex [Cu(sac)₂(mpyz)(H₂O)₂] (1) and a polymeric complex [Cu(sac)₂(μ-mpyz)]_n (2). Both complexes have been characterized by elemental analyses, magnetic measurements, FT-IR and ESR, TG-DTA and single-crystal X-ray diffraction analyses. Single-crystal X-ray analyses show that complex 1 consists of discrete molecules in which the copper(II) ions exhibits a square-pyramidal coordination geometry. The individual molecules of 1 are connected into a hydrogen-bonded chain structure, which is further assembled to form a three-dimensional network by π–π stacking interactions. Complex 2 is an 1D coordination polymer in which copper(II) centers are bridged by the mpyz ligand. The chains are further assembled to form two-dimensional frameworks by π–π and C–H···π stacking interactions.     

N,N-dimethylglycyl-amido derivative of minocycline and 6-demethyl-6-desoxytetracycline, two new glycylcyclines highly effective against tetracycline-resistant gram-positive cocci

The in vitro activities of the N,N-dimethylglycyl-amino derivative of minocycline (DMG-MINO) and 6-dimethyl-6-dexoxytetracycline (DMG-DMDOT), members of a new generation of tetracyclines, were evaluated by an agar dilution method and were compared with those of tetracycline and minocycline against 224 tetracycline-resistant and 73 tetracycline-susceptible recent clinical isolates of gram-positive cocci, including multiple-antibiotic-resistant methicillin-resistant Staphylococcus aureus and penicillin-resistant Streptococcus pneumoniae. The MICs of DMG-MINO and DMG-DMDOT were up to 500- to 2,000-fold lower than those of tetracycline against methicillin-resistant S. aureus and Streptococcus pneumoniae (MIC for 50% of strains tested [MIC50], < 0.06 microgram/ml). Against Streptococcus groups A, B, C, and G and Enterococcus faecalis, the MIC50 was 0.5 microgram/ml. MIC50s were greater only for coagulase-negative staphylococci (2 micrograms/ml). These data indicate that DMG-MINO and DMG-DMDOT are very potent drugs, and further in vitro and in vivo studies are warranted.     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Methacrylate-functionalized POSS influence on cross-linking and mechanical properties of styrene-butadiene rubber

Iranian Polymer Journal - The addition of methacrylate-functional polyhedral oligomeric silsesquioxane (MA-POSS) nanoparticles to styrene-butadiene rubber (SBR) composites was evaluated in terms of...     

Improvement of flexural bond strength of zirconia-resin cement by surface patterning using sub-nanosecond UV laser

Zirconia is a dental material that shows excellent biocompatibility and high strength in clinical applications. This study aims to evaluate the effects of ultrafast laser applications. The surface nanostructures were classified into three groups. Group 1 was generated using the burst mode, with three different distances between dots: 52 µm (Group 1a), 104 µm (Group 1b), and 156 µm (Group 1c). Group 2 was processed using the scanning mode configuration, with a set of parallel lines. Group 3 was also processed using this scanning configuration creating a set of square-shaped patterning. Group 4 was the control group. After the surface treatments, a pair of zirconia specimens was bonded end to end with resin cement. Flexural bond strength (FBS) test was applied in a universal test machine. Multiple comparisons were performed using a one-way analysis of variance and the Tukey's HSD test. All the samples that were treated with the laser showed higher FBS values than the untreated surface. Using the burst mode, preformed circular-shaped surface on an angle of 90⁰ at 52 µm distance (Group 1a) showed the highest FBS values among all groups (p < .05). Groups 2 and 3 had significantly higher values than 1b and 1c.     

Unsupervised Multiway Data Analysis: A Literature Survey

Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.     

Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.     

Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange

Prediction of stock price index movement is regarded as a challenging task of financial time series prediction. An accurate prediction of stock price movement may yield profits for investors. Due to the complexity of stock market data, development of efficient models for predicting is very difficult. This study attempted to develop two efficient models and compared their performances in predicting the direction of movement in the daily Istanbul Stock Exchange (ISE) National 100 Index. The models are based on two classification techniques, artificial neural networks (ANN) and support vector machines (SVM). Ten technical indicators were selected as inputs of the proposed models. Two comprehensive parameter setting experiments for both models were performed to improve their prediction performances. Experimental results showed that average performance of ANN model (75.74%) was found significantly better than that of SVM model (71.52%).     

A comprehensive study on mid-level representation and ensemble learning for emotional analysis of video material

Multimedia Tools and Applications - In today’s society where audio-visual content such as professionally edited and user-generated videos is ubiquitous, automatic analysis of this content is...