Poly-functional Catalyst Development for Benzene Production from Alkyl-aromatic Mixture via Hydrodealkylation

Development of a poly-functional catalyst that has higher hydrodealkylation activity based on 15% Cr/γ- Al₂ O₃ was researched for benzene production from an alkyl-aromatic mixture via hydrodealkylation. For this goal, the effects of the promoters like Fe, Co, or Ni and their concentrations on the activity of a Cr/Al₂ O₃ catalyst were investigated. Toluene + n-heptane + tetrahydrotiophene (89:10:1 w%) model mixture, which has the same content as industrial aromatics, was used as feedstock. The activities of catalysis in reactions of hydrodealkylation, hydrocracking, and hydrogenolize with respect to conversions of toluene, heptane, and tetrahydrotiophene were tested, respectively. As to the obtained results, the catalyst including 5% Fe has a higher hydrodealkylation activity, compared to the catalyst including Co, and especially to the alumocrom catalyst not including a promoter, while it has a lower coke formation and reduced activity to the catalyst including Ni.     

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.     

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.

     

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.     

Correlation of reactivity with chemical structure: Thermal hydrogenation of gas oils

The relationship between chemical structure and reactivity for thermal hydroprocessing was studied for five gas oils derived from Alberta bitumens. Chemical structure was characterized by combining data from ¹ H and ¹³ C nuclear magnetic resonance spectroscopy, class fractionation, and elemental analysis to calculate structural parameters. Thermal hydrotreating was performed in a continuous-flow stirred reactor at 420 and 440°C, 13.9 MPa hydrogen pressure, and 1.5 h¹ LHSV. Conversion of the 343–525°C boiling fraction of the gas oils was correlated with the concentration of naphthenic methylene groups in the feed. Formation of methane and ethane was dependent on the degree of condensation of the aromatic rings in the feed oils. Thermal desulfurization was highly correlated with the amount of saturates in the feed, and the aromaticity of the resin fraction. Hydrogen consumption increased with the aromaticity of the gas oil…     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set. Responsible Editor: Charu Aggarwal.     

Preparation of dye sensitized titanium oxide nanoparticles for solar cell applications

A new method for synthesis of titanium dioxide (TiO₂)-dye nanoparticles is reported. TiO₂ nanocrystals were obtained at 150 and 200 °C by using chemically bonded TiO₂-sensitizer dye as a precursor. Titanium tetraisopropoxide was first modified with a dye molecule and then precipitated by dropping into acidic water. A strongly colored precipitate was obtained. Hydrothermal growth of a colloidal solution was carried out in a Teflon-lined stainless steel autoclave. Dye sensitized solar cell efficiencies obtained were comparable and fill factor values were close to the analogous cells prepared by the use of conventional TiO₂ paste techniques. This method allows the use of different substrates together with nanocrystalline TiO₂ for many technological applications.     

Competitiveness analysis of automotive industry in Turkey using Bayesian networks

The purpose of this study is to analyze the relations between the factors that enable national competitive advantage and the establishment of competitive superiority in automotive industry through a comprehensive analytical model. Bayesian networks (BN) are used to investigate the associations of different factors in the automotive industry which lead to competitive advantage. The results of the study focus on building a road map for the automotive sector policy makers in their way to improve the competitiveness through scenario analysis. Using the probabilistic dependency structure of the Bayesian network all of the variables in the model can be estimated. Thus, with the proposed model the automotive industry can be analyzed as a whole system and not only in terms of single variables. Findings of the model indicate that technological developments in automotive industry can alter the nature of competition in this industry.