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.     

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.

     

On ℤ2ℤ2[u]-additive codes

In this paper, a new class of additive codes which is referred to as ?₂ ?₂[u]-additive codes is introduced. This is a generalization towards another direction of recently introduced ?₂ ?₄-additive codes [J. Borges, C. Fernández-Córdoba, J. Pujol, J. Rif´a, and M. Villanueva, ?₂ ?₄-linear codes: Generator matrices and duality, Designs Codes Cryptogr. 54(2) (2010), pp. 167–179]. ?₂ ?₄-additive codes have shown to provide a promising class of codes with their algebraic structure and applications such as steganography. The standard generator matrices are established and by introducing orthogonality the parity-check matrices are also obtained. A MacWilliams-type identity that relates the weight enumerator of a code with its dual is proved. Furthermore, a Gray map that maps these codes to binary codes is defined and some examples of optimal codes which are the binary Gray images of ?₂ ?₂[u]-additive codes are presented.     

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.     

Molecular weight control of poly(ethylene terephthalate) in extrusion process

A control strategy is developed to control molecular weight of recycled poly(ethylene terephthalate), PET, to overcome its degradation through an extrusion process. To obtain dynamic model of a twin screw extruder, steady‐state, and unsteady‐state experiments were performed. Discrete convolution models between inputs and outputs were obtained. Process inputs were considered as screw speed (SS), feed rate, and barrel temperatures and the output was viscosity average molecular weight (M_v) of the extrudate. SS and molecular weight of the product were chosen as the manipulated, controlled variable pair by considering singular value decomposition (SVD) technique. Model based PID controller and model predictive controller were used in the designed control scheme. By the simulation studies, both controllers were found to be successful for set‐point tracking, disturbance rejection cases; and were proven to be robust under modeling errors. POLYM. ENG. SCI., 54:459–465, 2014. © 2013 Society of Plastics Engineers     

A review on selected heterogeneous photocatalysts for hydrogen production

In this study, visible light‐driven heterogeneous photocatalysts for hydrogen production are comparatively assessed based on technical, environmental, and cost criteria. The photocatalysis systems are compared with respect to their (i) rate of hydrogen generation per gram; (ii) rate of hydrogen generation per m² of the specific surface area; and (iii) the band gap energy. The photocatalysis systems are also compared and discussed in terms of flammability, reactivity, and their impact on living systems' health. Furthermore, the costs of the required components of the photocatalysis systems are ranked. In addition to individual photocatalyst comparison, seven photocatalyst groups are ranked and compared. The results show that TiO₂‐C‐362 and Ag_0.03Mn_0.40Cd_0.60S show the highest in terms of µmol/h‐g_cat and µmol/h‐m²_cat, respectively, and TiO₂‐C‐362 has the highest overall rankings. The Zn/In/S‐based photocatalyst groups show the highest hydrogen production rate in terms of µmol/h‐gcat and µmol/h‐m²_cat. Overall, Cd/S/Zn has the highest rankings when cost and health and environmental impact criteria are taken into account. Copyright © 2014 John Wiley & Sons, Ltd.     

Nonlocal elasticity effect on vibration of in-plane loaded double-walled carbon nano-tubes

Summary Vibration and buckling of in-plane loaded simply supported double-walled carbon nanotubes were investigated using the nonlocal Timoshenko-beam theory. The influence of in-plane loads on the natural frequencies was determined. The results show that while the natural frequencies decrease with increasing compressive in-plane loads, an increase in frequencies is observed for tension type of in-plane loads. Effects of in-plane loads are more pronounced for lower modes, and some mode changes are observed at critical in-plane loads. A comparison of nonlocal elasticity solutions with local elasticity solutions indicates that the nonlocal effects should be considered for higher modes of vibration of double-walled carbon nanotubes.     

Machining performance of TiN coatings incorporating indium as a solid lubricant

The machining and wear performance of TiN-coated and patterned carbide inserts incorporating indium as a solid lubricant are reported in this study. Cutting tests were conducted by turning hardened 4340 steel in both lubricated and dry conditions. During turning, periodic flank wear measurements were made. The chips formed during cutting were examined by scanning electron microscopy, as the condition of the chip reflects the conditions obtained during machining. Inserts subject to dry machining were also examined using optical microscopy and X-ray photoelectron spectroscopy to determine the extent of damage on the rake surface as well as the degree of material transfer. The results showed indium to be effective in reducing flank wear during lubricated machining, but little additional benefit of patterning was observed. For dry machining, some degree of improvement was noted in the patterned sample, but the degree of lubricity brought about by the indium coating was not sufficient and the overall flank wear was higher than the lubricated tests. However, the wear and damage on the rake surface along the path of the chip was reduced by the presence of the In-containing microreservoirs. An additional test was conducted using an instrument that simulates temperature effects during machining, and it was found that the lubricity achieved by In coatings is lost above 450 °C. These results suggest that the use of indium is limited to below this temperature, and above this temperature transforms to a less lubricious indium oxide.