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.

     

One-dimensional partitioning for heterogeneous systems: Theory and practice

We study the problem of one-dimensional partitioning of nonuniform workload arrays, with optimal load balancing for heterogeneous systems. We look at two cases: chain-on-chain partitioning, where the order of the processors is specified, and chain partitioning, where processor permutation is allowed. We present polynomial time algorithms to solve the chain-on-chain partitioning problem optimally, while we prove that the chain partitioning problem is NP-complete. Our empirical studies show that our proposed exact algorithms produce substantially better results than heuristics, while solution times remain comparable.     

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.     

Investigation of the Equivalence of National Dew-Point Temperature Realizations in the ?50?°C to +?20?°C Range

In the field of humidity quantities, the first CIPM key comparison, CCT-K6 is at its end. The corresponding European regional key comparison, EUROMET.T-K6, was completed in early 2008, about 4?years after the starting initial measurements in the project. In total, 24 NMIs from different countries took part in the comparison. This number includes 22 EURAMET countries, and Russia and South Africa. The comparison covered the dew-point temperature range from ?50?°C to +20?°C. It was carried out in three parallel loops, each with two chilled mirror hygrometers as transfer standards in each loop. The comparison scheme was designed to ensure high quality results with evenly spread workload for the participants. It is shown that the standard uncertainty due to the long-term instability was smaller than 0.008?°C in all loops. The standard uncertainties due to links between the loops were found to be smaller than 0.025?°C at ?50?°C and 0.010?°C elsewhere. Conclusions on the equivalence of the dew-point temperature standards are drawn on the basis of calculated bilateral degrees of equivalence and deviations from the EURAMET comparison reference values (ERV). Taking into account 16 different primary dew-point realizations and 8 secondary realizations, the results demonstrate the equivalence of a large number of laboratories at an uncertainty level that is better than achieved in other multilateral comparisons so far in the humidity field.     

Thermal degradation characteristic of Tetra Pak panel boards under inert atmosphere

hermal degradation characteristics of Tetra Pak panel boards (TPPB) can be useful to improve usage of such panels as an alternative to wood-based products such as plywood, fiberboard, and particleboard. In the study, samples from the TPBBs manufactured from waste Tetra Pak packages (WTPP) were heated in a nitrogen atmosphere at different heating rates (10, 15 and 20 °C/min) using a thermal analysis system. The Coats-Redfern kinetic model was applied to calculate kinetic parameters. The degradation rate equations were then established. In addition, the kinetic compensation effect (KCE) was used to correlate the pre-exponential factor (k _o ) with activation energy (E _a ) and the existence of the KCE was accepted. TG-FT/IR analyses were applied to the TPPB degradation and then the FT-IR stack plot was used to analyze gas products (CO₂, CH₄, HCOOH, and CH₃OH). Infrared vibrational frequencies and the micro, crystal structure of the TPPBs were investigated by Fourier transform infrared spectroscopy (FT-IR), Scanning electron microscope (SEM) and X-Ray diffraction analysis (XRD), respectively.     

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