Some picture fuzzy Bonferroni mean operators with their application to multicriteria decision making

In this paper, we extend the Bonferroni mean (BM) operator with the picture fuzzy numbers (PFNs) to propose novel picture fuzzy aggregation operators and demonstrate their application to multicriteria decision making (MCDM). On the basis of the algebraic operational rules of PFNs and BM, we introduce some aggregation operators: the picture fuzzy Bonferroni mean, the picture fuzzy normalized weighted Bonferroni mean, and the picture fuzzy ordered weighted Bonferroni mean. Then, a new picture fuzzy MCDM method is proposed with the help of the proposed operators. Lastly, a practical application of proposed model is given to verify the developed model and related results of the proposed model is compared with the results of the existing models to indicate its applicability.     

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.     

Effects of maternal alcohol intake on fractal properties in humanfetal breathing dynamics

Fractal methods have been found to be useful in characterizing biomedical signals. The use of fractal estimation requires the estimation of parameter H, which is directly related to the fractal dimension D. Here, the authors propose a new approach which is a combination of the wavelet transform and fractal estimators to characterize the human fetal breathing signals before and after the intake of two glasses of wine by a mother. This study was performed on 26 fetuses. The variances of the wavelet coefficients were estimated at each scale. The slope of the representation on a logarithmic plot from the scales 5 to 1 was found to be increased after alcohol intake. The authors' results suggested that fetal breathing rates have a rough structure before the alcohol intake and a smooth structure after alcohol intake     

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.

     

A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding

Segmentation is a critical task in image processing. Bi-level segmentation involves dividing the whole image into partitions based on a threshold value, whereas multilevel segmentation involves multiple threshold values. A successful segmentation assigns proper threshold values to optimise a criterion such as entropy or between-class variance. High computational cost and inefficiency of an exhaustive search for the optimal thresholds leads to the use of global search heuristics to set the optimal thresholds. An emerging area in global heuristics is swarm-intelligence, which models the collective behaviour of the organisms. In this paper, two successful swarm-intelligence-based global optimisation algorithms, particle swarm optimisation (PSO) and artificial bee colony (ABC), have been employed to find the optimal multilevel thresholds. Kapur's entropy, one of the maximum entropy techniques, and between-class variance have been investigated as fitness functions. Experiments have been performed on test images using various numbers of thresholds. The results were assessed using statistical tools and suggest that Otsu's technique, PSO and ABC show equal performance when the number of thresholds is two, while the ABC algorithm performs better than PSO and Otsu's technique when the number of thresholds is greater than two. Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding. Moreover, segmentation methods are required to have a minimum running time in addition to high performance. Therefore, the CPU times of ABC and PSO have been investigated to check their validity in real-time. The CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases.     

Artificial bee colony algorithm for large-scale problems and engineering design optimization

Engineering design problems are generally large scale or nonlinear or constrained optimization problems. The Artificial Bee Colony (ABC) algorithm is a successful tool for optimizing unconstrained problems. In this work, the ABC algorithm is used to solve large scale optimization problems, and it is applied to engineering design problems by extending the basic ABC algorithm simply by adding a constraint handling technique into the selection step of the ABC algorithm in order to prefer the feasible regions of entire search space. Nine well-known large scale unconstrained test problems and five well-known constrained engineering problems are solved by using the ABC algorithm and the performance of ABC algorithm is compared against those of state-of-the-art algorithms.     

Achieving Full Frequency and Space Diversity in Wireless Systems via BICM, OFDM, STBC, and Viterbi Decoding

Orthogonal frequency-division multiplexing (OFDM) is known as an efficient technique to combat frequency-selective channels. In this paper, we show that the combination of bit-interleaved coded modulation (BICM) and OFDM achieves the full frequency diversity offered by a frequency-selective channel with any kind of power delay profile (PDP), conditioned on the minimum Hamming distance dfree of the convolutional code. This system has a simple Viterbi decoder with a modified metric. We then show that by combining such a system with space-time block coding (STBC), one can achieve the full space and frequency diversity of a frequency-selective channel with N transmit and M receive antennas. BICM-STBC-OFDM achieves the maximum diversity order of NML over L-tap frequency-selective channels regardless of the PDP of the channel. This latter system also has a simple Viterbi decoder with a properly modified metric. We verify our analytical results via simulations, including channels employed in the IEEE 802.11 standards     

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.