Synthesis,characterization and magnetic properties of polythiophene/SrFe12-x(ZrZn)0.5xO19 nanocomposite as a microwave absorber

Ferrites are an important group of magnetic materials which are used as absorbers. The incorporation of ferrite and conducting polymer achieves great enhancement in microwave absorption properties. The nanocomposites of hexagonal ferrites embedded by conducting polymers such as polypyrrole, polyaniline and polythiophene (PTH) have been paid much attention. In the present study, strontium hexagonal ferrite doped by Zr and Zn with the final formula of SrFe_12-x(ZrZn)_0.5xO19 considering x = 0.9 and embedded by PTH was produced to achieve a nanocomposite with the highest microwave absorbing ability. In this study, after synthesis of SrFe₁₂O₁₉(ZrZn)_0.5xO19 and PTH, the nanocomposite was prepared by in situ polymerization. Wrapping the ferrite particles and PTH chains could form nanocomposite properly, and therefore acceptable interactions were observable between SrFe_12-x(ZrZn)_0.5xO19ferrite particles and PTH polymer chains in the composites. Assessing the X-ray diffraction (XRD) patterns of SrFe_12-x(ZrZn)_0.5xO19, PTH, and PTH/SrFe_12-x(ZrZn)_0.5xO19 nanocomposite indicated that the PTH characteristic peak shifts slightly and its peak intensity reduces, which may be attribute to the coating of PTH polymer chains onto SrFe_12-x(ZrZn)_0.5xO19 particles. We revealed also lower magnetic properties in the obtained nanocomposite. The morphological assessment also suggested that PTH could effectively coat the SrFe_12-x(ZrZn)_0.5xO19 particles. The synergistic effect of SrFe_12-x(ZrZn)_0.5xO19 particle plus PTH leads to microwave absorption percentage higher than 95% by PTH/SrFe_12-x(ZrZn)_0.5xO19 nanocomposite. Overall, nanocomposite creating by coupling interaction between SrFe_12-x(ZrZn)_0.5xO19 particles (x = 0.9) and PTH can effectively lead to achieve the highest rate of absorption of electromagnetic waves.     

A Regioselective Approach to Synthesize Indolyl Diketone Derivatives via Magnetic Polymeric Copper-Catalyst

Catalysis Letters - In the present paper, an efficient Cu-catalyzed regioselective acylation of indoles with phenylglyoxals was developed which is the first example of indolyl diketones synthesis...     

Green synthesis of zinc oxide nanoparticles using Amygdalus scoparia Spach stem bark extract and their applications as an alternative antimicrobial,anticancer, and anti-diabetic agent

For the first time in this study, Zinc oxide nanoparticles were biosynthesized by the eco-friendly and cost-effective procedure using Amygdalus scoparia stem bark extract then used as antibacterial, antifungal, anticancer, and anti-diabetic agents. The characterization techniques confirmed the biosynthesis, crystalline nature, structure, size, elemental composition of ZnO NPs and bioactive compounds that exist in A. scoparia extract accounting for Zn²⁺ ion reduction, capping and stabilization of ZnO NPs. The ZnO NPs displayed remarkable inhibitory activity against E. coli, E. aerigenes, S. aureus, P. oryzae, F. thapsinum, and F. semitectum compared to antibiotic standards. The ZnO NPs showed significant inhibitory effects on cancer cell lines, while it had no toxic effect on Vero normal cell line. The ZnO NPs (30 mg/kg)-treated diabetic rats showed significantly higher levels of insulin and lower AST, ALT and blood glucose compared with the STZ induced diabetic group and other treated groups (P < 0.05). The ZnO NPs- and extract-treated rats showed significantly higher levels of IR, GluT2, and GCK expression and lower TNFα expression compared with the STZ induced diabetic rats. Our findings showed that ZnO NPs represented an outstanding performance for biological applications.     

A multi-criteria context-sensitive approach for social image collection summarization

Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features. The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identify attractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.     

A strategy for the electro-organic synthesis of new hydrocaffeic acid derivatives

Electrochemical treatment processes can significantly contribute to the protection of the environment through the minimization of waste and toxic materials in effluents. From a pharmaceutical point of view and due to the existing resemblance between the electrochemical and biological reactions, it can be assumed that the oxidation mechanisms on the electrode and in the body share similar principles. In this paper, the application of electrochemical studies in the design of an environmentally friendly method was delineated for the new hydrocaffeic acid (HCA, 3,4-dihydroxy hydrocinnamic acid) derivatives synthesis at carbon electrodes in an undivided cell. In this cell, the EC mechanism reaction was involved, comprising two steps alternatively; (1) electrochemical oxidation and (2) chemical reaction. In particular, the electro-organic reactions of HCA, an important biological molecule, were studied in a water–acetonitrile (90:10 v/v) mixture in the presence of benzenesulfinic acid (3) and p-toluenesulfinic acid (4). The research included the use of a variety of experimental techniques, such as cyclic voltammetry, controlled-potential electrolysis and product spectroscopic identification.     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

A learning framework for the optimization and automation of document binarization methods

Almost all binarization methods have a few parameters that require setting. However, they do not usually achieve their upper-bound performance unless the parameters are individually set and optimized for each input document image. In this work, a learning framework for the optimization of the binarization methods is introduced, which is designed to determine the optimal parameter values for a document image. The framework, which works with any binarization method, has a standard structure, and performs three main steps: (i) extracts features, (ii) estimates optimal parameters, and (iii) learns the relationship between features and optimal parameters. First, an approach is proposed to generate numerical feature vectors from 2D data. The statistics of various maps are extracted and then combined into a final feature vector, in a nonlinear way. The optimal behavior is learned using support vector regression (SVR). Although the framework works with any binarization method, two methods are considered as typical examples in this work: the grid-based Sauvola method, and Lu’s method, which placed first in the DIBCO’09 contest. The experiments are performed on the DIBCO’09 and H-DIBCO’10 datasets, and combinations of these datasets with promising results.     

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Journal of Intelligent Manufacturing - In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber...     

Multi-player detection in soccer broadcast videos using a blob-guided particle swarm optimization method

Soccer is the most popular sport around the world, and automatic processing of soccer images is a precious alternative to the manual solutions regarding the explosive growth of soccer videos. A new multi-player detection algorithm in far view frames as an initial step to a wide range of applications, such as player tracking, is addressed in this paper. In the proposed detector, a two-step blob detection (grass-based blob detection followed by an edge-based blob detection) is combined with an efficient search mechanism based on particle swarm optimization (PSO) by assigning sub-swarms to each detected blob. Then, a sub-swarm is initialized and tripled to search for three models corresponding to two teams and the referee. Therefore, the most player-like regions in detected blobs are simultaneously searched by all sub-swarms flying through the solution space, thus expanding the scope of single player detection to multi-player detection. Experimental results demonstrate the efficiency and robustness of the algorithm.     

Vehicle routing problem with uncertain demands: An advanced particle swarm algorithm

The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers’ demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers’ demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.