Multifunctional Gadolinium‐Doped Mesoporous TiO2 Nanobeads: Photoluminescence,Enhanced Spin Relaxation,and Reactive Oxygen Species Photogeneration,Beneficial for Cancer Diagnosis and Treatment

Materials with controllable multifunctional abilities for optical imaging (OI) and magnetic resonant imaging (MRI) that also can be used in photodynamic therapy are very interesting for future applications. Mesoporous TiO₂ sub‐micrometer particles are doped with gadolinium to improve photoluminescence functionality and spin relaxation for MRI, with the added benefit of enhanced generation of reactive oxygen species (ROS). The Gd‐doped TiO₂ exhibits red emission at 637 nm that is beneficial for OI and significantly improves MRI relaxation times, with a beneficial decrease in spin–lattice and spin–spin relaxation times. Density functional theory calculations show that Gd³⁺ ions introduce impurity energy levels inside the bandgap of anatase TiO₂, and also create dipoles that are beneficial for charge separation and decreased electron–hole recombination in the doped lattice. The Gd‐doped TiO₂ nanobeads (NBs) show enhanced ability for ROS monitored via ^?OH radical photogeneration, in comparison with undoped TiO₂ nanobeads and TiO₂ P25, for Gd‐doping up to 10%. Cellular internalization and biocompatibility of TiO₂@x Gd NBs are tested in vitro on MG‐63 human osteosarcoma cells, showing full biocompatibility. After photoactivation of the particles, anticancer trace by means of ROS photogeneration is observed just after 3 min irradiation.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

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.     

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the solutions (90% improvement in average) when it is applied to standard test benches.     

Synthesis and characterization of multiwall carbon nanotube/waterborne polyurethane nanocomposites

The preparation of thermoplastic nanocomposites of waterborne polyurethane (WBPU) and multiwall carbon nanotubes (MWCNTs) via an in situ polymerization approach is presented. The effects of the presence and content of MWCNTs on the morphology and thermal, mechanical and electrical properties of the nanocomposites were investigated. Carbon nanotubes were modified with amide groups in order to enhance their chemical affinity towards WBPU. Thermogravimetric studies show enhanced thermal stability of the nanocomposites. Scanning and transmission electronic microscopy images prove that functionalized carbon nanotubes can be effectively dispersed in WBPU matrix. Mechanical properties reveal that Young's modulus and tensile strength tend to increase when appropriate amounts of MWCNTs are loaded due to the reinforcing effect of the functionalized carbon nanotubes. Thermal properties show an increase in the glass transition temperature and storage modulus with an increase in MWCNT content. X‐ray diffraction reveals better crystallization of the WBPU in the presence of MWCNTs. The WBPU/MWCNT nanocomposite film containing 1 wt% of MWCNTs exhibits a conductivity nearly five orders of magnitude higher than that of WBPU film. © 2017 Society of Chemical Industry     

Facile synthesis of polyoxometalate-based composite with doped ternary NiCoFe cations as electrocatalyst for oxygen evolution reaction

The construction of efficient and low-cost electrocatalysts for oxygen evolution reactions (OER) to replace precious catalysts is a necessity to achieve economic production of hydrogen. Herein, we report an efficient tri-metallic electrocatalysts for the OER that is prepared by incorporate nickel, cobalt and iron cations on Triton X-100/phosphotungstic acid organic-inorganic composite without utilize any binders or energy consumer procedure. Considering to the synergy effect of simultaneous absorption of NiCoFe cations on composite substrate, the as-made tri-metallic catalyst exhibits excellent OER activity with a small overpotential of 210 and 330 mV at a current density of 10 and 100 mA cm^?2, respectively. Moreover, remarkable trends in electrocatalytic activity of mono-, bi- and tri-metallic electrocatalysts at low (10 mA) and high (100 mA) current density are observed. In addition, this new families of non-precious metal catalyst shows long-term durability in 1 M KOH.     

Preparation, Characterization and Photocatalytic Properties of Ba-Cd-Sr-Ti Doped Fe3O4 Nanohollow Spheres on Removal of Congo Red Under Visible-Light Irradiation

In this study, Ba-Cd-Sr-Ti doped Fe₃O₄ nanohollow spheres were successfully prepared via a simple solvothermal method. The crystal size, structure, morphology and elemental analysis of the as-prepared sample were investigated in detail by X-ray diffraction (XRD), FT-IR, scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) spectroscopy, respectively. Magnetic hysteresis measurement was carried out on a vibrant sample magnetometer (VSM) showing the soft ferromagnetic property at room temperature. The synthesized nanohollow spheres were employed as a photocatalyst to study the photocatalytic degradation of dye contaminations. The UV-Vis results showed that the specimen could well catalyze the decolorizing of congo red (CR) solution and a removal efficiency of 99.5 % was obtained at pH 6. The optical characteristic of the products was studied by estimating the band-gap energy based on diffuse-reflectance spectroscopy (DRS) which represented the doped magnetite with semiconductor metals to be more prone in the visible region as compared to UV region. Some factors such as initial dye concentration, pH, and contact time influencing the decomposition of CR were evaluated.     

Extended Algorithms for Approximating Variable Order Fractional Derivatives with Applications

This paper proposes accurate and robust algorithms for approximating variable order fractional derivatives of arbitrary order. The proposed schemes are based on finite difference approximations. We compare the performance of algorithms by introducing a new formulation of experimental convergence order. Two initial value problems are considered and solved by means of the proposed methods. Numerical results are provided justifying the usefulness of the proposed methods.