Biosynthesis of SeNPs by Mycobacterium bovis and their enhancing effect on the immune response against HBs antigens: an in vivo study

Conventional hepatitis B vaccine contains alum but is less effective to induce Th1 response. Selenium nanoparticles and Bacillus Calmette–Guerin were reported as immune modulators. In this study, SeNPs were extracted from Mycobacterium bovis and characterised. SeNPs were mixed with HBs‐Ag and administered to the mice to investigate he immune response pattern. With an addition of Se ions at a sub‐inhibitory concentration to the Sauton medium broth after 24 h, SeNPs were extracted from M. bovis and characterised by Fourier transform infrared spectroscopy, dynamic light scattering, atomic forcemicroscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, and thermogravimetric analysis. Furthermore, female inbred BALB/c mice were injected subcutaneously on the first, 14th, 28th day with 100 and 200 µg doses of that SeNPs supplemented with HBs‐Ag vaccine. Later, the total antibody, isotypes of Immunoglobulin G1, Interlukin 4, and interferon‐γ were measured by enzye‐linked immunosorbent assay. The size of the SeNPs was <150 nm. Level of total antibody and immunoglobulin G2a increased significantly in the group that received 200 µg/ml nano selenium extracted from M. bovis. SeNPs in dose of 200 µg coated with organic materials of M. bovis could induce an influential immune response in relation to the conventional HBs‐Ag vaccine.Inspec keywords: selenium, nanoparticles, nanofabrication, microorganisms, Fourier transform spectra, infrared spectra, light scattering, atomic force microscopy, X‐ray chemical analysis, transmission electron microscopy, thermal analysis, nanomedicineOther keywords: biosynthesis, selenium nanoparticles, Mycobacterium bovis, enhnced immune response, HBs antigens, Bacillus Calmette‐Guerin, immune modulators, biogenic SeNPs, immune response pattern, Sauton medium broth, Fourier transform infrared spectroscopy, dynamic light scattering, atomic force microscopy, energy dispersive X‐ray spectrum, transmission electron microscopy, thermogravimetric analysis, female inbred BALB/c mice, antibody, IgG1 isotype, IgG2a isotype, IL₄ isotype, interferon‐γ, enzyme‐linked immunosorbent assay, bacterium, time 24 h, time 14 day, time 28 day, Se     

Green synthesis of silver nanoparticles using Glaucium corniculatum (L.) Curtis extract and evaluation of its antibacterial activity

The metal nanoparticles, due to interesting features such as electrical, optical, chemical and magnetic properties, have been investigated repeatedly. Also, the mentioned nanoparticles have specific uses in terms of their antibacterial activity. The biosynthesis method is more appropriate than the chemical method for producing the nanoparticles because it does not need any special facilities; it is also economically affordable. In the current study, the silver nanoparticles (AgNPs) were obtained by using a very simple and low‐cost method via Glaucium corniculatum (L.) Curtis plant extract. The characteristics of the AgNPs were investigated using techniques including: X‐ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy. The SEM and TEM images showed that the nanoparticles had a spherical shape, and the mean diameter of them was 53.7 and 45 nm, respectively. The results of the disc diffusion test used for measuring the anti‐bacterial activity of the synthesised nanoparticles indicated that the formed nanoparticles possessed a suitable anti‐bacterial activity.Inspec keywords: silver, nanoparticles, antibacterial activity, nanomedicine, nanofabrication, X‐ray diffraction, transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectraOther keywords: green synthesis, silver nanoparticles, Glaucium corniculatum Curtis extract, antibacterial activity, metal nanoparticles, biosynthesis method, X‐ray diffraction, transmission electron microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, SEM, TEM, spherical shape, disc diffusion test, Ag     

The Modeling of Magnetorheological Elastomers: A State-of-the-Art Review

The magnetorheological elastomers (MREs) are novel multifunctional materials wherein their viscoelastic properties can be varied instantly under an application of applied magnetic field. Due to their field-dependent stiffness and damping properties, MREs are widely used in the development and design of MRE-based adaptive vibration isolators and absorbers and also biomedical engineering. Moreover, MREs due to their inherent magnetostriction effect have enormous potential for the development of soft actuators. The dynamic behavior of MREs is affected by various material parameters (e.g., matrix and particle types, particle concentration, additives) as well as mechanical and magnetic loading parameters (e.g., frequency, amplitude, temperature, magnetic flux density). Understanding and predicting the effect of materials and loading parameters on the response behavior of MREs are of paramount importance for the design of MRE-based adaptive structures and systems. This review paper mainly aims to provide a comprehensive study of material constitutive models to predict the nonlinear magnetomechanical behavior of MREs. Particular emphasis is paid to physics-based models including continuum- and microstructure-based models. Moreover, phenomenological models describing the dynamic magnetoviscoelastic behavior of MREs as well as the effect of temperature on the magnetomechanical behavior of such materials are properly addressed.     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration

In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed methodology, Levenberg–Marquardt second order method is used to train the multilayer perceptron. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters, trained ANN has therefore satisfactory generalization capability. Both single and three-phase line energizations are analyzed. The simulated results for 39-bus New England test system, indicate that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy.     

A hybrid method for flowshops scheduling with condition-based maintenance constraint and machines breakdown

One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expected makespan. Furthermore, we consider a condition-based maintenance (CBM) strategy which could be used in most industrial settings. The proposed algorithm is designed for non-resumable flowshop state where the processing of jobs after preventive maintenance is restarted from the beginning. We propose a hybrid algorithm based on genetic algorithm and simulated annealing. Additionally, we conduct an extensive parameter calibration with the utilization of Taguchi method and select the optimal levels of the algorithm’s performance influential factors. The preliminary results indicate that the proposed method provides significantly better results compared with other high performing algorithms in the literature.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

Multi-Verse Optimizer: a nature-inspired algorithm for global optimization

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.     

Impacts in multibody systems: modeling and experiments

This paper outlines a novel approach to the modeling and analysis of impact involving multibody systems. This approach is based on an analysis of energy absorption and restitution during impact, using a decomposition of the kinetic energy, which decouples the parts associated with the spaces of admissible and constrained motions of the underlying unilateral constraints. Such a decomposition turns out to be useful in the analysis of energy dissipation during impact, and leads to a generalized definition of the energetic coefficient of restitution, which targets particularly collisions in multibody systems. The applicability of the approach reported is investigated by conducting an experimental study on a robotic testbed. It is shown that impact between multibody systems is considerably affected not only by the local dynamics characteristics of the interacting bodies, but also the configuration of the whole multibody system. The results reported here show that our decomposition can offer a sound characterization of impact in several problems of multibody systems.     

Multi-product multi-chance-constraint stochastic inventory control problem with dynamic demand and partial back-ordering: A harmony search algorithm

In this paper, a multiproduct inventory control problem is considered in which the periods between two replenishments of the products are assumed independent random variables, and increasing and decreasing functions are assumed to model the dynamic demands of each product. Furthermore, the quantities of the orders are assumed integer-type, space and budget are constraints, the service-level is a chance-constraint, and that the partial back-ordering policy is taken into account for the shortages. The costs of the problem are holding, purchasing, and shortage. We show the model of this problem is an integer nonlinear programming type and to solve it, a harmony search approach is used. At the end, three numerical examples of different sizes are given to demonstrate the applicability of the proposed methodology in real world inventory control problems, to validate the results obtained, and to compare its performances with the ones of both a genetic and a particle swarm optimization algorithms.