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.     

Electronic properties of magnetically doped nanotubes

Effect of doping of carbon nanotubes by magnetic transition metal atoms has been considered in this paper. In the case of semiconducting tubes, it was found that the system has zero magnetization, whereas in metallic tubes the valence electrons of the tube screen the magnetization of the dopants: the coupling to the tube is usually antiferromagnetic (except for Cr).     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...     

A novel design of 8-bit adder/subtractor by quantum-dot cellular automata

Application of quantum-dot is a promising technology for implementing digital systems at nano-scale. QCA supports the new devices with nanotechnology architecture. This technique works based on electron interactions inside quantum-dots leading to emergence of quantum features and decreasing the problem of future integrated circuits in terms of size. In this paper, we will successfully design, implement and simulate a new full adder based on QCA with the minimum delay, area and complexities. Also, new XOR gates will be presented which are used in 8-bit controllable inverter in QCA. Furthermore, a new 8-bit full adder is designed based on the majority gate in the QCA, with the minimum number of cells and area which combines both designs to implement an 8-bit adder/subtractor in the QCA. This 8-bit adder/subtractor circuit has the minimum delay and complexity. Being potentially pipeline, the QCA technology calculates the maximum operating speed.     

A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and NVH Optimization

In this paper, metamodeling and five well-known metaheuristic optimization algorithms were used to reduce the weight and improve crash and NVH attributes of a vehicle simultaneously. A high-fidelity full vehicle model is used to analyze peak acceleration, intrusion and component’s internal-energy under Full-Frontal, Offset-Frontal, and Side crash scenarios as well as vehicle natural frequencies. The radial basis functions method is used to approximate the structural responses. A nonlinear surrogate-based mass minimization was formulated and solved by five different optimization algorithms under crash-vibration constraints. The performance of these algorithms is investigated and discussed.     

Parallel Lagrange interpolation on <Emphasis Type="Italic">k</Emphasis>-ary <Emphasis Type="Italic">n</Emphasis>-cubes with maximum channel utilization

This paper proposes an efficient parallel algorithm for computing Lagrange interpolation on k-ary n-cube networks. This is done using the fact that a k-ary n-cube can be decomposed into n link-disjoint Hamiltonian cycles. Using these n link-disjoint cycles, we interpolate Lagrange polynomial using full bandwidth of the employed network. Communication in the main phase of the algorithm is based on an all-to-all broadcast algorithm on the n link-disjoint Hamiltonian cycles exploiting all network channels, and thus, resulting in high-efficiency in using network resources. A performance evaluation of the proposed algorithm reveals an optimum speedup for a typical range of system parameters used in current state-of-the-art implementations.

Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures

In this research, we work with data of futures contracts on foreign exchange rates for British pound (BP), Canadian dollar (CD), and Japanese yen (JY) that are traded at the Chicago Mercantile Exchange (CME) against US dollars. We model relationships between exchange rates in these currencies using linear models, feed forward artificial neural networks (ANN), and three versions of recurrent neural networks (RNN1, RNN2 and RNN3) for predicting exchange rates in these currencies against the US dollar. Our results on forecast evaluations based on AGS test the tests of forecast equivalence between any two competing models among the entire models employed for each of the series show that ANN and the three versions of RNN models offer superior forecasts for predicting BP, CD and JY exchange rates although the forecast evaluations based on MGN test are in sharp contrast. On the other hand forecast based on SIGN test shows that ANN and all the versions of RNN models offer superior forecasts for BP and CD in exception of JY exchange rates. The results for forecast evaluation for all the models for each of the series based on summary measures of forecast evaluations show that RNN3 model appears to offer the most accurate predictions of BP and RNN1 for JP exchange rates. However, none of the RNN models appear to be statistically superior to the benchmark (i.e., linear model) for predicting CD exchange rates.      

An ant colony optimization algorithm for the bi-objective shortest path problem

Multi-objective shortest path problem (MOSP) is an extension of a traditional single objective shortest path problem that seeks for the efficient paths satisfying several conflicting objectives between two nodes of a network. MOSP is one of the most important problems in network optimization with wide applications in telecommunication industries, transportation and project management. This research presents an algorithm based on multi-objective ant colony optimization (ACO) to solve the bi-objective shortest path problem. To analyze the efficiency of the algorithm and check for the quality of solutions, experimental analyses are conducted. Two sets of small and large sized problems that generated randomly are solved. Results on the set problems are compared with those of label correcting solutions that is the most known efficient algorithm for solving MOSP. To compare the Pareto optimal frontiers produced by the suggested ACO algorithm and the label correcting algorithm, some performance measures are employed that consider and compare the distance, uniformity distribution and extension of the Pareto frontiers. The results on the set of instance problems show that the suggested algorithm produces good quality non-dominated solutions and time saving in computation of large-scale bi-objective shortest path problems.     

Revisiting the free transverse vibration of embedded single-layer graphene sheets acted upon by an in-plane magnetic field

Murmu et al. [23] recently presented a nonlocal model for the transverse vibration of simply supported graphene sheets in the presence of a unidirectional in-plane magnetic field. Further studies showed that the majority of Lorentz’s force components were improperly provided and led to invalid governing equations. To remove such deficiencies, the most general form of Lorentz’s force components is carefully extracted in the present work. The nonlocal equations of motion of the problem are reconstructed and solved again. The influences of crucial parameters on the flexural frequencies of magnetically affected graphene sheets and nanoribbons are examined in detail. Furthermore, the crucial discrepancies between the results obtained in this study and those of the abovementioned previous work are rationally discussed. Some erroneous results of the latter are also rectified.     

Synthesis of 3D nanostructured metal alloy of immiscible materials induced by megahertz-repetition femtosecond laser pulses

In this work, we have proposed a concept for the generation of three-dimensional (3D) nanostructured metal alloys of immiscible materials induced by megahertz-frequency ultrafast laser pulses. A mixture of two microparticle materials (aluminum and nickel oxide) and nickel oxide microparticles coated onto an aluminum foil have been used in this study. After laser irradiation, three different types of nanostructure composites have been observed: aluminum embedded in nickel nuclei, agglomerated chain of aluminum and nickel nanoparticles, and finally, aluminum nanoparticles grown on nickel microparticles. In comparison with current nanofabrication methods which are used only for one-dimensional nanofabrication, this technique enables us to fabricate 3D nanostructured metal alloys of two or more nanoparticle materials with varied composite concentrations under various predetermined conditions. This technique can lead to promising solutions for the fabrication of 3D nanostructured metal alloys in applications such as fuel-cell energy generation and development of custom-designed, functionally graded biomaterials and biocomposites.