Quantum confinement effect on trilayer graphene nanoribbon carrier concentration

In this study, one-dimensional vision of carrier movement based on the band structure of trilayer graphene nanoribbon in the presence of a perpendicular electric field is employed. An analytical model of ABA-stacked trilayer graphene nanoribbon carrier statistics as a fundamental parameter of field effect transistor (FET) in corporation with a numerical solution is presented in the degenerate and non-degenerate limits. The simulated results based on the presented model indicate that the model can be approximated by degenerate and non-degenerate approximations in some numbers of normalised Fermi energy. Analytical model specifies that carrier concentration in degenerate limit is strongly independent of normalised Fermi energy; however, in the non-degenerate limit, it is a strong function of normalised Fermi energy. The proposed model is then compared with other types of graphene. As a result, the developed model can assist in comprehending experiments involving trilayer graphene nanoribbon FET-based devices.     

Distributed consensus-based formation control for multiple nonholonomic mobile robots with a specified reference trajectory

In this paper, the distributed formation control problem for multiple nonholonomic mobile robots using consensus-based approach is considered. A transformation is given to convert the formation control problem for multiple nonholonomic mobile robots into a state consensus problem. Distributed control laws are developed for achieving the formation control objectives: a group of nonholonomic mobile robots at least exponentially converge to a desired geometric pattern with its centroid moving along the specified reference trajectory. Rigorous proofs are provided by using graph, matrix , and Lyapunov theories. Simulations are also given to verify the effectiveness of the theoretical results.     

Dynamic response of a double,single-walled carbon nanotube under a moving nanoparticle based on modified nonlocal elasticity theory considering surface effects

In the present study, the surface effect on the forced vibration of a double, single-walled carbon nanotube system (DSWNTS) under excitation of a moving nanoparticle is analyzed based on the modified nonlocal elasticity theory. The nanotube surroundings are modeled by an elastic medium and it is assumed that two nanotubes are connected to each other continuously, using elastic springs. In a parametric study, influences of the nonlocal parameter, velocity of the moving nanoparticle, the elastic layer between the nanotubes, and the order of derivative on dynamic responses of the DSWNTS are investigated in detail. The results demonstrate that the variation of order of derivative affects dynamic deflection and frequency of DSWNTS considerably. In this study, the influences of additional terms in nonlocal theory and improving the accuracy of results by presenting a modified version of nonlocal elasticity theory is investigated. As the results have presented, there is a noticeable difference in comparison with a previous case and this issue certifies the importance of the presented work. Also, a general and exact validation has been performed on the results, differences percentages have been observed, and effective factors on these differences have been reported.     

Development of solution-gated graphene transistor model for biosensors

The distinctive properties of graphene, characterized by its high carrier mobility and biocompatibility, have stimulated extreme scientific interest as a promising nanomaterial for future nanoelectronic applications. In particular, graphene-based transistors have been developed rapidly and are considered as an option for DNA sensing applications. Recent findings in the field of DNA biosensors have led to a renewed interest in the identification of genetic risk factors associated with complex human diseases for diagnosis of cancers or hereditary diseases. In this paper, an analytical model of graphene-based solution gated field effect transistors (SGFET) is proposed to constitute an important step towards development of DNA biosensors with high sensitivity and selectivity. Inspired by this fact, a novel strategy for a DNA sensor model with capability of single-nucleotide polymorphism detection is proposed and extensively explained. First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm. Based on the sensing mechanism of DNA sensors, detective parameters (I_ds and V_gmin) are suggested to facilitate the decision making process. Finally, the behaviour of graphene-based SGFET is predicted in the presence of single-nucleotide polymorphism with an accuracy of more than 98% which guarantees the reliability of the optimized model for any application of the graphene-based DNA sensor. It is expected to achieve the rapid, quick and economical detection of DNA hybridization which could speed up the realization of the next generation of the homecare sensor system.     

A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing

Workload scheduling in cloud computing is currently an active research field. Scheduling plays an important role in cloud computing performance, especially when the platform is used for big data analysis and as less predictable workloads dynamically enter the clouds. Finding the optimized scheduling solution with different parameters in different environments is still a challenging issue. In dynamic environments such as cloud, scheduling strategies should feature rapid altering to be able to adapt more easily to the changes in input workloads. However, achieving an optimized solution is an important issue, which has a trade-off with the speed of finding the solution. In this article, an ordinal optimization method is proposed that considers the volume of workloads, load balancing and the volume of exchanged messages among virtual clusters, considering the replications. The algorithm in the present paper is based on ordinal optimization (OO) and evolutionary OO. In any time periods, a criterion is calculated to determine the similarity of workloads in two-consequence time periods, which is appropriate for timely changes in the scheduling procedure. In this paper, considering more than one parameter, a proper scheduling would be created for each time period. This scheduler is an organization for the number of virtual machines for each virtual cluster, but if there is a desirable similarity between workloads of two-consequence time periods, this procedure would be ignored. The results show that a more optimized solution is obtained in comparison with the rated methods, such as blind pink, OO, Monte Carlo and eOO in a reasonable time. The suggested method is flexible and it is possible to change the weight ratio of the proposed criteria in different environments to be consistent with different environmental conditions. The results show that proposed method achieved up to 28% performance improvement in comparison with eOO.     

Techniques,perspectives, and challenges of bioactive peptide generation: A comprehensive systematic review

Due to the digestible refractory and absorbable structures of bioactive peptides (BPs), they could induce notable biological impacts on the living organism. In this regard, the current study was devoted to providing an overview regarding the available methods for BPs generation by the aid of a systematic review conducted on the published articles up to April 2019. In this context, the PubMed and Scopus databases were screened to retrieve the related publications. According to the results, although the characterization of BPs mainly has been performed using enzymatic and microbial in‐vitro methods, they cannot be considered as suitable techniques for further stimulation of digestion in the gastrointestinal tract. Therefore, new approaches for both in‐vivo and in‐silico methods for BPs identification should be developed to overcome the obstacles that belonged to the current methods. The purpose of this review was to compile the recent analytical methods applied for studying various aspects of food‐derived biopeptides, and emphasizing generation at in vitro, in vivo, and in silico.