High‐Performance Multifunctional Graphene‐PLGA Fibers: Toward Biomimetic and Conducting 3D Scaffolds

The development of electrically conducting fibers based on known cytocompatible materials is of interest to those engaged in tissue regeneration using electrical stimulation. Herein, it is demonstrated that with the aid of rheological insights, optimized formulations of graphene containing spinnable poly(lactic‐co‐glycolic acid) (PLGA) dopes can be made possible. This helps extend the general understanding of the mechanics involved in order to deliberately translate the intrinsic superior electrical and mechanical properties of solution‐processed graphene into the design process and practical fiber architectural engineering. The as‐produced fibers are found to exhibit excellent electrical conductivity and electrochemical performance, good mechanical properties, and cellular affinity. At the highest loading of graphene (24.3 wt%), the conductivity of as‐prepared fibers is as high as 150 S m^?1 (more than two orders of magnitude higher than the highest conductivity achieved for any type of nanocarbon‐PLGA composite fibers) reported previously. Moreover, the Young's modulus and tensile strength of the base fiber are enhanced 647‐ and 59‐folds, respectively, through addition of graphene.     

Thermal dynamic buckling of temperature-dependent sandwich nanocomposite quadrilateral microplates using visco-higher order nonlocal strain gradient theory

This research deals with the nonlocal temperature-dependent dynamic buckling analysis of embedded laminated quadrilateral micro plates reinforced by functionally graded carbon nanotubes (FG-CNTs). The material properties of structure are assumed viscoelastic based on Kelvin–Voigt model. The effective material properties of structure are considered based on mixture rule. The elastic medium is simulated by orthotropic visco-Pasternak medium. The motion equations are derived applying Sinusoidal shear deformation theory (SSDT) in which the size effects are considered using higher order nonlocal strain gradient theory. The transformed weighing (TW) and differential quadrature (DQ) method in conjunction with the Bolotin’s method are applied for calculating resonance frequency and dynamic instability region (DIR) of structure. The effects of different parameters such as volume percent of CNTs, distribution type of CNTs, temperature, nonlocal parameter and structural damping on the dynamic instability of visco-system are shown. The results are compared with other published works in the literature. Results indicate that the CNTs have an important role in dynamic stability of structure and FGX distribution type is the better choice.     

Discharge Coefficient for Sharp-Crested Side Weir in Subcritical Flow

To estimate the outflow over a rectangular sharp-crested side weir, the discharge coefficient in the weir equation needs to be known. Although this type of structure has been designed and used extensively by hydraulic engineers, a universally acceptable discharge coefficient does not exist. In this study over 250 laboratory tests were conducted, and the results were analyzed to find the influence of the flow hydraulics and the geometric, channel, and weir shapes on the coefficient. The results show that for subcritical flow the De-Marchi assumption of constant energy is acceptable, and the weir discharge can therefore be used. Furthermore, it was discovered that the De-Marchi coefficient of discharge is a function of the upstream Froude number and the ratios of weir height to upstream depth and weir length to channel width, whereas the channel slope in subcritical flow can be ignored. Hence, an accurate equation for the coefficient of discharge is introduced.     

A Review on the Role of Non-Coding RNAs in the Pathogenesis of Myasthenia Gravis

Myasthenia gravis (MG) is an autoimmune condition related to autoantibodies against certain proteins in the postsynaptic membranes in the neuromuscular junction. This disorder has a multifactorial inheritance. The connection between environmental and genetic factors can be established by epigenetic factors, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). XLOC_003810, SNHG16, IFNG-AS1, and MALAT-1 are among the lncRNAs with a possible role in the pathoetiology of MG. Moreover, miR-150-5p, miR-155, miR-146a-5p, miR-20b, miR-21-5p, miR-126, let-7a-5p, and let-7f-5p are among miRNAs whose roles in the pathogenesis of MG has been assessed. In the current review, we summarize the impact of miRNAs and lncRNAs in the development or progression of MG.     

Dynamic clustering using combinatorial particle swarm optimization

Combinatorial Particle Swarm Optimization (CPSO) is a relatively recent technique for solving combinatorial optimization problems. CPSO has been used in different applications, e.g., partitional clustering and project scheduling problems, and it has shown a very good performance. In partitional clustering problem, CPSO needs to determine the number of clusters in advance. However, in many clustering problems, the correct number of clusters is unknown, and it is usually impossible to estimate. In this paper, an improved version, called CPSOII, is proposed as a dynamic clustering algorithm, which automatically finds the best number of clusters and simultaneously categorizes data objects. CPSOII uses a renumbering procedure as a preprocessing step and several extended PSO operators to increase population diversity and remove redundant particles. Using the renumbering procedure increases the diversity of population, speed of convergence and quality of solutions. For performance evaluation, we have examined CPSOII using both artificial and real data. Experimental results show that CPSOII is very effective, robust and can solve clustering problems successfully with both known and unknown number of clusters. Comparing the obtained results from CPSOII with CPSO and other clustering techniques such as KCPSO, CGA and K-means reveals that CPSOII yields promising results. For example, it improves 9.26 % of the value of DBI criterion for Hepato data set.     

Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization

Microaggregation is a masking mechanism to protect confidential data in a public release. This technique can produce a k-anonymous dataset where data records are partitioned into groups of at least k members. In each group, a representative centroid is computed by aggregating the group members and is published instead of the original records. In a conventional microaggregation algorithm, the centroids are computed based on simple arithmetic mean of group members. This naïve formulation does not consider the proximity of the published values to the original ones, so an intruder may be able to guess the original values. This paper proposes a disclosure-aware aggregation model, where published values are computed in a given distance from the original ones to attain a more protected and useful published dataset. Empirical results show the superiority of the proposed method in achieving a better trade-off point between disclosure risk and information loss in comparison with other similar anonymization techniques.     

A biogeography-based optimization for optimum discrete design of skeletal structures

This article presents a modified biogeography-based optimization (MBBO) algorithm for optimum design of skeletal structures with discrete variables. The main idea of the biogeography-based optimization (BBO) algorithm is based on the science of biogeography, in which each habitat is a possible solution for the optimization problem in the search space. This algorithm consists of two main operators: migration and mutation. The migration operator helps the habitats to exploit the search space, while the mutation operator guides habitats to escape from the local optimum. To enhance the performance of the standard algorithm, some modifications are made and an MBBO algorithm is presented. The performance of the MBBO algorithm is evaluated by optimizing five benchmark design examples, and the obtained results are compared with other methods in the literature. The numerical results demonstrate that the MBBO algorithm is able to show very competitive results and has merits in finding optimum designs.     

Liquid-Metal-Templated Synthesis of 2D Graphitic Materials at Room Temperature

Room-temperature synthesis of 2D graphitic materials (2D-GMs) remains an elusive aim, especially with electrochemical means. Here, it is shown that liquid metals render this possible as they offer catalytic activity and an ultrasmooth templating interface that promotes Frank–van der Merwe regime growth, while allowing facile exfoliation due to the absence of interfacial forces as a nonpolar liquid. The 2D-GMs are formed at low onset potential and can be in situ doped depending on the choice of organic precursors and the electrochemical set-up. The materials are tuned to exhibit porous or pinhole-free morphologies and are engineered for their degree of oxidation and number of layers. The proposed liquid-metal-based room-temperature electrochemical route can be expanded to many other 2D materials.