Preferred design of recurrent neural network architecture using a multiobjective evolutionary algorithm with un-supervised information recruitment: a paradigm for modeling shape memory alloy actuators

Shape memory alloys (SMAs) are able to compensate any undergoing plastic deformations and return to their memorized shape. Such a behavior persuades industrialists to use them for different engineering applications, as smart actuators and sensors. Because of their vast applications, it is crucial to engineers to develop effective identification tools capable of simulating the behavior of SMAs. However, SMA actuators have complex and hysteric behavior that in turn obstructs the modeling process. The motivation behind the current study emanates in the pursuit of developing efficient prediction tools for effective modeling of SMA actuators. Actually, after several experiments and software simulations, the authors develop a hybrid intelligent tool which takes advantage of the self-organizing Pareto based evolutionary algorithm (SOPEA) and simultaneous recurrent neural network (SRNN), as a black-box model, to automatically identify the behavior of SMA. SOPEA is a multiobjective evolutionary algorithm which is based on the concepts of survival of the fittest, non-dominated sorting and information recruitment. The information recruitment is guaranteed by applying an un-supervised neuro computing technique, i.e. adaptive self organizing map (ASOM) with conscience mechanism. ASOM is an un-supervised network that assists SOPEA to recognize the non-dominated patterns and produce further non-dominated solutions. Together with the structure of SOPEA, the authors follow a comprehensive preference-based strategy to exploit the desired regions in the Pareto front. This occurs through introducing deliberate reference points. The outcome method is applied to the design of SRNN for modeling the SMA actuator. It is demonstrated that the designed optimization tool can show acceptable performance for the present case study within the imposed computational budget. Besides, through a rigorous experimental procedure, it is indicated that by applying an efficient artificial system, the behavior of SMA can be identified without any specific knowledge of the physical conditions and governing equations.     

Analysis of the occurrence of stick-slip in AFM-based nano-pushing

The use of the Atomic Force Microscope (AFM) as a tool to manipulate matter at the nanoscale is promising. However, the complexity of the corresponding physics and mechanics makes such nanomanipulation difficult and not very accurate. In the present paper, we analyze the dynamics of AFM-based nano-pushing manipulation. Simulation results show that the choice of the manipulation speed and loading force highly affect the manipulation outcome. In addition, simulations predict the existence of several threshold manipulation speeds. These thresholds mark the transitions between no stick-slip motion and either unique or multiple coexisting stick-slip. The obtained results bear significant implications and help get more insight into AFM-based nano-pushing.     

Selective BODIPY® based fluorescent chemosensor for imaging Pb ion in living cells

A new fluorescent probe has been designed and synthesized by linking dicarboxylate pseudocrown ether to the BODIPY® (4,4-difluoro-4-bora-3a,4a-diaza-s-indacene) fluorophore. The probe allows determination of free lead ions in living cells.     

A dynamical approach to the exterior geometry of a perfect fluid as a relativistic star

In this article, we assume that a cold charged perfect fluid is constructing a spherical relativistic star. Our purpose is the investigation of the dynamical properties of its exterior geometry, through simulating the geodesic motion of a charged test-particle, while moving on the star.     

Optimal transportation on non-compact manifolds

In this work, we show how to obtain for non-compact manifolds the results that have already been done for Monge Transport Problem for costs coming from Tonelli Lagrangians on compact manifolds. In particular, the already known results for a cost of the type d ^r , r > 1, where d is the Riemannian distance of a complete Riemannian manifold, hold without any curvature restriction.     

Bicontinuous nanodisc and nanospherical titania materials prepared by sol-gel process in reverse microemulsion

TiO₂ nanoparticles with different shapes and sizes were synthesised by the sol-gel route in Water/Brij78/Hexane reverse microemulsions. The aqueous cores of these microemulsions were used as nanoreactors to control sol-gel reactions. We studied the effect of water/surfactant mole ratio (W ₀) on the morphology, and textural properties of the final products. The materials thus obtained were characterised by different techniques. Thermogravimetric and differential thermal analysis (TG-DTA) was used to study the thermal behaviour of the products and X-ray diffraction (XRD) to identify the crystalline phases. The morphological and textural properties of the products were determined by scattering electron microscopy (SEM) and the Brunauer-Emmett-Teller (BET) method, respectively. We also studied the influence of thermal treatment on the structure and size of the TiO₂ particles. The effect of W ₀ on the anatase-rutile phase transition temperature was investigated.     

Longitudinal unzipping of carbon nanotubes and their electrochemical performance in supercapacitors

The capacitive properties of graphene nanoribbons (GNRs) with different reduction levels were investigated. GNRs have been synthesized through thermal reduction of oxidized GNRs in the temperature range 100–400 °C. Oxidized GNRs were synthesized by longitudinal unzipping of multi-walled carbon nanotubes (MWCNTs) by means of chemical treatments. Scanning electron microscopy and transmission electron microscopy observations showed, that the efficient tube unzipping yielded improved effective surface area without any tube annihilation by the unzipping process of MWCNTs. Electrochemical studies indicated that through unzipping of MWCNTs, specific capacitance increased from 8 to 28 F g⁻¹ at discharge current density of 0.5 A g⁻¹, confirming increased active surface area and increased defect density in the MWCNTs surface. Unzipping of MWCNTs resulted in decreased rate capability of the electrode because of low electrical conductivity due to oxidization during the unzipping process. Thermal reduction of unzipped sample affected both specific capacitance and rate capability of electrodes. The highest specific capacitance of 62 F g⁻¹ at discharge current density of 0.5 A g⁻¹ was obtained for the sample unzipped and thermally annealed at about 150 °C. The amount of oxygen-containing groups was shown to be an important factor influencing the performance of the GNRs. These results make unzipped MWCNTs promising electrode materials for supercapacitor applications.