Structural changes in quasi-one-dimensional many-electron systems: From linear to zigzag and beyond

Many-electron systems confined to a quasi-one-dimensional geometry by a cylindrical distribution of positive charge have been investigated by density functional computations in the unrestricted local spin density approximation. Our investigations have been focused on the low-density regime, in which electrons are localized. The results reveal a wide variety of different charge and spin configurations, including linear and zig-zag chains, single-and double-strand helices, and twisted chains of dimers. The spin-spin coupling turns from weakly antiferromagnetic at relatively high density, to weakly ferromagnetic at the lowest densities considered in our computations. The stability of linear chains of localized charge has been investigated by analyzing the radial dependence of the self-consistent potential and by computing the dispersion relation of low-energy harmonic excitations.

Position control of shape memory alloy actuators using self tuning fuzzy PID controller

Shape Memory Alloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

Locating binding poses in protein-ligand systems using reconnaissance metadynamics

A molecular dynamics-based protocol is proposed for finding and scoring protein-ligand binding poses. This protocol uses the recently developed reconnaissance metadynamics method, which employs a self-learning algorithm to construct a bias that pushes the system away from the kinetic traps where it would otherwise remain. The exploration of phase space with this algorithm is shown to be roughly six to eight times faster than unbiased molecular dynamics and is only limited by the time taken to diffuse about the surface of the protein. We apply this method to the well-studied trypsin-benzamidine system and show that we are able to refind all the poses obtained from a reference EADock blind docking calculation. These poses can be scored based on the length of time the system remains trapped in the pose. Alternatively, one can perform dimensionality reduction on the output trajectory and obtain a map of phase space that can be used in more expensive free-energy calculations.

A self-learning algorithm for biased molecular dynamics

A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally valid collective coordinates. These collective coordinates are obtained from trajectory analyses so that they adapt to any new features encountered during the simulation. We show how this methodology can be used to enhance sampling in real chemical systems citing examples both from the physics of clusters and from the biological sciences.

Physical characterisation and component release of poly(vinyl alcohol)-tetrahydroxyborate hydrogels and their applicability as potential topical drug delivery systems

Poly(vinyl alcohol)-tetrahydroxyborate (PVA-THB) hydrogels are dilatant formulations with potential for topical wound management. To support this contention, the physical properties, rheological behaviour and component release of candidate formulations were investigated. Oscillatory rheometry and texture profile analysis were used at room temperature and 37 °C. Results showed that it was possible to control the rheological and textural properties by altering component concentration and modifying the type of PVA polymer used. Hydrogels made using PVA grades with higher degrees of hydrolysis displayed favourable characteristics from a wound healing perspective. In vitro release of borate and PVA were assessed in order to evaluate potential clinical dosing of free species originating from the hydrogel structure. Component diffusion was influenced by both concentration and molecular weight, where relevant, with up to 5% free PVA cumulative release observed after 30 min. The results of this study demonstrated the importance of poly(vinyl alcohol) selection for ensuring appropriate gel formation in PVA-THB hydrogels. The benefits of higher degrees of hydrolysis, in particular, included lower excipient release and reduced bioadhesion. The unique physical characteristics of these hydrogels make them an appealing delivery vehicle for chronic and acute wound management purposes.

Supporting active learning in an undergraduate geotechnical engineering course using group-based audience response systems quizzes

The use of audience response systems (ARSs) or ‘clickers’ in higher education has increased over the recent years, predominantly owing to their ability to actively engage students, for promoting individual and group learning, and for providing instantaneous feedback to students and teachers. This paper describes how group-basedARSquizzes have been integrated into an undergraduate civil engineering course on foundation design. Overall, theARSsummary quizzes were very well received by the students. Feedback obtained from the students indicates that the majority believed the group-based quizzes were useful activities, which helped to improve their understanding of course materials, encouraged self-assessment, and assisted preparation for their summative examination. Providing students with clickers does not, however, necessarily guarantee the class will be engaged with the activity. If an ARS activity is to be successful, careful planning and design must be carried out and modifications adopted where necessary, which should be informed by the literature and relevant student feedback.

Nucleation-Induced Self-Assembly of Multiferroic BiFeO3-CoFe2O4 Nanocomposites

Large areas of perfectly ordered magnetic CoFe2O4 nanopillars embedded in a ferroelectric BiFeO3 matrix were successfully fabricated via a novel nucleation-induced self-assembly process. The nucleation centers of the magnetic pillars are induced before the growth of the composite structure using anodic aluminum oxide (AAO) and lithography-defined gold membranes as hard mask. High structural quality and good functional properties were obtained. Magneto-capacitance data revealed extremely low losses and magneto-electric coupling of about 0.9 mu C/cmOe. The present fabrication process might be relevant for inducing ordering in systems based on phase separation, as the nucleation and growth is a rather general feature of these systems.

Self-assembly using dendritic building blocks - towards controllable nanomaterials

Dendritic molecules have well defined, three-dimensional branched architectures, and constitute a unique nanoscale toolkit. This review focuses on examples in which individual dendritic molecules are assembled into more complex arrays via non-covalent interactions. In particular, it illustrates how the structural information programmed into the dendritic architecture controls the assembly process, and as a consequence, the properties of the supramolecular structures which are generated. Furthermore, the review emphasises how the use of non-covalent (supramolecular) interactions, provides the assembly process with reversibility, and hence a high degree of control. The review also illustrates how self-assembly offers an ideal approach for amplifying the branching of small, synthetically accessible, relatively inexpensive dendritic systems (e.g. dendrons), into highly branched complex nanoscale assemblies.

The review begins by considering the assembly of dendritic molecules to generate discrete, well-defined supramolecular assemblies. The variety of possible assembled structures is illustrated, and the ability of an assembled structure to encapsulate a templating unit is described. The ability of both organic and inorganic building blocks to direct the assembly process is discussed. The review then describes larger discrete assemblies of dendritic molecules, which do not exist as a single well-defined species, but instead exist as statistical distributions. For example, assembly around nanoparticles, the assembly of amphiphilic dendrons and the assembly of dendritic systems in the presence of DNA will all be discussed. Finally, the review examines dendritic molecules, which assemble or order themselves into extended arrays. Such systems extend beyond the nanoscale into the microscale or even the macroscale domain, exhibiting a wide range of different architectures. The ability of these assemblies to act as gel-phase or liquid crystalline materials will be considered.

Taken as a whole, this review emphasises the control and tunability that underpins the assembly of nanomaterials using dendritic building blocks, and furthermore highlights the potential future applications of these assemblies at the interfaces between chemistry, biology and materials science. 

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Sandwich nanoarchitecture of LiV3O8/graphene multilayer nanomembranes via layer-by-layer self-assembly for long-cycle-life lithium-ion battery cathodes

A lack of suitable high-performance cathode materials has become the major barrier to their applications in future advanced communication equipment and electric vehicle power systems. In this paper, we have developed a layer-by-layer self-assembly approach for fabricating a novel sandwich nanoarchitecture of multilayered LiV₃O₈ nanoparticle/graphene nanosheet (M-nLVO/GN) hybrid electrodes for potential use in high performance lithium ion batteries by using a porous Ni foam as a substrate. The prepared sandwich nanoarchitecture of M-nLVO/GN hybrid electrodes exhibited high performance as a cathode material for lithium-ion batteries, such as high reversible specific capacity (235 mA h g^-1 at a current density of 0.3 A g^-1), high coulombic efficiency (over 98%), fast rate capability (up to a current density of 10 A g^-1), and superior capacity retention during cycling (90% capacity retention with a current density of 0.3 A g^-1 after 300 cycles). Very significantly, this novel insight into the design and synthesis of sandwich nanoarchitecture would extend their application to various electrochemical energy storage devices, such as fuel cells and supercapacitors.

Self-consistent modelling of line-driven hot-star winds with Monte Carlo radiation hydrodynamics

Radiative pressure exerted by line interactions is a prominent driver of outflows in astrophysical systems, being at work in the outflows emerging from hot stars or from the accretion discs of cataclysmic variables, massive young stars and active galactic nuclei. In this work, a new radiation hydrodynamical approach to model line-driven hot-star winds is presented. By coupling a Monte Carlo radiative transfer scheme with a finite volume fluid dynamical method, line-driven mass outflows may be modelled self-consistently, benefiting from the advantages of Monte Carlo techniques in treating multiline effects, such as multiple scatterings, and in dealing with arbitrary multidimensional configurations. In this work, we introduce our approach in detail by highlighting the key numerical techniques and verifying their operation in a number of simplified applications, specifically in a series of self-consistent, one-dimensional, Sobolev-type, hot-star wind calculations. The utility and accuracy of our approach are demonstrated by comparing the obtained results with the predictions of various formulations of the so-called CAK theory and by confronting the calculations with modern sophisticated techniques of predicting the wind structure. Using these calculations, we also point out some useful diagnostic capabilities our approach provides. Finally, we discuss some of the current limitations of our method, some possible extensions and potential future applications.

A Novel Approach for Axle Detection in Bridge Weigh-In-Motion Systems

Bridge weigh-in-motion (B-WIM), a system that uses strain sensors to calculate the weights of trucks passing on bridges overhead, requires accurate axle location and speed information for effective performance. The success of a B-WIM system is dependent upon the accuracy of the axle detection method. It is widely recognised that any form of axle detector on the road surface is not ideal for B-WIM applications as it can cause disruption to the traffic (Ojio & Yamada 2002; Zhao et al. 2005; Chatterjee et al. 2006). Sensors under the bridge, that is Nothing-on-Road (NOR) B-WIM, can perform axle detection via data acquisition systems which can detect a peak in strain as the axle passes. The method is often successful, although not all bridges are suitable for NOR B-WIM due to limitations of the system. Significant research has been carried out to further develop the method and the NOR algorithms, but beam-and-slab bridges with deep beams still present a challenge. With these bridges, the slabs are used for axle detection, but peaks in the slab strains are sensitive to the transverse position of wheels on the beam. This next generation B-WIM research project extends the current B-WIM algorithm to the problem of axle detection and safety, thus overcoming the existing limitations in current state-of–the-art technology. Finite Element Analysis was used to determine the critical locations for axle detecting sensors and the findings were then tested in the field. In this paper, alternative strategies for axle detection were determined using Finite Element analysis and the findings were then tested in the field. The site selected for testing was in Loughbrickland, Northern Ireland, along the A1 corridor connecting the two cities of Belfast and Dublin. The structure is on a central route through the island of Ireland and has a high traffic volume which made it an optimum location for the study. Another huge benefit of the chosen location was its close proximity to a nearby self-operated weigh station. To determine the accuracy of the proposed B-WIM system and develop a knowledge base of the traffic load on the structure, a pavement WIM system was also installed on the northbound lane on the approach to the structure. The bridge structure selected for this B-WIM research comprised of 27 pre-cast prestressed concrete Y4-beams, and a cast in-situ concrete deck. The structure, a newly constructed integral bridge, spans 19 m and has an angle of skew of 22.7°.