Mucoadhesive, syringeable drug delivery systems for controlled application of metronidazole to the periodontal pocket: In vitro release kinetics, syringeability, mechanical and mucoadhesive properties

Novel mucoadhesive formulations containing hydroxyethylcellulose (HEC; 3 and 5%, w/w) or Carbopol (3 and 5%, w/w), polycarbophil (PC; 1 and 3%, w/w) and metronidazole (5%, w/w) at pH 6.8 were designed for the treatment of periodontal diseases. Each formulation was characterised in terms of hardness, compressibility, adhesiveness and cohesiveness (using Texture Profile Analysis), drug release, adhesion to a mucin disc (measured as a detachment force using the texture analyser in tensile mode) and, finally, syringeability (using the texture analyser in compression mode). Drug release from all formulations was non-diffusion controlled. Drug release was significantly decreased as the concentration of each polymeric component was increased, due to both the concomitant increased viscosity of the formulations and, additionally, the swelling kinetics of PC following contact with dissolution fluid. Increasing the concentrations of each polymeric component significantly increased formulation hardness, compressibility, adhesiveness, mucoadhesion and syringeability, yet decreased cohesiveness. Increased product hardness, compressibility and syringeability were due to polymeric effects on formulation viscosity. The effects on cohesiveness may be explained both by increased viscosity and also by the increasing semi-solid nature of products containing 5% HEC or Carbopol and PC (1 or 3%). The observations concerning formulation adhesiveness/mucoadhesion illustrate the adhesive nature of each polymeric component. Greatest adhesion was noted in formulations where neutralisation of PC was maximally suppressed. For the most part, increased time of contact between formulation and mucin significantly increased the required force of detachment, due to the greater extent of mucin polymer hydration and interpenetration with the formulations. Significant statistical interactions were observed between the effects of each polymer on drug release and mechanical/mucoadhesive properties. These interactions may be explained by formulatory effects on the extent of swelling of PC. In conclusion, the formulations described offered a wide range of mechanical and drug release characteristics. Formulations containing HEC exhibited superior physical characteristics for improved drug delivery to the periodontal pocket and are now the subject of long-term clinical investigations. (C) 1997 Elsevier Science B.V.

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.

The forgiving tree: a self-healing distributed data structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletesan arbitrary node from the network, then the network responds by quickly adding a small number of new edges.

We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than O(log Delta) times its original diameter, where Delta is the maximum degree of the network initially. We note that for many peer-to-peer systems, Delta is polylogarithmic, so the diameter increase would be a O(loglog n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

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.

Smart, Reusable labels for assessing self-cleaning films

Novel, reversible (reusable) photocatalyst activity indicator labels, which undergo a rapid colour change when in contact with a photocatalytic film via the photoreduction of methylene blue contained within the label’s adhesive, are explored as a method for assessing the activity of self-cleaning glass in situ and the laboratory, using digital photography.

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.