926 resultados para Pareto-optimal solutions
Resumo:
A bioassay technique, based on surface-enhanced Raman scattering (SERS) tagged gold nanoparticles encapsulated with a biotin functionalised polymer, has been demonstrated through the spectroscopic detection of a streptavidin binding event. A methodical series of steps preceded these results: synthesis of nanoparticles which were found to give a reproducible SERS signal; design and synthesis of polymers with RAFT-functional end groups able to encapsulate the gold nanoparticle. The polymer also enabled the attachment of a biotin molecule functionalised so that it could be attached to the hybrid nanoparticle through a modular process. Finally, the demonstrations of a positive bioassay for this model construct using streptavidin/biotin binding. The synthesis of silver and gold nanoparticles was performed by using tri-sodium citrate as the reducing agent. The shape of the silver nanoparticles was quite difficult to control. Gold nanoparticles were able to be prepared in more regular shapes (spherical) and therefore gave a more consistent and reproducible SERS signal. The synthesis of gold nanoparticles with a diameter of 30 nm was the most reproducible and these were also stable over the longest periods of time. From the SERS results the optimal size of gold nanoparticles was found to be approximately 30 nm. Obtaining a consistent SERS signal with nanoparticles smaller than this was particularly difficult. Nanoparticles more than 50 nm in diameter were too large to remain suspended for longer than a day or two and formed a precipitate, rendering the solutions useless for our desired application. Gold nanoparticles dispersed in water were able to be stabilised by the addition of as-synthesised polymers dissolved in a water miscible solvent. Polymer stabilised AuNPs could not be formed from polymers synthesised by conventional free radical polymerization, i.e. polymers that did not possess a sulphur containing end-group. This indicated that the sulphur-containing functionality present within the polymers was essential for the self assembly process to occur. Polymer stabilization of the gold colloid was evidenced by a range of techniques including, visible spectroscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis and Raman spectroscopy. After treatment of the hybrid nanoparticles with a series of SERS tags, focussing on 2-quinolinethiol the SERS signals were found to have comparable signal intensity to the citrate stabilised gold nanoparticles. This finding illustrates that the stabilization process does not interfere with the ability of gold nanoparticles to act as substrates for the SERS effect. Incorporation of a biotin moiety into the hybrid nanoparticles was achieved through a =click‘ reaction between an alkyne-functionalised polymer and an azido-functionalised biotin analogue. This functionalized biotin was prepared through a 4-step synthesis from biotin. Upon exposure of the surface-bound streptavidin to biotin-functionalised polymer hybrid gold nanoparticles, then washing, a SERS signal was obtained from the 2-quinolinethiol which was attached to the gold nanoparticles (positive assay). After exposure to functionalised polymer hybrid gold nanoparticles without biotin present then washing a SERS signal was not obtained as the nanoparticles did not bind to the streptavidin (negative assay). These results illustrate the applicability of the use of SERS active functional-polymer encapsulated gold nanoparticles for bioassay application.
Resumo:
Introduction: Why we need to base childrens’ sport and physical education on the principles of dynamical systems theory and ecological psychology As the childhood years are crucial for developing many physical skills as well as establishing the groundwork leading to lifelong participation in sport and physical activities, (Orlick & Botterill, 1977, p. 11) it is essential to examine current practice to make sure it is meeting the needs of children. In recent papers (e.g. Renshaw, Davids, Chow & Shuttleworth, in press; Renshaw, Davids, Chow & Hammond, in review; Chow et al., 2009) we have highlighted that a guiding theoretical framework is needed to provide a principled approach to teaching and coaching and that the approach must be evidence- based and focused on mechanism and not just on operational issues such as practice, competition and programme management (Lyle, 2002). There is a need to demonstrate how nonlinear pedagogy underpins teaching and coaching practice for children given that some of the current approaches underpinning children’s sport and P.E. may not be leading to optimal results. For example, little time is spent undertaking physical activities (Tinning, 2006) and much of this practice is not representative of the competition demands of the performance environment (Kirk & McPhail, 2002; Renshaw et al., 2008). Proponents of a non- linear pedagogy advocate the design of practice by applying key concepts such as the mutuality of the performer and environment, the tight coupling of perception and action, and the emergence of movement solutions due to self organisation under constraints (see Renshaw, et al., in press). As skills are shaped by the unique interacting individual, task and environmental constraints in these learning environments, small changes to individual structural (e.g. factors such as height or limb length) or functional constraints (e.g. factors such as motivation, perceptual skills, strength that can be acquired), task rules, equipment, or environmental constraints can lead to dramatic changes in movement patterns adopted by learners to solve performance problems. The aim of this chapter is to provide real life examples for teachers and coaches who wish to adopt the ideas of non- linear pedagogy in their practice. Specifically, I will provide examples related to specific issues related to individual constraints in children and in particular the unique challenges facing coaches when individual constraints are changing due to growth and development. Part two focuses on understanding how cultural environmental constraints impact on children’s sport. This is an area that has received very little attention but plays a very important part in the long- term development of sporting expertise. Finally, I will look at how coaches can manipulate task constraints to create effective learning environments for young children.
Resumo:
In this paper, a comprehensive planning methodology is proposed that can minimize the line loss, maximize the reliability and improve the voltage profile in a distribution network. The injected active and reactive power of Distributed Generators (DG) and the installed capacitor sizes at different buses and for different load levels are optimally controlled. The tap setting of HV/MV transformer along with the line and transformer upgrading is also included in the objective function. A hybrid optimization method, called Hybrid Discrete Particle Swarm Optimization (HDPSO), is introduced to solve this nonlinear and discrete optimization problem. The proposed HDPSO approach is a developed version of DPSO in which the diversity of the optimizing variables is increased using the genetic algorithm operators to avoid trapping in local minima. The objective function is composed of the investment cost of DGs, capacitors, distribution lines and HV/MV transformer, the line loss, and the reliability. All of these elements are converted into genuine dollars. Given this, a single-objective optimization method is sufficient. The bus voltage and the line current as constraints are satisfied during the optimization procedure. The IEEE 18-bus test system is modified and employed to evaluate the proposed algorithm. The results illustrate the unavoidable need for optimal control on the DG active and reactive power and capacitors in distribution networks.
Resumo:
A number of Game Strategies (GS) have been developed in past decades. They have been used in the fields of economics, engineering, computer science and biology due to their efficiency in solving design optimization problems. In addition, research in multi-objective (MO) and multidisciplinary design optimization (MDO) has focused on developing robust and efficient optimization methods to produce a set of high quality solutions with low computational cost. In this paper, two optimization techniques are considered; the first optimization method uses multi-fidelity hierarchical Pareto optimality. The second optimization method uses the combination of two Game Strategies; Nash-equilibrium and Pareto optimality. The paper shows how Game Strategies can be hybridised and coupled to Multi-Objective Evolutionary Algorithms (MOEA) to accelerate convergence speed and to produce a set of high quality solutions. Numerical results obtained from both optimization methods are compared in terms of computational expense and model quality. The benefits of using Hybrid-Game Strategies are clearly demonstrated
Resumo:
This paper considers an aircraft collision avoidance design problem that also incorporates design of the aircraft’s return-to-course flight. This control design problem is formulated as a non-linear optimal-stopping control problem; a formulation that does not require a prior knowledge of time taken to perform the avoidance and return-to-course manoeuvre. A dynamic programming solution to the avoidance and return-to-course problem is presented, before a Markov chain numerical approximation technique is described. Simulation results are presented that illustrate the proposed collision avoidance and return-to-course flight approach.
Resumo:
In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.
Resumo:
In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.
Resumo:
A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.