912 resultados para Optimization methods
Resumo:
Methods are presented for the preparation, ligand density analysis and use of an affinity adsorbent for the purification of a glutathione S-transferase (GST) fusion protein in packed and expanded bed chromatographic processes. The protein is composed of GST fused to a zinc finger transcription factor (ZnF). Glutathione, the affinity ligand for GST purification, is covalently immobilized to a solid-phase adsorbent (Streamline™). The GST–ZnF fusion protein displays a dissociation constant of 0.6 x10-6 M to glutathione immobilized to Streamline™. Ligand density optimization, fusion protein elution conditions (pH and glutathione concentration) and ligand orientation are briefly discussed.
Resumo:
Materials, methods and systems are provided for the purifn., filtration and/or sepn. of certain mols. such as certain size biomols. Certain embodiments relate to supports contg. at least one polymethacrylate polymer engineered to have certain pore diams. and other properties, and which can be functionally adapted to for certain purifications, filtrations and/or sepns. Biomols. are selected from a group consisting of: polynucleotide mols., oligonucleotide mols. including antisense oligonucleotide mols. such as antisense RNA and other oligonucleotide mols. that are inhibitory of gene function such as small interfering RNA (siRNA), polypeptides including proteinaceous infective agents such as prions, for example, the infectious agent for CJD, and infectious agents such as viruses and phage.
Resumo:
This paper reports on two lengthy studies in Physical education teacher education (PETE) conducted independently but which are epistemologically and methodologically linked. The paper describes how personal construct theory (PCT) and its associated methods provided a means for PETE students to reflexively construct their ideas about teaching physical education over an extended period. Data are drawn from each study in the form of a story of a single participant to indicate how this came about. Furthermore we suggest that PCT might be both a useful research strategy and an effective approach to facilitate professional development in a teacher education setting.
Resumo:
In this thesis a new approach for solving a certain class of anomalous diffusion equations was developed. The theory and algorithms arising from this work will pave the way for more efficient and more accurate solutions of these equations, with applications to science, health and industry. The method of finite volumes was applied to discretise the spatial derivatives, and this was shown to outperform existing methods in several key respects. The stability and convergence of the new method were rigorously established.
Resumo:
This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.
Resumo:
The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.
Resumo:
Integration of biometrics is considered as an attractive solution for the issues associated with password based human authentication as well as for secure storage and release of cryptographic keys which is one of the critical issues associated with modern cryptography. However, the widespread popularity of bio-cryptographic solutions are somewhat restricted by the fuzziness associated with biometric measurements. Therefore, error control mechanisms must be adopted to make sure that fuzziness of biometric inputs can be sufficiently countered. In this paper, we have outlined such existing techniques used in bio-cryptography while explaining how they are deployed in different types of solutions. Finally, we have elaborated on the important facts to be considered when choosing appropriate error correction mechanisms for a particular biometric based solution.
Resumo:
We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles.
Resumo:
Internationally there is interest in developing the research skills of pre-service teachers as a means of ongoing professional renewal with a distinct need for systematic and longitudinal investigation of student learning. The current study takes a unique perspective by exploring the research learning journey of pre-service teachers participating in a transnational degree programme. Using a case-study design that includes both a self-reported and direct measure of research knowledge, the results indicate a progression in learning, as well as evidence that this research knowledge is continued or maintained when the pre-service teachers return to their home university. The findings of this study have implications for both pre-service teacher research training and transnational programmes.
Resumo:
Background: Overviews of systematic reviews (SRs) are useful for public health policy; however there is an absence of Cochrane Overviews covering public health (PH) topics. Objectives: We sought to analyze the methodological approaches used in existing Cochrane Overviews and Protocols for overviews (primarily clinical in nature), and compare these to the methods and approaches used in PH overviews (non-Cochrane). The intent was to identify issues that would be relevant for undertaking Cochrane overviews. Methods: We conducted a descriptive analysis of overviews published between 1999 and 2014. We searched the Cochrane Database of Systematic Reviews for Cochrane Protocols for overviews and Cochrane Overviews, and the HealthEvidence.org for PH overviews. The primary characteristics of the overviews and elements of the methodology were extracted and compared. Results: A total of 61 overviews of SRs were included in our analysis; specifically, this included 21 Cochrane Protocols for overviews, 15 Cochrane Overviews, and 27 non-Cochrane PH overviews. Amongst the overviews, the most significant differences are that PH overviews (non-Cochrane) tend to: include earlier and more reviews, greater number of participants, allow lower levels of evidence, use assessment tools other than AMSTAR (A Measurement Tool to Assess Systematic Reviews, i.e. a tool for assessing quality of SRs), not assess quality of evidence in reviews, search more databases overall, specify search limits including English-only reviews, and not consider recent primary studies for inclusion. Some of these differences clearly related to quality, however many relate to the nuances of PH interventions. Conclusions: The methodology in Cochrane overviews and PH overviews varies widely. Future PH overviews may benefit from the Cochrane methodology but the Cochrane approach requires modification to accommodate PH research methodology. Additionally, the use of databases that pre-screen and quality assess relevant PH systematic reviews may help expedite the search process.