The influence of the precipitation rate on the properties of porous chromia

The properties were studied of heated (320°C) chromia samples, prepared by two precipitation methods: \ud \ud 1. (1) addition of ammonia to chromium salt solutions,\ud 2. (2) OH− formation in chromium salt solutions through hydrolysis of urea.\ud \ud Samples formed by means of the first method are macro or mesoporous and have a lower specific surface area (~200 m2·g−1) than those formed by urea hydrolysis (~300 m2·g−1). Only in the case of a very slow addition of the ammonia solution these properties of the chromia's become equal. Experiments show that the micro porous type samples with high surface area are only formed if the pH range 5.1 to 5.7 is passed slowly. The formation of polychromium complexes of uniform size is suggested.\ud \u

A study of high power argon laser optics Final report, 15 Apr. 1967 - 14 Apr. 1968

Window degradation in high power argon laser optic

A study of high power argon laser optics Interim scientific report, 15 Apr. - 31 Oct. 1967

Argon laser optimal component degredation at high power level

Reinforcement Learning and Tree Search Methods for the Unit Commitment Problem

The unit commitment (UC) problem, which determines operating schedules of generation units to meet demand, is a fundamental task in power systems operation. Existing UC methods using mixed-integer programming are not well-suited to highly stochastic systems. Approaches which more rigorously account for uncertainty could yield large reductions in operating costs by reducing spinning reserve requirements; operating power stations at higher efficiencies; and integrating greater volumes of variable renewables. A promising approach to solving the UC problem is reinforcement learning (RL), a methodology for optimal decision-making which has been used to conquer long-standing grand challenges in artificial intelligence. This thesis explores the application of RL to the UC problem and addresses challenges including robustness under uncertainty; generalisability across multiple problem instances; and scaling to larger power systems than previously studied. To tackle these issues, we develop guided tree search, a novel methodology combining model-free RL and model-based planning. The UC problem is formalised as a Markov decision process and we develop an open-source environment based on real data from Great Britain's power system to train RL agents. In problems of up to 100 generators, guided tree search is shown to be competitive with deterministic UC methods, reducing operating costs by up to 1.4\%. An advantage of RL is that the framework can be easily extended to incorporate considerations important to power systems operators such as robustness to generator failure, wind curtailment or carbon prices. When generator outages are considered, guided tree search saves over 2\% in operating costs as compared with methods using conventional $N-x$ reserve criteria

Corrigendum to `Orbit closures in the enhanced nilpotent cone', published in Adv. Math. 219 (2008)

In this note, we point out an error in the proof of Theorem 4.7 of [P. Achar and A.~Henderson, `Orbit closures in the enhanced nilpotent cone', Adv. Math. 219 (2008), 27-62], a statement about the existence of affine pavings for fibres of a certain resolution of singularities of an enhanced nilpotent orbit closure. We also give independent proofs of later results that depend on that statement, so all other results of that paper remain valid.Comment: 4 pages. The original paper, in a version almost the same as the published version, is arXiv:0712.107

The social, political, economic, and legal aspects of affirmative action admission litigation from 2002-2007 from five universities

Litigation against colleges and universities has prompted the need to re-examine the legalities of the means by which they strive for a diverse student population. Court decisions have resulted in mixed signals about the use of various types of affirmative action policies. This study\u27 method presented an analysis of archival data to provide a clear summary of requirements that should influence admissions and compared this summary with five universities\u27 admission policies. The research questions and the literature review are organized around the S.P.E.L. model. The social, political, economic, and legal implications of 2002-2007 affirmative action admission litigation are explored in this multiple case study. Three major conclusions were drawn: (a) the five universities use narrowly defined affirmative action criteria and include consideration of race/ethnicity or culture in their process for admitting students to their schools, (b) the universities provide some forms of economic support exclusively for students of certain ethnic or racial groups and/or socioeconomic backgrounds, and (c) the universities are in violation of the 14th Amendment in regards to their admission policies, and in addition all five universities are in conflict with state or voter approved legislation that limited or removed the use of race, gender, and ethnicity in admission programs and policies. The results section includes guidelines for improvement in admission policies and affirmative action programs in order to guide colleges and universities to a legally acceptable means of establishing diversity. This study also points the way for schools to effectively implement their diversity policies within the parameters set by law and legal precedent

Reinforcement learning and A* search for the unit commitment problem

Previous research has combined model-free reinforcement learning with model-based tree search methods to solve the unit commitment problem with stochastic demand and renewables generation. This approach was limited to shallow search depths and suffered from significant variability in run time across problem instances with varying complexity. To mitigate these issues, we extend this methodology to more advanced search algorithms based on A* search. First, we develop a problem-specific heuristic based on priority list unit commitment methods and apply this in Guided A* search, reducing run time by up to 94% with negligible impact on operating costs. In addition, we address the run time variability issue by employing a novel anytime algorithm, Guided IDA*, replacing the fixed search depth parameter with a time budget constraint. We show that Guided IDA* mitigates the run time variability of previous guided tree search algorithms and enables further operating cost reductions of up to 1%

Applying reinforcement learning and tree search to the unit commitment problem

Recent advances in artificial intelligence have demonstrated the capability of reinforcement learning (RL) methods to outperform the state of the art in decision-making problems under uncertainty. Day-ahead unit commitment (UC), scheduling power generation based on forecasts, is a complex power systems task that is becoming more challenging in light of increasing uncertainty. While RL is a promising framework for solving the UC problem, the space of possible actions from a given state is exponential in the number of generators and it is infeasible to apply existing RL methods in power systems larger than a few generators. Here we present a novel RL algorithm, guided tree search, which does not suffer from an exponential explosion in the action space with increasing number of generators. The method augments a tree search algorithm with a policy that intelligently reduces the branching factor. Using data from the GB power system, we demonstrate that guided tree search outperforms an unguided method in terms of computational complexity, while producing solutions that show no performance loss in terms of operating costs. We compare solutions against mixed-integer linear programming (MILP) and find that guided tree search outperforms a solution using reserve constraints, the current industry approach. The RL solutions exhibit complex behaviours that differ qualitatively from MILP, demonstrating its potential as a decision support tool for human operators

Discrimination of Semi-Quantitative Models by Experiment Selection: Method and Application in Population Biology

Modeling an experimental system often results in a number of alternative models that are justified equally well by the experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for the discrimination of models in the form of semiquantitative differential equations. The method is a generalization of previous work in model discrimination. It is based on an entropy criterion for the selection of the most informative experiment which can handle cases where the models predict multiple qualitative behaviors. The applicability of the method is demonstrated on a real-life example, the discrimination of a set of competing models of the growth of phytoplankton in a bioreactor

New results for Petrov type D pure radiation fields

We present a new family of Petrov type D pure radiation spacetimes with a shear-free, non-diverging geodesic principal null congruence.Comment: 4 pages; changed the appearance of some v's and nu'