Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments

In many chemical and biological applications, systems of differential equations containing unknown parameters are used to explain empirical observations and experimental data. The DEs are typically nonlinear and difficult to analyze, requiring numerical methods to approximate the solutions. Compounding this difficulty are the unknown parameters in the DE system, which must be given specific numerical values in order for simulations to be run. Estrogen receptor protein dimerization is used as an example to demonstrate model construction, reduction, simulation, and parameter estimation. Mathematical, computational, and statistical methods are applied to empirical data to deduce kinetic parameter estimates and guide decisions regarding future experiments and modeling. The process demonstrated serves as a pedagogical example of quantitative methods being used to extract parameter values from biochemical data models.Comment: 23 pages, 9 figures, to be published in SIAM Revie

Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

Inexpensive fusion methods for enhancing feature detection

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere

SciTech News Volume 70, No. 4 (2016)

Columns and Reports From the Editor 3 Division News Science-Technology Division 4 SLA Annual Meeting 2016 Report (S. Kirk Cabeen Travel Stipend Award recipient) 6 Reflections on SLA Annual Meeting (Diane K. Foster International Student Travel Award recipient) 8 SLA Annual Meeting Report (Bonnie Hilditch International Librarian Award recipient)10 Chemistry Division 12 Engineering Division 15 Reflections from the 2016 SLA Conference (SPIE Digital Library Student Travel Stipend recipient)15 Fundamentals of Knowledge Management and Knowledge Services (IEEE Continuing Education Stipend recipient) 17 Makerspaces in Libraries: The Big Table, the Art Studio or Something Else? (by Jeremy Cusker) 19 Aerospace Section of the Engineering Division 21 Reviews Sci-Tech Book News Reviews 22 Advertisements IEEE 17 WeBuyBooks.net 2

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Prognostics health management: perspectives in engineering systems reliability prognostics

The Prognostic Health Management (PHM) has been asserting itself as the most promising methodology to enhance the effective reliability and availability of a product or system during its life-cycle conditions by detecting current and approaching failures, thus, providing mitigation of the system risks with reduced logistics and support costs. However, PHM is at an early stage of development, it also expresses some concerns about possible shortcomings of its methods, tools, metrics and standardization. These factors have been severely restricting the applicability of PHM and its adoption by the industry. This paper presents a comprehensive literature review about the PHM main general weaknesses. Exploring the research opportunities present in some recent publications, are discussed and outlined the general guide-lines for finding the answer to these issues.(undefined