James Taylor

Bio: James Taylor is an academic researcher from Newcastle University. The author has contributed to research in topics: Laser & Fiber laser. The author has an hindex of 95, co-authored 1161 publications receiving 39945 citations. Previous affiliations of James Taylor include Institut national de la recherche agronomique & European Spallation Source.

Parental attitudes toward varicella vaccination

Abstract: Objectives To evaluate parental health beliefs regarding the varicella vaccine and to identify potential areas for interventions designed to increase immunization against varicella. Setting Data were collected in the offices of pediatricians who are members of the Puget Sound Pediatric Research Network, a regional practice-based research group in the Seattle, Wash, area. Methods At the time of an office visit, parents were asked to complete a survey on the varicella vaccine. Respondents indicated level of agreement with 10 health belief statements regarding the immunization using a 6-point Likert scale from "completely agree" to "completely disagree"; responses were subsequently transformed to an ordinal scale from 1 to 6, with 6 corresponding to highly positive beliefs. A composite health belief score for each respondent was computed by averaging responses to all statements. Parents also were asked to indicate the level of influence of their child's pediatrician on their decision to use the varicella vaccine. Results A total of 598 surveys were completed. Generally, parents agreed that the vaccine was worthwhile even if the only benefit was preventing a rare complication. Conversely, the majority of parents disagreed that varicella vaccine was worthwhile if the only benefit was preventing lost time from work, and that the immunization was worthwhile even if immunity was not lifelong. Parents who indicated that their child's pediatrician's opinion significantly influenced their decision to use the vaccine had higher composite health belief scores than those who indicated less influence (median scores, 4.3 and 4.0, respectively; P Conclusions In this sample, parents had more positive health beliefs about the ability of varicella vaccine to prevent rare complications than to save time lost from work. These data also suggest that pediatricians can have an important role in increasing positive health beliefs about the vaccine. These findings may help future interventions to increase the immunization rate against varicella.

Ketamine sedation for children in the emergency department.

Abstract: Objective: To introduce a protocol for dissociative sedation using ketamine for children requiring painful procedures within the emergency department. Method: A medical literature search was performed along with a review of existing ketamine-use protocols in emergency departments from Australia and the United States. Our hospital anaesthetic department and our emergency department nursing staff were closely involved in protocol design. A series of nursing education sessions were held prior to the drug being used within the emergency department. Twenty-eight children aged 1.5–12 years were administered ketamine sedation prior to undergoing a painful procedure. Ketamine was administered either i.m. (dose 3–4 mg/kg) or i.v. (dose 1.00–2.75 mg/kg) depending on physician preference. Midazolam (dose 0.02 mg/kg) and atropine (dose 0.02 mg/kg) were given as adjuncts in the majority of cases. Results: Onset of sedation was rapid (range 1–7 min) for both routes and provided excellent procedural conditions. Discharge time averaged 103 min (range 67–180 min) from time of drug administration. Side-effects included vomiting, rash, diplopia, salivation and one case of a distressing emergence reaction. On telephone follow up 1–3 days after the sedation, no delayed adverse events were reported. Conclusion: Ketamine is a safe and effective sedative agent for use in children requiring immobilization to enable performance of a painful procedure. It is important to involve both anaesthetic staff and emergency department nursing staff in sedation protocol development to ensure a smooth introduction. We present a protocol for ketamine use in children undergoing painful procedures within the emergency department. See Commentary, page 7.

A comparison of temperature density forecasts from GARCH and atmospheric models

Abstract: Density forecasts for weather variables are useful for the many industries exposed to weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. The distribution of the scenarios can be used as a density forecast, which is needed for pricing weather derivatives. We consider one to 10-day-ahead density forecasts provided by temperature ensemble predictions. More specifically, we evaluate forecasts of the mean and quantiles of the density. The mean of the ensemble scenarios is the most accurate forecast for the mean of the density. We use quantile regression to debias the quantiles of the distribution of the ensemble scenarios. The resultant quantile forecasts compare favourably with those from a GARCH model. These results indicate the strong potential for the use of ensemble prediction in temperature density forecasting.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Using Multivariate Statistics

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The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Linear Matrix Inequalities in System and Control Theory

Abstract: Preface 1. Introduction Overview A Brief History of LMIs in Control Theory Notes on the Style of the Book Origin of the Book 2. Some Standard Problems Involving LMIs. Linear Matrix Inequalities Some Standard Problems Ellipsoid Algorithm Interior-Point Methods Strict and Nonstrict LMIs Miscellaneous Results on Matrix Inequalities Some LMI Problems with Analytic Solutions 3. Some Matrix Problems. Minimizing Condition Number by Scaling Minimizing Condition Number of a Positive-Definite Matrix Minimizing Norm by Scaling Rescaling a Matrix Positive-Definite Matrix Completion Problems Quadratic Approximation of a Polytopic Norm Ellipsoidal Approximation 4. Linear Differential Inclusions. Differential Inclusions Some Specific LDIs Nonlinear System Analysis via LDIs 5. Analysis of LDIs: State Properties. Quadratic Stability Invariant Ellipsoids 6. Analysis of LDIs: Input/Output Properties. Input-to-State Properties State-to-Output Properties Input-to-Output Properties 7. State-Feedback Synthesis for LDIs. Static State-Feedback Controllers State Properties Input-to-State Properties State-to-Output Properties Input-to-Output Properties Observer-Based Controllers for Nonlinear Systems 8. Lure and Multiplier Methods. Analysis of Lure Systems Integral Quadratic Constraints Multipliers for Systems with Unknown Parameters 9. Systems with Multiplicative Noise. Analysis of Systems with Multiplicative Noise State-Feedback Synthesis 10. Miscellaneous Problems. Optimization over an Affine Family of Linear Systems Analysis of Systems with LTI Perturbations Positive Orthant Stabilizability Linear Systems with Delays Interpolation Problems The Inverse Problem of Optimal Control System Realization Problems Multi-Criterion LQG Nonconvex Multi-Criterion Quadratic Problems Notation List of Acronyms Bibliography Index.