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.

The discovery and preliminary geological and faunal descriptions of three new Steinahóll vent sites, Reykjanes Ridge, Iceland

Abstract: During RV MS Merian expedition MSM75, an international, multidisciplinary team explored the Reykjanes Ridge from June to August 2018. The first area of study, Steinaholl (150–350 m depth), was chosen based on previous seismic data indicating hydrothermal activity. The sampling strategy included ship- and AUV-mounted multibeam surveys, Remotely Operated Vehicle (ROV), Epibenthic Sledge (EBS), and van Veen grab (vV) deployments. Upon returning to Steinaholl during the final days of MSM75, hydrothermal vent sites were discovered using the ROV Phoca (Kiel, GEOMAR). Here we describe and name three new, distinct hydrothermal vent site vulnerable marine ecosystems (VMEs); Hafgufa, Stokkull, Lyngbakr. The hydrothermal vent sites consisted of multiple anhydrite chimneys with large quantities of bacterial mats visible. The largest of the three sites (Hafgufa) was mapped, and reconstructed in 3D. In total 23,310 individual biological specimens were sampled comprising 41 higher taxa. Unique fauna located in the hydrothermally venting areas included two putative new species of harpacticoid copepod (Tisbe sp. nov. and Amphiascus sp. nov.), as well as the sponge Lycopodina cupressiformis (Carter, 1874). Capitellidae Grube, 1862 and Dorvilleidae Chamberlin, 1919 families dominated hydrothermally influenced samples for polychaetes. Around the hydrothermally influenced sites we observed a notable lack of megafauna, with only a few species being present. While we observed hydrothermal associations, the overall species composition is very similar to that seen at other shallow water vent sites in the north of Iceland, such as the Mohns Ridge vent fields, particularly with peracarid crustaceans. We therefore conclude the community overall reflects the usual “background” fauna of Iceland rather than consisting of “vent endemic” communities as is observed in deeper vent systems, with a few opportunistic species capable of utilizing this specialist environment.

Guidelines for modelling and simulation of hybrid systems

Abstract: Hybrid systems are defined as systems that exhibit mixed properties of continuous-time systems.

Early season detection and mapping of Pseudomonas syringae pv. actinidae infected kiwifruit (Actinidia sp.) orchards

Abstract: Pseudomonas syringae pv. actinidiae (Psa) is an emerging disease of kiwifruit (Actinidia sp.). It has the potential to cause considerable production losses; therefore the ability to monitor and map the disease is important for industry-wide disease management. Using industry-collected infection data and an archived time-series of high-resolution satellite imagery, Psa disease monitoring in kiwifruit orchards was attempted for the 2010–11 growing season in the Bay of Plenty, New Zealand. Multiple vegetation indices were generated from imagery and a binomial logistic regression used to relate these vegetation indices to the Psa disease response. Results showed that the early season (2 October) photosynthetic vigour ratio was the most effective for differentiating infected and non-infected orchards. Omission and commission errors were observed, but were in part due to issues with data quality. The results were encouraging for the potential timely use of satellite imagery for monitoring and mapping Psa infect...

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

Abstract: Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

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.