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 …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

The Design and Analysis of Experiments

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.).

Machine learning

Additive manufacturing of metallic components – Process, structure and properties

Dissimilar metal joints between ferritic steels and nickel base alloys are currently fabricated using conventional arc welding processes with selected filler metal combinations. The dissimilar metal joints contain abrupt changes in composition over a relatively small distance. Many years of service at elevated temperatures has shown that these welds are susceptible to creep and creep fatigue failures. The primary mechanism for these creep failures involves carbon diffusion from the ferritic steel to the nickel base alloy. As a result, a carbon depleted zone is created that contains very few stable carbides. This work used additive manufacturing (AM) technologies as a highly controllable means for reducing carbon migration through theoreticallydesigned, compositionally graded transition joints guided by appropriate thermodynamic, kinetic and heat transfer and fluid flow modeling. The contents of the report include the objectives and tasks set forth by the PI and collaborators, major technical accomplishments throughout the research and additional details in the form of technical publications resulting from the NEUP grant and reports from the collaborating university and national laboratory.

Novel Dissimilar Joints Between Alloy 800H and 2.25%Cr and 1%Mo Steel

Reaction of molten kcn with co-n//2 gas mixtures.

Recent developments in metallurgical science and technology: process metallurgy.

This story is about a young engineer, who when faced with conflicting data, refused to ignore the discrepancies, followed them up and as a result, created a disruptive technology that has changed the scenario for stainless steels. It shows corporate research at its best, with generic lessons for those yearning for novelty that changes established practice for the better. The experiments that led to the breakthrough, how they were scaled for manufacture, the ubiquitous adoption of the new process, and much more are described.

A Remarkable Innovation in Stainless Steel Making

Les vitesses de vaporisation des elements d'alliage dans le bain de fusion ont ete correles avec les spectres d'emission du plasma au cours du soudage a l'aide d'un laser CO 2 de l'acier inoxydable AISI 201 sous des conditions operatoires variables

Tarasankar Debroy

Papers

Novel Dissimilar Joints Between Alloy 800H and 2.25%Cr and 1%Mo Steel

Reaction of molten kcn with co-n//2 gas mixtures.

Recent developments in metallurgical science and technology: process metallurgy.

A Remarkable Innovation in Stainless Steel Making

Emission spectroscopy of plasma during laser welding of AISI 201 stainless steel