This book presents, as a single package, three semingly contradictory and often competitive approaches to deal with ever present uncertainty in science and engineering. The book describes, as a unique view, probabilistic, fuzzy sets based and antioptimization based approaches, in order to remedy the present "tower ob Babel” situation, in which researchers in competing fields do not communicate. Integrative approach will attract scientists and engineers alike and provide a strong impetus towards integrative, hybrid approaches.

This volume addresses the issue of uncertainty in civil engineering from design to construction. Failures do occur in practice. Attributing them to a residual system risk or a faulty execution of the project does not properly cover the range of causes. A closer scrutiny of the adopted design, the engineering model, the data, the soil-construction-interaction and the model assumptions is required. Usually, the uncertainties in initial and boundary conditions are abundant. Current engineering practice often leaves these issues aside, despite the fact that new scientific tools have been developed in the past decades that allow a rational description of uncertainties of all kinds, from model uncertainty to data uncertainty. It is the aim of this volume to have a critical look at current engineering risk concepts in order to raise awareness of uncertainty in numerical computations, shortcomings of a strictly probabilistic safety concept, geotechnical models of failure mechanisms and their implications for construction management, execution, and the juristic question of responsibility. In addition, a number of the new procedures for modelling uncertainty are explained. The book is a result of a collaborate effort of mathematicians, engineers and construction managers who met regularly in a post graduate seminar at the University of Innsbruck during the past years.

What does it take to be considered 'real'? A physical presence that can be discerned by touch, taste, sound, sight or smell? Are socially important concepts such as justice or morality 'unreal' because they lack the physical traits that would enable us to touch, taste, hear, see or smell them? Are the distinctions many or us draw between non-fiction and fiction reflective of the manner in which we could confidently sort the 'real' from the 'unreal'? Is the apparent capacity of literature, philosophy and theology to influence our behavior as human beings a recurring instance of the allegedly 'unreal' guiding the 'real'? If I am prepared to acknowledge that ideas possess an apparent capacity to influence my own behavior, the viability of seperating the 'real' from the 'unreal' based upon physically extended properties is brought into question. If I dismiss this traditional method for assaying the presence of reality, what am I left with? A reality shared by rocks and emotions, objects of aesthetic expression that range from Michelangelo's "David" to Conan Doyle's "Sherlock Holmes, all of which I knowingly encounter and to each of which I respond in one way or another. Does this behavioral acknowledgment on my part play a role in making each of these entities real? Does my behavior have a role to play in the creation of reality? "How and Why; a layman's look at causation and reality", is a description of how reality is populated and why that population comes about. It is based upon a causal foundation that is characterized by uncertainty. It offers an explanatory paradigm in which novelty and creativity have indispensable roles to play, a paradigm of causal association that can be used to explain but not determine the behavior with which it is affiliated. To be considered 'real', one must possess and exercise the capacity 'to cause', which enables one to substantiate both the 'how' and the 'why' of one's existence as an expression of that singular capacity,

Have you ever wondered where the safety factors come from? Why is it that deterministic analysis has reached a very sophisticated level, but in the end empirical factors are still needed? Is there a way to select them, rather than assigning them arbitrarily as is often done? This book clearly shows that safety factors are closely related with the reliability of structures, giving yet another demonstration of Albert Einstein's maxim that "It is incomprehensible that Nature is comprehensible". The book shows that the safety factors are much more comprehensible if they are seen in a probabilistic context. Several definitions of the safety factors are given, analytical results on insightful numbers are presented, nonprobabilistic safety factors are shown, as well as their estimates derived by the inequalities of Bienayme, Markov, Chebushev and Camp-Meidell. A special chapter is devoted to important contributions by Japanese experts. This volume will help to critically re-think the issue of safety factors, which can create a false feeling of security. The deterministic paradigm can be enhanced by incorporating probabilistic concepts wisely where they are needed without treating all variables as probabilistic ones. The book shows that there is a need of their integration rather than separation. This book is intended for engineers, graduate students, lecturers and researchers.

Until recently, a majority of the applications of X-ray computed tomography (CT) scanning in plant sciences remained descriptive; some included a quantification of the plant materials when the root-soil isolation or branch-leaf separation was satisfactory; and a few involved the modeling of plant biology processes or the assessment of treatment or disease effects on plant biomass and structures during growth. In the last decade, repeated CT scanning of the same plants was reported in an increasing number of studies in which moderate doses of X-rays had been used. Besides the general objectives of Frontiers in Plant Science research topics, “Branching and Rooting Out with a CT Scanner” was proposed to meet specific objectives: (i) providing a non-technical update on knowledge about the application of CT scanning technology to plants, starting with the type of CT scanning data collected (CT images vs. CT numbers) and their processing in the graphical and numerical approaches; (ii) drawing the limits of the CT scanning approach, which because it is based on material density can distinguish materials with contrasting or moderately overlapping densities (e.g., branches vs. leaves, roots vs. non-organic soils) but not the others (e.g., roots vs. organic soils); (iii) explaining with a sufficient level of detail the main procedures used for graphical, quantitative and statistical analyses of plant CT scanning data, including fractal complexity measures and statistics appropriate for repeated plant CT scanning, in experiments where the research hypotheses are about biological processes such as light interception by canopies, root disease development and plant growth under stress conditions; (iv) comparing plant CT scanning with an alternative technology that applies to plants, such as the phenomics platforms which target leaf canopies; and (v) providing current and potential users of plant CT scanning with up-to-date information and exhaustive documentation, including clear perspectives and well-defined goals for the future, for them to be even more efficient or most efficient from start in their research work.

Looking at climate change through the lens of economics is interesting, useful and rewarding for the perplexed but interested citizen.

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance." The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields — probability, physics, biology, the “soft” sciences, computer science — because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling.