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\"Fishing Lessons shares the stories of success and decline, told by those at the ends of the long lines and hand lines. Kevin Bailey knows the waters, the small scale industry, and the relationships to ocean ecology. In a series of place based chapters, he channels readers through the changing dynamics of small-scale fisheries and the issues of sustainability they face--fiscal and ecological.\"-- Provided by publisher.

This book is a comprehensive guide to new design for testability (DFT) methods that will show the readers how to design a testable and quality product, drive down test cost, improve product quality and yield, and speed up time-to-market and time-to-volume. Key features include up-to-date coverage of design for testability, coverage of industry practices commonly found in commercial DFT tools but not discussed in other books, and numerous, practical examples in each chapter illustrating basic VLSI test principles and DFT architectures. Practitioners/Researchers in VLSI design and testing; design or test engineers, as well as research institutes will benefit from this book. This book is also appropriate for undergraduate and graduate-level courses in electronic testing, digital systems testing, digital logic test and simulation, and VLSI design.

Recognizing increasing returns disrupts much of the established wisdom in economic analysis, making money non-neutral, equity conflict with freedom, & encouraging goods with increasing returns efficient. This book discusses these problems & ways they can be handled, helping to explain phenomena in the real world.

Scale is a critical factor when studying patterns and the processes that cause them. A variety of approaches have been used to define the concept of scale but confusion and ambiguities remain regarding scale types and their definitions. The objectives of this study were therefore (1) to review existing types and definitions of scale, and (2) to systematically investigate the ambiguities in scale definitions and to determine the applicability of the various scale types and definitions. Through a comprehensive literature review, we identified seven types of scales and designed a survey for the seven definitions of scale and interviewed 150 scientists. The results show that the more cartography related types of scale are relatively well known while the more abstract dimensions are less known and are most ambiguous. Based on graphical examples, participants were asked which spatial scales are most relevant for their work. Surprisingly, composite objects such as a forest stand were most relevant followed by individual objects such as single trees and, lastly, more generalized categorizes or meta-objects such as \"forested area\". We have drawn some conclusions that will help to clarify the different types of scale in regard to their practical use.

Several scales are commonly used for assessing pain intensity. Among them, the numerical rating scale (NRS), visual analog scale (VAS), and verbal rating scale (VRS) are often used in clinical practice. However, no study has performed psychometric analyses of their reliability and validity in the measurement of osteoarthritic (OA) pain. Therefore, the present study examined the test-retest reliability, validity, and minimum detectable change (MDC) of the VAS, NRS, and VRS for the measurement of OA knee pain. In addition, the correlations of VAS, NRS, and VRS with demographic variables were evaluated. The study included 121 subjects (65 women, 56 men; aged 40-80 years) with OA of the knee. Test-retest reliability of the VAS, NRS, and VRS was assessed during two consecutive visits in a 24 h interval. The validity was tested using Pearson's correlation coefficients between the baseline scores of VAS, NRS, and VRS and the demographic variables (age, body mass index [BMI], sex, and OA grade). The standard error of measurement (SEM) and the MDC were calculated to assess statistically meaningful changes. The intraclass correlation coefficients of the VAS, NRS, and VRS were 0.97, 0.95, and 0.93, respectively. VAS, NRS, and VRS were significantly related to demographic variables (age, BMI, sex, and OA grade). The SEM of VAS, NRS, and VRS was 0.03, 0.48, and 0.21, respectively. The MDC of VAS, NRS, and VRS was 0.08, 1.33, and 0.58, respectively. All the three scales had excellent test-retest reliability. However, the VAS was the most reliable, with the smallest errors in the measurement of OA knee pain.

Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition. This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010 , under revision) and comprising: an enriched set of differential interest operators at a fixed scale including the Laplacian operator, the determinant of the Hessian, the new Hessian feature strength measures I and II and the rescaled level curve curvature operator, as well as an enriched set of scale selection mechanisms including scale selection based on local extrema over scale, complementary post-smoothing after the computation of non-linear differential invariants and scale selection based on weighted averaging of scale values along feature trajectories over scale. It is shown how the selected scales of different linear and non-linear interest point detectors can be analyzed for Gaussian blob models. Specifically it is shown that for a rotationally symmetric Gaussian blob model, the scale estimates obtained by weighted scale selection will be similar to the scale estimates obtained from local extrema over scale of scale normalized derivatives for each one of the pure second-order operators. In this respect, no scale compensation is needed between the two types of scale selection approaches. When using post-smoothing, the scale estimates may, however, be different between different types of interest point operators, and it is shown how relative calibration factors can be derived to enable comparable scale estimates for each purely second-order operator and for different amounts of self-similar post-smoothing. A theoretical analysis of the sensitivity to affine image deformations is presented, and it is shown that the scale estimates obtained from the determinant of the Hessian operator are affine covariant for an anisotropic Gaussian blob model. Among the other purely second-order operators, the Hessian feature strength measure I has the lowest sensitivity to non-uniform scaling transformations, followed by the Laplacian operator and the Hessian feature strength measure II. The predictions from this theoretical analysis agree with experimental results of the repeatability properties of the different interest point detectors under affine and perspective transformations of real image data. A number of less complete results are derived for the level curve curvature operator.