The Mechanics of Adhesion shows that adhesion science and technology is inherently an interdisciplinary field, requiring fundamental understanding of mechanics, surfaces, and materials. This volume comprises 19 chapters. Starting with a background and introduction to stress transfer principles; fracture mechanics and singularities; and an energy approach to debonding, the volume continues with analysis of structural lap and butt joint configurations. It then continues with discussions of test methods for strength and constitutive properties; fracture; peel; coatings, the case of adhesion to a single substrate; elastomeric adhesives such as sealants. The role of mechanics in determining the locus of failure in bonded joints is discussed, followed by a chapter on rheology relevant to adhesives and sealants. Pressure sensitive adhesive performance; the principles of tack and tack measurements; and contact mechanics relevant to wetting and surface energy measurements are then covered. The volume concludes with sections on fibermatrix bonding and reinforcement; durability considerations for adhesive bonds; ultrasonic non-destructive evaluation of adhesive bonds; and design of adhesive bonds from a strength perspective. This book will be of interest to practitioners in the fields of engineering and to those with an interest in adhesion science.

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.