David Rousseau – författare

This book features five peer-reviewed reviews on the different applications of phenotyping techniques across agriculture.The first chapter outlines how plant phenotyping has developed over recent decades, driven by factors such as advances in optical sensors, image analysis and automation as well as multidisciplinary cooperation in establishing facilities for high-throughput plant phenotyping.The second chapter reviews the importance of phenotyping in plant breeding programmes and considers the role of phenomics in boosting the genetic gain in grain yields. Using high-throughput phenotyping techniques as a cost-efficient method for phenotypic data acquisition is also discussed.The third chapter examines recent advances in phenotyping to identify drought-resistance traits in cereal roots. It also reviews current root phenotyping strategies and challenges.The fourth chapter considers the application of phenotyping techniques based on existing plant cohort research in plant factories with artificial lighting (PFALs). The chapter also explores the possibility of plant phenotype-based environmental control in PFALs.The final chapter provides an overview of the imaging techniques used in seed phenotyping, focussing primarily on the recent advances in X-ray imaging and the main parameters required to produce optimal image data.

This book expands the foundations of general systems theory to enable progress beyond the rich heuristic practices available today. It establishes a foundational framework for the development of scientific transdisciplinary systems principles and shows how these can amplify the potential of individuals and teams working in multi-, inter- and transdisciplinary contexts or striving to translate their progress across disciplinary boundaries. Three general scientific systems principles are presented, and their relevance to the design, analysis, management and transformation of systems is explored. Applying lessons from the history and philosophy science, this book disambiguates key concepts of general systemology, clarifies the role of general systemology within the field of systemology, and explains how general systemology supports other forms of transdisciplinarity. These insights are used to develop new perspectives, strategies and tools for addressing long-standing challenges to the advancement and transdisciplinary application of general insights into the nature of complex systems.The material presented in this book includes comprehensive models of the structure of systemology as a disciplinary field, the structure and significance of the general systems worldview, and the role of general systemology as the heart of systems science, systems engineering and systems practice. It explains what a fully-fledged general theory of systems would look like, what its potential is, what routes are available to us to develop it further, and how to leverage the knowledge we have attained so far. Many examples and analogies show how general systemology has the potential to enable scientific discovery, insightful theory building, and practical innovation in all the disciplines as they study, design, nurture or transform complex systems. This book is essential reading for anyone wishing to master the concepts, terminology, models and strategies needed to make effective use of current general systems knowledge and to engage in the further development of the philosophy, science, and practice of general systemology.

The Higgs boson discovery at the Large Hadron Collider in 2012 relied on boosted decision trees. Since then, high energy physics (HEP) has applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, the availability of high-quality simulators, the complexity of the data structures (which rarely are image-like), the control of uncertainties expected from scientific measurements, and the exabyte-scale datasets require the development of HEP-specific ML techniques. While these developments proceed at full speed along many paths, the nineteen reviews in this book offer a self-contained, pedagogical introduction to ML models' real-life applications in HEP, written by some of the foremost experts in their area.