How do you know what works and what doesn't? This book contains case studies highlighting the power of polytope projects for complex problem solving. Any sort of combinational problem characterized by a large variety of possibly complex constructions and deconstructions based on simple building blocks can be studied in a similar way. Although the m

This book presents a domain of extreme industrial and scientific interest: the study of smart systems and structures. It presents polytope projects as comprehensive physical and cognitive architectures that support the investigation, fabrication and implementation of smart systems and structures. These systems feature multifunctional components that can perform sensing, control, and actuation. In light of the fact that devices, tools, methodologies and organizations based on electronics and information technology for automation, specific to the third industrial revolution, are increasingly reaching their limits, it is essential that smart systems be implemented in industry. Polytope projects facilitate the utilization of smart systems and structures as key elements of the fourth industrial revolution. The book begins by presenting polytope projects as a reference architecture for cyber-physical systems and smart systems, before addressing industrial process synthesis in Chapter 2. Flow-sheet trees, cyclic separations and smart configurations for multi-component separations are discussed here. In turn, Chapter 3 highlights periodic features for drug delivery systems and networks of chemical reactions, while Chapter 4 applies conditioned random walks to polymers and smart materials structures. Chapter 5 examines self-assembly and self-reconfiguration at different scales from molecular to micro systems. Smart devices and technologies are the focus of chapter 6. Modular micro reactor systems and timed automata are examined in selected case studies. Chapter 7 focuses on inferential engineering designs, concept-knowledge, relational concept analysis and model driven architecture, while Chapter 8 puts the spotlight on smart manufacturing, industry 4.0, reference architectures and models for new product development and testing. Lastly, Chapter 9 highlights the polytope projects methodology and the prospects for smart systems and structures. Focusing on process engineering and mathematical modeling for the fourth industrial revolution, the book offers a unique resource for engineers, scientists and entrepreneurs working in chemical, biochemical, pharmaceutical, materials science or systems chemistry, students in various domains of production and engineering, and applied mathematicians.

How do you know what works and what doesn't? This book contains case studies highlighting the power of polytope projects for complex problem solving. Any sort of combinational problem characterized by a large variety of possibly complex constructions and deconstructions based on simple building blocks can be studied in a similar way. Although the majority of case studies are related to chemistry, the method is general and equally applicable to other fields for engineering or science.

High dimensional reference architectures presented here allows confronting and prevailing over the growing complexity of polytopic projects implementations. Such projects should be envisaged giving that conventional systems operations, equipments, methodologies or organizations will reach their limits for self-evolvability in high complexity conditions. Self-evolvable high complexity systems are based on high dimensional polytopic reference architectures. Polytope is the general term of the sequence: point, line, polygon, polyhedron and so on.The polytopic projects are targeting the artificiality, not only for materials where it is well known and applied, but also for biological, cognitive, intelligent and mathematical systems. The book highlights the polytopic projects basic similarity despite the noticeable difference as domains of application. The roads to follow and the algebra of changing roads are emphasized. The book is divided in 9 chapters. Chapter 1 introduces the Polytopic Roadmap to 4D and beyond. The role for the dialogue of processes in duality of the non-Aristotelian Logic of Contradiction and of Included Middle is emphasized for different domains. Chapter 2 refers to chemical systems. Supramolecular chemistry, metal organic frameworks, MOF, and reaction networks, are the examples considered in the frame of polytopic chemistry. Chapter 3 refers to biological systems. Biological dynamical hierarchies and quasi-species are the considered case studies. Technological and scientific projects targeting artificiality for cells and viruses are considered. Chapter 4 refers to cognitive systems. Developmental stages, formal and relational concepts analysis, and neural coding are considered here. The roles of the 4D systems of systems of systems and of conceptual 4D-cube are emphasized. Artificiality for cognitive systems is the object of study. Chapter 5 refers to mathematical systems. Modeling levels and the 4D digital twins are discussed. Hopf monoids as tools for the study of combinations and separations, dual graded graphs and V-models are informally presented. Chapter 6 refers to application of formal concept analysis, FCA, for high dimensional separations, nesting and drug delivery. Chapter 7 refers to polytopic engineering systems as multiscale transfer, distributors-collectors, cyclic operations, middle vessel columns, mixing, assembly and designs. Equipments have been characterized using Polytopic Roadmaps and classified by Periodic Tables. Chapter 8 introduces polytopic industry, economy, society and sustainability. Chapter 9 outlines new domains of interest as arts and architecture, transdisciplinarity, complex systems and unity of sciences and engineering. Polytopic Roadmaps are proposed as Method for experts from various fields to synthesize their thinking and capabilities into new projects implementation to face and surpass high complexity. A repetitive finding of this book is that self-evolvability observed in physical systems is based on the same directed sequence of reference architectures as the self-evolvability of concepts in our mind. Continuing to develop the field of self-evolvable systems and presenting the polytopic roadmaps for 4D and beyond advances in ever growing complexity domains, the book will be useful to engineers, researchers, entrepreneurs and students in different branches of production, complex systems sciences and engineering, ecology and applied mathematics.

This book focuses on new developments in polytopic projects, particularly on implementation domains and case studies, as well as high-dimensional methodology. Polytopic projects are based on a general reference architecture inspired and shared by the functional organization of organisms and enterprises as informational and cognitive systems, the scientific and engineering methodology and the operational structure of existing self-evolvable and self-sustainable systems.

The design and analysis of geometric algorithms have seen remarkable growth in recent years, due to their application in, for example, computer vision, graphics, medical imaging and CAD. The goals of this book are twofold: first to provide a coherent and systematic treatment of the foundations; secondly to present algorithmic solutions that are amenable to rigorous analysis and are efficient in practical situations. When possible, the algorithms are presented in their most general d-dimensional setting. Specific developments are given for the 2- or 3-dimensional cases when this results in significant improvements. The presentation is confined to Euclidean affine geometry, though the authors indicate whenever the treatment can be extended to curves and surfaces. The prerequisites for using the book are few, which will make it ideal for teaching advanced undergraduate or beginning graduate courses in computational geometry.

This monograph presents key method to successfully manage the growing complexity of systems where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted, self-properties such as self-organization, self-configuration, and self-repairing are introduced and challenges and limitations of the self-evolvable engineering systems are evaluated.

This book is useful to engineers, researchers, entrepreneurs, and students in different branches of production, engineering, and systems sciences. The polytopic roadmaps are the guidelines inspired by the development stages of cognitive-intelligent systems, and expected to become powerful instruments releasing an abundance of new capabilities and structures for complex engineering systems implementation. The 4D approach developed in previous monographs and correlated with industry 4.0and Fourth Industrial Revolution is continued here toward higher dimensions approaches correlated with polytopic operations, equipment, technologies, industries, and societies. Methodology emphasizes the role of doubling, iteration, dimensionality, and cyclicality around the center, of periodic tables and of conservative and exploratory strategies. Partitions, permutations, classifications, and complexification, as polytopic chemistry, are the elementary operations analyzed. Multi-scale transfer, cyclic operations, conveyors, and assembly lines are the practical examples of operations and equipment. Polytopic flow sheets, online analytical processing, polytopic engineering designs, and reality-inspired engineering are presented. Innovative concepts such as Industry 5.0, polytopic industry, Society 5.0, polytopic society, cyber physical social systems, industrial Internet, and digital twins have been discussed. The general polytopic roadmaps, (GPTR), are proposed as universal guidelines and as common methodologies to synthesize the systemic thinking and capabilities for growing complexity projects implementation.

Combinational optimization (CO) is a topic in applied mathematics, decision science and computer science that consists of finding the best solution from a non-exhaustive search. CO is related to disciplines such as computational complexity theory and algorithm theory, and has important applications in fields such as operations research/management science, artificial intelligence, machine learning, and software engineering.Advances in Combinatorial Optimization presents a generalized framework for formulating hard combinatorial optimization problems (COPs) as polynomial sized linear programs. Though developed based on the 'traveling salesman problem' (TSP), the framework allows for the formulating of many of the well-known NP-Complete COPs directly (without the need to reduce them to other COPs) as linear programs, and demonstrates the same for three other problems (e.g. the 'vertex coloring problem' (VCP)). This work also represents a proof of the equality of the complexity classes 'P' (polynomial time) and 'NP' (nondeterministic polynomial time), and makes a contribution to the theory and application of 'extended formulations' (EFs).On a whole, Advances in Combinatorial Optimization offers new modeling and solution perspectives which will be useful to professionals, graduate students and researchers who are either involved in routing, scheduling and sequencing decision-making in particular, or in dealing with the theory of computing in general.