The dynamics of functioning investigating societal transitions with partial differential equations

In this article a mathematical framework is introduced and explored for the study of processes in societal transitions. A transition is conceptualised as a fundamental shift in the functioning of a societal system. The framework views functioning as a real-valued field defined upon a real variable. The initial status quo prior to a transition is captured in a field called the regime and the alternative that possibly takes over is represented in a field called a niche. Think for example of a transition in an energy supply system, where the regime could be centrally produced, fossil fuel based energy supply and a niche decentralised renewable energy production. The article then proceeds to translate theoretical notions on the interactions and dynamics of regimes and niches from transition literature into the language of this framework. This is subsequently elaborated in some simple models and studied analytically or by means of computer simulation

Computational and mathematical approaches to societal transitions

After an introduction of the theoretical framework and concepts of transition studies, this article gives an overview of how structural change in social systems has been studied from various disciplinary perspectives. This overview first leads to the conclusion that computational and mathematical approaches and their practical form, modeling, up till now, have been almost absent in the research and theorizing of structural change or transitions in social systems. Second, this review of the social science literature suggests numerous theoretical constructs relevant for transition modeling. Relevant concepts include the conceptualization of the micro-to-macro link, the importance of explaining both stability and change, quantitative and qualitative definitions of structural change, the use of dichotomies, synchronic and diachronic reasoning in explaining structural change, definitions of basic patterns of social change, the conceptualization of resistance to change and intentional and normative aspects of social change. This article employs these theoretical concepts to describe and discuss the models presented in this special issue in order to develop an understanding of what exactly entails a computational or mathematical approach to societal transitions

French Roadmap for complex Systems 2008-2009

This second issue of the French Complex Systems Roadmap is the outcome of the Entretiens de Cargese 2008, an interdisciplinary brainstorming session organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It capitalizes on the first roadmap and gathers contributions of more than 70 scientists from major French institutions. The aim of this roadmap is to foster the coordination of the complex systems community on focused topics and questions, as well as to present contributions and challenges in the complex systems sciences and complexity science to the public, political and industrial spheres

Evaluating the predicted reliability of mechatronic systems: state of the art

Reliability analysis of mechatronic systems is a recent field and a dynamic branch of research. It is addressed whenever there is a need for reliable, available, and safe systems. The studies of reliability must be conducted earlier during the design phase, in order to reduce costs and the number of prototypes required in the validation of the system. The process of reliability is then deployed throughout the full cycle of development. This process is broken down into three major phases: the predictive reliability, the experimental reliability and operational reliability. The main objective of this article is a kind of portrayal of the various studies enabling a noteworthy mastery of the predictive reliability. The weak points are highlighted. Presenting an overview of all the quantitative and qualitative approaches concerned with modelling and evaluating the prediction of reliability is so important for future reliability studies and for academic research to come up with new methods and tools. The mechatronic system is a hybrid system, it is dynamic, reconfigurable, and interactive. The modeling carried out of reliability prediction must take into account these criteria. Several methodologies have been developed in this track of research. In this regard, the aforementioned methodologies will be analytically sketched in this paper.Comment: 13 page, Mechanical Engineering: An International Journal (MEIJ), Vol. 3, No. 2, May 201

EVALUATING THE PREDICTED RELIABILITY OF MECHATRONIC SYSTEMS: STATE OF THE ART

Reliability analysis of mechatronic systems is one of the most young field and dynamic branches of research. It is addressed whenever we want reliable, available, and safe systems. The studies of reliability must be conducted earlier during the design phase, in order to reduce costs and the number of prototypes required in the validation of the system. The process of reliability is then deployed throughout the full cycle of development; this process is broken down into three major phases: the predictive reliability, the experimental reliability and operational reliability. The main objective of this article is a kind of portrayal of the various studies enabling a noteworthy mastery of the predictive reliability. The weak points are highlighted, in addition presenting an overview of all approaches existing in quantitative and qualitative modeling and evaluating the reliability prediction is so important for the futures reliability studies, and for academic researches to innovate other new methods and tools. the Mechatronic system is a hybrid system; it is dynamic, reconfigurable, and interactive. The modeling carried out of reliability prediction must take into account these criteria. Several methodologies have been developed in this track of research. In this article, we will try to handle them from a critical angle

Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud \ud \ud KEYWORDS: Emergence of Life and Human Consciousness;\ud Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud \u

Stability and Fluctuations in Complex Ecological Systems

From 08-12 August, 2022, 32 individuals participated in a workshop, Stability and Fluctuations in Complex Ecological Systems, at the Lorentz Center, located in Leiden, The Netherlands. An interdisciplinary dialogue between ecologists, mathematicians, and physicists provided a foundation of important problems to consider over the next 5-10 years. This paper outlines eight areas including (1) improving our understanding of the effect of scale, both temporal and spatial, for both deterministic and stochastic problems; (2) clarifying the different terminologies and definitions used in different scientific fields; (3) developing a comprehensive set of data analysis techniques arising from different fields but which can be used together to improve our understanding of existing data sets; (4) having theoreticians/computational scientists collaborate closely with empirical ecologists to determine what new data should be collected; (5) improving our knowledge of how to protect and/or restore ecosystems; (6) incorporating socio-economic effects into models of ecosystems; (7) improving our understanding of the role of deterministic and stochastic fluctuations; (8) studying the current state of biodiversity at the functional level, taxa level and genome level.Comment: 22 page