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To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network's diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method in the OMNET++ simulator shows that the trust mechanism can lead to the identification of reliable sources and, as a result, reduce the time to complete tasks. [ABSTRACT FROM AUTHOR]

Over the past decade, Convergence Research has increasingly gained prominence as a research, development, and innovation (RDI) strategy to address grand societal challenges. However, a dearth of research-based evidence is available to aid researchers, research teams, and institutions with navigating the complexities attendant to the specifics of Convergence Research. This paper presents a multilevel research agenda that accounts for an integral understanding of Convergence Research as a complex adaptive system. Furthermore, by developing a framework that accounts for ancillary, yet essential, systems associated with Convergence Research, we enrich the agenda with a literature-steeped discussion that considers how systems-based practices of collaboration, inquiry, and context interact with the processes and products of Convergence Research. Finally, we synthesize and apply insights from the reviewed literature by providing paths for empirical exploration emphasizing systems-based practices. In so doing, we delineate an extended boundary for a research stream that both clarifies and enlarges our understanding of Convergence Research as a 'system-of-systems'. [ABSTRACT FROM AUTHOR]

In the present paper, we study a specific partition of a given network and establish the moderate deviation principle of modularity for the partition when the size of the network gets large. [ABSTRACT FROM AUTHOR]

Background: This paper discusses a paradox in mental health. It manifests as a relationship between adverse "bad" effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous "good" aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible. Discussion: This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies. Conclusion: Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society. [ABSTRACT FROM AUTHOR]

Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should be readily applied to complex and open social systems. In this paper we set forth a framework to understand when optimization is limited, particularly for complex and open social systems. [ABSTRACT FROM AUTHOR]

There is growing need for hybrid curricula that integrate constructivist methods from Science and Technology Studies (STS) into both engineering and policy courses at the undergraduate and graduate levels. However, institutional and disciplinary barriers have made implementing such curricula difficult at many institutions. While several programs have recently been launched that mix technical training with consideration of “societal” or “ethical issues,” these programs often lack a constructivist element, leaving newly-minted practitioners entering practical fields ill-equipped to unpack the politics of knowledge and technology or engage with skeptical publics. This paper presents a novel format for designing interdisciplinary coursework that combines conceptual content from STS with training in engineering and policy. Courses following this format would ideally be team taught by instructors with advanced training in diverse fields, and hence co-learning between instructors and disciplines is a key element of the format. Several instruments for facilitating both student and instructor collaborative learning are introduced. The format is also designed for versatility: in addition to being adaptable to both technical and policy training environments, topics are modularized around a conceptual core so that issues ranging from biotech to nuclear security can be incorporated to fit programmatic needs and resources. [ABSTRACT FROM AUTHOR]

In their recent book, Ladyman and Wiesner (What is a complex system?, Yale University Press, 2020) delineate the bounds of the exciting interdisciplinary field of complexity science. In this work, they provide examples of generally accepted complex systems and common features which these possess to varying degrees. In this paper, I plan to extend their list to include the formal study of natural language, i.e. linguistics. In fact, I will argue that language exhibits many of the hallmarks of a complex system, specifically a complex biological system. Thus, my aim is to advocate contra the the ‘Minimalist Program’ (Chomsky, The minimalist program, MIT Press, Cambridge, 1995), which motivates simple underlying mechanisms (i.e. Merge) in their idealisations, that biolinguistics should embrace a ‘Maximalist Program’ in which multiple subfields contribute component explanations to an emerging whole. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of finite-time dissipative-based distributive non-fragile filter design for a class of discrete-time complex systems subject to randomly occurring multiple delays, dynamic quantization and missing measurements. The main intention of this work is to propose a distributive non-fragile filter that ensures the stochastic finite-time boundedness together with prescribed dissipative performance in the presence of multiple delays. To characterize the random nature of delays, stochastic variables are introduced which satisfy the Bernoulli binary distribution. Moreover, the two factors such as missing measurements and dynamic quantization are implemented in the measurement signal. By employing S-procedure and constructing proper Lyapunov–Krasovskii functional, a set of linear matrix inequality (LMI)-based sufficient conditions that guarantee the stochastic finite-time boundedness with dissipative performance of the augmented filtering error system is obtained. Finally, the efficiency of the proposed distributive non-fragile filter design is proved by presenting three numerical examples including the continuous stirred tank reactor (CSTR) and a quarter-car suspension model. [ABSTRACT FROM AUTHOR]

This paper sets out the main findings from two rounds of interviews with senior representatives from the UK's urban development industry: the third and final phase of a 3-year pilot, Moving Health Upstream in Urban Development' (UPSTREAM). The project had two primary aims: firstly, to attempt to value economically the health cost-benefits associated with the quality of urban environments and, secondly, to interview those in control of urban development in the UK in order to reveal the potential barriers to, and opportunities for, the creation of healthy urban environments, including their views on the use of economic valuation of (planetary) health outcomes. Much is known about the 'downstream' impact of urban environments on human and planetary health and about how to design and plan healthy towns and cities ('midstream'), but we understand relatively little about how health can be factored in at key governance tipping points further 'upstream', particularly within dominant private sector areas of control (e.g. land, finance, delivery) at sub-national level. Our findings suggest that both public and private sector appeared well aware of the major health challenges posed by poor-quality urban environments. Yet they also recognized that health is not factored adequately into the urban planning process, and there was considerable support for greater use of non-market economic valuation to help improve decision-making. There was no silver bullet however: 110 barriers and 76 opportunities were identified across a highly complex range of systems, actors and processes, including many possible points of targeted intervention for economic valuation. Eight main themes were identified as key areas for discussion and future focus. This findings paper is the second of two on this phase of the project: the first sets out the rationale, approach and methodological lessons learned. [ABSTRACT FROM AUTHOR]

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already affects many sensitive realms (as in Politics, Social Sciences, or Law). The decision-making power handover to opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solutions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientific and humanistic areas. The first aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is beneficiated from part of the scientific and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the field of Data Science. The second objective is to develop some arguments justifying the authors' bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justification and the explainer-explainee agreement. [ABSTRACT FROM AUTHOR]

Market diffusion of new products is driven by the actions and reactions of consumers, distributors, competitors, and other stakeholders, all of whom can be heterogeneous in their individual characteristics, attitudes, needs, and objectives. These actors may also interact with others in various ways (e.g., through word of mouth or social influence). Thus, a typical consumer market constitutes a complex system whose behavior is difficult to foresee because stochastic impulses may give rise to complex emergent patterns of system reactions over time. Agent-based modeling, a relatively novel approach to understanding complex systems, is well equipped to deal with this complexity and, therefore, may serve as a valuable tool for both researchers studying particular market effects and practitioners seeking decision support for determining features of products under development or the appropriate combination of measures to accelerate product diffusion in a market. This paper provides an overview of the strengths and criticisms of such tools. It aims to encourage researchers in the field of innovation management, as well as practitioners, to consider agent-based modeling and simulation as a method for gaining deeper insights into market behavior and making better-informed decisions. [ABSTRACT FROM AUTHOR]

Chronic discrimination and associated socioeconomic inequalities have shaped the health and well-being of Black Americans. As a consequence of the intersection of these factors with rural deprivation, rural Black Americans live and work in particularly pathogenic environments that generate disproportionate and interacting chronic comorbidities (syndemics) compared to their White and/or urban counterparts. Traditional prevention research has been unable to fully capture the underlying complexity of rural minority health and has generated mostly low-leverage interventions that have failed to reverse adverse metabolic outcomes among rural Black Americans. In contrast, novel research approaches-such as system dynamics modeling-that seek to understand holistic system structure and determine complex health outcomes over time provide a robust framework to develop a more accurate understanding of the key factors contributing to type 2 diabetes. This framework can then be used to establish more efficacious interventions to address disparities among minorities in rural areas. This paper advocates for a unified complex systems epistemology and methodology in advancing rural minority health disparities research. Toward this goal, we (1) provide an overview of rural Black American metabolic health research, (2) introduce a complex systems framework in rural minority health disparities research, and (3) demonstrate how community-based system dynamics modeling and simulation can help us plow new ground in rural minority health disparities research and action. We anticipate that this paper can serve as a catalyst for a long-overdue discourse on the relevance of complex systems approaches in minority health research, with practical benefits for numerous disproportionately burdened communities. [ABSTRACT FROM AUTHOR]

Multi-fidelity approaches combine different models built on a scarce but accurate dataset (high-fidelity dataset), and a large but approximate one (low-fidelity dataset) in order to improve the prediction accuracy. Gaussian processes (GPs) are one of the popular approaches to exhibit the correlations between these different fidelity levels. Deep Gaussian processes (DGPs) that are functional compositions of GPs have also been adapted to multi-fidelity using the multi-fidelity deep Gaussian process (MF-DGP) model. This model increases the expressive power compared to GPs by considering non-linear correlations between fidelities within a Bayesian framework. However, these multi-fidelity methods consider only the case where the inputs of the different fidelity models are defined over the same domain of definition (e.g., same variables, same dimensions). However, due to simplification in the modeling of the low fidelity, some variables may be omitted or a different parametrization may be used compared to the high-fidelity model. In this paper, deep Gaussian processes for multi-fidelity (MF-DGP) are extended to the case where a different parametrization is used for each fidelity. The performance of the proposed multi-fidelity modeling technique is assessed on analytical test cases and on structural and aerodynamic real physical problems. [ABSTRACT FROM AUTHOR]

Knowledge, technology, and society as well as natural systems are increasingly coherent and complex, and new systems are continuously formed at their interfaces. Convergence is a problem-solving strategy to holistically understand, create, and transform a system for reaching a common goal, such as advancing an emerging technology in society. The systems may be either in natural, scientific, technological, economic, or societal settings. Convergence offers a unifying strategy applicable to all systems that can be modeled as evolving neural-like networks. The paper presents an overview of the convergence science including underlying theories, principles, and methods and illustrates its implementation in key areas of application. The convergence approach begins with deep integration of previously separated fields, communities, and modes of thinking, to form and improve a new system, from where solutions divergence to previously unattainable applications and outcomes. The worldwide science and technology (S&T) landscape is changing at the beginning of the twenty-first century because of convergence. First, there is the affirmation of three transdisciplinary general-purpose technologies—nanotechnology, digital technology, and artificial intelligence (AI). A second main characteristics is the deep integration of five foundational science and technology fields (NBICA: nanoscale, modern biology, information, cognition, and artificial intelligence) from their basic elements—atoms, genes, bits, neurons, and logic steps and their collective action—to address global challenges and opportunities. The affirmation of nanotechnology at the confluence of disciplines toward systematic control of matter at the nanoscale has been an enabling inspiration and foundation for other S&T fields, emerging industries, and convergence solutions in society. Several future opportunities for implementation of convergence principles are the global S&T system, realizing sustainable society, advancing human capabilities, and conflict resolution. [ABSTRACT FROM AUTHOR]

Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called 'Form 10-Ks,' contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company operates. In this paper, we analyze over 4.5 million references to U.S. Federal Acts and Agencies contained within these reports to measure the regulatory ecosystem, in which companies are organisms inhabiting a regulatory environment. While individuals across the political, economic, and academic world frequently refer to trends in this regulatory ecosystem, far less attention has been paid to supporting such claims with large-scale, longitudinal data. In this paper, in addition to positing a model of regulatory ecosystems, we document an increase in the regulatory energy per filing, i.e., a warming 'temperature.' We also find that the diversity of the regulatory ecosystem has been increasing over the past two decades. These findings support the claim that regulatory activity and complexity are increasing, and this framework contributes an important step towards improving academic and policy discussions around legal complexity and regulation. [ABSTRACT FROM AUTHOR]

Multidisciplinary design optimization (MDO) is concerned with solving design problems involving coupled numerical models of complex engineering systems. While various MDO software frameworks exist, none of them take full advantage of state-of-the-art algorithms to solve coupled models efficiently. Furthermore, there is a need to facilitate the computation of the derivatives of these coupled models for use with gradient-based optimization algorithms to enable design with respect to large numbers of variables. In this paper, we present the theory and architecture of OpenMDAO, an open-source MDO framework that uses Newton-type algorithms to solve coupled systems and exploits problem structure through new hierarchical strategies to achieve high computational efficiency. OpenMDAO also provides a framework for computing coupled derivatives efficiently and in a way that exploits problem sparsity. We demonstrate the framework's efficiency by benchmarking scalable test problems. We also summarize a number of OpenMDAO applications previously reported in the literature, which include trajectory optimization, wing design, and structural topology optimization, demonstrating that the framework is effective in both coupling existing models and developing new multidisciplinary models from the ground up. Given the potential of the OpenMDAO framework, we expect the number of users and developers to continue growing, enabling even more diverse applications in engineering analysis and design. [ABSTRACT FROM AUTHOR]

In this paper, we study the importance of a consistent description of real work in patient risk assessment in the primary healthcare domain. Through a case study in the context of primary health care, we address the research problem of finding ways to build consistent real work descriptions of the patient risk assessment system in the primary healthcare domain, in order to foster the design of improved work situations and support devices. This is a qualitative field study based on ethnographic observation and semi-structured interviews carried out among professionals involved in the risk assessment process in a primary healthcare facility. The objects of ergonomic work analysis were work places and work situations with focus on human activity, as well as surrounding aspects. The analysis identified elements in the work domain with high cognitive demand and operations that could increase mental workload, providing elements for the earlier stages of the design of work situations and support devices to improve the risk assessment in primary health care. This paper shows the usefulness of real work descriptions in the design for complex situations like the risk assessment in health care, as well the impact of poor descriptions in generating harmful situations for both the patient and healthcare practitioners in the explored domain. [ABSTRACT FROM AUTHOR]

This paper explores the concept of user experience (UX) within complex systems in the domain of nuclear power production. The specific context is the development of main control room solutions. As UX has become a prevalent concept in human-computer interaction and related research fields, it is a valid question, whether the concept could bring something new to the research concerning complex safety-critical systems also. In this paper, we introduce the concept of UX as an indicator of the users' subjective feeling of the appropriateness of the proposed tool for the activity. Based on activity theoretical contemplation, we presume that UX is grounded in the feelings and emotions evoked in the usage of the systems and which concern the status of the whole activity. In the paper, a theory-based characterization of UXs in the particular domain is developed based on three general functions of a tool: instrument, psychological, and communicative. We present the operationalization of UX and three studies in which UXs concerning different control room systems during a control room transformation process were followed. Based on the results of the three studies, we find that the significance of UX as an indicator of quality in use lies in the ability to bring the professional users' experiences, which are embedded in the inner characteristics of the work and not always observable by external evaluators, to the process of designing new systems. Evidence to support the initial background assumption that UXs may concern instrumental aspects of tools was identified in the studies. [ABSTRACT FROM AUTHOR]

There are two major types of approaches that are used for the multidisciplinary analysis (MDA) of coupled systems: fixed-point-iteration-based approaches and coupled Newton-based approaches. Fixed-point-iteration approaches are easier to implement, but can require a large number of iterations or diverge for strongly coupled problems. On the other hand, coupled-Newton approaches have superior convergence orders, but generally require more effort to implement and have more expensive iterations. Additionally, these two major approaches have many variations, including hybrid approaches where the MDA begins with a fixed-point iteration and then switches to a coupled-Newton approach after a certain number of iterations. However, there is a lack of criteria to govern how to select between these approaches, and when to switch between them in a hybrid approach. This paper compares these approaches and provides an algorithm that can be used to automatically select and switch between them. The proposed algorithm is implemented using OpenMDAO, NASA’s open-source framework for multidisciplinary analysis and optimization, and is tested using OpenAeroStruct, an open-source low-fidelity tool for aerostructural optimization. The results show that the proposed algorithm provides a balance of improved robustness and speed. [ABSTRACT FROM AUTHOR]

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012). [ABSTRACT FROM AUTHOR]

System evolvability is vital to the longevity of large-scale complex engineered systems. The need for evolvability in complex systems is a result of their long service lives, rapid advances to their integrated technologies, unforeseen operating conditions, and emerging system requirements. Building excess capability into complex systems can improve their ability to evolve while in service. However, excess capability increases initial build cost and operating cost, which is compounded across the service life of the system. Excess capability that is eventually used adds benefit by allowing for in-service evolution to meet emerging system requirements. Therefore, there is a trade-off between the cost of excess capability initially built into the system and the benefit that is added to the system by enabling future evolution. This paper introduces a process for optimizing the amount of excess capability in a complex system. This process results in a set of evolvable systems without excessive cost. We demonstrate how this process can be used to select the amount of excess capability that should be included in a military ground vehicle. [ABSTRACT FROM AUTHOR]

LED System-in-Package (SiP) aims to reduce manufacturing and material costs of LED lighting products through integration of components into one single package, based on semiconductor technology. This introduces multidisciplinary coupling in the system behavior which requires reconsideration of the existing LED design decomposition practice. This paper presents our method to do a decomposition analysis of the multidisciplinary coupling structure for an industry scale LED SiP design problem. The innovative aspect of our method is the use of a specification language to specify the input-output (binary) relationships between design variables, behavioral responses, objective and constraint responses. A design structure matrix, representing the mutual linkage, is automatically generated from the specification. The rows and columns of the DSM are subsequently re-ordered using partitioning and sequencing algorithms to provide insight in the coupling structure. The method is illustrated by means of a simplified example. Subsequently, the results for a recently manufactured LED SiP design prototype are presented. [ABSTRACT FROM AUTHOR]

Internal beliefs about the nature of social reality have been less examined in relation to new business development through microentrepreneurship than have tangible factors such as access to capital, business training, and social support. In this paper we present a view based in social ontology and suggest that regimes of internally consistent assumptions about the nature of the world are actualized through the entrepreneur's approach to his/her emerging business. Though social ontology is often addressed at the macro, philosophical level, we define it in micro, phenomenological terms as the actor's sense of being-in-the-world, which in turn conditions his/her perceived resources, strategic options, and approach to stakeholders. Theoretically we develop three social ontologies-dualist, interpretive, and complex systems-and we then examine empirical evidence from an action research study as expressive of these ontologies in practice. Action research is an ideal methodology for our study because it permits a tight focus on the actor's point of view in relation to his/her visible practice. Our results suggest that social ontology may indeed play a part in microentrepreneurial development, adding to but not replacing traditional resources for business success. In our concluding discussion of implications for practice and further research, we suggest that social ontologies are learned and habitual, and therefore that they may be recognized and potentially transcended when other ontologies offer new inroads that may not have been visible in a previous regime. [ABSTRACT FROM AUTHOR]

Self-organized regularities in terms of patient arrivals and wait times have been discovered in real-world healthcare services. What remains to be a challenge is how to characterize those regularities by taking into account the underlying patients' or hospitals' behaviors with respect to various impact factors. This paper presents a case study to address such a challenge. Specifically, it models and simulates the cardiac surgery services in Ontario, Canada, based on the methodology of Autonomy-Oriented Computing (AOC). The developed AOC-based cardiac surgery service model (AOC-CSS model) pays a special attention to how individuals' (e.g., patients and hospitals) behaviors and interactions with respect to some key factors (i.e., geographic accessibility to services, hospital resourcefulness, and wait times) affect the dynamics and relevant patterns of patient arrivals and wait times. By experimenting with the AOC-CSS model, we observe that certain regularities in patient arrivals and wait times emerge from the simulation, which are similar to those discovered from the real world. It reveals that patients' hospital-selection behaviors, hospitals' service-adjustment behaviors, and their interactions via wait times may potentially account for the self-organized regularities of wait times in cardiac surgery services. [ABSTRACT FROM AUTHOR]

This paper is an extension of a previous work which proposes a non-phenomenological model of population growth that is based on the interactions among the individuals of a population. In addition to what had already been studied-that the individuals interact competitively-in the present work it is also considered that the individuals interact cooperatively. As a consequence of this new consideration, a richer dynamics is observed. For instance, besides getting the population models already reached from the original version of the model (as the Malthus, Verhulst, Gompertz, Richards, Bertalanffy and power-law growth models), the new formulation also reaches the von Foerster growth model and also a regime of divergence of the population at a finite time. An agent-based model is also presented in order to give support to the analytical results. Moreover, this new approach of the model explains the Allee effect as an emergent behavior of the cooperative and competitive interactions among the individuals. The Allee effect is the characteristic of some populations of increasing the population growth rate in a small-sized population. Whereas the models presented in the literature explain the Allee effect with phenomenological ideas, the model presented here explains this effect by the interactions between the individuals. The model is tested with empirical data to justify its formulation. Another interesting macroscopic emergent behavior from the model proposed is the observation of a regime of population divergence at a finite time. It is interesting that this characteristic is observed in humanity's global population growth. It is shown that in a regime of cooperation, the model fits very well to the human population growth data from 1000 AD to nowadays. [ABSTRACT FROM AUTHOR]

Chaotic systems demonstrate complex behaviour in their state variables and their parameters, which generate some challenges and consequences. This paper presents a new synchronisation scheme based on integral sliding mode control (ISMC) method on a class of complex chaotic systems with complex unknown parameters. Synchronisation between corresponding states of a class of complex chaotic systems and also convergence of the errors of the system parameters to zero point are studied. The designed feedback control vector and complex unknown parameter vector are analytically achieved based on the Lyapunov stability theory. Moreover, the effectiveness of the proposed methodology is verified by synchronisation of the Chen complex system and the Lorenz complex systems as the leader and the follower chaotic systems, respectively. In conclusion, some numerical simulations related to the synchronisation methodology is given to illustrate the effectiveness of the theoretical discussions. [ABSTRACT FROM AUTHOR]