The cross-disciplinary activity of modelling and simulation is the core of the scientific activities addressing the complexity of nature. In this context, we need reliable computational environments to integrate heterogeneous representations coming from different scientific fields. Therefore, such environments must be able to integrate heterogeneous formalisms in the same model and assist the modeller for the design and implementation of models, the definition of the experimental frames and the analysis of simulation results. The aim of this article is to introduce a tool supporting all these features, the Virtual Laboratory Environment (VLE). VLE is a software and an API which supports multi-modelling, simulation and analysis. It addresses the reliability issue by using recent developments in the theory of modelling and simulation proposed by Zeigler. We present VLE in the context of the modelling and simulation cycle and show the effectiveness of the tool with a multimodel of fireman fighting a fire spread.

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Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.

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This article deals with the coupling of analytical models with individual based models design with the reactive agents paradigm. Such a coupling of models of different natures is motivated by the need to find a way to model scale transfer in large complex systems, i.e. to model how low level of organization can be made to influence upper level and vice versa. This is a fundamental issue, and more particularly in ecological modeling where models are a real scientific tool of investigation. Individuals and populations are not described at the same scale of time and space but it is known that they act on each others. Based on this example, we model individuals in their environment and the population dynamics. While behavior is best modeled using an algorithmic framework (the reactive agent paradigm), population dynamics (because of the number of interacting entities) is best modeled using numerical models. We propose the use of the concept of emergent computation as a framework for coupling heterogeneous formalisms. In the same time, it is crucial to be aware of the consequences of the simplifications and of the choices that are made in the reactive agent model, such as the topology of space and various parameters. In this article, we discuss these issues and our approach on a case study drawn from marine ecology and we show that it is possible to find classical mathematical functional responses with a reactive agent system. Then, we propose a methodology to deal with the coupling of heterogeneous formalism useful in any kind of system modeling.

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