Architectures in parametric component-based systems: Qualitative and quantitative modelling

One of the key aspects in component-based design is specifying the software architecture that characterizes the topology and the permissible interactions of the components of a system. To achieve well-founded design there is need to address both the qualitative and non-functional aspects of architectures. In this paper we study the qualitative and quantitative formal modelling of architectures applied on parametric component-based systems, that consist of an unknown number of instances of each component. Specifically, we introduce an extended propositional interaction logic and investigate its first-order level which serves as a formal language for the interactions of parametric systems. Our logics achieve to encode the execution order of interactions, which is a main feature in several important architectures, as well as to model recursive interactions. Moreover, we prove the decidability of equivalence, satisfiability, and validity of first-order extended interaction logic formulas, and provide several examples of formulas describing well-known architectures. We show the robustness of our theory by effectively extending our results for parametric weighted architectures. For this, we study the weighted counterparts of our logics over a commutative semiring, and we apply them for modelling the quantitative aspects of concrete architectures. Finally, we prove that the equivalence problem of weighted first-order extended interaction logic formulas is decidable in a large class of semirings, namely the class (of subsemirings) of skew fields.

HIA and EIA Are Different, but Maybe Not in the Way We Thought They Were: A Bibliometric Analysis

Background: The fields of Health Impact Assessment (HIA) and Environmental Impact Assessment (EIA) have grown with increasing numbers of disciplines and sectors contributing to their advancements, but with it, perceived conflict over methodological and disciplinary approaches to integrate health in impact assessments. This study maps the current field of HIA and health in EIA to examine the scientific landscape of the field. Methods: We carried out a bibliometric analysis of HIA papers and EIA papers that included a health focus in peer-reviewed journals in the Web of Science Core Collection (n = 229). We carried out co-authorship and co-citation network analyses of authors and documents in VOSviewer. Results: We identified two main co-authorship and co-citation groupings. Our document co-citation analysis also identified four clusters with two major groups, the Defining HIA cluster and the Describing the fields cluster versus the Active transport quantitative HIA cluster, and the Quantitative modelling tools cluster. Conclusion: Our findings strongly suggest that there exist two groups of thought in the scholarly fields of HIA and health in EIA. Barriers to developing more methodologically integrated approaches to considering health within EIA are related more to disciplinary differences than field (HIA versus EIA)-based differences and we advocate for the development of transdisciplinary approaches to both HIA and EIA.

A model-based approach to characterize enzyme-mediated response to antibiotic treatments: going beyond the SIR classification

To design appropriate treatments, one must be able to characterize accurately the response of bacteria to antibiotics. When exposed to β-lactam treatments, bacteria can be resistant and/or tolerant, and populations can exhibit resilience. Disentangling these phenomena is challenging and no consolidated understanding has been proposed so far. Because these responses involve processes happening at several levels, including the molecular level (e.g. antibiotic degradation), the cell physiology level (filamentation) and the population level (release of β-lactamases into the environment), quantitative modelling approaches are needed. Here, we propose a model of bacterial response to β-lactam treatments that accounts for bacterial resistance, tolerance, and population resilience. Our model can be calibrated solely based on optical density readouts, can predict the inoculum effect, and leads to a mechanistically relevant classification of bacterial response to treatments that goes beyond the classical susceptible / intermediate / resistant classification. Filamentation-mediated tolerance and collective enzyme-mediated antibiotic degradation are essential model features to explain the complex observed response of cell populations to antibiotic treatments.