A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits

Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks

Location-based software modeling and analysis: Tropos-based approach

Abstract. The continuous growth of interest in mobile applications makes the concept of location essential to design and develop software systems. Location-based software is supposed to be able to monitor the surrounding location and choose accordingly the most appropriate behavior. In this paper, we propose a novel conceptual framework to model and analyze location-based software. We mainly focus on the social facets of locations adopting concepts such as actor, resource, and location-based behavior. Our approach is based on Tropos methodology and allows the analyst to elicit and model software requirements according to the different locations where the software will operate. We propose an extension of Tropos modeling and adapt its process to suit well with the development of location-based software. The proposed framework also includes automated analysis techniques to reason about the relation between location and location-based software.

Location-based Modeling and Analysis: Tropos-based Approach

The continuous growth of interest in mobile applications makes the concept of location essential to design and develop software systems. Location-based software is supposed to be able to monitor the location and choose accordingly the most appropriate behavior. In this paper, we propose a novel conceptual framework to model and analyze location-based software. We mainly focus on the social facets of locations adopting concepts such as social actor, resource, and location-based behavior. Our approach is based on Tropos methodology and allows the analyst to elicit and model software requirements according to the different locations where the software will operate. We propose an extension of Tropos modeling and adapt its process to suit well with the development of location-based software. The proposed framework also includes automated analysis techniques to reason about the relation between location and location-based behavior

Big Data and Changing Concepts of the Human

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human

A formal verification framework and associated tools for enterprise modeling : application to UEML

The aim of this paper is to propose and apply a verification and validation approach to Enterprise Modeling that enables the user to improve the relevance and correctness, the suitability and coherence of a model by using properties specification and formal proof of properties