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The Internet of Things (IoT) leads to an ever-growing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fog-based computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied. ; Commission of the European Union within the CREMAH2020-RIA ; 427 ; 443 ; 17

The use of cloud-based storage systems for storing data is a popular alternative to local storage systems. Beside several benefits of cloud-based storages, there are also downsides like vendor lock-in or unavailability. Moreover, the selection of the best fitting storage solution can be a tedious and cumbersome task and the storage requirements may change over time. In this paper, we formulate a system model that uses multiple cloud-based services to realize a redundant and cost-efficient storage. Within this system model, we formulate a local and a global optimization problem that considers historical data access information and predefined quality of service requirements to select a cost-efficient storage solution. Furthermore, we present a heuristic optimization approach for the global optimization. Extensive evaluations show the benefits of our work in comparison with a baseline that follows a state-of-the-art approach. We show that our solutions save up to 30% of the cumulative cost in comparison with the baseline. ; Commission of the European Union - CREMA H2020-RIA

There is considerable interest in adverse outcome pathways (AOPs) as a means of organizing biological and toxicological information to assist in data interpretation and method development. While several chemical sectors have shown considerable progress in applying this approach, this has not been the case in the food sector. In the present study, safety evaluation reports of food additives listed in Annex II of Regulation (EC) No 1333/2008 of the European Union were screened to qualitatively and quantitatively characterize toxicity induced in laboratory animals. The resulting database was used to identify the critical adverse effects used for risk assessment and to investigate whether food additives share common AOPs. Analysis of the database revealed that often such scrutiny of AOPs was not possible or necessary. For 69% of the food additives, the report did not document any adverse effects in studies based on which the safety evaluation was performed. For the remaining 31% of the 326 investigated food additives, critical adverse effects and related points of departure for establishing health-based guidance values could be identified. These mainly involved effects on the liver, kidney, cardiovascular system, lymphatic system, central nervous system and reproductive system. AOPs are available for many of these apical endpoints, albeit to different degrees of maturity. For other adverse outcomes pertinent to food additives, including gastrointestinal irritation and corrosion, AOPs are lacking. Efforts should focus on developing AOPs for these particular endpoints.