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Universities, R&D Groups and Academic Networks

MODELSWARD is a unique forum for universities, research groups and research projects to present their research and scientific results, be it by presenting a paper, hosting a tutorial or instructional course or demonstrating its research products in demo sessions, by contributing towards panels and discussions in the event's field of interest or by presenting their project, be it by setting up an exhibition booth, by being profiled in the event's web presence or printed materials or by suggesting keynote speakers or specific thematic sessions.

Special conditions are also available for Research Projects which wish to hold meetings at INSTICC events.

Current Academic Partners:


DRIVE-AB (Driving reinvestment in research and development and responsible antibiotic use) is a project composed of 16 public and 7 private partners from 12 countries that is funded by the Innovative Medicines Initiative (IMI): IMI is a joint undertaking between the European Union and the European Pharmaceutical Industry Association (EFPIA). It is established to search and provide answers to the following areas that are critical to the future success of healthcare globally:
Reducing antimicrobial resistance through responsible antibiotic use Identifying how, through new economic models, to incentivise the discovery and development of new novel antibiotics for use now and in the future



OPEN-SME aims to develop a set of methodologies, associated tools, and business models centred on SME Associations, which will enable software SMEs to effectively introduce Open Source Software Reuse practises in their production processes. In this scope, software reuse is regarded as the sharing of software modules across different development teams, organisations, and diverse application domains. The potential benefits from the adoption of Software Reuse practises by software SMEs could provide substantial competitive advantages against large players by improving productivity, increasing competitiveness (offer more competitive prices), and facilitating entrance to new markets. The main reasons prohibiting the adoption of Software Reuse solutions by software SMEs are the following: (1) The large initial investments in technology know-how and tools required for a successful software reuse program are inhibiting reuse for SMEs, since their budget is limited. (2) Effective accumulation of modules requires multiple products in different application domains.

Software development SMEs typically develop only a limited number of products or just a single system that evolves in different versions. (3) The market presently lacks services that would allow the effective identification, cataloguing, reshaping and utilisation of open source software components. This significantly prohibits the reuse of code concerning application functionality, which is common to a limited set of domains. The OPEN-SME project aims to provide a viable solution to the aforementioned SME problems, by developing appropriate

methodologies and tools, and defining adequate business models that will allow SME Associations to effectively provide Open Source Software Reuse services to their member software SMEs. In this sense, SME Associations will act as the “software reuse departments” of their members.



The growing number of fine-granular data streams opens up new opportunities for improved risk analysis, situation and evolution monitoring as well as event detection. However, there are still some major roadblocks for leveraging the full potential of data stream processing, as it would, for example, be needed the highly relevant systemic risk analysis in the financial domain.The QualiMaster project will address those road blocks by developing novel approaches for autonomously dealing with load and need changes in large-scale data stream processing, while opportunistically exploiting the available resources for increasing analysis depth
whenever possible. For this purpose, the QualiMaster infrastructure will enable autonomous proactive,
reflective and cross-pipeline adaptation, in addition to the more traditional reactive adaptation. Starting from
configurable stream processing pipelines, adaptation will be based on quality-aware component description,
pipelines optimization and the systematic exploitation of families of approximate algorithms with different
quality/performance tradeoffs. However, adaptation will not be restricted to the software level alone: We will
go a level further by investigating the systematic translation of stream processing algorithms into code for
reconfigurable hardware and the synergistic exploitation of such hardware-based processing in adaptive high
performance large-scale data processing. The project focuses on financial analysis based on combining financial
data streams and social web data, especially for systemic risk analysis. Our user-driven approach involves
two SMEs from the financial sector. Rigorous evaluation with real world data loads from the financial domain
enriched with relevant social Web content will further stress the applicability of QualiMaster results.



Infertility affects 12% to 15% of reproductive age couples in Europe, costs approximately 1 billion Euros per year, and experts agree that these figures will double in a decade. In about 50% of such couples, infertility is caused by female health problems, more than 40% of which are related to endocrinological diseases impairing women’s health independently from fertility. Such considerations motivate our three-pillar project focusing on quantitative models for Infertility Related Endocrinological Diseases (IREDs). Our first pillar (modelling) develops patient-specific computer-based models for IRED. Such models account for the physiological and pathophysiological mechanisms regulating the menstrual cycle and how this is influenced by external (e.g., drugs) as well as environmental (e.g., obesity) factors. Our models enable a quantitative understanding of the mechanisms behind endocrine disorders such as Polycystic Ovarian Syndrome (PCOS), hyperprolactinemia or endometriosis. Our second pillar (computation) develops general purpose methods and tools to support effective exploitation of patient-specific models to reliably predict the outcome of a treatment on a specific patient and to support individualisation of a treatment for a specific patient. Our third pillar (clinical trial) gathers data (e.g. hormonal secretion patterns in different physiological and pathophysiological settings) to enable validation of the models and tools developed in our project and carries out such a validation thereby providing feedback to the previous pillars. In our presentation we will show how the models and computational tools developed in the project can be effectively used to predict outcome of treatments as well as to support design of new personalised treatments.