NOSTROMO - D5.1 - ATM Performance Metamodels - Preliminary Release

This deliverable presents the results obtained with the meta-modelling process presented in D3.1 and D3.2 applied to the two micromodels (or simulators), Mercury and FLITAN, themselves implementing concepts from four SESAR solutions, PJ01.01, PJ07.02, PJ08-01, and PJ02.08. The objective of the meta-modelling process is explained briefly again in the introduction, in particular with respect to performance assessment. The rationale for the selection of the SESAR solutions implemented in the simulators are briefly explained too. The simulators are presented in two distinct chapters. First, a general presentation of each simulator is given, with past challenges and development, before explaining the development steps carried out to implement the concepts from the chosen solutions. Domain research questions that could be answered by these implementations are highlighted along the way. The meta-modelling process is then briefly explained again, followed by the results obtained with the two simulators, in distinct sections. The results highlight the performance of the meta-model with respect to approximating the output of the micromodels, but not the performance of the models themselves with respect to the research questions, which will be explored in WP7 instead. The deliverable closes with some considerations on the meta-modelling performance and next steps for this line of work

Mental health-related communication in a virtual community: text mining analysis of a digital exchange platform during the Covid-19 pandemic.

BACKGROUND Virtual communities played an important role in mental health and well-being during the Covid-19 pandemic by providing access to others and thereby preventing loneliness. The pandemic has accelerated the urge for digital solutions for people with pre-existing mental health problems. So far, it remains unclear how the people concerned communicate with each other and benefit from peer-to-peer support on a moderated digital platform. OBJECTIVE The aim of the project was to identify and describe the communication patterns and verbal expression of users on the inCLOUsiv platform during the first lockdown in 2020. METHODS Discussions in forums and live chats on inCLOUsiv were analysed using text mining, which included frequency, correlation, n-gram and sentiment analyses. RESULTS The communication behaviour of users on inCLOUsiv was benevolent and supportive; and 72% of the identified sentiments were positive. Users addressed the topics of 'corona', 'anxiety' and 'crisis' and shared coping strategies. CONCLUSIONS The benevolent interaction between users on inCLOUsiv is in line with other virtual communities for Covid-19 and the potential for peer-to-peer support. Users can benefit from each other's experiences and support each other. Virtual communities can be used as an adjuvant to existing therapy, particularly in times of reduced access to local health services

Hypertrophic Scar Formation Following Burns and Trauma: New Approaches to Treatment

The authors examine the process of hypertrophic scar formation, the results of current treatments, and areas of research likely to lead to significant advances in the field

Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

An Empirically grounded Agent Based simulator for Air Traffic Management in the SESAR scenario

In this paper we present a simulator allowing to perform policy experiments relative to the air traffic management. Different SESAR solutions can be implemented in the model to see the reaction of the different stakeholders as well as other relevant metrics (delay, safety, etc). The model describes both the strategic phase associated to the planning of the flight trajectories and the tactical modifications occurring in the en-route phase. An implementation of the model is available as an open-source software and is freely accessible by any user. More specifically, different procedures related to business trajectories and free-routing are tested and we illustrate the capabilities of the model on an airspace which implements these concepts. After performing numerical simulations with the model, we show that in a free-routing scenario the controllers perform less operations but the conflicts are dispersed over a larger portion of the airspace. This can potentially increase the complexity of conflict detection and resolution for controllers. In order to investigate this specific aspect, we consider some metrics used to measure traffic complexity. We first show that in non-free-routing situations our simulator deals with complexity in a way similar to what humans would do. This allows us to be confident that the results of our numerical simulations relative to the free-routing can reasonably forecast how human controllers would behave in this new situation. Specifically, our numerical simulations show that most of the complexity metrics decrease with free-routing, while the few metrics which increase are all linked to the flight level changes. This is a non-trivial result since intuitively the complexity should increase with free-routing because of problematic geometries and more dispersed conflicts over the airspace

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Explainable Metamodels for ATM Performance Assessment

Fast-time simulation constitutes a well-known and long-established technique within the Air Traffic Management (ATM) community. However, it is often the case that simulation input and output spaces are underutilized, limiting the full understandability, transparency, and interpretability of the obtained results. In this paper, we propose a methodology that combines simulation metamodeling and SHapley Additive exPlanations (SHAP) values, aimed at uncovering the intricate hidden relationships among the input and output variables of a simulated ATM system in a rather practical way. Whereas metamodeling provides explicit functional approximations mimicking the behavior of the simulators, the SHAP-based analysis delivers a systematic framework for improving their explainability. We illustrate our approach using a state-of-the-art ATM simulator across two case studies in which two delay-centered performance metrics are analyzed. The results show that the proposed methodology can effectively make simulation and its results more explainable, facilitating the interpretation of the obtained emergent behavior, and additionally opening new opportunities towards novel performance assessment processes within the ATM research field

Best practice in undertaking and reporting health technology assessments : Working Group 4 report

[Executive Summary] The aim of Working Group 4 has been to develop and disseminate best practice in undertaking and reporting assessments, and to identify needs for methodologic development. Health technology assessment (HTA) is a multidisciplinary activity that systematically examines the technical performance, safety, clinical efficacy, and effectiveness, cost, costeffectiveness, organizational implications, social consequences, legal, and ethical considerations of the application of a health technology (18). HTA activity has been continuously increasing over the last few years. Numerous HTA agencies and other institutions (termed in this report “HTA doers”) across Europe are producing an important and growing amount of HTA information. The objectives of HTA vary considerably between HTA agencies and other actors, from a strictly political decision making–oriented approach regarding advice on market licensure, coverage in benefits catalogue, or investment planning to information directed to providers or to the public. Although there seems to be broad agreement on the general elements that belong to the HTA process, and although HTA doers in Europe use similar principles (41), this is often difficult to see because of differences in language and terminology. In addition, the reporting of the findings from the assessments differs considerably. This reduces comparability and makes it difficult for those undertaking HTA assessments to integrate previous findings from other HTA doers in a subsequent evaluation of the same technology. Transparent and clear reporting is an important step toward disseminating the findings of a HTA; thus, standards that ensure high quality reporting may contribute to a wider dissemination of results. The EUR-ASSESS methodologic subgroup already proposed a framework for conducting and reporting HTA (18), which served as the basis for the current working group. New developments in the last 5 years necessitate revisiting that framework and providing a solid structure for future updates. Giving due attention to these methodologic developments, this report describes the current “best practice” in both undertaking and reporting HTA and identifies the needs for methodologic development. It concludes with specific recommendations and tools for implementing them, e.g., by providing the structure for English-language scientific summary reports and a checklist to assess the methodologic and reporting quality of HTA reports

Evaluation of a therapy protocol for the treatment of chronic digital dermatitis in European bison (Bison bonasus)

Digital dermatitis (DD) associated with the presence of multiple Treponema spp. was recently described for the first time in European bison (Bison bonasus). DD is characterized by skin inflammation in the distal foot area in various ungulates. The objective of this proof of concept study was to test a treatment protocol adopted from cattle for its applicability in this wildlife species using five animals. Keratolytic salicylic acid paste was administered topically under bandages for seven days to enable removal of the

NOSTROMO - D5.2 - ATM Performance Metamodels - Final Release

This deliverable presents the third iteration of the development of the two micromodels Flitan and Mercury and the results obtained with them with the active learning process, as described in the deliverables D3.X. In this iteration, Flitan implemented concepts from PJ08.01 and PJ02.08, and Mercury implemented a module related to PJ07.02. Mercury also developed an additional module related to PJ01.01, which description is presented in Annex only, since no results could be produced in time with it for this deliverable. The development is presented in two different chapters for each simulator, with general descriptions referred to from D5.1. The modules related to each SESAR solution are described separately. The latest version of the meta-modelling process is described briefly, followed by the results obtained with the two simulators, in distinct sections. This chapter shows the performance of the meta-model with respect to approximating micro simulators