Adults with dyslexia appear to display most of the deficits commonly seen in children with dyslexia. Like children with dyslexia, who are most often studied, adults with dyslexia have difficulties in segmenting words into sounds, in working memory, and in rapid naming, among others, in addition to difficulties in reading and writing. However, while in children with dyslexia, difficulties with accuracy in reading and writing (i.e., spelling) may be the main symptom of dyslexia, some adults with dyslexia, particularly those with a high level of education, may have overcome or mitigated this type of difficulty. Some deficits may have been compensated, while others may manifest themselves differently, possibly more pronounced when compared to children. Furthermore, the characteristics of the writing system where they learned to read and write may influence the manifestation of difficulties. European Portuguese (EP) has an orthography of intermediate depth, with characteristics distinct from other systems where this population (college students diagnosed with dyslexia) has been studied. How to identify dyslexia and characterise it and what its cognitive predictors are the research questions of this project.

ImAFUSA focuses on quantifying a variety of understudied factors influencing societal acceptance of UAM in urban, peri-urban and inter-urban areas, especially when these are deployed using U3 and U4 services. The Project will deliver an “Impact and capacity Assessment Framework for U-space Societal Acceptance” (ImAFUSA) to assist Local Authorities and other U-space stakeholders and users with the delivery of socially acceptable and beneficial UAM deployment in cities. The framework and its tools will provide insights into 3 major areas influencing societal acceptance: environmental impact (including noise, visual pollution and air quality), safety and socioeconomic impact (including affordability, accessibility, economic development, public space use and connectivity). In each area, innovative performance indicators will be described while mathematical formulas and algorithms will be developed to quantify them. Data on citizen noise perceptions, perceptions of visual pollution, safety perceptions and overall UAM acceptance will be collected during 3 immersive citizen experiences of UAM applications in the city of Athens, Greece. The usability of the developed indicators will be tested in a simulation environment simulating real future use cases with a varying volume and configuration of movements in U-space.

Despite the Union’s effort to fight against online hate speech (OHS), several reports showed an increase in OHS during 2020-21. The current pandemic provided a context for increased scapegoating and stigmatization, and minority groups are disproportionally targets of hatred discourse. OHS is a persistent threat to the Union’s values and there is a need for more knowledge on its content, detection and countering, as highlighted in the current Call. Portugal, as other member states, has seen an escalation of hate speech against immigrants, racial/ethnic groups, and LGBTIQ communities. However, there is no systematized knowledge nor tools designed to detect, monitor and prevent OHS against these communities. Our project aims at addressing this need, offering a comprehensive, participatory and culturally sensitive approach to analyse, detect, and counter, direct and indirect OHS in Portuguese language. 

To date, research about mobile usage to conduct surveys has focused on a comparative analysis with fixed phones (e.g. Keeter et al 2007, Vicente et al 2009, Lynn and Kaminska 2010). Research focused exclusively on mobile phones, specifically in the evaluation of its strengths and weakness as a survey mode is still scarce.  Non-response is one major problem for surveys’ activity. Mobile phone surveys are no exception to this situation. If nonresponse is unaddressed, the resulting damage to data quality may have serious consequences for data analyses underpinning social science research. Mobile phones have specific characteristics that other modes don’t have – they are of personal use, carried at all time to every places. This specificity may affect the likelihood of getting a successful contact when soliciting people to participate. On one side, the time period for contact is large (theoretically, all day) which can improve the likelihood of contact, on the other side, it is easy to reject a call coming from an unknown number. If the proportion of non-responses is high and/or non-respondents are much different from respondents surveys estimates are subject to non-response error.  Paradata are data collected during the survey data collection process (Couper 1998). Examples of paradata are call records, interviewer observations, keystroke data. Paradata can also be obtained by means of questions included on the questionnaire (e.g. the location of the respondent in the time of the interview, whether he/she is alone or accompanied by someone else). Paradata is most used to study unit nonresponse (Steeh et al 2001; De Leeuw and De Heer 2002). Nonresponse research is often limited by the small amount of information available for all sample units. However, call records can guide responsive survey design decisions aimed at reducing nonresponse rates and bias (Groves et al 2009), as they provide important clues on when and how best to contact and to achieve cooperation from...