Abstract
This research investigates the use of artificial intelligence algorithms to identify behavioural patterns in computer use, with the aim of detecting trends that help to flag cases of depression by analysing the human-computer interaction records of these users, thereby increasing the quality of the data for early detection of these situations. Following design science methodology, a case study will be conducted using an existing mental health screening questionnaire, integrating an artificial intelligence layer to map mouse and keyboard interactions, followed by machine learning analysis of the records. The results of the machine learning assisted questionnaires will be compared with the results of the questionnaires without the mapping. If there is a significant difference, this model could be useful for making predictions about emotional states, contributing to the field of artificial intelligence and helping to prevent depression, which is the focus of the research, although the aim is to look at mental health in a global way.

Abstract
Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.

Abstract
Depression is expected to be the disorder with the highest disease burden in high-income countries by the year 2030. ICT4Depression (ICT4D) is a European FP7 project, which aims to contribute to the alleviation of this burden by making use of depression treatment and ICT innovations. In this project we developed an ICT-based system for use in primary care that aims to improve access as well as actual care delivery for depressed adults. Innovative technologies within the ICT4D system include 1) flexible self-help treatments for depression, 2) automatic assessment of the patient using mobile phone and web-based communication 3) wearable biomedical sensor devices for monitoring activities and electrophysiological indicators, 4) computational methods for reasoning about the state of a patient and the risk of relapse (reasoning engine) and 5) a flexible system architecture for monitoring and supporting people using continuous observations and feedback via mobile phone and the web. The general objective of the ICT4D project is to test the feasibility and acceptability of the ICT4D system within a pilot study in the Netherlands and in Sweden during 2012 and 2013. © 2012 Interactive Media Institute and IOS Press.

Abstract
SINUP consists of a geographical information system whose purpose is to store, in a coherent manner, data resulting from key activities of Oporto local authority, allowing to better structure the knowledge about the urban reality. In the possession of such knowledge, and with the revision of Oporto's Municipal Master Plan taking place soon, the municipality is making an effort to develop an electronic citizen service that will allow a large number of citizens to consult it, and more important, participate in its public discussion prior to approval thus creating a major instrument of e-democracy in Oporto's municipality.

Abstract
In this article we focus on interoperable geographic information (GI) services from the crisis management perspective. Based on Open Geospatial Consortium [10] standards and initiatives, we present the building blocks of the interoperable solution for supporting crisis management that is proposed as a result of the EU sponsored project, MEDSI [1]. In particular, we focus on the application and operationalization of several OGC standards, some adopted and some still under discussion, such as WMS, WFS, WMC, SLD and SMS, as well as their integration and cooperation within a single software framework.

Abstract
In this paper we discuss the development and clinical evaluation of a wireless platform for health signs sensing. The sensors measure physical activity, ECG, blood oxygen saturation, temperature and respiratory rate. An important aspect of the approach is that the sensors are integrated into one waist-worn device. A mobile phone collects data from this device and uses data fusion in the scope of a decision support system to trigger additional measurements, classify health conditions or schedule future observations. In these decisions, the user's current physical activity plays an important role as the validity of many health signs measurements is strongly related to physical activity. Due to the integration of the sensors and the use of data fusion it is possible to accurately identify health risks and to react promptly. During clinical trials, for which proper ethical approval was obtained, the system was used by healthy elderly volunteers in Limerick (Ireland) and Ancona (Italy). Results of these trials are also discussed in this paper.