Abstract
Points of Interest (POI) are widely used in many applications nowadays mainly due to the increasing amount of related data available online, notably from volunteered geographic information (VGI) sources. Being able to connect these data from different sources is useful for many things like validating, correcting and also removing duplicated data in a database. However, there is no standard way to identify the same POIs across different sources and doing it manually could be very expensive. Therefore, automatic POI matching has been an attractive research topic. In our work, we propose a novel data-driven machine learning approach based on an outlier detection algorithm to match POIs automatically. Surprisingly, works that have been presented so far do not use data-driven machine learning approaches. The reason for this might be that such approaches need a training dataset to be constructed by manually matching some POIs. To mitigate this, we have taken advantage of the Crosswalk API, available at the time we started our project, which allowed us to retrieve already matched POI data from different sources in US territory. We trained and tested our model with a dataset containing Factual, Facebook and Foursquare POIs from New York City and were able to successfully apply it to another dataset of Facebook and Foursquare POIs from Porto, Portugal, finding matches with an accuracy around 95%. These are encouraging results that confirm our approach as an effective way to address the problem of automatically matching POIs. They also show that such a model can be trained with data available from multiple sources and be applied to other datasets with different locations from those used in training. Furthermore, as a data-driven machine learning approach, the model can be continuously improved by adding new validated data to its training dataset. © Springer Nature Switzerland AG 2018.

Abstract
The increasing acceptance of WiFi has created unprecedented levels of congestion in the unlicensed frequency bands, especially in densely populated areas. This results mainly because of the unmanaged interference and uncoordinated operation between WiFi access points. Radio Environment Maps (REM) have been suggested as a support for coordination strategies that optimize the overall WiFi network performance. In this context, the main objective of this experiment is to assess the benefit of a coordinated management of radio resources in dense WiFi networks at 5 GHz band, using REMs for indoor scenarios. It was shown that REMs can detect the presence of interfering links on the network or coverage holes, and a suitable coordination strategy can use this information to reconfigure Access Points (AP) channel assignment and re-establish the client connection, at a cost of diminishing the aggregate throughput of the network. The technique of AP hand-off was tested to balance the load from one AP to another. Using REMs, the Radio Resource Management (RRM) strategy could reconfigure the network to optimize the client distribution among available APs. Although the aggregate throughput is lower after load balancing, the RRM could increase the throughput of the overloaded AP. © 2018 IEEE.

Abstract
Firefighting is a hazardous profession commonly exposed to high stress that can interfere with firefighter's health and performance. Nevertheless, on-duty stress levels quantitative evaluations are very rare in the literature. In order to investigate firefighters' occupational health in terms of stress perceptions, symptoms, and quantified physiological reactions under real-world conditions, an ambulatory assessment protocol was developed. Therefore, cardiac signal from firefighters (N =6) was continuously monitored during two shifts within a working week with a medical clinically certified equipment (VitalJacket®), allowing continuous electrocardiogram (ECG) and actigraphy measurement. Psychological data were collected with an android application, collecting potential stressful events, stress symptoms, and stress appraisal. A total of 130 hours of medical-quality ECG were collected, from which heart rate variability (HRV) metrics were extracted and analyzed. Statistical significant differences were found in some HRV metrics - AVNN, RMSSD, pNN50 and LF/HF - between events and non-events, showing higher levels of physiological stress during events (p<0.05). Stress symptoms increase from the beginning to the end of the shift (from 1.54 ± 0.52 to 2.01 ± 0.73), however the mean stress self-perception of events was very low (3.22 ± 2.38 in a scale ranging from 0 to 10). Negative and strong correlations were also found between stress symptoms and some time-domain ECG measures (AVNN, SDNN and pNN50). It can be concluded that stress may not always be detected when using merely self-reports. These results enhance the importance of combining both self-report and ambulatory high-quality physiological stress measures in occupational health settings. Future studies should investigate not only what causes stress but also its impact on health and well-being of these professionals, in order to contribute to the design of efficient stress-management interventions.

Abstract
O documento fotográfico constitui um enorme desafio para aquela que é chamada de Era de Informação e que vem aprumada com o avanço tecnológico, nomeadamente a imposição do digital, através de dispositivos de captura de imagem. O tratamento de informação fotográfica, embora, à primeira vista, espelhe um leque imenso de oportunidades e benefícios ainda padece de um estudo limitado e pouco desenvolvido. Se por um lado, os documentos de caráter textual estão presentes, em esmagadora maioria, nos centros de informação e, por esse motivo, o sua análise e tratamento são realizadas quotidianamente, por outro, a imagem tem vindo a posicionar-se, ainda que lentamente, na sociedade de informação como um meio implacável na geração de conhecimento.

Abstract
Mobility has become one of the most difficult challenges that cities must face. More than half of world’s population resides in urban areas and with the continuously growing population it is imperative that cities use their resources more efficiently. Obtaining and gathering data from different sources can be extremely important to support new solutions that will help building a better mobility for the citizens. Crowdsensing has become a popular way to share data collected by sensing devices with the goal to achieve a common interest. Data collected by crowdsensing applications can be a promising way to obtain valuable mobility information from each citizen. In this paper, we study the current work on the integrated mobility services exploring the crowdsensing applications that were used to extract and provide valuable mobility data. Also, we analyze the main current techniques used to characterize urban mobility.

Abstract
Background: The tip of an optical fiber has been considered an attractive platform in Biology. The simple cleaved end of an optical fiber can be machined, patterned and/or functionalized, acquiring unique properties enabling the exploitation of novel optical phenomena. Prompted by the constant need to measure and manipulate nanoparticles, the invention of the Scanning Near-field Optical Microscopy (SNOM) triggered the optimization and development of novel fiber tip microfabrication methods. In fact, the fiber tip was soon considered a key element in SNOM by confining light to sufficiently small extensions, challenging the diffraction limit. As result and in consequence of the newly proposed "Lab On Tip" concept, several geometries of fiber tips were applied in three main fields: imaging (in Microscopy/Spectroscopy), biosensors and micromanipulation (Optical Fiber Tweezers, OFTs). These are able to exert forces on microparticles, trap and manipulate them for relevant applications, as biomolecules mechanical study or protein aggregates unfolding. Scope of review: This review presents an overview of the main achievements, most impactful studies and limitations of fiber tip-based configurations within the above three fields, along the past 10 years. Major conclusions: OFTs could be in future a valuable tool for studying several cellular phenomena such as neurodegeneration caused by abnormal protein fibrils or manipulating organelles within cells. This could contribute to understand the mechanisms of some diseases or biophenomena, as the axonal growth in neurons.