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From an invisible organ to one of the most explored non-invasively, the liver is, today, one of the cornerstones for current cross-sectional imaging techniques and minimally invasive procedures. After the achievements of US, CT and, most recently, MRI in providing highly accurate morphological and structural information about the organ, a significant scientific development has gained momentum for the last decades, coupling morphology to liver function and contributing far most to what we know today as precision medicine. In fact, dedicated tailor-made investigations are now possible in order to detect and, most of all, quantify physiopathological processes with unprecedented certitude. It is the intention of this review to provide a better insight to the reader of several functional imaging techniques applied to liver imaging. Contrast enhanced imaging, diffusion weighted imaging, elastography, spectral computed tomography and fat and iron assessment techniques are commonly performed clinically. Diffusion kurtosis imaging, magnetic resonance spectroscopy, T1 relaxometry and radiomics remain largely limited to advanced clinical research. Each of them has its own value and place on the diagnostic armamentarium and provide unique qualitative and quantitative information regarding the pathophysiology of diseases, contributing at a large scale to model therapeutic decisions and patient follow-up. Therefore, state-of-the-art liver imaging acts today as a non-invasive surrogate biomarker of many focal and diffuse liver diseases.

Satellite-based systems are the most widespread solution for outdoor localization. However, they present well-known limitations in multipath environments and non-line-of-sight satellite conditions, e.g. tunnels, underground, urban canyons, and multilevel roads, being frequently combined with dead reckoning techniques. Inertial sensors present cumulative errors, and geomagnetic-field information is often distorted by strong local magnetic fields caused by road infrastructure. We turn this magnetic weakness into strength by proposing MagLand, an approach with detection and matching steps to leverage these anomalies as signatures for localization purposes. For anomaly detection, we adopt a window-based technique and apply supervised binary classification, choosing a random forest. We select one nearest centroid algorithm with dynamic time warping to match input data streams to reference signatures, providing guidelines to define and collect them. Real data experiments with off-the-shelf devices in challenging road scenarios show MagLand's feasibility with anomaly detection accuracy of 91% and matching with lane distinction of up to 93%. Magnetic landmarks can be extremely useful to address limitations of current localization systems and improve their performance, e.g. by providing an alternative in GPS limited areas, anchors for integrity monitoring, or resetting dead reckoning cumulative errors.

Wireless sensor networks comprising nodes equipped with cameras have become common in many scenarios, providing valuable visual data for some relevant services such as localization, tracking, patterns identification and emergencies detection. In this context, algorithms and optimization approaches have been designed to perform different types of quality assessment or performance enhancement tasks, addressing challenging issues such as networking, compression, availability, reliability, security, energy efficiency and virtually any subject related to the operational challenges of those networks. However, the dynamics of coverage failures have not been properly modelled in visual sensor networks, resulting in unrealistic perceptions when optimizing or assessing quality in most visual sensing scenarios. Particularly, the Field of View of visual sensors will be affected by occlusion caused by obstacles in the monitored field, which may turn such sensors inadequate for the expected monitoring services of the considered network. Therefore, this article proposes a mathematical model to assess occlusion caused by mobile obstacles such as vehicles on a road or forklifts in an industrial plant, aiming at the selection of the visual sensor nodes that will not have their coverage significantly restricted by those obstacles. Doing so, the proposed model can be exploited by any optimization or quality assessment approach in wireless visual sensor networks, providing a preprocessing method when selecting visual nodes.

The main goal of this work was to assess if the Supercritical CO2-Assisted Spray Drying (SASD) is a competitive technology for the production of inhalable trehalose and leucine composite particles with advantageous properties and aerodynamic performance while ensuring a high process throughput and yield. For that purpose, a systematic Quality-by-Design approach using the design of experiments tool, followed by a statistical analysis were implemented. A full-factorial design was used to assess the impact of the static mixer pressure, inlet drying gas temperature and feed flowrate on the powder physical properties and in vitro aerodynamic performance. The powders were produced through SASD with yields up to 70%, while enabling the optimization of the overall throughput. Improved in-vitro aerodynamic performance was driven by the successful manipulation of the process parameters, namely by decreasing the feed flowrate and increasing the inlet drying temperature, yielding powders with fine particle fraction values up to 86%.

COVID-19 vaccines are considered promising agents in the control of the pandemic. Although their safety was assessed in randomised clinical trials, severe adverse events (AEs) have been reported after large-scale administration. This study aims to evaluate thromboembolic AEs reported after vaccination in a real-world context and how they led to the interruption of vaccination campaigns. We also review the benefits and risks of the vaccines approved in the European Union and provide recommendations. A review of the literature was performed using Medline/PubMed electronic database as well as institutional and pharmacovigilance official reports. Our findings show that vaccine-induced prothrombotic immune thrombocytopenia has been suggested as a very rare AE associated with viral vector vaccines. Unusual thrombotic events combined with moderate-to-severe thrombocytopenia were reported mainly in women under 60 years of age. As safety signals emerged, Vaxzevria and Janssen´s COVID-19 vaccine campaigns have been paused while investigations proceed. On the other hand, the number of deep vein thrombosis and pulmonary embolism reports have not increased. Post-marketing surveillance indicated that mRNA vaccines are safe and should continue to be used. The thrombotic events report rate is not increased in people over 60 years. As they are at greater risk for COVID-19 complications and death, no vaccine restrictions are recommended in this group. Risk factors for vaccine-induced prothrombotic immune thrombocytopenia should be established so that evidence-based decisions can be made. Systematic monitoring of COVID-19 vaccine safety is essential to ensure that the benefits of vaccination outweigh the risks. All authors had full access to the complete data in the study and accepted the responsibility to submit it for publication. AA holds a PhD Grant (Ref. 2020. 09390.BD), co-funded by the Fundação para a Ciência e a Tecnologia, Lisbon, Portugal, and the Fundo Social Europeu Programme.

Understanding the distribution of people's transportation mode is a crucial facet of today's urban mobility for proper transportation planning. The penetration of smart-phones combined with their sensing capability is an enabler for crowdsourcing large mobility data such as commuters' GPS records. In this paper, we leverage the G PS traces of commuters to infer five different transportation modes frequently used in urban areas including foot, bike, bus, car and metro. We compare three different approaches commonly reported in the literature for transportation mode detection from the family of machine learning algorithms (random forest -RF) and deep learning architectures (convolutional neural network -CNN and ensemble of autoencoders -EAE). By splitting the dataset into train-test by the period of data collection, as well as the conventional 80–20 split, we evaluate the impact of several data pre-processing decisions on overall classifiers' performance. Our results show RF and CNN performing better upon evaluation on classification metrics such as the f1 score and the area under the Receiver Operating Characteristics (ROC) curve.

Background During these unprecedented times, individuals who experience the sudden death of a family member or friend, already miss the anticipatory grief and are now also prevented from a proper farewell. This study aimed to address prolonged grief disorder since the beginning of the COVID-19 pandemic Methods A virtual snowball sampling survey was disseminated through social networking channels. The study enrolled participants from general resident population in Portugal with ≥18 years. Characteristics of the sample were summarized using descriptive statistics. Anxiety and depression symptoms are described by absolute and relative frequencies and compared through the Chi-square test. Results A total of 929 participants completed the survey; 17.9% (n = 166) have lost someone since the beginning of the pandemic, making our final sample. Most were female (66.9%); median age of 35 years and 70.5% had a high education degree. Participants who had lost a grandparent consisted of 28.3% of the sample, father and/or mother 9.0%, and a friend 22.3%. We found a prevalence of 30.7% of anxiety symptoms and 10.2% of depression symptoms (p = 0.030). Also, 16.8% have had a high score on the PG13, which show that these people can mourned for longer periods. Conclusions Paying the last tribute to a loved one is a mental health gesture that allows individuals to make amends and reconcile with life. Key messages The need arises to give timely attention to the psychological consequences due to the COVID-19 pandemic concerning grief

Portugal was hit by COVID-19 on the 2nd of March 2020. For almost two months, the country was confined due to the declaration of the emergency state. The confinement was always conveyed by the media, who clearly guided citizens toward preventive behaviors and so became a means of fighting this pandemic by helping the country stay home. Several authors recognize the importance of health communication and health literacy in fighting COVID-19. In a public health crisis like this, media are important actors in the promotion of health and prevention of disease, and they can have a central role in behavior change. To further understand how the media guided citizens toward preventive behaviors we applied a questionnaire survey to Portuguese journalists. More than 90% assumed citizens' behavior guidance. Journalists' perceptions about their work reflect a true concern with the promotion of health literacy, and their assumption of a public service mission was crucial in people's adherence to confinement. Health literacy achieved its maximum during this confinement period, not only because these were extraordinary times but also because Portuguese journalists had an exemplar behavior.

To improve range, 802.11ad uses directional communication, the first step of which is to choose the best antenna configuration, or sector. We studied the sector selection behavior of Commercial Off-The-Shelf (COTS) 802.11ad equipment in an experimental Vehicle-to-Infrastructure (V2I) communication scenario. Analysis of the collected data revealed the following inefficiencies: (i) a large number of sector selection attempts that do not result in a sector change; and (ii) a “ping-pong” effect in which a node oscillates between two sectors. With this in mind we studied an alternative antenna sector selection scheme that uses spatially-indexed historical performance data to pick the statistically-best sector for any given geolocation. A trace-based analysis showed that such a strategy can potentially improve throughput by up to 60%, depending on location.

Highlights • The use of HP-UAE as a strategy to intensify extraction is still incipient. • The latest trends and state of the art of HP-UAE extraction processes are detailed. • The influence of physical and medium parameters on the HP-UAE processes was discussed. • The main applications of HP-UAE, mostly in the last ten years, were presented. • The scale of application and the main mathematical models were discussed., Natural products are a source of a wide range of chemical compounds, from pigments to bioactive compounds, which can be extracted and used in different applications. Due to consumer awareness, the interest in natural compounds significantly increased in the last decades, prompting the search for more efficient and environmentally friendly extraction techniques and methods. Pressurized liquids and fluids (sub and supercritical) are being explored to extract natural compounds within the green process concept. The combination of these techniques with ultrasound has emerged as an alternative to intensify the extraction process efficiently. In this context, this work presents a comprehensive review and current insights into the use of high-pressure systems, specifically supercritical fluid extraction and pressurized liquid extraction assisted by ultrasound, as emerging technologies for extracting bioactive compounds from natural products. The extraction mechanisms, applications, and the influence of operational parameters in the process are addressed, in addition to an analysis of the main challenges to be overcome for widespread application.

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Infectious diseases, such as tuberculosis (TB) and the novel coronavirus (COVID-19) relate to environmental factors, understanding of which is essential to inform policy and practice and tackle them effectively. The review follows the conceptual framework offered by the World Health Organization Commission on Social Determinants of Health (defined as "all those material, psychological and behavioural circumstances linked to health and generically indicated as risk factors' in the conventional epidemiological language"). It describes the social factors behind TB and COVID-19, the commonalities between the two diseases, and what can be learned so far from the published best practices. The social determinants sustaining TB and COVID-19 underline the importance of prioritising health and allocating adequate financial and human resources to achieve universal health coverage and health-related social protection while addressing the needs of vulnerable populations. Rapid and effective measures against poverty and other major social determinants and sources of inequality are urgently needed to develop better health in the post-COVID-19 world.

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Cooperative perception allows exchanging on-board sensor information through V2X networks for overcoming the limitations of local sensors. We present a cooperative perception platform based on COTS hardware, including an initial implementation of the ETSI TR 103 562 standard. Preliminary results show the performance of the system using two setups, namely the high impact of the 3D object detection task.

Bicycles are becoming increasingly more equipped with embedded connected devices, by design or through after market products, to support applications such as fitness monitoring and tracking. Bluetooth (BT) and BT Low Energy (BLE) technology is often embedded in such devices to support connectivity to a personal mobile device or a dock, when parked. BT/BLE transmit periodic beacons for node discovery that can be explored for $\mathrm{V}2\mathrm{X}$ applications, such as safety and fleet management. We present a distributed system that explores periodic BT beacons sent by a module embedded in a bicycle to opportunistically locate nodes of interest (NOI). We address the particular application of stolen bicycle detection. In a scenario in which a bicycle is stolen and has its communication system tampered with but BLE remains functional, a service provider (e.g., fleet operator, authorities) is informed of this new NOI and shares an updated NOI list with the NOI detection-enabled bicycles. In turn, the bicycles flag contacts with stolen bicycles to the provider backoffice, at the earliest convenience (depending on available communications interfaces: immediately if cellular is available, or opportunistically when passing by a dock). We describe the operation and software architecture of the system, and an actual implementation in COTS equipment. Experimental measurements of the communication range and a demonstration of the system for oroof-of-concept are also reported.

Covidmonitor is a crowdsensing tool to support epidemologists and public health authorities in monitoring the covid-19 pandemic. The tool collects data to support transdisciplinary studies aiming at improving the knowledge of the pandemic evolution as well as monitor the citizens' behaviour and mental health. Covidmonitor leverages a previously existing mobile crowdsensing platform, SenseMyCity, adapted in collaboration with epidemology, public health and psychology researchers. Our biggest challenge was to identify the relevant metrics for the target trans-disciplinary studies and map them to collectable data. Covidmonitor explores the concept of citizens as probes to sample collective behaviour. The mobile application launches questionnaires about hygiene practices, use of personal protection equipment, health and emotional state. The questionnaires are triggered by different logic, adequate to the multi-dimensional perspectives of the target studies. Covidmonitor also seamlessly collects relevant mobility data without significant battery consumption. Finally, it enables voluntary sharing of location and symptom history, to facilitate tracing in case of infection. The tool considers user privacy and data minimisation by design, and is currently under preliminary scrutiny of the data protection regulator in Portugal.

The adoption of sensors-based monitoring systems supported by Internet of Things technologies has opened new possibilities for data retrieving and processing in urban areas. Among such possibilities, emergencies management is expected to play an important role in how modern cities will evolve, reducing the negative impacts of critical events and improving the quality of life perceived by their inhabitants. Actually, when an emergency is detected and alerted, emergency vehicles, notably ambulances, fire trucks, police cars and transit agents vehicles, should be quickly assigned to respond to that situation, as soon as possible. In this context, we propose a dynamic algorithm to automatically assign emergency vehicles in smart city scenarios, exploiting for that a sensors-based emergency detection system to provide emergency alerts. The proposed algorithm can then be used to quickly assign a number of emergency vehicles in the first moments of an emergency, which can potentially save lives and improve existing crisis management applications in smart cities.

Background Antiretroviral therapy (ART) has the most effective treatment for people with HIV, but its effectiveness depends on the individual medication adherence. Morisky Medication Adherence Scale (MMAS-8) is one of the most widely used scales to assess patient adherence. Thus, we aimed to validate a Portuguese version of MMAS-8 and determine its psychometric properties in HIV positive patients. Methods A cross-sectional survey was conducted in Centro Hospitalar Universitário São João (Porto, northern Portugal) at the infectious diseases department. After authorization to use the scale - granted by the author - and, a standard forward-backwards procedure to translate MMAS-8 to Portuguese, the questionnaire was applied to 233 patients with HIV doing ART. Reliability was assessed using Cronbach's alpha and test-retest reliability. Three levels of adherence were considered: 0 to Results In the studied sample, the mean age was 45.03 years (SD = 11.63), 80.3% men, 19.3% women and 1 transgender, and 53.8% had ≤9 years of education. The mean number of prescribed ART per patient was 1.76. The mean score for the medication adherence scale was 7.29 (SD = 6.74). For the reliability analysis, 12 patients were excluded due to missing data (n = 221). Regarding the level of adherence, 22.5% were low adhering, 71.6% medium and 5.9% high. Corrected item-total correlations showed that 1 item does not correlate very well with the overall scale and was dropped. Scale reliability analysis for the remaining 7 items revealed an overall Cronbach's alpha of 0.661. Women had a protective effect on adherence (OR = 0.31;95%CI:0.15-0.66). Number of years doing ART, age of participants, and type of residence didn't show to be correlated with adherence. Conclusions MMAS-8 is a reliable and valid measure to detect patients at risk of non-adherence. A satisfactory Cronbach's alfa (0.661) was obtained. In general, adherence to medication was medium or high. Key messages This scale can be applied nationwide in other different hospitals, as it could serve as a tool for measuring adherence to ART that can allow for better health care to the ones that are low adhering. A Portuguese version of the MMAS-8 was created for measuring adherence to ART that maintained a similar structure to the original MMAS-8 and good psychometric properties.

Family farming has been on the agenda worldwide. Portugal is no exception. Act No. 64/2018, published in 2018, establishes the Family Farming Statute. Its preponderance in food and in the sustainability of families and territories has been reinforced by the sustainable development paradigm. However, competitiveness constraints assigned to the production system is often seen as an obstacle. Combining the theoretical frameworks of integrated agriculture and work organizations, we offer a lean sociological perspective on family farms producing lettuce. We concluded that their production system is characterized by a set of innovative practices very close to a lean approach. However, there is room for improvements in a production system that seems to maintain a family-based workforce and traditional know-how. info:eu-repo/semantics/publishedVersion

We developed a patented jaboticaba peel extract (PJE) aiming to investigate PJE dose-dependent effect in liver and metabolism of high-fat-fed aging mice. Male FVB mice were distributed as follows: YG(young; 3-months-old), AG(aged; 11-months-old), HfAG(aged + high-fat diet), JAGI(aged + 2.9gPJE/Kg), JAGII(aged + 5.8gPJE/Kg), HfJAGI(aged + high-fat diet + 2.9gPJE/Kg) and HfJAGII(aged + high-fat diet + 5.8gPJE/Kg). PJE showed a potent antioxidant activity and high bioactive compounds content. Both PJE doses prevented weight gain, dyslipidemia, hyperglycemia, reduced COX-2 level and improved HDL-cholesterol, pIRS-1, and PPARγ levels in high-fat-fed aging mice. Only HfJAGII showed lower TNFα level, insulin resistance, and glucose intolerance relative to HfAG. Taking aging into consideration, PJE prevented dyslipidemia and hyperglycemia, reduced TNFα besides increasing pIRS-1 and PPARγ levels, and restoring the hepatic structure in aged mice. Thus, PJE anti-inflammatory, hypoglycemic and lipid modulation capacity prevented the pre-diabetes and NAFLD in this model, being indicated as potential therapy to prevent hepatic and metabolic disorders associated to obesity, diabetes, and aging.

Unintentional associations of mobile devices to on-board WiFi access points (APs) can affect the outdoor Internet experience of mobile device users, as the on-going cellular connection is broken and a short-lived WiFi connection is initiated. This disruption of the user experience can be avoided if the on-board AP learns whether the user device is inside or outside the bus and decides to accept its connection request or not. In this article, we present a classifier-based mechanism for on-board APs that accepts or denies user device associations based on a classification of the relative position of the device. An analysis of the problem in terms of connection duration and RSSI is presented to motivate the selected approach. We then describe a classifier to identify the user relative position trained on features extracted from contextual information. The classifier was trained with a large dataset of real-world WiFi-usage and mobility patterns of a public bus fleet from Porto, Portugal. The training procedure indicated bus speed as the most relevant feature, and that the RSSI measured at the on-board AP does not contribute. Finally, we propose a mechanism that grants or denies connection access to users based on the classifier output. We discuss how to integrate this mechanism in the AP network stack and evaluate its performance in real-world tests. Our solution can avoid 40% of the associations from users outside of the bus.

Smart cities aim to improve the citizens’ quality of life by leveraging information about urban scale processes extracted from heterogeneous data sources collected on city-wide deployments. The Internet-of-Things (IoT) is, thus, the enabler of smart city technologies at urban scale. In this paper, we present PortoLivingLab, a multisource sensing infrastructure that leverages IoT technology to achieve city-scale sensing of four phenomena: weather, environment, public transport, and people flows. To sense these processes on a city scale, we deployed a vehicular network with over 600 vehicles and 19 static environmental sensors. We also developed an easily reconfigurable crowdsensing platform and carried out several crowdsensing campaigns with more than 600 participants. The data is collected in a common backend and stored using similar spatio-temporal data models to simplify sharing and joint analysis for the characterization of urban dynamics. We describe the architecture and composing elements of PortoLivingLab, highlighting the IoT technologies, and challenges faced. We present several proof-of-concept use cases (e.g., passenger flows from WiFi connections) that provide new insights into different components of an evolving and moving city. Finally, we lay out the future lines of work that will strive for finding hidden phenomena by leveraging data from the three complementary platforms.

The use of supercritical fluid technology applied to the production of particles by atomization is an area of increasing interest. A wide variety of supercritical atomization techniques have been described in the literature. They have something in common: the importance of phase behavior governing the scCO2-assisted processes. This minireview intends to provide a critical overview of the supercritical CO2–assisted atomization process (SAA), the different systems already reported in the literature, and discuss the importance of phase equilibria and other parameters in the process optimization and hence in the final properties of the engineered particles.

Fire is used in the management of pastures, renewal and expansion of areas, and agricultural activities in South America (SA). The objectives of this study were: i) to identify the countries and regions with the highest number of fire foci in SA, and ii) to evaluate the spatial dynamics of fire foci based on the Meteorological Fire Danger Index (MFDI) and future scenarios through numerical simulations. Fire foci time series comprised 21 years (1998–2018) from the BDQueimadas database. Cluster Analysis (CA), descriptive and exploratory statistics were employed. Fire foci maps for SA were made in 10-km pixel dimensions. MFDI was used to assess fire danger via SPEEDY (Simplified Parametrizations, primitivE-Equation DYnamics) model simulations. Three simulations were performed: control scenario (1980–2015), RCP2.6 scenario (optimistic - 2016 and 2050), and RCP8.5 scenario (pessimistic - 2015 and 2050). Regionally, three homogeneous groups of fire foci (G1, G2 and G3) and one atypical (NA - Not Grouped) were identified for Brazil via CA. The highest fire foci occurred in Brazil (62.72%), followed by Bolivia (9.03%), Argentina (8.28%), Venezuela (6.11%), Paraguay (5.94%), and Colombia (3.87%), respectively. The highest density of fire foci occurred in the MATOPIBA region, the confluence of Maranhao, Tocantins, Piaui, and Bahia, - (agricultural frontier), and also in the Cerrado-Amazon transition and the states of Mato Grosso and Mato Grosso do Sul in Brazil, followed by Paraguay, Bolivia, Venezuela, and Argentina. The countries and regions of Brazil do not change, only intensify from year to year, and such fire foci variability may be associated with the El Nino-Southern Oscillation (ENSO) phases. The control scenario identified areas extremely vulnerable to fire risk in east-central Brazil, western Bolivia, Paraguay, and northern Argentina. The optimistic scenario showed an improvement in some countries and a worsening in the territorial distribution in Brazil, Venezuela, and Colombia. The pessimistic scenario identified increased degradation compared to the previous scenarios in almost all SA countries.

The Internet of Things (IoT) emerges as a myriad of devices and services that interact to build complex distributed applications. Interoperability and standardization are imperative for the realization of this vision. Machine-to-machine (M2M) communications standards can be the middleware that glues together the IoT. However, standards are highly complex and require a large amount of interpretation, deployments are currently scarce, and performance evaluations simplistic or speculative. In this paper, we focus on the experimental evaluation of latency in IoT service composition with mobile gateways (GWs). We measure latency between system components and quantify application protocol overheads to assess the capabilities and limitations of a standard M2M middleware. We designed and implemented a mobile e-health use case on top of ETSI M2M and openEHR standards. We ran a pilot remote monitoring ten people for three weeks, collecting nearly 480 h of data. Our results show that while the latency added by a broker lies around 25 ms, the cellular network often exceeds 1 s, becoming a problem for interactive applications. Moreover, we observe that latencies between a smartphone GW and cloud hosted services vary largely depending on the user mobility, and on the promotion delay of the used wireless network.

In emerging Internet-of-Things (IoT) realities, sensor nodes near roads will leverage opportunistic connections to vehicles to forward their data to the cloud. In planning such IoT platforms, node placement must be informed by an assessment of infrastructure-to-vehicle (I2V) transfers at a tentative location. It is not feasible to measure I2V volumes at all potential locations, so predictive models are necessary. We propose that qualitative characteristics of a potential site, in particular the existence of traffic and fleet-related points-of-interest (POIs), can inform about the vehicles’ mobility patterns and can ultimately be related with the quality of I2V service. In this work, we analyze a real-world dataset of Wi-Fi I2V link measurements in an urban setting with an urban fleet. We observe that most connections occur with the vehicles stopped and show that stopping regions are related with POIs. Our conclusions include that traffic lights and fleet POIs account for a considerable part of the collected I2V samples, whereas crosswalks account for few transfer occasions.

3D maps of urban environments are useful in various fields ranging from cellular network planning to urban planning and climatology. These models are typically constructed using expensive techniques such as manual annotation with 3D modeling tools, extrapolated from satellite or aerial photography, or using specialized hardware with depth sensing devices. In this work, we show that 3D urban maps can be extracted from standard GNSS data, by analyzing the received satellite signals that are attenuated by obstacles, such as buildings. Furthermore, we show that these models can be extracted from low-accuracy GNSS data, crowdsourced opportunistically from standard smartphones during their user's uncontrolled daily commute trips, unleashing the potential of applying the principle to wide areas. Our proposal incorporates position inaccuracies in the calculations, and accommodates different sources of variability of the satellite signals' SNR. The diversity of collection conditions of crowdsourced GNSS positions is used to mitigate bias and noise from the data. A binary classification model is trained and evaluated on multiple urban scenarios using data crowdsourced from over 900 users. Our results show that the generalization accuracy for a Random Forest classifier in typical urban environments lies between 79% and 91% on 4 m wide voxels, demonstrating the potential of the proposed method for building 3D maps for wide urban areas.

In-vehicle wireless networks (e.g., Wi-Fi, Bluetooth) are experiencing a faster market penetration than dedicated V2V technologies and are compatible with non-V2X devices. In this paper, we assess whether commodity in-vehicle networks can leverage opportunistic V2X communication to nodes outside of the vehicle, particularly with Vulnerable Road Users. We characterize the radiation pattern and performance of communication links in the 2.4 GHz band between in-car wireless networks and a wireless-enabled bicycle in two representative interaction scenarios (i.e. parallel and perpendicular vehicle-VRU trajectories) using both production hardware (i.e. built-in WiFi hotspot) and dedicated measurement equipment. Empirical results show that (i) the signal propagation to the outside of the vehicle is strongly affected (up to 20 dB) by the vehicle elements (e.g. pillars) and by the placement of the wireless system inside the car, and (ii) the communication performance (in terms of RSSI, IRT, Throughput) is also impaired by the spatial arrangement of vehicle and VRU, and other time-varying phenomena (e.g., human body and bicycle shadowing). We conclude that the in-car system performance allows supporting a wide range of safety and infotainment applications (e.g., IRT under 300 ms) even at large TX-RX distances, and that the placement of an in-car wireless system should be tailored according to the target application.

The Internet of Things (IoT) emerged simultaneously in various research fields and application domains, leading to the appearence of a multitude of hardware, software technologies, horizontal platforms and data models. Lost in this diversity, adoption decisions are made to a large extent based on familiarity with a technology, or because there is a broad community support for it. In this context, bechmarking different technologies at different horizontal levels would provide a more solid justification for the adoption of a specific technology in a specific context. In this paper, we reflect on our previous work on benchmarking publish-subscribe IoT platforms as a middleware for IoT applications, reporting lessons learned and identifying challenges, thus contributing to the discussion on open topics and relevant downstream work.

Understanding delay performance is essential for successful development and deployment of real-time networked sensing applications. In this work, we characterize the end-to-end delay on a multi-hop Wireless Sensor Network (WSN) with an analytical method based on Stochastic Network Calculus (SNC) with Moment Generating Functions (MGF). The particularity of the sensing scenario is that all nodes generate and cooperatively forward their traffic through the network to a main location, known as the sink. We tackled the problem by modeling the service process of fading wireless channels from a high-layer perspective with Finite-State Markov Chains (FSMC). Numerical performance bounds are provided for an example WSN with IEEE 802.11g ad-hoc links in which the effects of delay bound violation probability, per-node offered load, data rate and fading speed are quantified for different network sizes. Finally, the presented analysis is validated through a comparison between analytical and numerical simulation results.

For communications, in certain scenarios, the spatial proximity between nodes obviates the need for a cellular connection, and MANETs can provide better solutions. An example is audio communication in bicycle platoons: cyclists ride in a cluster, but may be spread over hundreds of meters and their spatial arrangement can vary, leading to volatile links. In this work, we showcase a novel implementation of VoIP that was tailored for a MANET composed of embedded devices installed in bicycles, operating over multicast. We describe the architecture of our system, which builds on off-the-shelf hardware and software, and list the parameter values that result in reasonable audio quality. Preliminary results show that high packet delivery ratios (above 70%) can be attained with our system in platoon-riding conditions, despite no link (or network) retransmissions.

The absence of a timely and cost-effective way to perform over-the-air (OTA) software updates is contributing to defer the deployment of large fleets of connected vehicles. This work presents the design and implementation of a cooperative content dissemination protocol that takes advantage of Vehicle-to-Vehicle (V2V) communication links to distribute data across a network with reduced costs. The protocol uses probing instead of gossiping to reduce the risk of congestion, and parameter design follows statistics from a real vehicular network. Further, we carry out a thorough performance evaluation using 17 vehicles and 5 roadside units (RSUs) of a VANET operator. The results show that the dissemination time is significantly sped up by V2V cooperation and that is heavily influenced by vehicle mobility. Moreover, results show that the chunk size and decision algorithm can have a considerable impact on the performance of the protocol.

We present a particle-based framework for estimating the position of a vehicle using map information and measurements of speed. The filter propagates the particles’ position estimates by means of dead-reckoning, and then updates the particle weights using two measurement functions. The first measurement function is based on the assumption that the lateral force on the vehicle does not exceed critical limits derived from physical constraints. The second is based on the assumption that the driver approaches a target speed derived from the speed limits along the upcoming trajectory. Assuming some prior knowledge of the initial position, performance evaluations of the proposed method indicate that end destinations often can be estimated with an accuracy in the order of $100\,[\mathrm{m}]$ . These results expose the sensitivity and commercial value of speed data collected in many of today's insurance telematics programs, where the data is used to adjust premiums and provide driver feedback. We end by discussing the strengths and weaknesses of different methods for anonymization and privacy preservation in telematics programs.

Whipple’s disease is a rare and difficult-to-diagnose infectious disease, related to infection by gram-positive bacillum Tropheryma whipplei. Clinical manifestations are very variable, but the classic form usually begins with recurring arthritis, followed several years later by non-specific abdominal symptoms, leading to late diagnosis. We present the case of a 52-year-old man who was admitted in the emergency department with an insidious clinical picture characterised by weight loss, abdominal pain, diarrhoea and arthralgias. An abdominal ultrasound was performed, showing findings suggestive of Whipple’s disease, which, in conjunction with the clinical and laboratory findings, allowed the diagnosis to be correctly addressed. Upper endoscopy with duodenal biopsy revealed findings compatible with Whipple’s disease, and the diagnosis was also confirmed through PCR techniques of blood. The patient was given antibiotic therapy, with rapid and substantial clinical improvement.

International audience; Due to the limited coverage of WiFi APs, users' mobility has a severe impact on the performance of mobile offloading systems. The present study is a contribution in this context as offloading zones are identified and characterized from individual GPS trajectories when small offloading time windows are considered. The results show that (i) attending to users mobility, ten seconds is the minimum offloading time window that can be considered; (ii) offloading predictive methods can have variable performance according to the period of the day; and (iii) per-user opportunistic decision models can determine offloading system design and performance.

Firefighters often operate in remote areas where there is little to no cellular coverage. In that context, a Mobile Ad-Hoc Network (MANET) among a firefighter team would provide a communication infrastructure, e.g., for monitoring, early warnings and basic communication. In this paper, we analyse trajectories simultaneously collected from a firefighter team during a controlled fire exercise, showing that the distance among any two firefighters only rarely exceeds 300~m. We assess connectivity using 802.11g links, which we argue to be a viable commercial off-the-shelf (COTS) technology. Results show a probability of being connected to another firefighter larger than 80%, although a few disconnection periods of more than 5 minutes may occur. Finally, we show that the Raspberry Pi (RPi) could be an inexpensive, power-feasible platform to deploy such a network for testing upper layer protocols and applications.

International audience; Setting up a MANET is a complex task, involving the configuration of a routing protocol and the network parameters of the nodes, since there is no centralized point for distribution of IP addresses. This complexity is further compounded by the absence of a name resolution system. We describe NetService, a service that frees application developers and deployers from this burden. NetService runs on the nodes and auto-configures a predefined MANET at the request of applications. It handles IP address assignment and includes a name resolution mechanism that provides a hierarchical naming scheme and is compatible with a standard resolver. The service provides an API to configure the nodes and another one for exchanging messages, abstracting applications from the details of network access.

Aiming to improve sustainability and life quality, urban space research is prompting an intensive use of communication and information technologies. With it, researchers are also facing more challenges regarding research data management and therefore seeking clear guidelines and tools for proper data organization, sharing and reuse. In the context of a smart cities research project, UrbanSense, held in the city of Porto, we proposed a data management plan, to support researchers from the moment they start to collect data up to the point of data publication. We also developed an ontology for the description of smart cities data, validated by UrbanSense researchers. Descriptions based on this ontology were evaluated by external parties, after the data was published in an institutional data repository.

Bicycle-to-bicycle (Bi2Bi) communication can be implemented by well-established technologies in the 2.4GHz ISM band: IEEE 802.11, Bluetooth or IEEE 802.15.4. These technologies have distinct performance due to different physical and data link layers. In this paper, we characterize the mentioned 2.4 GHz-operating technologies over opportunistic links established between bicycles using commodity hardware. We find that, in Bi2Bi links, Blue-tooth, IEEE 802.11 at 24 Mbit/s, and IEEE 802.11 with automatic rate adaptation can communicate only in the immediate surroundings (under 15m of range), to maxima of 1.5 Mbit/s, 17 Mbit/s and 25 Mbit/s, respectively. IEEE 802.15.4 and IEEE 802.11 at 1 Mbit/s sustain connectivity up to 30 and 40 meters and peak transfer rates of 50 kbit/s and 800 kbit/s respectively. In addition, we observed that, in all measurement scenarios, link performance depended strongly on whether bicycles were approaching or moving away, rather than on whether one was at the front or back of the other.

Renewable energy sources may ease the deployment of IoT nodes in smart city scenarios where access to a steady power supply is often limited. In this paper, we report experimental work to evaluate the feasibility of sustaining the operation of an IoT node from an autonomous power supply, composed of a photo-voltaic (PV) energy source, batteries and a controller. We measured a tentative deployment, observe that typical forecasting tools are inadequate to dimension such micro-supplies, and used greedy search to find the best system configuration.

Machine-to-Machine (M2M) communications are a key enabler of Internet of Things (IoT) applications. One domain with growing interest in M2M/IoT is e-health, either for self-monitoring, home monitoring, or hospital systems. However, current sensing devices in this domain rely on short-range communication protocols that require a gateway (GW) for Internet connection. Smartphones have been proposed as GWs in mobile M2M communications due to their enhanced connectivity and sensing capabilities. However, the GW functionality impacts on the smartphone usability, causing undesirable battery depletion and the smartphone itself increases the overall cost of e- health solutions. In this work, we propose converging e-health devices and Wi-Fi towards direct Internet access through the existing Wi-Fi infrastructure and by-passing current GWs. We use recent low-cost ultra low-power Wi-Fi modules and feature them with M2M capabilities supporting their integration in an interoperable e-health framework. We present results on end-to-end latency and power requirements within a concrete e-health use case that show the feasibility of the proposed GW-less solution.

With the rise in popularity of the Internet of Things in all kinds of different application scenarios, various middleware solutions have appeared with different use-cases and optimizations in mind. The design space for any specific deployment is thus increasingly large, but little objective support exists to help choose the best middleware for each use-case. From this stems the need to evaluate how different IoT middleware solutions perform in different use-cases. Measuring the performance of IoT middleware in a way that 1) provides common ground among experiments, and 2) makes it easier to integrate new IoT middleware in the benchmark is not straightforward. In this paper, we propose a generic architecture for comparing the performance of publish/subscribe middleware, develop a tool that implements this architecture, and show the benefits in time and effort that can be reaped from our approach. We further validate our approach by using the architecture and tool to benchmark different middleware solutions, taking lessons from the changes necessary to support new middleware, and attempting to quantify the effort through lines of code and to qualitatively assess code structure similarity.

Mobile crowdsensing can provide mobility researchers with fine grained spatio-temporal location data. But crowdsourcing impacts data quality both due to device and OS heterogeneity, and to annotation errors. Additionally, it is often necessary to deal with multimodality, i.e. participants using different travel modes often in the same trip. In this paper, we address how to draw value from a crowdsensed dataset for characterising mobility demand through origin-destination (OD) matrices, highlighting challenges and providing some solutions.First, we identify typical errors in heterogeneous location data, propose and compare methods to automatically improve data quality. Then, we devise a method to detect among 5 transport modes (walk, car, bus, metro, bike) offline a posteriori. We segment trips on stopped periods and propose a random forest model to detect transportation mode per segment using only location data. Our results show that with adequate pre-processing and robust features, an RF classifier is able to achieve accuracy and precision of 85% in trip segments. This is similar to the literature, but our work uses a very heterogeneous crowdsourced trajectory dataset when compared to the others. Finally, we quantify the impact of the model on mulit-modal OD matrices and whole trip characterisation. We can correctly identify used transportation modes accurately, but the precision is impaired by the high likelihood of at least one false positive in the whole trip.

Anti-Mullerian hormone (AMH) is a useful marker of ovarian reserve. Obesity/overweight are increasing and may affect the reproductive health. Previous studies regarding the effect of body mass index (BMI) on AMH levels are discordant. Our main goal was to evaluate the influence of BMI on AMH levels in women without polycystic ovarian syndrome. Revision of medical records of 951 women who performed AMH determinations as part of their fertility workup, between 2011 and 2016. Median AMH concentration was 1.75 [interquartile range (IQR) 2] ng/mL (12.9 pmol/mL) and median age at AMH determination was 35 (IQR 6) years. These women evidenced a median BMI of 23 (IQR 5) kg/m2. Caucasian women were more represented [889(89.3%)]. Smoking habits (present/past) were present in 359(36.1%), and 147(14.8%) harboured a history of ovarian surgery. On univariable analysis AMH was not correlated with BMI (r = 0.048/p = 0.135); the only factors influencing AMH were age (p