Young people's well-being and the economic crisis: How does parental unemployment and family wealth affect the downturn experience?

Background: Portugal is among the European countriesmost severely hit by the economic recession and the fifth with the highest unemployment rate. Given that adolescents' development is highly influenced by their living contexts, monitoring the repercussions of the economic recession is essential for the evaluation and improvement of their current and future public health. Objective: To investigate youth perceived repercussions of the economic recession, its association with life satisfaction, as well as to assess differences across parental employment status and family perceived wealth. Methods: Data were drawn from the Portuguese 2014 Health Behaviour in School-aged children survey, aWHO collaborative cross-national study, with a nationally representative sample of 2748 students (Mage = 14.7 years ± 1.2; 48% boys). Descriptive statistics and linear regression models were performed. Results: Levels of life satisfaction are lower when young people perceive that the economic recession generated negative lifestyle changes. Having unemployed parents was found to be significantly associated with perceiving such repercussions and family wealth to decrease the perception of repercussions of the recession. Conclusions: Findings enhance our understanding of how Portuguese youth are being affected by the socioeconomic conditions surrounding them. Such information contributes to improve future research and also allow some considerations about the policies aimed at protecting young people'swellbeing during a period of high unemployment and socioeconomic downturn.

Baran: an interaction-centred user monitoring framework

User Quality of Experience (QoE) is a subjective entity and difficult to measure. One important aspect of it, User Experience (UX), corresponds to the sensory and emotional state of a user. For a user interacting through a User Interface (UI), precise information on how they are using the UI can contribute to understanding their UX, and thereby understanding their QoE. As well as a user’s use of the UI such as clicking, scrolling, touching, or selecting, other real-time digital information about the user such as from smart phone sensors (e.g. accelerometer, light level) and physiological sensors (e.g. heart rate, ECG, EEG) could contribute to understanding UX. Baran is a framework that is designed to capture, record, manage and analyse the User Digital Imprint (UDI) which, is the data structure containing all user context information. Baran simplifies the process of collecting experimental information in Human and Computer Interaction (HCI) studies, by recording comprehensive real-time data for any UI experiment, and making the data available as a standard UDI data structure. This paper presents an overview of the Baran framework, and provides an example of its use to record user interaction and perform some basic analysis of the interaction.

Multi-Scale Governance of Sustainable Natural Resource Use—Challenges and Opportunities for Monitoring and Institutional Development at the National and Global Level

In a globalized economy, the use of natural resources is determined by the demand of modern production and consumption systems, and by infrastructure development. Sustainable natural resource use will require good governance and management based on sound scientific information, data and indicators. There is a rich literature on natural resource management, yet the national and global scale and macro-economic policy making has been underrepresented. We provide an overview of the scholarly literature on multi-scale governance of natural resources, focusing on the information required by relevant actors from local to global scale. Global natural resource use is largely determined by national, regional, and local policies. We observe that in recent decades, the development of public policies of natural resource use has been fostered by an “inspiration cycle” between the research, policy and statistics community, fostering social learning. Effective natural resource policies require adequate monitoring tools, in particular indicators for the use of materials, energy, land, and water as well as waste and GHG emissions of national economies. We summarize the state-of-the-art of the application of accounting methods and data sources for national material flow accounts and indicators, including territorial and product-life-cycle based approaches. We show how accounts on natural resource use can inform the Sustainable Development Goals (SDGs) and argue that information on natural resource use, and in particular footprint indicators, will be indispensable for a consistent implementation of the SDGs. We recognize that improving the knowledge base for global natural resource use will require further institutional development including at national and international levels, for which we outline options.

Seasonal dynamics and operational monitoring of hedgerow olive tree transpiration in response to applied water

We used 2012 sap flow measurements to assess the seasonal dynamics of daily plant transpiration (ETc) in a high-density olive orchard (Olea europaea L. cv. ‘Arbequina’) with a well-watered (HI) control treatment A to supply 100 % of the crop water needs, and a moderately (MI) watered treatment B that replaced 70% of crop needs. To assure that treatment A was well-watered, we compared field daily ETc values against ETc obtained with the Penman-Monteith (PM) combination equation incorporating the Orgaz et al. (2007) bulk daily canopy conductance (gc) model, validated for our non-limiting conditions. We then tested the hypothesis of indirectly monitoring olive ETc from readily available vegetation index (VI) and ground-based plant water stress indicator. In the process we used the FAO56 dual crop coefficient (Kc) approach. For the HI olive trees we defined Kcb as the basal transpiration coefficient, and we related Kcb to remotely sensed Soil Adjusted Vegetation Index (SAVI) through a Kcb-SAVI functional relationship. For the MI treatment, we defined the actual transpiration ETc as the product of Kcb and the stress reduction coefficient Ks obtained as the ratio of actual to crop ETc, and we correlated Ks with MI midday stem water potential (ψst) values through a Ks-ψ functional relationship. Operational monitoring of ETc was then implemented with the ETc = Kcb(SAVI)Ks(ψ)ETo relationship stemmed from the FAO56 approach and validated taking as inputs collected SAVI and ψst data reporting to year 2011. Low validation error (6%) and high goodness-of-fit of prediction were observed (R2 = 0.94, RSME = 0.2 mm day-1, P = 0.0015), allowing to consider that under field conditions it is possible to predict ETc values for our hedgerow olive orchards if SAVI and water potential (ψst) values are known.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Design and Development of Network Monitoring Strategies in P4-enabled Programmable Switches

Network monitoring is of paramount importance for effective network management: it allows to constantly observe the network’s behavior to ensure it is working as intended and can trigger both automated and manual remediation procedures in case of failures and anomalies. The concept of SDN decouples the control logic from legacy network infrastructure to perform centralized control on multiple switches in the network, and in this context, the responsibility of switches is only to forward packets according to the flow control instructions provided by controller. However, as current SDN switches only expose simple per-port and per-flow counters, the controller has to do almost all the processing to determine the network state, which causes significant communication overhead and excessive latency for monitoring purposes. The absence of programmability in the data plane of SDN prompted the advent of programmable switches, which allow developers to customize the data-plane pipeline and implement novel programs operating directly in the switches. This means that we can offload certain monitoring tasks to programmable data planes, to perform fine-grained monitoring even at very high packet processing speeds. Given the central importance of network monitoring exploiting programmable data planes, the goal of this thesis is to enable a wide range of monitoring tasks in programmable switches, with a specific focus on the ones equipped with programmable ASICs. Indeed, most network monitoring solutions available in literature do not take computational and memory constraints of programmable switches into due account, preventing, de facto, their successful implementation in commodity switches. This claims that network monitoring tasks can be executed in programmable switches. Our evaluations show that the contributions in this thesis could be used by network administrators as well as network security engineers, to better understand the network status depending on different monitoring metrics, and thus prevent network infrastructure and service outages.

Real-time PMU monitoring of an IEEE 5-Bus Network implemented on OPAL system

This thesis studies the state-of-the-art of phasor measurement units (PMUs) as well as their metrological requirements stated in the IEEE C37.118.1 and C37.118.2 Standards for guaranteeing correct measurement performances. Communication systems among PMUs and their possible applicability in the field of power quality (PQ) assessment are also investigated. This preliminary study is followed by an analysis of the working principle of real-time (RT) simulators and the importance of hardware-in-the-loop (HIL) implementation, examining the possible case studies specific for PMUs, including compliance tests which are one of the most important parts. The core of the thesis is focused on the implementation of a PMU model in the IEEE 5-bus network in Simulink and in the validation of the results using OPAL RT-4510 as a real-time simulator. An initial check allows one to get an idea about the goodness of the results in Simulink, comparing the PMU data with respect to the load-flow steady-state information. In this part, accuracy indices are also calculated for both voltage and current synchrophasors. The following part consists in the implementation of the same code in OPAL-RT 4510 simulator, after which an initial analysis is carried out in a qualitative way in order to get a sense of the goodness of the outcomes. Finally, the confirmation of the results is based on an examination of the attained voltage and current synchrophasors and accuracy indices coming from Simulink models and from OPAL system, using a Matlab script. This work also proposes suggestions for an upcoming operation of PMUs in a more complex system as the Digital Twin (DT) in order to improve the performances of the already-existing protection devices of the distribution system operator (DSO) for a future enhancement of power systems reliability.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Structural health monitoring of storage tanks

In this thesis, Ph.D candidate presents a compact sensor node (SN) designed for long-term and real-time acoustic emission (AE) monitoring of above ground storage tanks (ASTs). Each SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms for effective leakage detection, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. Alternatively, capability to plug three high frequency AE sensors to a SN for corrosion simulated phenomena detection is envisaged and demonstrated. Another innovative aspect that the Ph.D candidate presents in this work is an alternative mathematical model of corrosion location on the bottom of the AST. This approach implies considering the three-dimensional localization model versus the two-dimensional commonly used according to the literature. This approach is aimed at significant optimization in the number of sensors in relation to the standard approach for solving localization problems as well as to allow filtering the false AE events related to the condensate droplets from AST ceiling. The technological implementation of this concept required the solution of a number of technical problems, such as the precise time of arrival (ToA) signal estimation, vertical localization of the AE source and multilaration solution that were discussed in detail in this work. To validate the developed prototype, several experimental campaigns were organized that included the simulation of target phenomena both in laboratory conditions and on a real water storage tank. The presented test results demonstrate the successful application of the developed AE system both for simulated leaks and for corrosion processes on the tank bottom. Mathematical and technological algorithms for localization and characterization of AE signals implemented during the development of the prototype are also confirmed by the test results.

Smart Textile Sensors for Healthcare Monitoring

Wearable electronic textiles are an emerging research field playing a pivotal role among several different technological areas such as sensing, communication, clothing, health monitoring, information technology, and microsystems. The possibility to realise a fully-textile platform, endowed with various sensors directly realised with textile fibres and fabric, represents a new challenge for the entire research community. Among several high-performing materials, the intrinsically conductive poly(3,4-ethylenedioxythiophene) (PEDOT), doped with poly(styrenesulfonic acid) (PSS), or PEDOT:PSS, is one of the most representative and utilised, having an excellent chemical and thermal stability, as well as reversible doping state and high conductivity. This work relies on PEDOT:PSS combined with sensible materials to design, realise, and develop textile chemical and physical sensors. In particular, chloride concentration and pH level sensors in human sweat for continuous monitoring of the wearer's hydration status and stress level are reported. Additionally, a prototype smart bandage detecting the moisture level and pH value of a bed wound to allow the remote monitoring of the healing process of severe and chronic wounds is described. Physical sensors used to monitor the pressure distribution for rehabilitation, workplace safety, or sport tracking are also presented together with a novel fully-textile device able to measure the incident X-ray dose for medical or security applications where thin, comfortable, and flexible features are essential. Finally, a proof-of-concept for an organic-inorganic textile thermoelectric generator that harvests energy directly from body heat has been proposed. Though further efforts must be dedicated to overcome issues such as durability, washability, power consumption, and large-scale production, the novel, versatile, and widely encompassing area of electronic textiles is a promising protagonist in the upcoming technological revolution.

Qualification of optical fiber sensors for the structural health monitoring of aerospace structures

In recent years, composite materials have revolutionized the design of many structures. Their superior mechanical properties and light weight make composites convenient over traditional metal structures for many applications. However, composite materials are susceptible to complex and challenging to predict damage behaviors due to their anisotropy nature. Therefore, structural Health Monitoring (SHM) can be a valuable tool to assess the damage and understand the physics underneath. Distributed Optical Fiber Sensors (DOFS) can be used to monitor several types of damage in composites. However, their implementation outside academia is still unsatisfactory. One of the hindrances is the lack of a rigorous methodology for uncertainty quantification, which is essential for the performance assessment of the monitoring system. The concept of Probability of Detection (POD) must function as the guiding light in this process. However, precautions must be taken since this tool was established for Non-Destructive Evaluation (NDE) rather than Structural Health Monitoring (SHM). In addition, although DOFS have been the object of numerous studies, a well-established POD methodology for their performance assessment is still missing. This thesis aims to develop a methodology to produce POD curves for DOFS in composite materials. The problem is analyzed considering several critical points, such as the strain transfer characterizing the DOFS and the development of an experimental and model-assisted methodology to understand the parameters that affect the DOFS performance.

Monitoring of Mediterranean marine benthic biodiversity through novel, standardized, and integrated approaches

Marine healthy ecosystems support life on Earth and human well-being thanks to their biodiversity, which is proven to decline mainly due to anthropogenic stressors. Monitoring how marine biodiversity changes trough space and time is needed to properly define and enroll effective actions towards habitat conservation and preservation. This is particularly needed in those areas that are very rich in species compared to their low surface extension and are characterized by strong anthropic pressures, such as the Mediterranean Sea. Subtidal rocky benthic Mediterranean habitats have a complex structural architecture, hosting a panoply of tiny organisms (cryptofauna) that inhabit crevices and caves, but that are still unknown. Different artificial standardized sampling structures (SSS) and methods have been developed and employed to characterize the cryptofauna, allowing for data replicability and comparability across regions. Organisms growing on these artificial structures can be identified coupling morphological taxonomy and DNA barcoding and metabarcoding. The metabarcoding allows for the identification of organisms in a bulk sample without morphological analysis, and it is based on comparing the genetic similarities of the assessed organisms with barcoding sequences present in online barcoding repositories. Nevertheless, barcoded species nowadays represent only a small portion of known species, and barcoding reference databases are not always curated and updated on a regular basis. In this Thesis I used an integrative approach to characterize benthic marine biodiversity, specifically coupling morphological and molecular techniques with the employment of SSS. Moreover, I upgraded the actual status of COI (cytochrome c oxidase subunit I) barcoding of marine metazoans, and I built a customized COI barcoding reference database for metabarcoding studies on temperate biogenic reefs. This work implemented the knowledge about diversity of Mediterranean marine communities, laying the groundworks for monitoring marine and environmental changes that will occur in the next future as consequences of anthropic and climate threats.

Pedagogy of healthcare interactions. A study on pediatric well-child visits as culture-oriented and culture-making sites

This study investigates interactions between parents and pediatricians during pediatric well-child visits. Despite constituting a pivotal moment for monitoring and evaluating children’s development during the critical ‘first thousand days of life’ and for family support, no study has so far empirically investigated the in vivo realization of pediatrician-parent interactions in the Italian context, especially not from a pedagogical perspective. Filling this gap, the present study draws on a corpus of 23 videorecorded well-child visits involving two pediatricians and twenty-two families with children aged between 0 and 18 months. Combining an ethnographic perspective and conversation analysis theoretical-analytical constructs, the micro-analysis of interactions reveals how well-child visits unfold as culture-oriented and culture-making sites. By zooming into what actually happens during these visits, the analysis shows that there is much more than the “mere” accomplishment of institutionally relevant activities like assessing children’s health or giving parents advice on baby care. Rather, through the interactional ways these institutional tasks are carried out, parents and pediatricians presuppose, ratify, and transmit culturally-informed models of “normal” growth, “healthy” development, “good” caring practices, and “competent” parenting, thereby enacting a pervasive yet unnoticed educational and moral work. Inaugurating a new promising line of inquiry within Italian pedagogical research, this study illuminates how a) pediatricians work as a “social antenna”, bridging families’ private “small cultures” and broader socio-cultural models of children’s well-being and caregiving practices, and b) parents act as agentive, knowledgeable, (communicatively) competent, and caring parents, while also sensitive to the pediatrician’s ultimate epistemic and deontic authority. I argue that a video-based, micro-analysis of interactions represents a heuristically powerful instrument for raising pediatricians’ and parents’ awareness of the educational and moral density of well-child visits. Insights from this study can constitute a valuable empirical resource for underpinning medical and parental training programs aimed at fostering pediatricians’ and parents’ reflexivity.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.

Cross-sensors analysis of multispectral Earth observation data for environmental monitoring

Time Series Analysis of multispectral satellite data offers an innovative way to extract valuable information of our changing planet. This is now a real option for scientists thanks to data availability as well as innovative cloud-computing platforms, such as Google Earth Engine. The integration of different missions would mitigate known issues in multispectral time series construction, such as gaps due to clouds or other atmospheric effects. With this purpose, harmonization among Landsat-like missions is possible through statistical analysis. This research offers an overview of the different instruments from Landsat and Sentinel missions (TM, ETM, OLI, OLI-2 and MSI sensors) and products levels (Collection-2 Level-1 and Surface Reflectance for Landsat and Level-1C and Level-2A for Sentinel-2). Moreover, a cross-sensors comparison was performed to assess the interoperability of the sensors on-board Landsat and Sentinel-2 constellations, having in mind a possible combined use for time series analysis. Firstly, more than 20,000 pairs of images almost simultaneously acquired all over Europe were selected over a period of several years. The study performed a cross-comparison analysis on these data, and provided an assessment of the calibration coefficients that can be used to minimize differences in the combined use. Four of the most popular vegetation indexes were selected for the study: NDVI, EVI, SAVI and NDMI. As a result, it is possible to reconstruct a longer and denser harmonized time series since 1984, useful for vegetation monitoring purposes. Secondly, the spectral characteristics of the recent Landsat-9 mission were assessed for a combined use with Landsat-8 and Sentinel-2. A cross-sensor analysis of common bands of more than 3,000 almost simultaneous acquisitions verified a high consistency between datasets. The most relevant discrepancy has been observed in the blue and SWIRS bands, often used in vegetation and water related studies. This analysis was supported with spectroradiometer ground measurements.