Empregabilidade nos recém licenciados da Faculdade de Motricidade Humana

Esta investigação enquadra-se nos estudos sobre o percurso académico e inserção profissional dos recém-licenciados dos anos letivos de 2007/08, 2008/09 e 2009/10 da Faculdade de Motricidade Humana (FMH) em colaboração com o Observatório da Empregabilidade da FMH. Tem como principal objetivo a caracterização do emprego dos recém-licenciados pela Faculdade. A metodologia aproveitou e aperfeiçoou uma plataforma eletrónica proprietária (AgonScopio v.1.7.51), para o desenvolvimento de questionários online, no meio Web. O universo do estudo foi representado pelos recém-licenciados dos três (3) anos letivos em estudo, das seguintes Licenciaturas: Ciências do Desporto, Ergonomia, Gestão do Desporto, Reabilitação Psicomotora e Dança. A amostra foi representada pelos resultados obtidos das duzentas e vinte e quatro (224) respostas conseguidas, de um universo de seiscentos e oitenta e seis (686) licenciados, permitindo caracterizar o comportamento dos recém-licenciados, de acordo com nove (9) dimensões estudadas, nomeadamente: dados gerais, enquadramento sociocultural com o objeto da FMH, primeiro emprego, formação, experiência profissional, trabalho e remuneração, expetativas, mobilidade e formação pós licenciatura. Aferimos que os recém-licenciados da FMH têm uma boa emprega-bilidade e o emprego é maioritariamente na sua área de formação. A maioria dos licenciados está empregada ao fim de 12 meses após a conclusão das suas licenciaturas (79,4%).

Empregabilidade nos récem licenciados da Faculdade de Motricidade Humana

A presente investigação enquadra-se nos estudos sobre o percurso académico e inserção profissional dos recém-licenciados dos anos letivos de 2010/11 e 2011/12 da Faculdade de Motricidade Humana, em colaboração com o Observatório da Empregabilidade da FMH. Tem como principal objetivo a caraterização do emprego dos recém-licenciados pela Faculdade. A metodologia aproveitou e aperfeiçoou uma plataforma eletrónica proprietária (AgonScopio v.1.7.51), para o desenvolvimento de questionários online, no meio Web. O universo do estudo foi representado pelos recém-licenciados dos dois anos letivos em estudo, das seguintes licenciaturas: Ciências do Desporto, Dança, Ergonomia, Gestão do Desporto e Reabilitação Psicomotora. A amostra foi representada pelos resultados obtidos das 105 respostas conseguidas, de um universo de 334 licenciados, permitindo caraterizar o comportamento dos recém-licenciados, de acordo com nove dimensões estudadas, nomeadamente: dados gerais, enquadramento sociocultural com o objeto da FMH, primeiro emprego, formação, experiência profissional, trabalho e remuneração, expetativas, mobilidade e formação pós licenciatura. Aferimos que os recém-licenciados da FMH possuem um bom índice de empregabilidade e o emprego é maioritariamente na sua área de formação. A maioria dos licenciados obtém emprego até 12 meses após a conclusão das respetivas licenciaturas (71%).

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

Reducing Uncertainties in Estimation of Wind Effects on Tall Buildings Using Aerodynamic Wind Tunnel Tests

Tall buildings are wind-sensitive structures and could experience high wind-induced effects. Aerodynamic boundary layer wind tunnel testing has been the most commonly used method for estimating wind effects on tall buildings. Design wind effects on tall buildings are estimated through analytical processing of the data obtained from aerodynamic wind tunnel tests. Even though it is widely agreed that the data obtained from wind tunnel testing is fairly reliable the post-test analytical procedures are still argued to have remarkable uncertainties. This research work attempted to assess the uncertainties occurring at different stages of the post-test analytical procedures in detail and suggest improved techniques for reducing the uncertainties. Results of the study showed that traditionally used simplifying approximations, particularly in the frequency domain approach, could cause significant uncertainties in estimating aerodynamic wind-induced responses. Based on identified shortcomings, a more accurate dual aerodynamic data analysis framework which works in the frequency and time domains was developed. The comprehensive analysis framework allows estimating modal, resultant and peak values of various wind-induced responses of a tall building more accurately. Estimating design wind effects on tall buildings also requires synthesizing the wind tunnel data with local climatological data of the study site. A novel copula based approach was developed for accurately synthesizing aerodynamic and climatological data up on investigating the causes of significant uncertainties in currently used synthesizing techniques. Improvement of the new approach over the existing techniques was also illustrated with a case study on a 50 story building. At last, a practical dynamic optimization approach was suggested for tuning structural properties of tall buildings towards attaining optimum performance against wind loads with less number of design iterations.

Regionen och EU? : Uppfattningar och attityder till EU-relaterade frågor i Västsverige

This thesis explores perceptions and preferences on regional action in EU-related frameworks among regional actors in Western Sweden. Building upon the literature on Europeanisation and the Fusion approach, three dimensions of Europeanisation are clarified and explored– download, upload and crossload – and together with a set of five variables that constitute the Micro Fusion Framework; a comprehensive analytical tool is developed. The thesis analyses the intense debate among the members of West Sweden that took place from 2011 to 2013 that focused on how to functionally organise the regional office in Brussels in order to meet future challenges. Surprisingly, the members eventually decided to terminate their cooperation and close the jointly owned office in Brussels in spite of the fact that it has been widely regarded as successful and effective. Diverging perceptions and preferences is understood in terms of three positions on regional action; a download-, upload- and a coherent oriented position. Finally, the thesis presents the empirical findings and discusses in relation to three fusion scenarios, infusion, defusion and clustered fusion. In terms of Micro Fusion Framework, the dynamics shaping why West Sweden was finally regarded as a dysfunctional arena for regional action are explained by a shift of attention and action among regional actors in Western Sweden that led to pressure for further institutional adaptation in order to meet the demand of how ‘to get the best out of the EU’. Further, this redefinition of how to handle EU-affairs within the upload-oriented position was accompanied by positive attitudes towards the potential to bypass the state and thereby pursue regional priorities directly in Brussels given the compound nature of the EU. In contrast, those regional actors that are found to be more download-oriented often question the benefits of uploading activities in practice and advocate close relations to the state. A coherent oriented position recognises the importance of activities related to both of the vertical dimensions of Europeanisation.

Surveillance risks in IoT applied to smart cities

Nowadays, cities deal with unprecedented pollution and overpopulation problems, and Internet of Things (IoT) technologies are supporting them in facing these issues and becoming increasingly smart. IoT sensors embedded in public infrastructure can provide granular data on the urban environment, and help public authorities to make their cities more sustainable and efficient. Nonetheless, this pervasive data collection also raises high surveillance risks, jeopardizing privacy and data protection rights. Against this backdrop, this thesis addresses how IoT surveillance technologies can be implemented in a legally compliant and ethically acceptable fashion in smart cities. An interdisciplinary approach is embraced to investigate this question, combining doctrinal legal research (on privacy, data protection, criminal procedure) with insights from philosophy, governance, and urban studies. The fundamental normative argument of this work is that surveillance constitutes a necessary feature of modern information societies. Nonetheless, as the complexity of surveillance phenomena increases, there emerges a need to develop more fine-attuned proportionality assessments to ensure a legitimate implementation of monitoring technologies. This research tackles this gap from different perspectives, analyzing the EU data protection legislation and the United States and European case law on privacy expectations and surveillance. Specifically, a coherent multi-factor test assessing privacy expectations in public IoT environments and a surveillance taxonomy are proposed to inform proportionality assessments of surveillance initiatives in smart cities. These insights are also applied to four use cases: facial recognition technologies, drones, environmental policing, and smart nudging. Lastly, the investigation examines competing data governance models in the digital domain and the smart city, reviewing the EU upcoming data governance framework. It is argued that, despite the stated policy goals, the balance of interests may often favor corporate strategies in data sharing, to the detriment of common good uses of data in the urban context.

La nuova eGovernance sanitaria europea: verso una piena integrazione della digitalizzazione nei sistemi sanitari degli Stati membri

L’elaborato approfondisce il diritto alla salute nell’ordinamento dell’Unione europea, con la consapevolezza che il settore della sanità, nella complessità di una tensione perdurante tra la sua matrice sociale e l’applicazione di logiche europee di mercato, rappresenta un ambito da sempre sottratto all’intervento diretto e vincolante delle istituzioni. Contemporaneamente, prende spunto dall’osservazione della transizione digitale dei sistemi sanitari nazionali per proporre una rilettura dei tradizionali equilibri istituzionali tra ordinamenti e constatare il grado di influenza dell’Unione oltre la dimensione transfrontaliera. Infatti, per le attuali esigenze di sostenibilità dei sistemi di tutela della salute e per il valore aggiunto riconosciuto alle azioni europee nel corso della gestione della pandemia da Covid-19, l’eHealth ha rappresentato l’occasione per una vigorosa intrusione delle istituzioni europee entro prerogative tipicamente statuali, fino all’emersione di una eGovernance sanitaria europea. Pertanto, la trattazione compie un percorso evolutivo che muove dalla Direttiva 2011/24 sull’assistenza transfrontaliera e l’assistenza sanitaria online, in combinato disposto con il complesso degli atti di soft law connessi, per verificarne l’esiguo impatto sui sistemi sanitari degli Stati membri e, alla luce dei recenti investimenti strategici ed interventi normativi rilevanti in tema di tecnologie applicate alla sanità, riconoscerne il sostanziale superamento. In particolare, il confronto tra l’insufficiente livello di digitalizzazione raggiunto finora nei sistemi sanitari degli Stati membri ed il tenore della Proposta di regolamento sullo European Health Data Space suggerisce l’evoluzione dell’impianto di governo dei dati sanitari stabilito nella Direttiva, a partire dalla previsione di una disciplina comune sulla cartella sanitaria. A questo proposito, l’interoperabilità tra tecnologie diviene un presupposto operativo indefettibile, che corrobora la natura ‘tecnologicamente condizionata’ del diritto alla salute e propone l’idea che la sanità digitale rappresenti un passo in avanti verso un’assistenza europea uniforme.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.

Trasformazione digitale nei processi di misura e fatturazione - il caso azienda Alfa

Con il lancio di nuove applicazioni tecnologiche come l'Internet of Things, Big Data, Cloud computing e tecnologie mobili che stanno accelerando in maniera spropositata la velocità di cambiamento, i comportamenti, le abitudini e i modi di vivere sono completamente mutati nel favorire un mondo di tecnologie digitali che agevolino le operazioni quotidiane. Questi progressi stanno velocemente cambiando il modo in cui le aziende fanno business, con grandi ripercussioni in tutto quello che è il contesto aziendale, ma non solo. L’avvento della Digital Transformation ha incrementato questi fenomeni e la si potrebbe definire come causa scatenante di tutti i mutamenti che stiamo vivendo. La velocità e l’intensità del cambiamento ha effetti disruptive rispetto al passato, colpendo numerosi settori economici ed abitudini dei consumatori. L’obiettivo di questo elaborato è di analizzare la trasformazione digitale applicata al caso dell’azienda Alfa, comprendendone le potenzialità. In particolare, si vogliono studiare i principali risvolti portati da tale innovazione, le più importanti iniziative adottate in merito alle nuove tecnologie implementate e i benefici che queste portano in campo strategico, di business e cultura aziendale.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring and management

Two major factors are likely to impact the utilisation of remotely sensed data in the near future: (1)an increase in the number and availability of commercial and non-commercial image data sets with a range of spatial, spectral and temporal dimensions, and (2) increased access to image display and analysis software through GIS. A framework was developed to provide an objective approach to selecting remotely sensed data sets for specific environmental monitoring problems. Preliminary applications of the framework have provided successful approaches for monitoring disturbed and restored wetlands in southern California.

Expert opinion on best practice guidelines and competency framework for visual screening in children

PURPOSE: Screening programs to detect visual abnormalities in children vary among countries. The aim of this study is to describe experts' perception of best practice guidelines and competency framework for visual screening in children. METHODS: A qualitative focus group technique was applied during the Portuguese national orthoptic congress to obtain the perception of an expert panel of 5 orthoptists and 2 ophthalmologists with experience in visual screening for children (mean age 53.43 years, SD ± 9.40). The panel received in advance a script with the description of three tuning competencies dimensions (instrumental, systemic, and interpersonal) for visual screening. The session was recorded in video and audio. Qualitative data were analyzed using a categorical technique. RESULTS: According to experts' views, six tests (35.29%) have to be included in a visual screening: distance visual acuity test, cover test, bi-prism or 4/6(Δ) prism, fusion, ocular movements, and refraction. Screening should be performed according to the child age before and after 3 years of age (17.65%). The expert panel highlighted the influence of the professional experience in the application of a screening protocol (23.53%). They also showed concern about the false negatives control (23.53%). Instrumental competencies were the most cited (54.09%), followed by interpersonal (29.51%) and systemic (16.4%). CONCLUSIONS: Orthoptists should have professional experience before starting to apply a screening protocol. False negative results are a concern that has to be more thoroughly investigated. The proposed framework focuses on core competencies highlighted by the expert panel. Competencies programs could be important do develop better screening programs.

Linking Global Youth Tobacco Survey (GYTS) data to the WHO Framework Convention on Tobacco Control (FCTC): the case for the Seychelles

Tobacco control has been recognized as a main public health concern in Seychelles for the past two decades. Tobacco advertising, sponsoring and promotion has been banned for years, tobacco products are submitted to high taxes, high-profile awareness programs are organized regularly, and several other control measures have been implemented. The Republic of Seychelles was the first country to ratify the WHO Framework Convention on Tobacco Control (FCTC) in the African region. Three population-based surveys have been conducted in adults in Seychelles and results showed a substantial decrease in the prevalence of smoking among adults between 1989 and 2004. A first survey in adolescents was conducted in Seychelles in 2002 (the Global Youth Tobacco Survey, GYTS) in a representative sample of 1321 girls and boys aged 13-15 years. The results show that approximately half of students had tried smoking and a quarter of both boys and girls had smoked at least one cigarette during the past 30 days. Although "current smoking" is defined differently in adolescents (>or=1 cigarette during the past 30 days) and in adults (>or=1 cigarette per day), which precludes direct comparison, the high smoking prevalence in youth in Seychelles likely predicts an increasing prevalence of tobacco use in the next adult generation, particularly in women. GYTS 2002 also provides important data on a wide range of specific individual and societal factors influencing tobacco use. Hence, GYTS can be a powerful tool for monitoring the situation of tobacco use in adolescents, for highlighting the need for new policy and programs, and for evaluating the impact of current and future programs.

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.