Higher education policy in the EU, an institutional account

This research examines the development of the EU higher education policy under the theoretical lenses of historical institutionalism. Starting from the assumption that institutions matter, this thesis follows the evolution of higher education policy in the EU premises from its emergence in the early 1970s to date. Unfolding in four phases, this case study focuses on the institutional parameters of the policy and the polity context in order to explain the critical factors that shaped the policy outcomes and the scope of higher education. In a story development full of unanticipated consequences and normative building, this thesis critically examines the relation between the levels of governance to assess their impact on the policy outcome. The main finding is that higher education has been developed as a `market-supporting' policy. The human capital role of higher education has been the main attribute identified in the EU level. As such, higher education gradually evolved from being a policy field aimed at battling unemployment to becoming one of the driving forces behind the knowledge driven society. At the same time higher education moved from the doldrums of EU competence and activity to the centre of policy action to become a policy example of applying the new modes of EU governance. In between the formal EU settings and the Bologna process, institutions and actors have withheld the idea that academic and professional mobility, recognition, comparability are the main areas for the future European workforce

Higher education policy in the EU, an institutional account

This research examines the development of the EU higher education policy under the theoretical lenses of historical institutionalism. Starting from the assumption that institutions matter, this thesis follows the evolution of higher education policy in the EU premises from its emergence in the early 1970s to date. Unfolding in four phases, this case study focuses on the institutional parameters of the policy and the polity context in order to explain the critical factors that shaped the policy outcomes and the scope of higher education. In a story development full of unanticipated consequences and normative building, this thesis critically examines the relation between the levels of governance to assess their impact on the policy outcome. The main finding is that higher education has been developed as a `market-supporting' policy. The human capital role of higher education has been the main attribute identified in the EU level. As such, higher education gradually evolved from being a policy field aimed at battling unemployment to becoming one of the driving forces behind the knowledge driven society. At the same time higher education moved from the doldrums of EU competence and activity to the centre of policy action to become a policy example of applying the new modes of EU governance. In between the formal EU settings and the Bologna process, institutions and actors have withheld the idea that academic and professional mobility, recognition, comparability are the main areas for the future European workforce

Predicting cardiac functional and structural abnormalities from electrogram morphology using supervised machine learning

The extracellular contact electrogram, which is the signature of the interaction of electrical activation and architecture of the local myocardium, is recorded clinically in contact with myocardium. The morphology of the signal could show relationships between the local electrogram and conduction abnormalities that influence the electrophysiology. In this thesis, I sought to address the hypothesis that the local electro-architecture, which is responsible for identifiable features of local action potentials, can be predicted from specific characteristics of electrogram recordings using supervised machine learning algorithms. In addressing this hypothesis, I utilised in vitro multicellular preparations for obtaining unipolar electrogram data. The recordings were collected under a variety of experimental conditions, in order to investigate the effects of functional abnormalities, such as ion channel blockade and gap junction uncoupling, as well as structural determinants, such as increasing amounts of fibroblasts co-cultured with cardiac myocytes. A signal processing and feature extraction process was developed and applied on electrograms. The relationships between the abnormalities, which were introduced to experimental models, and specific electrogram characteristics were then investigated. Electrograms were then used inversely for the development of prediction models. To demonstrate the translational potential of these tools, they were tested on tissue slices derived from human end-stage heart failure hearts. It was found that EGM morphology was significantly modified due to the different heart failure phenotypes. These differences in morphology allowed accurate predictions. Paced data were also obtained from patients with a history of persistent AF. The functional and structural determinants of unipolar electrogram morphology, which are also responsible for a variety of cardiac arrhythmias, can be predicted accurately using supervised machine learning. By better understanding the role of electro-architecture on electrogram morphology and utilising machine learning, we are provided with new insights that could contribute to a progress in diagnostics and treatment of cardiac diseases.Open Acces

Higher education policy in the EU : an institutional account

This research examines the development of the EU higher education policy under the theoretical lenses of historical institutionalism. Starting from the assumption that institutions matter, this thesis follows the evolution of higher education policy in the EU premises from its emergence in the early 1970s to date. Unfolding in four phases, this case study focuses on the institutional parameters of the policy and the polity context in order to explain the critical factors that shaped the policy outcomes and the scope of higher education. In a story development full of unanticipated consequences and normative building, this thesis critically examines the relation between the levels of governance to assess their impact on the policy outcome. The main finding is that higher education has been developed as a `market-supporting' policy. The human capital role of higher education has been the main attribute identified in the EU level. As such, higher education gradually evolved from being a policy field aimed at battling unemployment to becoming one of the driving forces behind the knowledge driven society. At the same time higher education moved from the doldrums of EU competence and activity to the centre of policy action to become a policy example of applying the new modes of EU governance. In between the formal EU settings and the Bologna process, institutions and actors have withheld the idea that academic and professional mobility, recognition, comparability are the main areas for the future European workforce.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analytical methods for optimal placement of distributed generation sources in power distribution systems

168 σ.Η απελευθέρωση της αγοράς ηλεκτρικής ενέργειας και η συμφόρηση των γραμμών μεταφοράς έχουν προκαλέσει αυξημένο ενδιαφέρον για τις τεχνολογίες διεσπαρμένης παραγωγής. Η κατάλληλη τοποθέτηση των μονάδων διεσπαρμένης παραγωγής στα δίκτυα διανομής είναι απαραίτητη προϋπόθεση προκειμένου να μεγιστοποιηθούν τα οφέλη που προκύπτουν από την ένταξή τους σε αυτά. Σκοπός της διπλωματικής εργασίας είναι η βέλτιστη τοποθέτηση μονάδων διεσπαρμένης παραγωγής μοναδιαίου συντελεστή ισχύος σε δίκτυα διανομής, με κριτήριο την ελαχιστοποίηση των απωλειών ενεργού ισχύος. Για την αντιμετώπιση του ζητήματος παρουσιάζονται ξεχωριστές αναλυτικές μέθοδοι για τα ακτινικά και τα βροχοειδή δίκτυα διανομής, αντίστοιχα. Η ανάλυση στα ακτινικά δίκτυα διανομής γίνεται με τη χρήση του μοντέλου του διανεμημένου φορτίου, ενώ στα βροχοειδή δίκτυα διανομής βασίζεται στον πίνακα αγωγιμότητων και στα στοιχεία των ζυγών. Επιπλέον, διερευνάται η επίδραση της βέλτιστης τοποθέτησης των μονάδων διεσπαρμένης παραγωγής στο προφίλ των τάσεων των δικτύων. Στην παρούσα εργασία, αναπτύχθηκε λογισμικό με γραφικό περιβάλλον (GUI) σε περιβάλλον MATLAB, το οποίο επιλύει τα ανωτέρω προβλήματα βέλτιστης τοποθέτησης μονάδων διεσπαρμένης παραγωγής σε ακτινικά και βροχοειδή δίκτυα διανομής. Το λογισμικό αυτό χρησιμοποιήθηκε για την επίλυση του προβλήματος της βέλτιστης τοποθέτησης μονάδων διεσπαρμένης παραγωγής : (α) σε ακτινικό δίκτυο διανομής 11 ζυγών κάτω από τέσσερις διαφορετικές συνθήκες φόρτισης, και (β) στα πρότυπα βροχοειδή δίκτυα 5, 6, 14, 30 και 57 ζυγών της ΙΕΕΕ και σε τροποποιημένες εκδοχές τους. Για την επιβεβαίωση των αποτελεσμάτων των αναλυτικών μεθόδων χρησιμοποιήθηκε η κλασσική μέθοδος προσομοίωσης με ροές φορτίου.Power system deregulation and the shortage of transmission capacities have led to increased interest in distributed generation technologies. Proper location of distributed generation sources in power distribution systems is important for obtaining their maximum potential benefits. The scope of this thesis is the optimal placement of distributed generation sources with unity power factor in power distribution systems, in order to achieve minimum active power losses. Analytical approaches are presented for both radial and meshed networks. Distributed load model is used for radial feeders analysis, while the proposed method for meshed systems is based on bus admittance matrix and load and generation data of the buses. Additionally, the impact of distributed generation allocation on bus voltage profile is examined. In the present diploma thesis, software with graphical user interface (GUI) was developed, under MATLAB environment. The software solves the above distributed generation placement problems in both radial and meshed networks. This software was used for the solution of the optimal distributed generation problem : (a) in an 11-bus radial feeder under four different types of loading, and (b) in IEEE 5, 6, 14, 30 and 57-bus meshed test systems and their modified versions. A classical power flow algorithm was used in order to verify the validity of the results obtained from the implementation of the proposed approaches.Κωνσταντίνος Θ. Τζώρτζη

Granular cell tumors of the urethra

Granular cell tumors (GCTs) are a rare type of mesenchymal tumors that are histologically derived by Schwann cells and rise within soft tissues such as skin and mucosal surfaces. Differentiation between benign and malignant GCTs is often difficult and relies on their biological behavior and metastatic potential. While there are no standard guidelines for management, upfront surgical resection, whenever feasible, is key as a definitive measure. Systemic therapy is often limited by poor chemosensitivity of these tumors; however, accumulating knowledge of their underlying genomic landscape has opened some opportunities for targeted approaches, for example, the vascular endothelial growth factor tyrosine kinase inhibitor pazopanib, which is already in clinical use for the treatment of many types of advanced soft tissue sarcomas

Automatic inspection of cultural monuments using deep and tensor-based learning on hyperspectral imagery

In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-$R$ tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimental results on real-world data from UNESCO protected areas indicate the superiority of the proposed scheme compared to conventional deep learning models.Comment: Accepted for presentation in IEEE International Conference on Image Processing (ICIP 2022

Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms

In this study, we propose a tensor-based learning model to efficiently detect abnormalities on digital mammograms. Due to the fact that the availability of medical data is limited and often restricted by GDPR (general data protection regulation) compliance, the need for more sophisticated and less data-hungry approaches is urgent. Accordingly, our proposed artificial intelligence framework utilizes the canonical polyadic decomposition to decrease the trainable parameters of the wrapped Rank-R FNN model, leading to efficient learning using small amounts of data. Our model was evaluated on the open source digital mammographic database INBreast and compared with state-of-the-art models in this domain. The experimental results show that the proposed solution performs well in comparison with the other deep learning models, such as AlexNet and SqueezeNet, achieving 90% ± 4% accuracy and an F1 score of 84% ± 5%. Additionally, our framework tends to attain more robust performance with small numbers of data and is computationally lighter for inference purposes, due to the small number of trainable parameters