Multi-Period Attack-Aware Optical Network Planning under Demand Uncertainty

In this chapter, novel attack‐aware routing and wavelength assignment (Aa‐RWA) algorithms for multiperiod network planning are proposed. The considered physical layer attacks addressed in this chapter are high‐power jamming attacks. These attacks are modeled as interactions among lightpaths as a result of intra‐channel and/or inter‐channel crosstalk. The proposed Aa‐RWA algorithm first solves the problem for given traffic demands, and subsequently, the algorithm is enhanced in order to deal with demands under uncertainties. The demand uncertainty is considered in order to provide a solution for several periods, where the knowledge of demands for future periods can only be estimated. The objective of the Aa‐RWA algorithm is to minimize the impact of possible physical layer attacks and at the same time minimize the investment cost (in terms of switching equipment deployed) during the network planning phase

A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction

The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnormal behavior of the UAV. This work addresses this problem by proposing an innovative multi-task learning framework (MLF-ST) for UAV state identification and trajectory prediction, that aims to optimize the performance of both tasks simultaneously. A deep neural network with shared layers to extract features from the input data is employed, utilizing drone sensor measurements and historical trajectory information. Moreover, a novel loss function is proposed that combines the two objectives, encouraging the network to jointly learn the features that are most useful for both tasks. The proposed MLF-ST framework is evaluated on a large dataset of UAV flights, illustrating that it is able to outperform various state-of-the-art baseline techniques in terms of both state identification and trajectory prediction. The evaluation of the proposed framework, using real-world data, demonstrates that it can enable applications such as UAV-based surveillance and monitoring, while also improving the safety and efficiency of UAV operations

Edge Learning of Vehicular Trajectories at Regulated Intersections

Trajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology

Progressive fluid removal can avoid electrolyte disorders in severely burned patients

Introduction: Extensive burn injury has systemic consequences due to capillary leak. After restoration of cellular integrity, infused fluid volume has to be removed partially. This can provoke electrolyte disorders

SafeDrones: Real-Time Reliability Evaluation of UAVs using Executable Digital Dependable Identities

The use of Unmanned Arial Vehicles (UAVs) offers many advantages across a variety of applications. However, safety assurance is a key barrier to widespread usage, especially given the unpredictable operational and environmental factors experienced by UAVs, which are hard to capture solely at design-time. This paper proposes a new reliability modeling approach called SafeDrones to help address this issue by enabling runtime reliability and risk assessment of UAVs. It is a prototype instantiation of the Executable Digital Dependable Identity (EDDI) concept, which aims to create a model-based solution for real-time, data-driven dependability assurance for multi-robot systems. By providing real-time reliability estimates, SafeDrones allows UAVs to update their missions accordingly in an adaptive manner

Co-expression of fibrotic genes in inflammatory bowel disease; A localized event?

IntroductionExtracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis.Methods and resultsIn this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn’s disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively.DiscussionThese findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies

Key Learning Outcomes for Clinical Pharmacology and Therapeutics Education in Europe: A Modified Delphi Study.

Harmonizing clinical pharmacology and therapeutics (CPT) education in Europe is necessary to ensure that the prescribing competency of future doctors is of a uniform high standard. As there are currently no uniform requirements, our aim was to achieve consensus on key learning outcomes for undergraduate CPT education in Europe. We used a modified Delphi method consisting of three questionnaire rounds and a panel meeting. A total of 129 experts from 27 European countries were asked to rate 307 learning outcomes. In all, 92 experts (71%) completed all three questionnaire rounds, and 33 experts (26%) attended the meeting. 232 learning outcomes from the original list, 15 newly suggested and 5 rephrased outcomes were included. These 252 learning outcomes should be included in undergraduate CPT curricula to ensure that European graduates are able to prescribe safely and effectively. We provide a blueprint of a European core curriculum describing when and how the learning outcomes might be acquired

Δοκίμια για τους πολιτικούς ανταγωνισμούς

In a multi-party competition under proportional representation, the government is a coalition of parties that agreed on a policy and distribution of portfolio benefits in a legislative bargaining game with concession costs. The equilibrium bargaining outcome is based on the ideological proximity of the parties and their relative weights in the legislature. The model can generate two party political equilibria when entry costs are prohibitively high while conditions are given for multi-party equilibria. In a three party competition with purely office motivated parties and strategic voters, two large parties adopt the ideal policy of the median voter and a smaller a distinct to the median’s ideal policy. The government is formed by one of the large and the smaller party.In the second chapter it is found that a party’s voting power in the government is statistically significant in explaining the utility it obtains in a coalition government. By using a new data set on formateur parties, the formateur is found to be rather disadvantaged to its critical partners regarding the distribution of benefits.In the third chapter I analyse a model of tripartite wage negotiations where the government participates in by assigning the relative bargaining power to the negotiating partners: the labour union and the employers’ association. The government’s decision to support one of the negotiating partners depends on their lobbying behaviour. Due to conflict of interest of the social partners, the government has to balance the monetary transfers that it receives from the lobby to the loss that it incurs from departing from its ideal policy and its reelection motives. In a two party electoral competition framework, the lobby improves its utility the closer the two candidates’ bliss points are to that of the median voter.Σε ανταγωνισμό μεταξύ πολλών κομμάτων με απλή αναλογική, η κυβέρνηση είναι ένας σχηματισμός κομμάτων που συμφωνεί στην πολιτική και την κατανομή των ωφελειών χαρτοφυλακίου σε παίγνιο κοινοβουλευτικής διαπραγμάτευσης με κόστη παραχωρήσεων. Το διαπραγματευτικό αποτέλεσμα ισορροπίας βασίζεται στην ιδεολογική εγγύτητα των κομμάτων και των σχετικών ποσοστών στο κοινοβούλιο. Το μοντέλο υποστηρίζει ισορροπία δύο πολιτικών κομμάτων όταν το κόστη εισόδου στις εκλογές είναι απαγορευτικά υψηλά, συνθήκες δίνονται για ισορροπία περισσότερων κομμάτων. Σε ανταγωνισμό τριών κομμάτων με κόμματα που έχουν ως κίνητρο τα οφέλη χαρτοφυλακίου και στρατηγικούς ψηφοφόρους, δύο μεγάλα κόμματα υιοθετούν την θέση του διάμεσου ψηφοφόρου και ένα μικρότερη διαφορετική θέση πολιτικής. Η κυβέρνηση σχηματίζεται από ένα μεγάλο και το μικρότερο κόμμα.Στο δεύτερο κεφάλαιο αποδεικνύεται ότι η εκλογική δύναμη ενός κόμματος στην κυβέρνηση είναι στατιστικά σημαντική στην επεξήγηση της χρησιμότητας που αποκτά το κόμμα σε έναν κυβερνητικό συνασπισμό. Χρησιμοποιώντας ένα νέο σύνολο δεδομένων αναφορικά με τα κόμματα με την πρωτοβουλία σχηματισμού κυβερνητικού συνασπισμού, το κόμμα με αυτήν την πρωτοβουλία εμφανίζεται να είναι μάλλον σε μειονεκτική θέση έναντι κρίσιμων εταίρων αναφορικά με την κατανομή ωφελειών χαρτοφυλακίου.Στο τρίτο κεφάλαιο αναλύω ένα μοντέλο τριμερούς μισθολογικής διαπραγμάτευσης όπου η κυβέρνηση συμμετέχει καθορίζοντας την σχετική διαπραγματευτική δύναμη των εταίρων στην διαπραγμάτευση: το εργατικό συνδικάτο και την ένωση εργοδοτών. Η απόφαση της κυβέρνησης έναν από τους δύο εταίρους στην διαπραγμάτευση εξαρτάται από την λόμπι συμπεριφορά τους. Λόγω της σύγκρουσης συμφερόντων μεταξύ των κοινωνικών εταίρων, η κυβέρνηση πρέπει να ισορροπήσει τις χρηματικές εισφορές που λαμβάνει από το λόμπι με την απώλεια που υφίσταται απομακρυνόμενη από την ιδανική της πολιτική και τα κίνητρα επανεκλογής. Σε εκλογικό ανταγωνισμό μεταξύ δύο κομμάτων το λόμπι βελτιώνει την χρησιμότητά του όσο εγγύτερα βρίσκονται τα σημεία ιδανικής πολιτικής σε αυτό του διάμεσου ψηφοφόρου

Chapter Multi-Period Attack-Aware Optical Network Planning under Demand Uncertainty

In this chapter, novel attack‐aware routing and wavelength assignment (Aa‐RWA) algorithms for multiperiod network planning are proposed. The considered physical layer attacks addressed in this chapter are high‐power jamming attacks. These attacks are modeled as interactions among lightpaths as a result of intra‐channel and/or inter‐channel crosstalk. The proposed Aa‐RWA algorithm first solves the problem for given traffic demands, and subsequently, the algorithm is enhanced in order to deal with demands under uncertainties. The demand uncertainty is considered in order to provide a solution for several periods, where the knowledge of demands for future periods can only be estimated. The objective of the Aa‐RWA algorithm is to minimize the impact of possible physical layer attacks and at the same time minimize the investment cost (in terms of switching equipment deployed) during the network planning phase