Distributed Selfish Coaching

Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node's resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the "outlier" characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

Urothelial Carcinoma of the Urinary Bladder in Young Adults: Presentation, Clinical behavior and Outcome

Introduction. There is not much evidence regarding clinical behavior of bladder cancer in younger patients. We evaluated clinical characteristics, tumor recurrence and progression in patients younger than 40 years old with urothelial bladder carcinoma. Methods. We retrospectively reviewed the medical records of 31 patients less than 40 years old who were firstly managed with bladder urothelial carcinoma in our department. Data were analysed with the Chi-square test. Results. Mean age was 31.7 years. Mean followup was 38.52 months (11–72 months). Nineteen (61%) patients were diagnosed with GII and 2 (6%) patients with GIII disease. Five (16%) patients presented with T1 disease. Three (9%) patients with invasive disease underwent cystectomy and adjuvant chemotherapy and one developed metastatic disease. Ten (32%) patients recurred during followup with a disease free recurrence rate of 65% the first 2 years after surgery. From those, 1 patient progressed to higher stage and three to higher grade disease. No patient died during followup. Conclusions. Bladder urothelial carcinoma in patients younger than 40 years is usually low stage and low grade. Management of these patients should be according to clinical characteristics and no different from older patients with the same disease

A Feedback Control Approach to Mitigating Mistreatment

Abstract. We consider distributed collaborative caching groups where individual members are autonomous and self-aware. Such groups have been emerging in many new overlay and peer-to-peer applications. In a recent work of ours, we considered distributed caching protocols where group members (nodes) cooperate to satisfy requests for information objects either locally or remotely from the group, or otherwise from the origin server. In such setting, we identified the problem of a node being mistreated, i.e., its access cost for fetching information objects becoming worse with cooperation than without. We identified two causes of mistreatment: (1) the use of a common caching scheme which controls whether a node should not rely on other nodes in the group by keeping its own local copy of the object once retrieved from the group; and (2) the state interaction that can take place when the miss-request streams from other nodes in the group are allowed to affect the state of the local replacement algorithm. We also showed that both these issues can be addressed by introducing two simple additional parameters that affect the caching behavior (the reliance and the interaction parameters). In this paper, we argue against a static rule-of-thumb policy of setting these parameters since the performance, in terms of average object access cost, depends on a multitude of system parameters (namely, group size, cache sizes, demand skewness, and distances). We then propose a feedback control approach to mitigating mistreatment in distributed caching groups. In our approach, a node independently emulates its performance as if it were acting selfishly and then adapts its reliance and interaction parameters in the direction of reducing its measured access cost below its emulated selfish cost. To ensure good convergence and stability properties, we use a (Proportional-Integral-Differential) PID-style controller. Our simulation results show that our controller adapts to the minimal access cost and outperforms static-parameter schemes

A decision support system for the development of voyage and maintenance plans for ships

The waterborne sector faces nowadays significant challenges due to several environmental, financial and other concerns. Such challenges may be addressed, among others, by optimising voyage plans, and diagnosing as early as possible engine failures that may lead to performance degradation. These two issues are addressed by the Decision Support System (DSS) presented herein, which focuses on the operation of merchant ships. For the development of voyage plans, a multicriteria decision problem is developed and handled with the PROMETHE method, while a multivariable control chart is used for the fault diagnosis problem. A MATLAB-based software implementation of the DSS has been developed adopting a modular architecture, while, in order to provide a generic software solution, the required input data are retrieved from dedicated web-services, following specific communication and data exchange protocols

A Deep Learning and GIS Approach for the Optimal Positioning of Wave Energy Converters

Renewable Energy Sources provide a viable solution to the problem of ever-increasing climate change. For this reason, several countries focus on electricity production using alternative sources. In this paper, the optimal positioning of the installation of wave energy converters is examined taking into account geospatial and technical limitations. Geospatial constraints depend on Land Use classes and seagrass of the coastal areas, while technical limitations include meteorological conditions and the morphology of the seabed. Suitable installation areas are selected after the exclusion of points that do not meet the aforementioned restrictions. We implemented a Deep Neural Network that operates based on heterogeneous data fusion, in this case satellite images and time series of meteorological data. This fact implies the definition of a two-branches architecture. The branch that is trained with image data provides for the localization of dynamic geospatial classes in the potential installation area, whereas the second one is responsible for the classification of the region according to the potential wave energy using wave height and period time series. In making the final decision on the suitability of the potential area, a large number of static land use data play an important role. These data are combined with neural network predictions for the optimizing positioning of the Wave Energy Converters. For the sake of completeness and flexibility, a Multi-Task Neural Network is developed. This model, in addition to predicting the suitability of an area depending on seagrass patterns and wave energy, also predicts land use classes through Multi-Label classification process. The proposed methodology is applied in the marine area of the city of Sines, Portugal. The first neural network achieves 98.7% Binary Classification accuracy, while the Multi-Task Neural Network 97.5% in the same metric and 93.5% in the F1 score of the Multi-Label classification output

Mathematical modeling of thermal comfort and indoor air quality and advanced techniques for optimizing building design: applications on naturally ventilated buildings

The present dissertation investigates the airflow pattern in naturally ventilated buildings using advanced computer simulation techniques such as field or Computational Fluid Dynamics (CFD) models. In particular, two-equation turbulence models, such as the Standard k-ε, the RNG k-ε, the “Realizable” k-ε, as well as the Reynolds Stress Model (RSM) are used to predict the flow field in and around the buildings considered. In the case of the Standard k-ε model the potential of improving the prediction accuracy is investigated by means of the following ways: (a) Application of a Two-layer Standard k-ε model, and (b) Modification according to the inlet flow variables profiles of the Atmospheric Boundary Layer (ABL). The aforementioned turbulence models and the proposed modifications are passive ventilation of a pilot building and of a real-scale building subject to actual passive ventilation (for which an experimental procedure is conducted). The CFD model is then used for the prediction of thermal comfort in naturally ventilated buildings by implementing the most appropriate thermal comfort indices (TCI), such as the Predicted Mean Vote (PMV), which, according to literature information, is modified for pure natural ventilation as follows: (a) Extension to account for the metabolic-rate reduction expected in warm environments and for the expectancy factor due to occupant’s habitat, and (b) Correction of the PMV for humidity effects, based on the Standard Effective Temperature (SET*). The present thermal comfort model is completed with the implementation of the Percentage Dissatisfied (PD) index to account for discomfort due to air draughts as well. Additionally, Indoor Air Quality (IAQ) indices are also implemented in the CFD model, i.e. the ventilation effectiveness related to the removal of common pollutants. Following the aforementioned coupled model (CFD-TC/IAQ) a database of input-output pairs is formed, which is used to train and validate radial basis functions (RBF) artificial neural networks (ANN) according to the fuzzy means method. The ANN distributions are then utilized to formulate a multiobjective optimization problem, which accounts for the available thermal comfort and indoor air quality indices, occupant activity level and special constraints provided by design guidelines. The problem is confronted using a gradient-based algorithm associated with a special handling of constraints and of the initialization space of the design variables (Initialization Grid Concept, IGC). Additionally, a technique to account for the non-dominated solutions of the objective functions according to the Pareto criteria is also introduced. Finally, for each case studied the optimal designs are presented by means of nomographs, which may serve as look-up tables for either decision-making strategies or automated systems to create “Intelligent Buildings”.Στην παρούσα διατριβή γίνεται διερεύνηση του πεδίου ροής σε φυσικά αεριζόμενα κτίρια με εξελιγμένες υπολογιστικές μεθόδους προσομοίωσης, όπως είναι τα μοντέλα πεδίου ή μοντέλα υπολογιστικής ρευστοδυναμικής (Computational Fluid Dynamics, CFD). Συγκεκριμένα, εφαρμόζονται μοντέλα τύρβης δύο εξισώσεων, όπως είναι το συμβατικό (Standard) k-ε, το επανακανονικοποιημένο (RNG) k-ε, το μοντέλο εφικτών λύσεων (Realizable) k-ε καθώς και το μοντέλο εξισώσεων τάσεων Reynolds (RSM). Στην περίπτωση του συμβατικού k-ε διερευνάται η δυνατότητα βελτίωσης της πρόβλεψης με δύο τρόπους: (α) Εφαρμογή ενός μοντέλου δύο στρωμάτων, και (β) Τροποποίηση με βάση τις κατανομές των μεταβλητών εισόδου (Μοντέλο k-ε βασισμένο στο Ατμοσφαιρικό Οριακό Στρώμα, ABL-based Standard k-ε model). Τα παραπάνω μοντέλα και οι προτεινόμενες τροποποιήσεις εφαρμόζονται σε πιλοτικό κτίριο (πείραμα αεροσήραγγας που βρίσκεται στη βιβλιογραφία) και σε κτίριο πραγματικής κλίμακας (Διεξαγωγή πειραματικής διαδικασίας). Επιχειρείται η σύζευξη του CFD μοντέλου με αντιπροσωπευτικά μοντέλα υπολογισμού δεικτών θερμικής άνεσης, όπως είναι ο μέσος αναμενόμενος θερμικός δείκτης (Predicted Mean Vote, PMV), ο οποίος τροποποιείται κατάλληλα για αμιγώς φυσικά αεριζόμενους χώρους με δύο τρόπους: (α) Επέκταση του PMV για αμιγώς παθητικά αεριζόμενα κτίρια, λαμβάνοντας υπόψη την ταπείνωση του ρυθμού μεταβολισμού του ενοίκου σε θερμές συνθήκες και τον συντελεστή προσδοκίας βάσει καταγωγής, και (β) Διόρθωση του PMV για την επίδραση της υγρασίας, με βάση την πρότυπη ενεργό θερμοκρασία (SET*). Το μοντέλο θερμικής άνεσης ολοκληρώνεται με την ενσωμάτωση του δείκτη δυσφορίας λόγω ελκυσμού (Percentage Dissatisfied, PD). Συμπληρωματικά, ενσωματώνονται και δείκτες ποιότητας εσωτερικού αέρα (Indoor Air Quality, IAQ) με βάση την εκτόπιση συνήθων ρύπων από την κατειλημμένη ζώνη. Με χρήση του παραπάνω συζευγμένου μοντέλου CFD-TCI/IAQ διαμορφώνεται μια βάση δεδομένων μεταβλητών εισόδου-εξόδου, η οποία χρησιμοποιείται για την εκπαίδευση και επαλήθευση ενός τεχνητού νευρωνικού δικτύου (Artificial Neural Network, ANN) ακτινικών συναρτήσεων βάσης (Radial Basis Function, RBF) με την μέθοδο των ασαφών μέσων (Fuzzy Means Method). Οι ΑΝΝ κατανομές χρησιμοποιούνται για την διαμόρφωση ενός προβλήματος πολυκριτηριακής βελτιστοποίησης με βάση τους διατιθέμενους δείκτες TC-IAQ, το αναμενόμενο επίπεδο δραστηριότητας και ειδικούς περιορισμούς που βρίσκονται στη βιβλιογραφία. Το πρόβλημα επιλύεται με μεθόδους καθόδου κλίσης που συνοδεύεται με κατάλληλο χειρισμό των περιορισμών και του πεδίου αρχικοποίησης των μεταβλητών σχεδιασμού. Συμπληρωματικά, εισάγεται μία τεχνική στον αλγόριθμο έτσι ώστε να προκύπτει το πεδίο των μη κυριαρχούμενων λύσεων σύμφωνα με τα κριτήρια Pareto. Τελικά, για κάθε περίπτωση προκύπτουν οι βέλτιστοι σχεδιασμοί υπό την μορφή νομογραφημάτων που μπορούν να χρησιμοποιηθούν ως πίνακες αναφοράς (look-up tables) στην λήψη αποφάσεων ή στην εφαρμογή συστημάτων αυτομάτου ελέγχου για την εξέλιξη των «Έξυπνων κτιρίων»