Optimizing dosage-specific treatments in a multi-Scale model of a tumor growth

The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.This research has received funding from the Horizon 2020 INFORE Project, GA n° 825070 and the Horizon 2020 PerMedCoE Project, GA n° 951773.Peer ReviewedPostprint (published version

Parallel model exploration for tumor treatment simulations

Abstract Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a genetic algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070Peer ReviewedPostprint (author's final draft

Πολυπρακτορική διαχείριση απόκρισης στη ζήτηση για ενεργειακούς συνεταιρισμούς στον πραγματικό κόσμο

Balancing energy demand and production in modern Smart Grids with increased penetration of intermittent renewable energy resources is a challenging problem and sophisticated Demand-Side Management (DSM) mechanisms have been hailed as means to deal with this problem. In this dissertation, we propose mechanisms for the formation of agent cooperatives offering large-scale DSM services, and put forward a complete framework for their operation. Our scheme employs several algorithms to promote the formation of the most effective shifting coalitions. It takes into account the shifting costs of the individuals, and rewards them according to their shifting efficiency. Moreover, to also allow the decentralized coordination of cooperatives of prosumers, we combine a strictly proper scoring rule with a cryptocurrency framework based on blockchain. Furthermore, we propose a vehicle-to-grid/grid-to-vehicle (V2G/G2V) algorithm for environments populated with electric vehicles that promotes new business models that make use of the capability of electric vehicles to store energy. Additionally, to assess participating agents’ uncertainty and predict their future behavior, we adopt machine learning techniques. Finally, we provide a methodology for delivering large scale DSM services in the real world based on an IoT service-oriented architecture. Our simulation results based on real-world data show that using the proposed methods in real-world large-scale settings can significantly benefit the end-users, the Grid, and the environment.Η εξισορρόπηση της ζήτησης και της παραγωγής ενέργειας στα σύγχρονα έξυπνα δίκτυα με αυξημένη διείσδυση των ανανεώσιμων πηγών ενέργειας είναι ένα μεγάλο πρόβλημα, για τη λύση του οποίου απαιτούνται περίπλοκοι μηχανισμοί για την αποτελεσματική διαχείριση της ζήτησης ενέργειας (DSM). Σε αυτή τη διατριβή, προτείνουμε μηχανισμούς για το σχηματισμό συνεταιρισμών ευφυών πρακτόρων που προσφέρουν υπηρεσίες DSM μεγάλης κλίμακας, και παρουσιάζουμε ένα ολοκληρωμένο πλαίσιο λειτουργίας. Τα σχήματα λαμβάνουν υπόψη τα ατομικά κόστη μετατόπισης κατανάλωσης και ανταμείβουν τους συνεισφέροντες ανάλογα με την αποδοτικότητα τους. Επίσης, για να επιτρέψουμε τον αποκεντρωμένο συντονισμό των συνεταιρισμών καταναλωτών-παραγωγών ενέργειας,συνδυάζουμε έναν αυστηρά αρμόζοντα κανόνα βαθμολόγησης με ένα κρυπτονόμισμα βασισμένο σε “αλυσίδα από μπλοκ” (blockchain). Επιπλέον, προτείνουμε αλγόριθμο για φόρτιση και αποφόρτιση ηλεκτρικών οχημάτων που προωθεί επιχειρηματικά μοντέλα που κάνουν χρήση της δυνατότητας των ηλ. οχημάτων να αποθηκεύουν ενέργεια. Συμπληρωματικά, για να αξιολογήσουμε την αβεβαιότητα των συμμετεχόντων, και να προβλέψουμε σωστά τη μελλοντική τους συμπεριφορά, υιοθετούμε τεχνικές μηχανικής μάθησης. Τέλος, παρέχουμε μεθοδολογία για την παροχή υπηρεσιών διαχείρισης ζήτησης (DSM) μεγάλης κλίμακας βασισμένη σε αρχιτεκτονική διαδικτύου των πραγμάτων (IoT). Τα αποτελέσματα από τις προσομοιώσεις που χρησιμοποιούν δεδομένα από τον πραγματικό κόσμο, δείχνουν ότι η χρήση των προτεινόμενων μεθόδων σε συνθήκες μεγάλης κλίμακας μπορεί να ωφελήσει σημαντικά τους τελικούς χρήστες, το δίκτυο, και το περιβάλλον

A Benchmark for Early Time-Series Classification (Extended Abstract)

<p>A framework that allows the comparison of ETSC algorithms, and a new method that is based on the selective truncation of time-series principle are developed, which includes a bundle of datasets originating from real-world time-critical applications.</p&gt

A Multi-Protocol IoT Platform Based on Open-Source Frameworks

Internet of Things (IoT) technologies have evolved rapidly during the last decade, and many architecture types have been proposed for distributed and interconnected systems. However, most systems are implemented following fragmented approaches for specific application domains, introducing difficulties in providing unified solutions. However, the unification of solutions is an important feature from an IoT perspective. In this paper, we present an IoT platform that supports multiple application layer communication protocols (Representational State Transfer (REST)/HyperText Transfer Protocol (HTTP), Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), Constrained Application Protocol (CoAP), and Websockets) and that is composed of open-source frameworks (RabbitMQ, Ponte, OM2M, and RDF4J). We have explored a back-end system that interoperates with the various frameworks and offers a single approach for user-access control on IoT data streams and micro-services. The proposed platform is evaluated using its containerized version, being easily deployable on the vast majority of modern computing infrastructures. Its design promotes service reusability and follows a marketplace architecture, so that the creation of interoperable IoT ecosystems with active contributors is enabled. All the platform’s features are analyzed, and we discuss the results of experiments, with the multiple communication protocols being tested when used interchangeably for transferring data. Developing unified solutions using such a platform is of interest to users and developers as they can test and evaluate local instances or even complex applications composed of their own IoT resources before releasing a production version to the marketplace

Πολυπρακτορική διαχείριση απόκρισης στη ζήτηση για ενεργειακούς συνεταιρισμούς στον πραγματικό κόσμο

Summarization: Balancing energy demand and production in modern Smart Grids with increased penetration of intermittent renewable energy resources is a challenging problem. Demand-Side Management (DSM), i.e., the design and application of sophisticated mechanisms for managing and coordinating energy demand, has been hailed as a means to deal with this problem. In this dissertation, we propose mechanisms for the formation of agent cooperatives offering large-scale DSM services, and put forward a complete framework for their operation. Individuals, being either mere consumers, or even prosumers of electricity, are represented by rational agents and form coalitions to offer demand shifting from peak to non-peak intervals. For cooperatives of consumers, we present an effective consumption shifting scheme, equipped with desirable guarantees, such as individual rationality, truthfulness, and (weak) budget balancedness. Our scheme employs several algorithms to promote the formation of the most effective shifting coalitions. It takes into account the shifting costs of the individuals, and rewards them according to their shifting efficiency. Moreover, to also allow the decentralized coordination of cooperatives of prosumers, we combine, for the first time in the literature, a strictly proper scoring rule with a specialized cryptocurrency framework. Using our approach, prosumers collaborate with the use of a blockchain-oriented framework to manage their demand, in order to make more profits from the selling of their energy. Furthermore, we propose a vehicle-to-grid/grid-to-vehicle (V2G/G2V) algorithm that balances demand and local renewable supply in environments populated with electric vehicles. The approach promotes new business models that make effective use of the capability of electric vehicles to store energy in their batteries. Additionally, to assess participating agents’ uncertainty, and correctly predict their future behavior regarding power consumption shifting actions, promoting in this way accuracy and effectiveness, we adopt various machine learning techniques, adapt them to fit the problem domain, and use these to effectively monitor the trustworthiness of agent statements regarding their final shifting actions. Finally, we provide the methodology for delivering large-scale DSM services in the real world. To this purpose, we devise an IoT service-oriented architecture for DSM applications, through which we test different GUIs and incentive types for managing energy consumption. In this context, we present a “serious game” solution that was tested by real human subjects. Our approach comes complete with the adoption of a statistical analysis methodology to validate reductions in consumption and the promotion of renewable energy usage in real world settings. Our simulation results based on real-world data show that using the proposed methods in real-world large-scale settings can significantly benefit the end-users, the Grid, and the environment.Περίληψη: Η εξισορρόπηση της ζήτησης και της παραγωγής ενέργειας στα σύγχρονα έξυπνα δίκτυα με αυξημένη διείσδυση των ανανεώσιμων πηγών ενέργειας είναι ένα μεγάλο πρόβλημα, για τη λύση του οποίου απαιτούνται περίπλοκοι μηχανισμοί για την αποτελεσματική διαχείριση της ζήτησης ενέργειας (Demand-Side Management - DSM). Σε αυτή τη διατριβή, προτείνουμε μηχανισμούς για το σχηματισμό συνεταιρισμών ευφυών πρακτόρων που προσφέρουν υπηρεσίες DSM μεγάλης κλίμακας, και παρουσιάζουμε ένα ολοκληρωμένο πλαίσιο για τη λειτουργία τους. Για συνεταιρισμούς καταναλωτών, σχεδιάσαμε ένα κατανεμημένο σχήμα μετατόπισης της κατανάλωσης, το οποίο προσφέρει συγκεκριμένες εγγυήσεις, όπως ορθολογισμό κατ' ανεξαρτησία (individually rational), ειλικρίνεια (truthfulness) και ασθενή ισολογισμό του κεφαλαίου (weak budget balancedness). Το σχήμα λαμβάνει υπόψη τα ατομικά κόστη μετατόπισης κατανάλωσης και ανταμείβει τους συνεισφέροντες πράκτορες ανάλογα με την αποδοτικότητα τους. Επιπροσθέτως, για να επιτρέψουμε επίσης τον αποκεντρωμένο συντονισμό των συνεταιρισμών καταναλωτών-παραγωγών ενέργειας, συνδυάζουμε για πρώτη φορά έναν αυστηρά αρμόζοντα κανόνα βαθμολόγησης με ένα εξειδικευμένο κρυπτονόμισμα (cryptocurrency). Χρησιμοποιώντας την προσέγγισή μας, οι καταναλωτές-παραγωγοί συντονίζονται με τη χρήση ενός πλαισίου βασισμένου σε "αλυσίδα από μπλοκ" (blockchain), για να διαχειριστούν τη ζήτησή τους προκειμένου να κερδίσουν περισσότερα από την πώληση της παραγωγής τους. Επιπλέον, προτείνουμε έναν αλγόριθμο για την φόρτιση και αποφόρτιση ηλεκτρικών οχημάτων (υπηρεσία V2G / G2V) που έχει επίσης ως στόχο την εξισορρόπηση της ζήτησης και της τοπικής ανανεώσιμης προσφοράς, αλλά προωθεί επίσης και νέα επιχειρηματικά μοντέλα που κάνουν αποτελεσματική χρήση της δυνατότητας των ηλεκτρικών οχημάτων να αποθηκεύουν ενέργεια στη μπαταρία τους. Συμπληρωματικά, για να αξιολογήσουμε την αβεβαιότητα των συμμετεχόντων, και να προβλέψουμε σωστά τη μελλοντική τους συμπεριφορά όσον αφορά τη διαχείριση ενεργειακής κατανάλωσης, προωθώντας έτσι την ακρίβεια και την αποτελεσματικότητα, υιοθετούμε διάφορες τεχνικές μηχανικής μάθησης, τις προσαρμόζουμε στα συγκεκριμένα προβλήματα, και τις χρησιμοποιούμε για την αποτελεσματική παρακολούθηση της αξιοπιστίας των δηλώσεων του κάθε πράκτορα σχετικά με τις πραγματικές του ενέργειες. Τέλος, παρέχουμε τη μεθοδολογία για την παροχή υπηρεσιών διαχείρισης ζήτησης (DSM) μεγάλης κλίμακας στον πραγματικό κόσμο. Παρουσιάζουμε μια αρχιτεκτονική διαδικτύου των πραγμάτων (IoT) για εφαρμογές DSM, μέσω της οποίας δοκιμάσαμε διαφορετικές γραφικές διεπαφές (GUI) και τύπους κινήτρων για τη διαχείριση της κατανάλωσης ενέργειας. Οι λύσεις αυτές οργανώθηκαν στην μορφή ενός "σοβαρού παιγνίου (serious game)'', το οποίο παρουσιάστηκε σε, και χρησιμοποιήθηκε από πραγματικούς ανθρώπους. Η προσέγγιση μας σε αυτή τη διατριβή συνοδεύεται από μια μεθοδολογία στατιστικής ανάλυσης για την επικύρωση των μεγεθών μείωσης της κατανάλωσης και της προώθησης της χρήσης ανανεώσιμης ενέργειας σε πραγματικές συνθήκες. Τα αποτελέσματα από τις προσομοιώσεις μας που χρησιμοποιούν δεδομένα από τον πραγματικό κόσμο, δείχνουν ότι η χρήση των προτεινόμενων μεθόδων σε συνθήκες μεγάλης κλίμακας πραγματικού κόσμου μπορεί να ωφελήσει σημαντικά τους τελικούς χρήστες, το δίκτυο, και το περιβάλλον

A novel electricity demand management scheme via multiagent cooperatives in the Smart Grid

Μη διαθέσιμη περίληψηNot available summarizatio

Exploring energy saving policy measures by renewable energy supplying cooperatives (REScoops)

Cooperatives for renewable energy supply (REScoops) provide their members renewably generated energy within a cooperative model that enables members to co-decide on the cooperative’s future. REScoops do not only collectively own renewable energy production facilities and supply this to their members, they also use their specific position as energy suppliers to take several actions to persuade their members to save energy. Although the activities that REScoops undertake to some extent resemble those of other organizations, because of their particular organisational and business model as citizens initiatives, the cooperative model, REScoops are supposed to be very well positioned for activities to influence and help their members to save energy. The paper discusses arguments why the REScoop model in energy supply can be an important contributor to reduce energy use by their members. Further this paper discusses measures that have been undertaken by REScoops studied in the REScoop Plus project. We use some illustrative examples to discuss if REScoops are in a relatively good position to take certain measures and succeed in persuading customers to lower their energy consumption level and elaborate on future experiments to explore the proposition that REScoop members save more energy due to actions of these REScoops towards their members

Agent cooperatives for effective power consumption shifting

Summarization: In this paper, we present a directly applicable scheme for electricity consumption shifting and effective de- mand curve flattening. The scheme can employ the services of either individual or cooperating consumer agents alike. Agents participating in the scheme, how- ever, are motivated to form cooperatives, in order to reduce their electricity bills via lower group prices granted for sizable consumption shifting from high to low demand time intervals. The scheme takes into ac- count individual costs, and uses a strictly proper scor- ing rule to reward contributors according to efficiency. Cooperative members, in particular, can attain vari- able reduced electricity price rates, given their different load shifting capabilities. This allows even agents with initially forbidding shifting costs to participate in the scheme, and is achieved by a weakly budget-balanced, truthful reward sharing mechanism. We provide four variants of this approach, and evaluate it experimentally.Παρουσιάστηκε στο: The Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13

Incentives for Rescheduling Residential Electricity Consumption to Promote Renewable Energy Usage

Managing energy consumption and production is a challenging problem and proactive balancing between the amount of electricity produced and consumed is needed. In this work, we examine mechanisms that give incentives to consumers to efficiently reschedule their demand, thus balancing the overall energy production and consumption. Viewing the smart grid as a MAS, each agent represents a consumer; this agent takes into account its user's preferences and proposes an optimal energy consumption plan via a gamified GUI. To implement this we propose a distributed architecture through which we give the incentives (either economic, or social); we test a number of pricing mechanisms and we develop a very fast agent optimization strategy. We also present experiments both from software simulations on real data and pilot tests with human participants: the simulations allow to evaluate the mechanisms and agents, whilst the gamified tests are useful to assess the usability of the GUI and the usefulness of the agent suggestions. With human subjects, we evaluated which type of incentives is more compelling: economic or social. Results validate that by using our agent optimization approach the performance of the smart grid can be improved, and that specific mechanisms allow better utilization of renewable sources