Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

Avanzando en la sostenibilidad del nexo agua-energía. Optimización de la recuperación de energía de gradientes salinos mediante electrodiálisis reversa

La energía del gradiente salino (EGS) es una fuente renovable abundante ampliamente desaprovechada para complementar y diversificar el mix energético actual, intensivo en emisiones y uso de agua, y apoyar la sostenibilidad y circularidad del, intensivo en energía, sector del agua. Esta tesis doctoral propone un marco metodológico para avanzar en el diseño óptimo en costes y medioambientalmente sostenible del proceso electrodiálisis reversa (EDR) en la recuperación de EGS de corrientes residuales del sector del agua. Este marco integra un modelo matemático del dispositivo EDR validado experimentalmente, una caracterización medioambiental mediante análisis de ciclo de vida, y un modelo de programación disyuntivo generalizado para la optimización de sistemas EDR a gran escala implementados en plantas desaladoras o estaciones depuradoras de aguas residuales. La herramienta de diseño propuesta puede ser de interés en del proceso de toma de decisiones que apoye la promoción y despliegue comercial de la EDR.Salinity gradient energy (SGE) is a vast yet largely untapped renewable source for complementing and diversifying the current carbon- and water-intensive energy mix and supporting the sustainability and circularity of the energy-intensive water sector. This doctoral thesis proposes a methodological framework for advancing the cost-optimal and environmentally sustainable design of reverse electrodialysis (RED) process for SGE recovery from waste streams in the water sector. This framework combines an experimentally validated mathematical model of the RED device, an environmental characterization through life cycle assessment, and a generalized disjunctive programming model to optimize large-scale RED systems deployed in desalination plants or wastewater treatment plants. The proposed design tool may be of interest in the decision-making process that supports the promotion and commercial deployment of RED technology.This research was financially supported through the R&D Projects CTM2017-87850-R and RTI2018-093310-B-I00 funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/501100011033) and by “ERDF A way of making Europe”, the R&D Project PDC2021-120786-I00 funded by the MCIN/AEI/10.13039/501100011033 and “European Union NextGenerationEU/PRTR, the R&D Project RM16-XX-046- SODERCAN/FEDER funded by SODERCAN, and the R&D Project LIFE19 ENV/ES/000143 funded by the LIFE Programme of the European Union. Carolina Tristán acknowledges the financial support from the research fellowship PRE2018-086454 funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/501100011033) and “ESF Investing in your future”

Artificial intelligence and model checking methods for in silico clinical trials

Model-based approaches to safety and efficacy assessment of pharmacological treatments (In Silico Clinical Trials, ISCT) hold the promise to decrease time and cost for the needed experimentations, reduce the need for animal and human testing, and enable personalised medicine, where treatments tailored for each single patient can be designed before being actually administered. Research in Virtual Physiological Human (VPH) is harvesting such promise by developing quantitative mechanistic models of patient physiology and drugs. Depending on many parameters, such models define physiological differences among different individuals and different reactions to drug administrations. Value assignments to model parameters can be regarded as Virtual Patients (VPs). Thus, as in vivo clinical trials test relevant drugs against suitable candidate patients, ISCT simulate effect of relevant drugs against VPs covering possible behaviours that might occur in vivo. Having a population of VPs representative of the whole spectrum of human patient behaviours is a key enabler of ISCT. However, VPH models of practical relevance are typically too complex to be solved analytically or to be formally analysed. Thus, they are usually solved numerically within simulators. In this setting, Artificial Intelligence and Model Checking methods are typically devised. Indeed, a VP coupled together with a pharmacological treatment represents a closed-loop model where the VP plays the role of a physical subsystem and the treatment strategy plays the role of the control software. Systems with this structure are known as Cyber-Physical Systems (CPSs). Thus, simulation-based methodologies for CPSs can be employed within personalised medicine in order to compute representative VP populations and to conduct ISCT. In this thesis, we advance the state of the art of simulation-based Artificial Intelligence and Model Checking methods for ISCT in the following directions. First, we present a Statistical Model Checking (SMC) methodology based on hypothesis testing that, given a VPH model as input, computes a population of VPs which is representative (i.e., large enough to represent all relevant phenotypes, with a given degree of statistical confidence) and stratified (i.e., organised as a multi-layer hierarchy of homogeneous sub-groups). Stratification allows ISCT to adaptively focus on specific phenotypes, also supporting prioritisation of patient sub-groups in follow-up in vivo clinical trials. Second, resting on a representative VP population, we design an ISCT aiming at optimising a complex treatment for a patient digital twin, that is the virtual counterpart of that patient physiology defined by means of a set of VPs. Our ISCT employs an intelligent search driving a VPH model simulator to seek the lightest but still effective treatment for the input patient digital twin. Third, to enable interoperability among VPH models defined with different modelling and simulation environments and to increase efficiency of our ISCT, we also design an optimised simulator driver to speed-up backtracking-based search algorithms driving simulators. Finally, we evaluate the effectiveness of our presented methodologies on state-of-the-art use cases and validate our results on retrospective clinical data

Agent-based modelling and Swarm Intelligence in systems engineering

El objetivo de la tesis doctoral es evaluar la utilidad de las técnicas Modelado Basado en Agentes, algoritmos de optimización Swarm Intelligence y programación paralela sobre tarjeta gráfica en el campo de la Ingeniería de Sistemas y Automática. Se ha realizado un revisión bibliográfica y desarrollado un marco de desarrollo de la técnica de Modelado Basado en Agentes. Esta técnica se ha empleado para realizar un modelo de un reactor de fangos activados (que se engloba dentro del proceso de depuración de aguas residuales). Se ha desarrollado una notación complementaria para la descripción de modelos basados en agentes desde el punto de vista de la ingeniería de sistemas. Se ha presentado asimismo un algoritmo de optimización basado en agentes bajo la filosofía Swarm Intelligence. Se han trabajado con las técnicas de paralelización sobre tarjeta gráfica para reducir los tiempos de simulación de modelos y algoritmos. Se trata por lo tanto de un tesis de integración de varias tecnologías.Departamento de Ingeniería de Sistemas y Automátic

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence