34 research outputs found

    Bio-Inspired Systems: Computational and Ambient Intelligence. 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, June 10-12, 2009. Proceedings, Part I

    Get PDF
    This book constitutes the refereed proceedings of the 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, held in Salamanca, Spain in June 2009. The 167 revised full papers presented together with 3 invited lectures were carefully reviewed and selected from over 230 submissions. The papers are organized in thematic sections on theoretical foundations and models; learning and adaptation; self-organizing networks, methods and applications; fuzzy systems; evolutionary computation and genetic algoritms; pattern recognition; formal languages in linguistics; agents and multi-agent on intelligent systems; brain-computer interfaces (bci); multiobjetive optimization; robotics; bioinformatics; biomedical applications; ambient assisted living (aal) and ambient intelligence (ai); other applications

    Cost-Effectiveness Analysis Using Agent-Based Modelling: A General Framework with Case Studies

    Get PDF
    In recent years, agent-based modelling (ABM) has been increasingly used to elucidate complex adaptive systems. An ABM is a structural computational system that consists of a collection of abstract objects (agents) embedded in a virtual environment that interact based on a set of prescribed rules. While traditional approaches such as differential equation-based compartmental models span a vast literature, they often impose restrictive assumptions such as homogeneity and determinism that limit their application to real settings. ABM overcomes these limitations through a bottom-up approach in which macro dynamics emerge from micro level phenomena. During the past decade, there has been a surge of interest in the use of ABM in human health and disease dynamics. While this is rapidly growing, its application to other relevant areas such as health economics is still in infancy, and frameworks that could systematically apply ABM are still lacking. In this thesis, we develop a general framework for cost-effectiveness analysis in which ABM is designed to project the system dynamics. We argue that ABM improves the empirical reliability of policy-oriented simulation models and that it presents an ideal tool to address the complexity of disease processes, project the impact of interventions and inform their optimal implementation. We use this framework in an epidemiological context to quantify the economic impact of vaccination strategies for prevention of infectious diseases. We present two case studies for a human-to-human infection transmission (i.e., Haemophilus influenzae) and a vector-borne disease (i.e., Zika). In each case, we detail the construction of ABM and its utilization to conduct Bayesian cost-effectiveness analysis of potential vaccine candidates. In addition to uncovering important characteristics of these diseases in epidemic dynamics, we present their first cost-effectiveness analysis and implications for vaccination strategies in different populations settings

    Book of abstracts

    Get PDF

    Development of a nanobody-based amperometric immunocapturing assay for sensitive and specific detection of Toxocara canis excretory-secretory antigen

    Full text link
    Introduction Human Toxocariasis (HT) is a zoonosis that, despite of its wide distribution around the world, remains poorly diagnosed. The identification of specific IgG immunoglobulins against the Toxocara canis Excretory-Secretory antigen (TES), a mix of glycoproteins that the parasite releases during its migration to the target organs in infected patients, is currently the only laboratory tool to detect the disease. The main drawbacks of this test are the inability to distinguish past and active infections together with lack of specificity. These factors seriously hamper the diagnosis, follow-up and control of the disease. Aim To develop an amperometric immunocapturing diagnostic assay based on single domain immunoglobulins from camelids (nanobodies) for specific and sensitive detection of TES. Methods After immunization of an alpaca (Vicugna pacos) with TES, RNA from peripheral blood lymphocytes was used as template for cDNA amplification with oligo dT primers and library construction. Isolation and screening of TES-specific nanobodies were carried out by biopanning and the resulting nanobodies were expressed in Escherichia coli. Two-epitopes amperometric immunocapturing assay was designed using paramagnetic beads coated with streptavidin and bivalent nanobodies. Detection of the system was carried out with nanobodies chemically coupled to horseradish peroxidase. The reaction was measured by amperometry and the limit of detection (LOD) was compared to conventional sandwich ELISA. Results We obtained three nanobodies that specifically recognize TES with no-cross reactivity to antigens of Ascaris lumbricoides and A. suum. The LOD of the assay using PBST20 0.05% as diluent was 100 pg/ml, 10 times more sensitive than sandwich ELISA. Conclusion Sensitive and specific detection of TES for discrimination of active and past infections is one of the most difficult challenges of T. canis diagnosis. The main advantage of our system is the use of two different nanobodies that specifically recognize two different epitopes in TES with a highly sensitive and straightforward readout. Considering that the amounts of TES available for detection in clinical samples are in the range of picograms or a few nanograms maximum, the LOD found in our experiments suggests that the test is potentially useful for the detection of clinically relevant cases of HT

    International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

    Get PDF
    The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio

    Modelling Biological Systems From Molecular Interactions to Population Dynamics

    Get PDF
    Biological systems are examples of complex systems, which consist of several interacting components. Understanding the behaviour of such systems requires a multidisciplinary approach that encompasses biology, mathematics, computer science, physiscs and chemistry. New research areas are emerging as the result of this multidisciplinarity, such as bioinformatics, systems biology and computational biology. Computer science plays an important role in the newly emerging research areas by offerring techniques, algorithms, languages and software to solve research problems efficiently. On the other hand, the efforts to solve these research problems stimulate the development of new and better computer science techniques, algorithms, languages and software. This thesis describes our approach in modelling biological systems as a way to better understand their complex behaviours. Our approach is based on the Calculi of Looping Sequences, a class of formalisms originally developed to model biological systems involving cells and their membrane-based structures. We choose Stochastic CLS and Spatial CLS, two variants of the calculi that support quantitative analysis of the model, and define an approach that support simulation, statistical model-checking and visualisation as analysis techniques. Moreover, we found out that this class of formalisms can be easily extended to model population dynamics of animals, a kind of biological systems that does not involve membrane-based structures

    Formal Modelling for Population Dynamics

    Get PDF
    The spirit of sustainable development has inspired our research work. Ecologically sus- tainable development needs preventative strategies and measures against environmental degradation. In our work we focus on constructing a formalism that enables modellers to model the population dynamics within an ecosystem and to analyse them. Furthermore, preventative strategies can be put into the model so that their effectiveness for ecosystems can be measured. An ecosystem consists of many interacting components. These components have many behaviours which are not easy to put together in a model. Work on such modelling started a long time ago, and even more has been done recently. These approaches have been taken from ordinary differential equations to stochastic processes. There are also some existing formalisms that have already been used for this modelling. In ecosystems there are several important aspects that need to be incorporated into the model, especially: stochasticity, spatiality and parallelism. One formalism has strengths in a certain aspect but weaknesses in others. Being motivated by this situation our work is to construct a formalism that could accommodate these aspects. Besides this, the formalism is intended to facilitate the modellers, who are generally biologists, to define the behaviours in the model in a more intuitive way. This has led our work to adopt features from existing formalisms: Cellular Automata and P Systems. Then, after adding new features, our work results in a new formalism called Grid Systems. Grid Systems have the spatiality of Cellular Automata but also provide a way to define behaviours differently in each cell (also called membrane) according to the reaction rules of P Systems. Therefore, Grid Systems have a richer spatiality compared to CA and the parallelism and stochaticity of P Systems. Besides these, we incorporate stochastic reaction duration for the reaction rules so that Grid Systems have stochasticity in rule selection and stochasticity in reaction termination. This enables us to define scheduled external events which are important aspects in modelling ecosystems. In addition to these, we extend Grid Systems with a new feature called ‘links’. A link is an object that can carry pointers. The pointer of a link can be used in the rule to transfer objects to another membrane. Because a link is also an object, its existence as well as its pointer are dynamic. By using the links, the membranes of Grid Systems can be structured as a tree to imitate the membrane structure of P Systems, or even more as a graph for a more general computation. The property of the links enables the structure to be dynamic, in a similar way to the dissolving membrane in the P Systems. The features of Grid Systems are defined in terms of syntax and semantics. The syntax describes how the model should be expressed by the modeller. The semantics describes what will happen to the model when the model evolves. From the semantics a software tool can be developed for analysing the model. In our research work we have developed the models in two case studies. In the first case study, we focus on the interacting events and external events that affect the population dynamics of mosquitoes. We observe how the impacts of events are propagated in space and time. In the second case study, we focus on the spatiality movement created by the seasonal migration of wildebeests. We observe that the pathways in the migration can be modelled well using links. The models of both case studies are analysed by using our simulation tool. From both case studies we conclude that our formalism can be used as a modelling framework especially for population dynamics, and in general for analysing the models of ecosystems

    Agent-based modeling for environmental management. Case study: virus dynamics affecting Norwegian fish farming in fjords

    Get PDF
    Background: Norwegian fish-farming industry is an important industry, rapidly growing, and facing significant challenges such as the spread of pathogens1, trade-off between locations, fish production and health. There is a need for research, i.e. the development of theories (models), methods, techniques and tools for analysis, prediction and management, i.e. strategy development, policy design and decision making, to facilitate a sustainable industry. Loss due to the disease outbreaks in the aquaculture systems pose a large risk to a sustainable fish industry system, and pose a risk to the coastal and fjord ecosystem systems as a whole. Norwegian marine aquaculture systems are located in open areas (i.e. fjords) where they overlap and interact with other systems (e.g. transport, wild life, tourist, etc.). For instance, shedding viruses from aquaculture sites affect the wild fish in the whole fjord system. Fish disease spread and pathogen transmission in such complex systems, is process that it is difficult to predict, analyze, and control. There are several time-variant factors such as fish density, environmental conditions and other biological factors that affect the spread process. In this thesis, we developed methods to examine these factors on fish disease spread in fish populations and on pathogen spread in the time-space domain. Then we develop methods to control and manage the aquaculture system by finding optimal system settings in order to have a minimum infection risk and a high production capacity. Aim: The overall objective of the thesis is to develop agent-based models, methods and tools to facilitate the management of aquaculture production in Norwegian fjords by predicting the pathogen dynamics, distribution, and transmission in marine aquaculture systems. Specifically, the objectives are to assess agent-based modeling as an approach to understanding fish disease spread processes, to develop agent-based models that help us predict, analyze and understand disease dynamics in the context of various scenarios, and to develop a framework to optimize the location and the load of the aquaculture systems so as to minimize the infection risk in a growing fish industry. Methods: We use agent-based method to build models to simulate disease dynamics in fish populations and to simulate pathogen transmission between several aquaculture sites in a Norwegian fjord. Also, we use particle swarm optimization algorithm to identify agent-based models’ parameters so as to optimize the dynamics of the system model. In this context, we present a framework for using a particle swarm optimization algorithm to identify the parameter values of the agent-based model of aquaculture system that are expected to yield the optimal fish densities and farm locations that avoid the risk of spreading disease. The use of particle swarm optimization algorithm helps in identifying optimal agent-based models’ input parameters depending on the feedback from the agentbased models’ outputs. Results: As the thesis is built on three main studies, the results of the thesis work can be divided into three components. In the first study, we developed many agent-based models to simulate fish disease spread in stand-alone fish populations. We test the models in different scenarios by varying the agents (i.e. fish and pathogens) parameters, environment parameters (i.e. seawater temperature and currents), and interactions (interaction between agents-agents, and agents-environment) parameters. We use sensitivity analysis method to test different key input parameters such as fish density, fish swimming behavior, seawater temperature, and sea currents to show their effects on the disease spread process. Exploring the sensitivity of fish disease dynamics to these key parameters helps in combatting fish disease spread. In the second study, we build infection risk maps in a space-time domain, by developing agent-based models to identify the pathogen transmission patterns. The agent-based method helps us advance our understanding of pathogen transmission and builds risk maps to help us reduce the spread of infectious fish diseases. By using this method, we may study the spatial and dynamic aspects of the spread of infections and address the stochastic nature of the infection process. In the third study, we developed a framework for the optimization of the aquaculture systems. The framework uses particle swarm optimization algorithm to optimize agent-based models’ parameters so as to optimize the objective function. The framework was tested by developing a model to find optimal fish densities and farm locations in marine aquaculture system in a Norwegian fjord. Results show so that the rapid convergence of the presented particle swarm optimization algorithm to the optimal solution, - the algorithm requires a maximum of 18 iterations to find the best solution which can increase the fish density to three times while keeping the risk of infection at an accepted level. Conclusion: There are many contributions of this research work. First, we assessed the agent-based modeling as a method to simulate and analyze fish disease spread dynamics as a foundation for managing aquaculture systems. Results from this study demonstrate how effective the use of agentbased method is in the simulation of infectious diseases. By using this method, we are able to study spatial aspects of the spread of fish diseases and address the stochastic nature of infections process. Agent-based models are flexible, and they can include many external factors that affect fish disease dynamics such as interactions with wild fish and ship traffic. Agent-based models successfully help us to overcome the problem associated with lack of data in fish disease transmission and contribute to our understanding of different cause-effects relationships in the dynamics of fish diseases. Secondly, we developed methods to build infection risk maps in a space-time domain conditioned upon the identification of the pathogen transmission patterns in such a space-time domain, so as to help prevent and, if needed, combat infectious fish diseases by informing the management of the fish industry in Norway. Finally, we developed a method by which we may optimize the fish densities and farm locations of aquaculture systems so as to ensure a sustainable fish industry with a minimum risk of infection and a high production capacity. This PhD study offers new research-based approaches, models and tools for analysis, predictions and management that can be used to facilitate a sustainable development of the marine aquaculture industry with a maximal economic outcome and a minimal environmental impact

    Women in Artificial intelligence (AI)

    Get PDF
    This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

    Forecasting: theory and practice

    Get PDF
    Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice
    corecore