We study scientific collaboration at the level of universities. The scope of this study is to ans... more We study scientific collaboration at the level of universities. The scope of this study is to answer two fundamental questions: (i) can one indicate a category (i.e., a scientific discipline) that has the greatest impact on the rank of the university and (ii) do the best universities collaborate with the best ones only? Restricting ourselves to the 100 best universities from year 2009 we show how the number of publications in certain categories correlates with the university rank. Strikingly, the expected negative trend is not observed in all cases -for some categories even positive values are obtained. After applying Principal Component Analysis we observe clear categorical separation of scientific disciplines, dividing the papers into almost separate clusters connected to natural sciences, medicine and arts and humanities. Moreover, using complex networks analysis, we give hints that the scientific collaboration is still embedded in the physical space and the number of common papers decays with the geographical distance between them. The idea of so-called science of science is not entirely new: 20th century is well known for its critical works of Kuhn 1 , Popper 2 , Lakatos 3 and Feyerabend 4 who tried to build models describing how science should work or, which is far more important, to show how it in fact does work. However it is only in recent times that, owing to the start of the era of overwhelming data, it is now possible to track this problem quantitatively . Several studies are on a journey to answer such intriguing questions like "Who is the best scientist?", "What makes the best university" etc . There are at least three separate factors that can be regarded as key components of today's science and the way it is recognized: papers, citations and rankings. The last one is devoted rather to whole unities like universities or departments although recent studies consider it also in the scope of individuals 14 . It has been argued that rankings still can be perceived as not enough deep measures "providing finalized, seemingly unrelated indicator values" 15 . On the other hand it is well known that scientific impact is a multi-dimensional construct and that using a single measure is not advisable . Nonetheless, rankings are clearly a derivative of the number of published papers. However apart from just raw numbers the quality of science comes often with two additional factors: specialization and collaboration. Interestingly the type of the scientific category can dramatically change both the way the paper is written and received, e.g., in the case of simple lexical factors as title length its impact on the acquired citations change significantly from one category to another 17 . In the same manner it is possible to spot that the number of citations per paper can vary by several orders of magnitude and are highest in multidisciplinary sciences, general internal medicine, and biochemistry and lowest in literature, poetry, and dance 18 . These studies can go even as deep as to fascinating notion of scientific meme propagating along the citation graph . Collaboration has been in the scope of interest for a long time and it is generally considered that it leads to high impact publications 23 . One of recognized factors affecting the level of collaboration is undoubtedly geographic proximity: usually one expects to find a decaying probability of citation as well as common papers with distance , however it can also be connected to such features as ethnicity or level of economic development . In this study we perform an investigation for a selected group of 100 best universities to unravel how the scientific productivity measured in the number of published papers per scientific categories (e.g, physics, art etc) correlates with the rank of the university. Using Principal Component Analysis (PCA) we study whether scientific categories coming from different areas (natural science, humanities etc) tend to stick together. In the second part of the paper we examine the complex network 27 of scientific collaboration among 100 best universities and study the properties of such a network using the concept of weight threshold 28 .
We study the influence of noise on information transmission in the form of packages shipped betwe... more We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Baraba ´si networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. A challenging research issue is the understanding of the effect of a network topology on the transfer of information 1-3 . Many biological, technical and social systems posses hierarchical structures, e.g., metabolic networks 4,5 , protein interaction networks 6-8 , the Internet 9 , theWorld WideWeb 10 , and social networks . Topologies of networks evolve over time in order to maximize the systemic efficiency. An important part of this efficiency is the optimal information exchange, even though the meaning and spreading of information varies considerably among such fields as mathematics 15 , physics 16 , biology , social science, communication and computer science . When information is transmitted through a complex network, the process can be represented as an evolution of a distributed information field that is related to the states of network nodes (and/or links) or as paths of localized packages traveling through the network. In the first case, nodes and links can form systems of coupled threshold devices or oscillators 21 that make it possible to propagate a signal from the sender to the receiver. In the second case, the nodes possess some binary variables that can be transmitted from one node to another. Several authors have considered various features of network typologies that influence information transfers (e.g., if the information flow in a scale-free network is more efficient than in a regular lattice) . Furthermore, it is shown to be crucial wether a network consists of heterogeneous (e.g. routers and peripheral nodes) 1,2 or homogeneous nodes with similar connection degrees. Another important attribute is the edge heterogenity in weighted networks or the network clustering coefficient since a lack of loops in a network can impose a less efficient information transfer . There exist several ways to incorporate packet dynamics, from rule simple random walks (e.g. ,) to more elaborate ways, like biased random walks with some local and/or global navigation rules (e.g. 1,3,28-30 ,). It is also possible to implement epidemic models such as SIR (e.g. 1,18,31 ,). Moreover, we can consider single particles (e.g. 1,3,26,28 ,) or interacting particles systems (e.g. 2,27,30 ,). The types of dynamics we choose strongly depends on the interpretation of the modeled information transfer. This paper considers a prototypical model for the flow of information represented by packages travelling on networks with hierarchical structures. We are particularly interested in the dependency between network structures, noise, and information transmission efficiency. We take into account two types of noise in the system. The first one corresponds to a non-deterministic part of system dynamics while the second one to the level of difference between the current network structure in comparison to original one. Numerical simulations show a resonancelike behavior of information transfer efficiency in the presence of both types of noise. This paper is organized as follows. Section Networks topology and topological noise introduces the topology of hierarchical networks. Section Packet navigation rules describes the imposed packet dynamics. Section Results for artificial networks presents the numerical results for the artificial networks, while Section Real network is devoted SUBJECT AREAS: STATISTICAL PHYSICS, THERMODYNAMICS AND NONLINEAR DYNAMICS COMPUTATIONAL SCIENCE INFORMATION THEORY AND COMPUTATION SCIENTIFIC DATA
Elevated walking speed is an indicator of increased pace of life in cities, caused by environment... more Elevated walking speed is an indicator of increased pace of life in cities, caused by environmental pressures inherent to urban environments, which lead to short-and long-term consequences for health and well-being. In this paper we investigate the effect of walking speed on heat stress. We define the heat-stress-optimal walking speed and estimate its values for a wide range of air temperatures with the use of computational modelling of metabolic heat production and thermal regulation. The heat-stress-optimal walking speed shows three distinct phases in relation to air temperature, determined by different modes of interaction between the environment and physiology. Simulation results suggest that different temperature regimes require walking speed adaptation to preserve heat balance. Empirical data collected for Singapore reveals elevated average walking speed, which is not responsive to slight changes in microclimate (4-5°C). The proposed computational model predicts the amount of additional heat produced by an individual due to the high pace of life. We conclude that there are direct implications of the high pace of life in cities on the immediate heat stress of people, and we show how a lower walking speed significantly reduces self-overheating and improves thermal comfort.
Thermal comfort of people in outdoor urban spaces is a growing concern in cities due to climate c... more Thermal comfort of people in outdoor urban spaces is a growing concern in cities due to climate change and urbanization. In outdoor settings the climate and behavior of people are more dynamic than in indoor situations, therefore a steady state of the thermoregulatory system is rarely reached. Understanding the dynamics of outdoor thermal comfort requires accurate predictive models. In this paper we extend a classical two-node model of human body thermal regulation. We give a detailed description and interpretation of all the components and parameter values and test the dynamics of the model against experimental data. We propose a modification of the skin blood flow model which, while keeping realistic values and responsiveness, improves skin temperature prediction nearly fourfold. We further analyze the sensitivity of the model with respect to climatic and personal parameters. This analysis reveals the relative importance of, for instance, air temperature, wind speed and clothing, in thermoregulatory processes of the human body in various climatic settings. We conclude, that our model realistically reproduces the dynamics of aggregate measures of human body thermal regulation. Validated for cool, warm and hot environments, the model is shown to be accurate in terms of its dynamics and it is conceptually and computationally far more efficient than any existing multi-node and multi-part model.
Communications in computer and information science, 2016
In this paper we present the first step towards the development of a mathematical model of human ... more In this paper we present the first step towards the development of a mathematical model of human immune system for advanced individualized healthcare, where medication plan is fine-tuned for each patient to fit his conditions. We reproduce two representative models of the innate immune system. The first model by Rocha et al. describes the dynamics of the innate immune response by ordinary differential equations, focusing on LPS, neutrophils, resting macrophages, and activated macrophages. The second model by Pigozzo et al. describes the spatial dynamics of LPS, neutrophils, and pro-inflammatory cytokines by partial differential equations. We found that the results of the first model are fully reproducible. However, the second model is only partially reproducible. Several parameters had to be adjusted in order to reproduce the dynamics of the immune response: diffusion coefficients and the rates of LPS phagocytosis, cytokine production, neutrophils chemotaxis and apoptosis.
We aim to develop a mathematical model of the human immune system for advanced individualized hea... more We aim to develop a mathematical model of the human immune system for advanced individualized healthcare where medication plan is fine-tuned to fit a patient's conditions through monitored biochemical processes. One of the challenges is calibrating model parameters to satisfy existing experimental data or prior knowledge about the system behavior. In this paper, we apply genetic algorithm to find model parameters reproducing the results of modeling human innate immune system by Pigozzo et al.
Current studies of outdoor thermal comfort are limited to calculating thermal indices or intervie... more Current studies of outdoor thermal comfort are limited to calculating thermal indices or interviewing people. The first method does not take into account the way people use this space, whereas the second one is limited to one particular study area. Simulating people's thermal perception along with their activities in public urban spaces will help architects and city planners to test their concepts and to design smarter and more liveable cities. In this paper, we propose an agent-based modelling approach to simulate people's adaptive behaviour in space. Two levels of pedestrian behaviour are considered: reactive and proactive, and three types of thermal adaptive behaviour of pedestrians are modelled with single-agent scenarios: speed adaptation, thermal attraction/repulsion and vision-motivated route alternation. An "accumulated heat stress" parameter of the agent is calculated during the simulation, and pedestrian behaviour is analysed in terms of its ability to reduce the accumulated heat stress. This work is the first step towards the "human component" in urban microclimate simulation systems. We use these simulations to drive the design of real-life experiments, which will help calibrating model parameters, such as the heat-speed response, thermal sensitivity and admissible turning angles.
UvA-DARE (Digital Academic Repository) Data-driven modeling of transportation systems and traffic... more UvA-DARE (Digital Academic Repository) Data-driven modeling of transportation systems and traffic data analysis during a major power outage in the Netherlands
The International Conference on Computational Science is an annual conference that brings togethe... more The International Conference on Computational Science is an annual conference that brings together researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering computational methods in sciences such as physics, chemistry, life sciences, and engineering, as well as in arts and humanitarian fields, to discuss problems and solutions in the area, to identify new issues, and to shape future directions for research.
Chapter 18, "Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices" was previously ... more Chapter 18, "Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices" was previously published non-open access. This have now been changed to open access under a CC BY 4.0 license and the copyright holders updated to 'The Author(s)' and the acknowledgement section added. The book has also been updated with this change.
Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psycho... more Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that are capable of surrogative rea- soning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews formal models of addiction developed in two very different but relevant fields of study: (neuro)psychological mod- eling of intra-individual dynamics, and social modeling of inter-individual dynamics. We find that these modeling approaches to addiction are quite disjoint and argue that in order to unravel the complexities of biopsychoso- cial processes of addiction, models should integrate intra- and interpersonal factors.
The analysis of questionnaires often involves representing the high-dimensional responses in a lo... more The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common statistical model assumptions rarely hold. Here we present a non-parametric approach based on Fisher Information which obtains a low-dimensional embedding of a statistical manifold (SM). The SM has deep connections with parametric statistical models and the theory of phase transitions in statistical physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health inequality in Amsterdam. Compare to previous analysis and known anthropological knowledge we conclude that our method best discriminates between different behaviours, pavi...
The existence of slums or informal settlements is common to most cities of developing countries. ... more The existence of slums or informal settlements is common to most cities of developing countries. In India, slums contain a wealth of diversity that is masked by a high level of poverty and rather insufficient access to resources. Recent studies have identified that the conventional perception of slums as distinctive homogeneous settlements is incorrect, rather slums are diverse and complex systems that cannot be addressed through one-size fits all approaches. In this paper we investigate Tilly's theory on group segregation and how it reproduces or reinforces inequality within the slums of Bengaluru. We apply statistical techniques (correspondence analysis and regression) to novel field data from 37 slums in Bengaluru. First, we find high levels of spatial and group segregation by religion across the slums of Bengaluru. Second, we find that segregation leads to opportunity bias among slum dwellers, which inhibits equitable access to jobs in the labour market. Finally, the results show that insufficient access to resources constrain the income generation and leads to emerging coping strategies. The results indicate that group identity is key to addressing disparity and how solving inequality can drastically impact group identity. Our results show that targeting horizontal inequality (as compared to vertical inequality) may increase the rate of successful interventions for each of the segregated groups of slum dwellers.
We describe two approaches to detecting anomalies in time series of multi-parameter clinical data... more We describe two approaches to detecting anomalies in time series of multi-parameter clinical data: (1) metric and model-based indicators and (2) information surprise. (1) Metric and model-based indicators are commonly used as early warning signals to detect transitions between alternate states based on individual time series. Here we explore the applicability of existing indicators to distinguish critical (anomalies) from non-critical conditions in patients undergoing cardiac surgery, based on a small anonymized clinical trial dataset. We find that a combination of time-varying autoregressive model, kurtosis, and skewness indicators correctly distinguished critical from non-critical patients in 5 out of 36 blood parameters at a window size of 0.3 (average of 37 hours) or higher. (2) Information surprise quantifies how the progression of one patient's condition differs from that of rest of the population based on the cross-section of time series. With the maximum surprise and slope features we detect all critical patients at the 0.05 significance level. Moreover we show that a naive outlier detection does not work, demonstrating the need for the more sophisticated approaches explored here. Our preliminary results suggest that future developments in early warning systems for patient condition monitoring may predict the onset of critical transition and allow medical intervention preventing patient death. Further method development is needed to avoid overfitting and spurious results, and verification on large clinical datasets.
We developed a robust approach for real-time levee condition monitoring based on combination of d... more We developed a robust approach for real-time levee condition monitoring based on combination of data-driven methods (one-side classification) and finite element analysis. It was implemented within a flood early warning system and validated on a series of full-scale levee failure experiments organised by the IJkdijk consortium in August-September 2012 in the Netherlands. Our approach has detected anomalies and predicted levee failures several days before the actual collapse. This approach was used in the UrbanFlood decision support system for routine levee quality assessment and for critical situations of a potential levee breach and inundation. In case of emergency, the system generates an alarm, warns dike managers and city authorities, and launches advanced urgent simulations of levee stability and flood dynamics, thus helping to make informed decisions on preventive measures, to evaluate the risks and to alleviate adverse effects of a flood.
We study scientific collaboration at the level of universities. The scope of this study is to ans... more We study scientific collaboration at the level of universities. The scope of this study is to answer two fundamental questions: (i) can one indicate a category (i.e., a scientific discipline) that has the greatest impact on the rank of the university and (ii) do the best universities collaborate with the best ones only? Restricting ourselves to the 100 best universities from year 2009 we show how the number of publications in certain categories correlates with the university rank. Strikingly, the expected negative trend is not observed in all cases -for some categories even positive values are obtained. After applying Principal Component Analysis we observe clear categorical separation of scientific disciplines, dividing the papers into almost separate clusters connected to natural sciences, medicine and arts and humanities. Moreover, using complex networks analysis, we give hints that the scientific collaboration is still embedded in the physical space and the number of common papers decays with the geographical distance between them. The idea of so-called science of science is not entirely new: 20th century is well known for its critical works of Kuhn 1 , Popper 2 , Lakatos 3 and Feyerabend 4 who tried to build models describing how science should work or, which is far more important, to show how it in fact does work. However it is only in recent times that, owing to the start of the era of overwhelming data, it is now possible to track this problem quantitatively . Several studies are on a journey to answer such intriguing questions like "Who is the best scientist?", "What makes the best university" etc . There are at least three separate factors that can be regarded as key components of today's science and the way it is recognized: papers, citations and rankings. The last one is devoted rather to whole unities like universities or departments although recent studies consider it also in the scope of individuals 14 . It has been argued that rankings still can be perceived as not enough deep measures "providing finalized, seemingly unrelated indicator values" 15 . On the other hand it is well known that scientific impact is a multi-dimensional construct and that using a single measure is not advisable . Nonetheless, rankings are clearly a derivative of the number of published papers. However apart from just raw numbers the quality of science comes often with two additional factors: specialization and collaboration. Interestingly the type of the scientific category can dramatically change both the way the paper is written and received, e.g., in the case of simple lexical factors as title length its impact on the acquired citations change significantly from one category to another 17 . In the same manner it is possible to spot that the number of citations per paper can vary by several orders of magnitude and are highest in multidisciplinary sciences, general internal medicine, and biochemistry and lowest in literature, poetry, and dance 18 . These studies can go even as deep as to fascinating notion of scientific meme propagating along the citation graph . Collaboration has been in the scope of interest for a long time and it is generally considered that it leads to high impact publications 23 . One of recognized factors affecting the level of collaboration is undoubtedly geographic proximity: usually one expects to find a decaying probability of citation as well as common papers with distance , however it can also be connected to such features as ethnicity or level of economic development . In this study we perform an investigation for a selected group of 100 best universities to unravel how the scientific productivity measured in the number of published papers per scientific categories (e.g, physics, art etc) correlates with the rank of the university. Using Principal Component Analysis (PCA) we study whether scientific categories coming from different areas (natural science, humanities etc) tend to stick together. In the second part of the paper we examine the complex network 27 of scientific collaboration among 100 best universities and study the properties of such a network using the concept of weight threshold 28 .
We study the influence of noise on information transmission in the form of packages shipped betwe... more We study the influence of noise on information transmission in the form of packages shipped between nodes of hierarchical networks. Numerical simulations are performed for artificial tree networks, scale-free Ravasz-Baraba ´si networks as well for a real network formed by email addresses of former Enron employees. Two types of noise are considered. One is related to packet dynamics and is responsible for a random part of packets paths. The second one originates from random changes in initial network topology. We find that the information transfer can be enhanced by the noise. The system possesses optimal performance when both kinds of noise are tuned to specific values, this corresponds to the Stochastic Resonance phenomenon. There is a non-trivial synergy present for both noisy components. We found also that hierarchical networks built of nodes of various degrees are more efficient in information transfer than trees with a fixed branching factor. A challenging research issue is the understanding of the effect of a network topology on the transfer of information 1-3 . Many biological, technical and social systems posses hierarchical structures, e.g., metabolic networks 4,5 , protein interaction networks 6-8 , the Internet 9 , theWorld WideWeb 10 , and social networks . Topologies of networks evolve over time in order to maximize the systemic efficiency. An important part of this efficiency is the optimal information exchange, even though the meaning and spreading of information varies considerably among such fields as mathematics 15 , physics 16 , biology , social science, communication and computer science . When information is transmitted through a complex network, the process can be represented as an evolution of a distributed information field that is related to the states of network nodes (and/or links) or as paths of localized packages traveling through the network. In the first case, nodes and links can form systems of coupled threshold devices or oscillators 21 that make it possible to propagate a signal from the sender to the receiver. In the second case, the nodes possess some binary variables that can be transmitted from one node to another. Several authors have considered various features of network typologies that influence information transfers (e.g., if the information flow in a scale-free network is more efficient than in a regular lattice) . Furthermore, it is shown to be crucial wether a network consists of heterogeneous (e.g. routers and peripheral nodes) 1,2 or homogeneous nodes with similar connection degrees. Another important attribute is the edge heterogenity in weighted networks or the network clustering coefficient since a lack of loops in a network can impose a less efficient information transfer . There exist several ways to incorporate packet dynamics, from rule simple random walks (e.g. ,) to more elaborate ways, like biased random walks with some local and/or global navigation rules (e.g. 1,3,28-30 ,). It is also possible to implement epidemic models such as SIR (e.g. 1,18,31 ,). Moreover, we can consider single particles (e.g. 1,3,26,28 ,) or interacting particles systems (e.g. 2,27,30 ,). The types of dynamics we choose strongly depends on the interpretation of the modeled information transfer. This paper considers a prototypical model for the flow of information represented by packages travelling on networks with hierarchical structures. We are particularly interested in the dependency between network structures, noise, and information transmission efficiency. We take into account two types of noise in the system. The first one corresponds to a non-deterministic part of system dynamics while the second one to the level of difference between the current network structure in comparison to original one. Numerical simulations show a resonancelike behavior of information transfer efficiency in the presence of both types of noise. This paper is organized as follows. Section Networks topology and topological noise introduces the topology of hierarchical networks. Section Packet navigation rules describes the imposed packet dynamics. Section Results for artificial networks presents the numerical results for the artificial networks, while Section Real network is devoted SUBJECT AREAS: STATISTICAL PHYSICS, THERMODYNAMICS AND NONLINEAR DYNAMICS COMPUTATIONAL SCIENCE INFORMATION THEORY AND COMPUTATION SCIENTIFIC DATA
Elevated walking speed is an indicator of increased pace of life in cities, caused by environment... more Elevated walking speed is an indicator of increased pace of life in cities, caused by environmental pressures inherent to urban environments, which lead to short-and long-term consequences for health and well-being. In this paper we investigate the effect of walking speed on heat stress. We define the heat-stress-optimal walking speed and estimate its values for a wide range of air temperatures with the use of computational modelling of metabolic heat production and thermal regulation. The heat-stress-optimal walking speed shows three distinct phases in relation to air temperature, determined by different modes of interaction between the environment and physiology. Simulation results suggest that different temperature regimes require walking speed adaptation to preserve heat balance. Empirical data collected for Singapore reveals elevated average walking speed, which is not responsive to slight changes in microclimate (4-5°C). The proposed computational model predicts the amount of additional heat produced by an individual due to the high pace of life. We conclude that there are direct implications of the high pace of life in cities on the immediate heat stress of people, and we show how a lower walking speed significantly reduces self-overheating and improves thermal comfort.
Thermal comfort of people in outdoor urban spaces is a growing concern in cities due to climate c... more Thermal comfort of people in outdoor urban spaces is a growing concern in cities due to climate change and urbanization. In outdoor settings the climate and behavior of people are more dynamic than in indoor situations, therefore a steady state of the thermoregulatory system is rarely reached. Understanding the dynamics of outdoor thermal comfort requires accurate predictive models. In this paper we extend a classical two-node model of human body thermal regulation. We give a detailed description and interpretation of all the components and parameter values and test the dynamics of the model against experimental data. We propose a modification of the skin blood flow model which, while keeping realistic values and responsiveness, improves skin temperature prediction nearly fourfold. We further analyze the sensitivity of the model with respect to climatic and personal parameters. This analysis reveals the relative importance of, for instance, air temperature, wind speed and clothing, in thermoregulatory processes of the human body in various climatic settings. We conclude, that our model realistically reproduces the dynamics of aggregate measures of human body thermal regulation. Validated for cool, warm and hot environments, the model is shown to be accurate in terms of its dynamics and it is conceptually and computationally far more efficient than any existing multi-node and multi-part model.
Communications in computer and information science, 2016
In this paper we present the first step towards the development of a mathematical model of human ... more In this paper we present the first step towards the development of a mathematical model of human immune system for advanced individualized healthcare, where medication plan is fine-tuned for each patient to fit his conditions. We reproduce two representative models of the innate immune system. The first model by Rocha et al. describes the dynamics of the innate immune response by ordinary differential equations, focusing on LPS, neutrophils, resting macrophages, and activated macrophages. The second model by Pigozzo et al. describes the spatial dynamics of LPS, neutrophils, and pro-inflammatory cytokines by partial differential equations. We found that the results of the first model are fully reproducible. However, the second model is only partially reproducible. Several parameters had to be adjusted in order to reproduce the dynamics of the immune response: diffusion coefficients and the rates of LPS phagocytosis, cytokine production, neutrophils chemotaxis and apoptosis.
We aim to develop a mathematical model of the human immune system for advanced individualized hea... more We aim to develop a mathematical model of the human immune system for advanced individualized healthcare where medication plan is fine-tuned to fit a patient's conditions through monitored biochemical processes. One of the challenges is calibrating model parameters to satisfy existing experimental data or prior knowledge about the system behavior. In this paper, we apply genetic algorithm to find model parameters reproducing the results of modeling human innate immune system by Pigozzo et al.
Current studies of outdoor thermal comfort are limited to calculating thermal indices or intervie... more Current studies of outdoor thermal comfort are limited to calculating thermal indices or interviewing people. The first method does not take into account the way people use this space, whereas the second one is limited to one particular study area. Simulating people's thermal perception along with their activities in public urban spaces will help architects and city planners to test their concepts and to design smarter and more liveable cities. In this paper, we propose an agent-based modelling approach to simulate people's adaptive behaviour in space. Two levels of pedestrian behaviour are considered: reactive and proactive, and three types of thermal adaptive behaviour of pedestrians are modelled with single-agent scenarios: speed adaptation, thermal attraction/repulsion and vision-motivated route alternation. An "accumulated heat stress" parameter of the agent is calculated during the simulation, and pedestrian behaviour is analysed in terms of its ability to reduce the accumulated heat stress. This work is the first step towards the "human component" in urban microclimate simulation systems. We use these simulations to drive the design of real-life experiments, which will help calibrating model parameters, such as the heat-speed response, thermal sensitivity and admissible turning angles.
UvA-DARE (Digital Academic Repository) Data-driven modeling of transportation systems and traffic... more UvA-DARE (Digital Academic Repository) Data-driven modeling of transportation systems and traffic data analysis during a major power outage in the Netherlands
The International Conference on Computational Science is an annual conference that brings togethe... more The International Conference on Computational Science is an annual conference that brings together researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering computational methods in sciences such as physics, chemistry, life sciences, and engineering, as well as in arts and humanitarian fields, to discuss problems and solutions in the area, to identify new issues, and to shape future directions for research.
Chapter 18, "Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices" was previously ... more Chapter 18, "Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices" was previously published non-open access. This have now been changed to open access under a CC BY 4.0 license and the copyright holders updated to 'The Author(s)' and the acknowledgement section added. The book has also been updated with this change.
Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psycho... more Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that are capable of surrogative rea- soning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews formal models of addiction developed in two very different but relevant fields of study: (neuro)psychological mod- eling of intra-individual dynamics, and social modeling of inter-individual dynamics. We find that these modeling approaches to addiction are quite disjoint and argue that in order to unravel the complexities of biopsychoso- cial processes of addiction, models should integrate intra- and interpersonal factors.
The analysis of questionnaires often involves representing the high-dimensional responses in a lo... more The analysis of questionnaires often involves representing the high-dimensional responses in a low-dimensional space (e.g., PCA, MCA, or t-SNE). However questionnaire data often contains categorical variables and common statistical model assumptions rarely hold. Here we present a non-parametric approach based on Fisher Information which obtains a low-dimensional embedding of a statistical manifold (SM). The SM has deep connections with parametric statistical models and the theory of phase transitions in statistical physics. Firstly we simulate questionnaire responses based on a non-linear SM and validate our method compared to other methods. Secondly we apply our method to two empirical datasets containing largely categorical variables: an anthropological survey of rice farmers in Bali and a cohort study on health inequality in Amsterdam. Compare to previous analysis and known anthropological knowledge we conclude that our method best discriminates between different behaviours, pavi...
The existence of slums or informal settlements is common to most cities of developing countries. ... more The existence of slums or informal settlements is common to most cities of developing countries. In India, slums contain a wealth of diversity that is masked by a high level of poverty and rather insufficient access to resources. Recent studies have identified that the conventional perception of slums as distinctive homogeneous settlements is incorrect, rather slums are diverse and complex systems that cannot be addressed through one-size fits all approaches. In this paper we investigate Tilly's theory on group segregation and how it reproduces or reinforces inequality within the slums of Bengaluru. We apply statistical techniques (correspondence analysis and regression) to novel field data from 37 slums in Bengaluru. First, we find high levels of spatial and group segregation by religion across the slums of Bengaluru. Second, we find that segregation leads to opportunity bias among slum dwellers, which inhibits equitable access to jobs in the labour market. Finally, the results show that insufficient access to resources constrain the income generation and leads to emerging coping strategies. The results indicate that group identity is key to addressing disparity and how solving inequality can drastically impact group identity. Our results show that targeting horizontal inequality (as compared to vertical inequality) may increase the rate of successful interventions for each of the segregated groups of slum dwellers.
We describe two approaches to detecting anomalies in time series of multi-parameter clinical data... more We describe two approaches to detecting anomalies in time series of multi-parameter clinical data: (1) metric and model-based indicators and (2) information surprise. (1) Metric and model-based indicators are commonly used as early warning signals to detect transitions between alternate states based on individual time series. Here we explore the applicability of existing indicators to distinguish critical (anomalies) from non-critical conditions in patients undergoing cardiac surgery, based on a small anonymized clinical trial dataset. We find that a combination of time-varying autoregressive model, kurtosis, and skewness indicators correctly distinguished critical from non-critical patients in 5 out of 36 blood parameters at a window size of 0.3 (average of 37 hours) or higher. (2) Information surprise quantifies how the progression of one patient's condition differs from that of rest of the population based on the cross-section of time series. With the maximum surprise and slope features we detect all critical patients at the 0.05 significance level. Moreover we show that a naive outlier detection does not work, demonstrating the need for the more sophisticated approaches explored here. Our preliminary results suggest that future developments in early warning systems for patient condition monitoring may predict the onset of critical transition and allow medical intervention preventing patient death. Further method development is needed to avoid overfitting and spurious results, and verification on large clinical datasets.
We developed a robust approach for real-time levee condition monitoring based on combination of d... more We developed a robust approach for real-time levee condition monitoring based on combination of data-driven methods (one-side classification) and finite element analysis. It was implemented within a flood early warning system and validated on a series of full-scale levee failure experiments organised by the IJkdijk consortium in August-September 2012 in the Netherlands. Our approach has detected anomalies and predicted levee failures several days before the actual collapse. This approach was used in the UrbanFlood decision support system for routine levee quality assessment and for critical situations of a potential levee breach and inundation. In case of emergency, the system generates an alarm, warns dike managers and city authorities, and launches advanced urgent simulations of levee stability and flood dynamics, thus helping to make informed decisions on preventive measures, to evaluate the risks and to alleviate adverse effects of a flood.
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Papers by Peter Sloot