Comparing System Dynamics and Agent-Based Simulation for Tumour Growth and its Interactions with Effector Cells

There is little research concerning comparisons and combination of System Dynamics Simulation (SDS) and Agent Based Simulation (ABS). ABS is a paradigm used in many levels of abstraction, including those levels covered by SDS. We believe that the establishment of frameworks for the choice between these two simulation approaches would contribute to the simulation research. Hence, our work aims for the establishment of directions for the choice between SDS and ABS approaches for immune system-related problems. Previously, we compared the use of ABS and SDS for modelling agents' behaviour in an environment with nomovement or interactions between these agents. We concluded that for these types of agents it is preferable to use SDS, as it takes up less computational resources and produces the same results as those obtained by the ABS model. In order to move this research forward, our next research question is: if we introduce interactions between these agents will SDS still be the most appropriate paradigm to be used? To answer this question for immune system simulation problems, we will use, as case studies, models involving interactions between tumour cells and immune effector cells. Experiments show that there are cases where SDS and ABS can not be used interchangeably, and therefore, their comparison is not straightforward.Comment: 8 pages, 8 figures, 2 tables, International Summer Computer Simulation Conference 201

A Systemic Dynamics Model of Text Production

This paper introduces a quantitative model of text as it unfolds in time. The model conceptualizes text as a functional unit of language. This organization can be difficult to identify because it forms complex patterns of linguistic laws, probability and dynamics. These patterns are covert configurations and need complex methods to be investigated. One such method is to draw from qualitative frameworks derived from the quantitative properties of language. Previous studies (Plum & Cowling, 1987; Rybicki & Eder, 2011; Zhang & Liu, 2017) have shown that covert configurations can be obtained through qualitative frameworks. When dynamics is considered, however, a model of text production including the variable time is needed. This paper therefore aims at addressing this research gap by proposing a dynamics model of text unfolding. It draws from systemic theory and models its categories quantitatively. Time is introduced as variation of choice. The model is applied to a sample of text. Results show how individual choices contribute to text unfolding – describing the amount of meanings at any given moment in text time. In addition, the dynamic accumulation indicates core characteristics of a text, which can be further explored in text behavior and typology

System Dynamics Modelling of the Processes Involving the Maintenance of the Naive T Cell Repertoire

The study of immune system aging, i.e. immunosenescence, is a relatively new research topic. It deals with understanding the processes of immunodegradation that indicate signs of functionality loss possibly leading to death. Even though it is not possible to prevent immunosenescence, there is great benefit in comprehending its causes, which may help to reverse some of the damage done and thus improve life expectancy. One of the main factors influencing the process of immunosenescence is the number and phenotypical variety of naive T cells in an individual. This work presents a review of immunosenescence, proposes system dynamics modelling of the processes involving the maintenance of the naive T cell repertoire and presents some preliminary results.Comment: 6 pages, 2 figures, 1 table, 9th Annual Workshop on Computational Intelligence (UKCI 2009), Nottingham, U

Translating simulation approaches for immunology

This thesis presents a novel set of guidelines to convert between simulation modelling approaches, namely, Ordinary differential Equations (ODEs), System Dynamics (SD) and Agent-based Modelling and Simulation (ABMS). In our literature review we identify a gap in establishing translation techniques between these approaches. We therefore focus our research in developing these techniques and assessing the impact of these conversions in the simulation outcomes. In particular, our interest lies in investigating our techniques applied to simulation problems for the immune system, as we wish to aid immunologists with the choice of the most appropriate approach for a certain problem. The aims of this thesis are therefore defined as: (1) with no explicit guidelines available from the literature, we want to develop, test and validate our own set of guidelines for converting between approaches: from ODE models to SD, from SD to ABMS and from ABMS to SD; and (2) we seek to discuss the merits of SD and ABMS for Immunology to assist researchers with the choice between both approaches. The assessment of the effectiveness of the conversion guidelines is achieved by using a case study approach involving six cases of established mathematical models describing immunological phenomena. These case studies are chosen by considering aspects such as the behaviour of the entities of the model (whether they are static or interact with other entities and whether they have spatial representation or not), the type of hypothesis to be tested, the empirical embeddedness of real data, population sizes, number of elements involved and the modelling effort. In order to conduct our conversion for the case studies, we first convert their original ODE model into an SD model, and then perform the translation from SD to ABMS. For the last three case studies, we also test the conversion guidelines from ABMS to SD. Evidence from the experiments reveal that for all cases it was possible to obtain equivalent approaches by using the conversion guidelines developed. However, outcome differences occur given the intrinsic characteristics of each simulation modelling paradigm. By observing these differences we could conclude that (1) SD is incapable of reflecting exactly the same variability as that obtained from the agent-based simulation, as it is a deterministic approach; (2) SD variables change continuously in time and therefore population numbers over time might be different from those obtained by the agent-based simulation; (3) as the number of different agents and behaviours increase, the corresponding SD becomes very intricate and difficult to develop and understand; (4) there are cases where it is preferable not to convert from ABMS to SD, as the agent-based model is easier to conceptualise and implement; (5) For other circumstances, ABMS outcomes are the same as those produced by the ODEs and SD, with the disadvantage to be more resource consuming in terms of computational memory and processing capacity; and (6) For some cases SD is less informative than ABMS, as it does not produce multiple scenarios or variations over the course of more than one run within the same parameters

Translating simulation approaches for immunology

This thesis presents a novel set of guidelines to convert between simulation modelling approaches, namely, Ordinary differential Equations (ODEs), System Dynamics (SD) and Agent-based Modelling and Simulation (ABMS). In our literature review we identify a gap in establishing translation techniques between these approaches. We therefore focus our research in developing these techniques and assessing the impact of these conversions in the simulation outcomes. In particular, our interest lies in investigating our techniques applied to simulation problems for the immune system, as we wish to aid immunologists with the choice of the most appropriate approach for a certain problem. The aims of this thesis are therefore defined as: (1) with no explicit guidelines available from the literature, we want to develop, test and validate our own set of guidelines for converting between approaches: from ODE models to SD, from SD to ABMS and from ABMS to SD; and (2) we seek to discuss the merits of SD and ABMS for Immunology to assist researchers with the choice between both approaches. The assessment of the effectiveness of the conversion guidelines is achieved by using a case study approach involving six cases of established mathematical models describing immunological phenomena. These case studies are chosen by considering aspects such as the behaviour of the entities of the model (whether they are static or interact with other entities and whether they have spatial representation or not), the type of hypothesis to be tested, the empirical embeddedness of real data, population sizes, number of elements involved and the modelling effort. In order to conduct our conversion for the case studies, we first convert their original ODE model into an SD model, and then perform the translation from SD to ABMS. For the last three case studies, we also test the conversion guidelines from ABMS to SD. Evidence from the experiments reveal that for all cases it was possible to obtain equivalent approaches by using the conversion guidelines developed. However, outcome differences occur given the intrinsic characteristics of each simulation modelling paradigm. By observing these differences we could conclude that (1) SD is incapable of reflecting exactly the same variability as that obtained from the agent-based simulation, as it is a deterministic approach; (2) SD variables change continuously in time and therefore population numbers over time might be different from those obtained by the agent-based simulation; (3) as the number of different agents and behaviours increase, the corresponding SD becomes very intricate and difficult to develop and understand; (4) there are cases where it is preferable not to convert from ABMS to SD, as the agent-based model is easier to conceptualise and implement; (5) For other circumstances, ABMS outcomes are the same as those produced by the ODEs and SD, with the disadvantage to be more resource consuming in terms of computational memory and processing capacity; and (6) For some cases SD is less informative than ABMS, as it does not produce multiple scenarios or variations over the course of more than one run within the same parameters

A genetic algorithm with composite chromosome for shift assignment of part-time employees

Personnel scheduling problems involve multiple tasks, including assigning shifts to workers. The purpose is usually to satisfy objectives and constraints arising from management, labour unions and employee preferences. The shift assignment problem is usually highly constrained and difficult to solve. The problem can be further complicated (i) if workers have mixed skills; (ii) if the start/end times of shifts are flexible; and (iii) if multiple criteria are considered when evaluating the quality of the assignment. This paper proposes a genetic algorithm using composite chromosome encoding to tackle the shift assignment problem that typically arises in retail stores, where most employees work part-time, have mixed-skills and require flexible shifts. Experiments on a number of problem instances extracted from a real-world retail store, show the effectiveness of the proposed approach in finding good-quality solutions. The computational results presented here also include a comparison with results obtained by formulating the problem as a mixed-integer linear programming model and then solving it with a commercial solver. Results show that the proposed genetic algorithm exhibits an effective and efficient performance in solving this difficult optimisation problem

Adaptive data communication interface: a user-centric visual data interpretation framework

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provides user-centric, meaningful visual information to assist owners to make sense of their data collection. The proposed framework comprises four stages: (i) the knowledge base compilation, where we search and collect existing state-of-the-art visualisation techniques per domain and user preferences; (ii) the development of the learning and inference system, where we apply artificial intelligence techniques to learn, predict and recommend new graphic interpretations (iii) results evaluation; and (iv) reinforcement and adaptation, where valid outputs are stored in our knowledge base and the system is iteratively tuned to address new demands. These stages, as well as our overall vision, limitations and possible challenges are introduced in this article. We also discuss further extensions of this framework for other knowledge discovery tasks

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions