Distributed Individual-Based Environmental Simulation

This paper describes the development and construction of a distributed model allowing the simulation of a large population. Particular attention will be paid to allowing the modelling of an individual's behaviour, communication and interaction with a shared environment. Individual based modelling is not a new concept, nor is the idea of distributed simulations, the system detailed here offers a means of combining these two paradigms into one large-scale modelling environment. A key concept in this system is that each individual being modelled is implemented as a separate process. This atomisation of the model allows the simulation a greater flexibility, individuals can be rapidly developed and the simulation can be spread over a wide number of machines of varying architectures. In an attempt to produce a flexible, extensible, individual based model of a large number of individual subjects the client-server paradigm has been employed. Combining the individual-based modelling techniques with a client-server network architecture has been found to be quite straightforward with the added bonus of having communication between individuals included for free. The idea of considering the problem as one of interaction between an individual and the environment means that the problems normally associated with distributed simulations, those of continuity of world-views for different clients and of communication between clients, are easily solved. Although this system has been developed originally to allow simulations of the Mountain Gorilla (Gorilla Gorilla Beringe) population, the modelling methods employed have meant that almost any entity can be simulated with very little change to the basic simulation processes

Specifying and Analysing SOC Applications with COWS

COWS is a recently defined process calculus for specifying and combining service-oriented applications, while modelling their dynamic behaviour. Since its introduction, a number of methods and tools have been devised to analyse COWS specifications, like e.g. a type system to check confidentiality properties, a logic and a model checker to express and check functional properties of services. In this paper, by means of a case study in the area of automotive systems, we demonstrate that COWS, with some mild linguistic additions, can model all the phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, orchestration, deployment, reconfiguration and execution. We also provide a flavour of the properties that can be analysed by using the tools mentioned above

An affinity analysis based CIM-to-PIM transformation

To tackle the problems such as the imperfection and inconsistency in software requirements in traditional Computation Independent Model (CIM) modelling, the low degree of automation as well as the imperfection in the description of Platform Independent Model (PIM) in CIM-to-PIM transforming, in this article, we propose a Business-Process-based CIM modelling method and a CIM-to-PIM transformation approach. Business Process Model is used to express CIM, and UML‘s Sequence Diagram, State Chart Diagram as well as Class Diagram are used to express PIM. Firstly, the users’ requirements are obtained through business process models. We extract use cases from business processes and create use case specifications. A verification mechanism is also added for the use case specification. Secondly, we transform CIMs into PIMs automatically with use case specifications as the inputs as well as combining with use case based thinking, responsibility based thinking and affinity analysis. Finally, by comparing with the methods in other studies, we conclude that methods proposed in this article can ensure model integrity and increase the degree of model transformation automation

Constructing Fuzzy Time Series Model Using Combination of Table Lookup and Singular Value Decomposition Methods and Its Application to Forecasting Inflation Rate

Fuzzy time series is a dynamic process with linguistic values as its observations. Modelling fuzzy time series data developed by some researchers used discrete membership functions and table lookup method from training data. This paper presents a new method to modelling fuzzy time series data combining table lookup and singular value decomposition methods using continuous membership functions. Table lookup method is used to construct fuzzy relations from training data. Singular value decomposition of firing strength matrix and QR factorization are used to reduce fuzzy relations. Furthermore, this method is applied to forecast inflation rate in Indonesia based on six-factors one-order fuzzy time series. This result is compared with neural network method and the proposed method gets a higher forecasting accuracy rate than the neural network method

Evaluating Process-Based Integrated Assessment Models of Climate Change Mitigation

Process-based integrated assessment models (IAMs) analyse transformation pathways to mitigate climate change. Confidence in models is established by testing their structural assumptions and comparing their behaviour against observations as well as other models. Climate model evaluation is concerted, and prominently reported in a dedicated chapter in the IPCC WG1 assessments. By comparison, evaluation of process-based IAMs tends to be less visible and more dispersed among modelling teams, with the exception of model inter-comparison projects. We contribute the first comprehensive analysis of process-based IAM evaluation, drawing on a wide range of examples across eight different evaluation methods testing both structural and behavioural validity. For each evaluation method, we compare its application to process-based IAMs with its application to climate models, noting similarities and differences, and seeking useful insights for strengthening the evaluation of process-based IAMs. We find that each evaluation method has distinctive strengths and limitations, as well as constraints on their application. We develop a systematic evaluation framework combining multiple methods that should be embedded within the development and use of process-based IAMs

Turning teachers into entrepreneurship role models: development of a measurement scale of useful characteristics

The circumstances under which students consider their teachers of entrepreneurship as role models have not received in-depth exploration in the literature. This paper focuses on determining the main personal, professional and pedagogical characteristics that would turn teachers of entrepreneurship into role models and thereby improve the entrepreneurial intentions of students. A three-step empirical research process combining documentary, qualitative and quantitative methods is developed in order to propose and test a measurement scale of teacher characteristics that is reliable, valid and useful for causal modelling. A total of 26 characteristics are identified and classified into personal, professional and pedagogical categories

Combining Kernel and Model Based Learning for HIV Therapy Selection

We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods

Studying dietary intake in daily life through multilevel two-part modelling: a novel analytical approach and its practical application

Background Understanding which factors influence dietary intake, particularly in daily life, is crucial given the impact diet has on physical as well as mental health. However, a factor might influence whether but not how much an individual eats and vice versa or a factor’s importance may differ across these two facets. Distinguishing between these two facets, hence, studying dietary intake as a dual process is conceptually promising and not only allows further insights, but also solves a statistical issue. When assessing the association between a predictor (e.g. momentary affect) and subsequent dietary intake in daily life through ecological momentary assessment (EMA), the outcome variable (e.g. energy intake within a predefined time-interval) is semicontinuous. That is, one part is equal to zero (i.e. no dietary intake occurred) and the other contains right-skewed positive values (i.e. dietary intake occurred, but often only small amounts are consumed). However, linear multilevel modelling which is commonly used for EMA data to account for repeated measures within individuals cannot be applied to semicontinuous outcomes. A highly informative statistical approach for semicontinuous outcomes is multilevel two-part modelling which treats the outcome as generated by a dual process, combining a multilevel logistic/probit regression for zeros and a multilevel (generalized) linear regression for nonzero values. Methods A multilevel two-part model combining a multilevel logistic regression to predict whether an individual eats and a multilevel gamma regression to predict how much is eaten, if an individual eats, is proposed. Its general implementation in R, a widely used and freely available statistical software, using the R-package brms is described. To illustrate its practical application, the analytical approach is applied exemplary to data from the Eat2beNICE-APPetite-study. Results Results highlight that the proposed multilevel two-part model reveals process-specific associations which cannot be detected through traditional multilevel modelling. Conclusions This paper is the first to introduce multilevel two-part modelling as a novel analytical approach to study dietary intake in daily life. Studying dietary intake through multilevel two-part modelling is conceptually as well as methodologically promising. Findings can be translated to tailored nutritional interventions targeting either the occurrence or the amount of dietary intake