Mechanism based emulation of dynamic simulation models : concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.

Self-exciting hurdle models for terrorist activity

A predictive model of terrorist activity is developed by examining the daily number of terrorist attacks in Indonesia from 1994 through 2007. The dynamic model employs a shot noise process to explain the self-exciting nature of the terrorist activities. This estimates the probability of future attacks as a function of the times since the past attacks. In addition, the excess of nonattack days coupled with the presence of multiple coordinated attacks on the same day compelled the use of hurdle models to jointly model the probability of an attack day and corresponding number of attacks. A power law distribution with a shot noise driven parameter best modeled the number of attacks on an attack day. Interpretation of the model parameters is discussed and predictive performance of the models is evaluated.

Autism : an introduction to behavioural therapy models used for autism and nursing the person with autism in the primary care setting

Childhood autism falls under the guise of autism spectrum disorders and is generally found in children over two years of age. There are of course variations in severity and clinical manifestations, however the most common features being disinterest in social interaction and engagement in ritualistic and repetitive behaviours. In Singapore the incidence of autism is on the rise as parents are becoming more aware of the early signs of autism and seek healthcare programmes to ensure the quality of life for their child is optimised. Two such programmes, Applied Behaiour Analysis and Floortime approach have proven successful in alleviating some of the behavioural and social skills problems associated with autism. Using positive behaviour reinforcement both Applied Behaviour Analysis and Floortime approach reward behaviour associated with positive social responses.

Surface functionalization on the thermal conductivity of graphene–polymer nanocomposites

Exploring thermal transport in graphene-polymer nanocomposite is significant to its applications with better thermal properties. Interfacial thermal conductance between graphene and polymer matrix plays a critical role in the improvement of thermal conductivity of graphene-polymer nanocomposite. Unfortunately, it is still challenging to understand the interfacial thermal transport between graphene nanofiller and polymer matrix at small material length scale. To this end, using non-equilibrium molecular dynamics simulations, we investigate the interfacial thermal conductance of graphene-polyethylene (PE) nanocomposite. The influence of functionalization with hydrocarbon chains on the interfacial thermal conductance of graphene-polymer nanocomposites was studied, taking into account of the effects of model size and thermal conductivity of graphene. An analytical model is also used to calculate the thermal conductivity of nanocomposite. The results are considered to contribute to development of new graphene-polymer nanocomposites with tailored thermal properties.

On the use of complex numbers in quantum models for information retrieval

Quantum-inspired models have recently attracted increasing attention in Information Retrieval. An intriguing characteristic of the mathematical framework of quantum theory is the presence of complex numbers. However, it is unclear what such numbers could or would actually represent or mean in Information Retrieval. The goal of this paper is to discuss the role of complex numbers within the context of Information Retrieval. First, we introduce how complex numbers are used in quantum probability theory. Then, we examine van Rijsbergen’s proposal of evoking complex valued representations of informations objects. We empirically show that such a representation is unlikely to be effective in practice (confuting its usefulness in Information Retrieval). We then explore alternative proposals which may be more successful at realising the power of complex numbers.

Quality Information System: Investigating the conception, role, models and suggesting conceptual and hierarchal models

In the current business world which companies’ competition is very compact in the business arena, quality in manufacturing and providing products and services can be considered as a means of seeking excellence and success of companies in this competition arena. Entering the era of e-commerce and emergence of new production systems and new organizational structures, traditional management and quality assurance systems have been challenged. Consequently, quality information system has been gained a special seat as one of the new tools of quality management. In this paper, quality information system has been studied with a review of the literature of the quality information system, and the role and position of quality Information System (QIS) among other information systems of a organization is investigated. The quality Information system models are analyzed and by analyzing and assessing presented models in quality information system a conceptual and hierarchical model of quality information system is suggested and studied. As a case study the hierarchical model of quality information system is developed by evaluating hierarchical models presented in the field of quality information system based on the Shetabkar Co.

Fire performance of cold-formed steel wall panels and prediction of their fire resistance rating

Recent research at the Queensland University of Technology has investigated the structural and thermal behaviour of load bearing Light gauge Steel Frame (LSF) wall systems made of 1.15 mm G500 steel studs and varying plasterboard and insulation configurations (cavity and external insulation) using full scale fire tests. Suitable finite element models of LSF walls were then developed and validated by comparing with test results. In this study, the validated finite element models of LSF wall panels subject to standard fire conditions were used in a detailed parametric study to investigate the effects of important parameters such as steel grade and thickness, plasterboard screw spacing, plasterboard lateral restraint, insulation materials and load ratio on their performance under standard fire conditions. Suitable equations were proposed to predict the time–temperature profiles of LSF wall studs with eight different plasterboard-insulation configurations, and used in the finite element analyses. Finite element parametric studies produced extensive fire performance data for the LSF wall panels in the form of load ratio versus time and critical hot flange (failure) temperature curves for eight wall configurations. This data demonstrated the superior fire performance of externally insulated LSF wall panels made of different steel grades and thicknesses. It also led to the development of a set of equations to predict the important relationship between the load ratio and the critical hot flange temperature of LSF wall studs. Finally this paper proposes a simplified method to predict the fire resistance rating of LSF walls based on the two proposed set of equations for the load ratio–hot flange temperature and the time–temperature relationships.

Geometric sensitivity of patient-specific finite element models of the spine to variability in user-selected anatomical landmarks

Software to create individualised finite element (FE) models of the osseoligamentous spine using pre-operative computed tomography (CT) data-sets for spinal surgery patients has recently been developed. This study presents a geometric sensitivity analysis of this software to assess the effect of intra-observer variability in user-selected anatomical landmarks. User-selected landmarks on the osseous anatomy were defined from CT data-sets for three scoliosis patients and these landmarks were used to reconstruct patient-specific anatomy of the spine and ribcage using parametric descriptions. The intra-observer errors in landmark co-ordinates for these anatomical landmarks were calculated. FE models of the spine and ribcage were created using the reconstructed anatomy for each patient and these models were analysed for a loadcase simulating clinical flexibility assessment. The intra-observer error in the anatomical measurements was low in comparison to the initial dimensions, with the exception of the angular measurements for disc wedge and zygapophyseal joint (z-joint) orientation and disc height. This variability suggested that CT resolution may influence such angular measurements, particularly for small anatomical features, such as the z-joints, and may also affect disc height. The results of the FE analysis showed low variation in the model predictions for spinal curvature with the mean intra-observer variability substantially less than the accepted error in clinical measurement. These findings demonstrate that intra-observer variability in landmark point selection has minimal effect on the subsequent FE predictions for a clinical loadcase.

Fast re-parameterisation of Gaussian mixture models for robotics applications

Autonomous navigation and picture compilation tasks require robust feature descriptions or models. Given the non Gaussian nature of sensor observations, it will be shown that Gaussian mixture models provide a general probabilistic representation allowing analytical solutions to the update and prediction operations in the general Bayesian filtering problem. Each operation in the Bayesian filter for Gaussian mixture models multiplicatively increases the number of parameters in the representation leading to the need for a re-parameterisation step. A computationally efficient re-parameterisation step will be demonstrated resulting in a compact and accurate estimate of the true distribution.

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

Selecting volatility forecasting models for portfolio allocation purposes

Techniques for evaluating and selecting multivariate volatility forecasts are not yet understood as well as their univariate counterparts. This paper considers the ability of different loss functions to discriminate between a set of competing forecasting models which are subsequently applied in a portfolio allocation context. It is found that a likelihood-based loss function outperforms its competitors, including those based on the given portfolio application. This result indicates that considering the particular application of forecasts is not necessarily the most effective basis on which to select models.

Generating natural language texts from business process models

Process Modeling is a widely used concept for understanding, documenting and also redesigning the operations of organizations. The validation and usage of process models is however affected by the fact that only business analysts fully understand them in detail. This is in particular a problem because they are typically not domain experts. In this paper, we investigate in how far the concept of verbalization can be adapted from object-role modeling to process models. To this end, we define an approach which automatically transforms BPMN process models into natural language texts and combines different techniques from linguistics and graph decomposition in a flexible and accurate manner. The evaluation of the technique is based on a prototypical implementation and involves a test set of 53 BPMN process models showing that natural language texts can be generated in a reliable fashion.

Location of hydrogen atoms in hydronium jarosite

Various models for the crystal structure of hydronium jarosite were determined from Rietveld refinements against neutron powder diffraction patterns collected at ambient temperature and also single-crystal X-ray diffraction data. The possibility of a lower symmetry space group for hydronium jarosite that has been suggested by the literature was investigated. It was found the space group is best described as R3¯m, the same for other jarosite minerals. The hydronium oxygen atom was found to occupy the 3¯m site (3a Wyckoff site). Inadequately refined hydronium bond angles and bond distances without the use of restraints are due to thermal motion and disorder of the hydronium hydrogen atoms across numerous orientations. However, the acquired data do not permit a precise determination of these orientations; the main feature up/down disorder of hydronium is clear. Thus, the highest symmetry model with the least disorder necessary to explain all data was chosen: The hydronium hydrogen atoms were modeled to occupy an m (18 h Wyckoff site) with 50 % fractional occupancy, leading to disorder across two orientations. A rigid body description of the hydronium ion rotated by 60° with H–O–H bond angles of 112° and O–H distances of 0.96 Å was optimal. This rigid body refinement suggests that hydrogen bonds between hydronium hydrogen atoms and basal sulfate oxygen atoms are not predominant. Instead, hydrogen bonds are formed between hydronium hydrogen atoms and hydroxyl oxygen atoms. The structure of hydronium alunite is expected to be similar given that alunite supergroup minerals are isostructural.

Practical aspects of frequency-domain identification of dynamic models of marine structures from hydrodynamic data

The motion response of marine structures in waves can be studied using finite-dimensional linear-time-invariant approximating models. These models, obtained using system identification with data computed by hydrodynamic codes, find application in offshore training simulators, hardware-in-the-loop simulators for positioning control testing, and also in initial designs of wave-energy conversion devices. Different proposals have appeared in the literature to address the identification problem in both time and frequency domains, and recent work has highlighted the superiority of the frequency-domain methods. This paper summarises practical frequency-domain estimation algorithms that use constraints on model structure and parameters to refine the search of approximating parametric models. Practical issues associated with the identification are discussed, including the influence of radiation model accuracy in force-to-motion models, which are usually the ultimate modelling objective. The illustration examples in the paper are obtained using a freely available MATLAB toolbox developed by the authors, which implements the estimation algorithms described.

Structuring acyclic process models

This article studies the problem of transforming a process model with an arbitrary topology into an equivalent well-structured process model. While this problem has received significant attention, there is still no full characterization of the class of unstructured process models that can be transformed into well-structured ones, nor an automated method for structuring any process model that belongs to this class. This article fills this gap in the context of acyclic process models. The article defines a necessary and sufficient condition for an unstructured acyclic process model to have an equivalent well-structured process model under fully concurrent bisimulation, as well as a complete structuring method. The method has been implemented as a tool that takes process models captured in the BPMN and EPC notations as input. The article also reports on an empirical evaluation of the structuring method using a repository of process models from commercial practice.