Converging evidence that common timing processes underlie temporal-order and simultaneity judgments: a model-based analysis

Perception of simultaneity and temporal order is studied with simultaneity judgment (SJ) and temporal-order judgment (TOJ) tasks. In the former, observers report whether presentation of two stimuli was subjectively simultaneous; in the latter, they report which stimulus was subjectively presented first. SJ and TOJ tasks typically give discrepant results, which has prompted the view that performance is mediated by different processes in each task. We looked at these discrepancies from a model that yields psychometric functions whose parameters characterize the timing, decisional, and response processes involved in SJ and TOJ tasks. We analyzed 12 data sets from published studies in which both tasks had been used in within-subjects designs, all of which had reported differences in performance across tasks. Fitting the model jointly to data from both tasks, we tested the hypothesis that common timing processes sustain simultaneity and temporal order judgments, with differences in performance arising from task-dependent decisional and response processes. The results supported this hypothesis, also showing that model psychometric functions account for aspects of SJ and TOJ data that classical analyses overlook. Implications for research on perception of simultaneity and temporal order are discussed.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

Model based integration of management systems (MSs) – case study

Abstract Purpose – The purpose of this paper is to present a case study regarding the deployment of a previously developed model for the integration of management systems (MSs). The case study is developed at a manufacturing site of an international enterprise. The implementation of this model in a real business environment is aimed at assessing its feasibility. Design/methodology/approach – The presented case study takes into account different management systems standards (MSSs) progressively implemented, along the years, independently. The implementation of the model was supported by the results obtained from an investigation performed according to a structured diagnosis that was conducted to collect information related to the organizational situation of the enterprise. Findings – The main findings are as follows: a robust integrated management system (IMS), objectively more lean, structured and manageable was found to be feasible; this study provided an holistic view of the enterprise’s global management; clarifications of job descriptions and boundaries of action and responsibilities were achieved; greater efficiency in the use of resources was attained; more coordinated management of the three pillars of sustainability – environmental, economic and social, as well as risks, providing confidence and added value to the company and interested parties was achieved. Originality/value – This case study is pioneering in Portugal in respect to the implementation, at the level of an industrial organization, of the model previously developed for the integration of individualized MSs. The case study provides new insights regarding the implementation of IMSs including the rationalization of several resources and elimination of several types of organizational waste leveraging gains of efficiency. Due to its intrinsic characteristics, the model is able to support, progressively, new or revised MSSs according to the principles of annex SL (normative) – proposals for MSSs – of the International Organization for Standardization and the International Electrotechnical Commission, that the industrial organization can adopt beyond the current ones.

Definition of Model-based diagnosis problems with Altarica

International audience

Meta-analysis and cross-validation of a hyperglycemic status predictor model based on salivary glucose

Poster presented at the First International Congress of CiiEM - From Basic Sciences To Clinical Research. Egas Moniz, Caparica, Portugal, 27-28 November 2015.

Collaborative Networked Organizations as System of Systems: A Model-Based Engineering Approach

Part 6: Engineering and Implementation of Collaborative Networks

A novel time computation model based on algorithm complexity for data intensive scientific workflow design and scheduling

Scientific workflow offers a framework for cooperation between remote and shared resources on a grid computing environment (GCE) for scientific discovery. One major function of scientific workflow is to schedule a collection of computational subtasks in well-defined orders for efficient outputs by estimating task duration at runtime. In this paper, we propose a novel time computation model based on algorithm complexity (termed as TCMAC model) for high-level data intensive scientific workflow design. The proposed model schedules the subtasks based on their durations and the complexities of participant algorithms. Characterized by utilization of task duration computation function for time efficiency, the TCMAC model has three features for a full-aspect scientific workflow including both dataflow and control-flow: (1) provides flexible and reusable task duration functions in GCE;(2) facilitates better parallelism in iteration structures for providing more precise task durations;and (3) accommodates dynamic task durations for rescheduling in selective structures of control flow. We will also present theories and examples in scientific workflows to show the efficiency of the TCMAC model, especially for control-flow. Copyright©2009 John Wiley & Sons, Ltd.

An epidemic model based temporal violation prediction strategy for large batch of parallel business cloud workflows

Temporal violations often take place during the running of large batch of parallel business cloud workflow, which have a serious impact on the on-time completion of massive concurrent user requests. Existing studies have shown that local temporal violations (namely the delays of workflow activities) occurring during cloud workflow execution are the fundamental causes for failed on-time completion. Therefore, accurate prediction of temporal violations is a very important yet challenging task for business cloud workflows. In this paper, based on an epidemic model, a novel temporal violation prediction strategy is proposed to estimate the number of local temporal violations and the number of violations that must be handled so as to achieve a certain on-time completion rate before the execution of workflows. The prediction result can be served as an important reference for temporal violation prevention and handling strategies such as static resource reservation and dynamic provision. Specifically, we first analyze the queuing process of the parallel workflow activities, then we predict the number of potential temporal violations based on a novel temporal violation transmission model inspired by an epidemic model. Comprehensive experimental results demonstrate that our strategy can achieve very high prediction accuracy under different situations.

Cost and feasibility of a barrier to halt the spread of invasive cane toads in arid Australia: incorporating expert knowledge into model-based decision-making

1. Active engagement with practitioners is a crucial component of model-based decision-making in conservation management; it can assist with data acquisition, improve models and help narrow the 'knowing-doing' gap.
2. We worked with practitioners of one of the worst invasive species in Australia, the cane toad Rhinella marina, to revise a model that estimates the effectiveness of landscape barriers to contain spread. The original model predicted that the invasion could be contained by managing artificial watering points on pastoral properties, but was initially met with scepticism by practitioners, in part due to a lack of engagement during model development.
3. We held a workshop with practitioners and experts in cane toad biology. Using structured decision-making, we elicited concerns about the original model, revised its structure, updated relevant input data, added an economic component and found the most cost-effective location for a barrier across a range of fixed budgets and management scenarios. We then conducted scenario analyses to test the sensitivity of management decisions to model revisions.
4. We found that toad spread could be contained for all of the scenarios tested. Our modelling suggests a barrier could cost $4·5 M (2015 AUD) over 50 years for the most likely landscape scenario. The incorporation of practitioner knowledge into the model was crucial. As well as improving engagement, when we incorporated practitioner concerns (particularly regarding the effects of irrigation and dwellings on toad spread), we found a different location for the optimal barrier compared to a previously published study (Tingley et al. 2013).
5. Synthesis and applications. Through engagement with practitioners, we turned an academic modelling exercise into a decision-support tool that integrated local information, and considered more realistic scenarios and constraints. Active engagement with practitioners led to productive revisions of a model that estimates the effectiveness of a landscape barrier to contain spread of the invasive cane toad R. marina. Benefits also include greater confidence in model predictions, improving our assessment of the cost and feasibility of containing the spread of toads.

A SCR Model based on Reactor and Engine Experimental Studies for a Cu-zeolite Catalyst

A NOx reduction efficiency higher than 95% with NH3 slip less than 30 ppm is desirable for heavy-duty diesel (HDD) engines using selective catalytic reduction (SCR) systems to meet the US EPA 2010 NOx standard and the 2014-2018 fuel consumption regulation. The SCR performance needs to be improved through experimental and modeling studies. In this research, a high fidelity global kinetic 1-dimensional 2-site SCR model with mass transfer, heat transfer and global reaction mechanisms was developed for a Cu-zeolite catalyst. The model simulates the SCR performance for the engine exhaust conditions with NH3 maldistribution and aging effects, and the details are presented. SCR experimental data were collected for the model development, calibration and validation from a reactor at Oak Ridge National Laboratory (ORNL) and an engine experimental setup at Michigan Technological University (MTU) with a Cummins 2010 ISB engine. The model was calibrated separately to the reactor and engine data. The experimental setup, test procedures including a surrogate HD-FTP cycle developed for transient studies and the model calibration process are described. Differences in the model parameters were determined between the calibrations developed from the reactor and the engine data. It was determined that the SCR inlet NH3 maldistribution is one of the reasons causing the differences. The model calibrated to the engine data served as a basis for developing a reduced order SCR estimator model. The effect of the SCR inlet NO2/NOx ratio on the SCR performance was studied through simulations using the surrogate HD-FTP cycle. The cumulative outlet NOx and the overall NOx conversion efficiency of the cycle are highest with a NO2/NOx ratio of 0.5. The outlet NH3 is lowest for the NO2/NOx ratio greater than 0.6. A combined engine experimental and simulation study was performed to quantify the NH3 maldistribution at the SCR inlet and its effects on the SCR performance and kinetics. The uniformity index (UI) of the SCR inlet NH3 and NH3/NOx ratio (ANR) was determined to be below 0.8 for the production system. The UI was improved to 0.9 after installation of a swirl mixer into the SCR inlet cone. A multi-channel model was developed to simulate the maldistribution effects. The results showed that reducing the UI of the inlet ANR from 1.0 to 0.7 caused a 5-10% decrease in NOx reduction efficiency and 10-20 ppm increase in the NH3 slip. The simulations of the steady-state engine data with the multi-channel model showed that the NH3 maldistribution is a factor causing the differences in the calibrations developed from the engine and the reactor data. The Reactor experiments were performed at ORNL using a Spaci-IR technique to study the thermal aging effects. The test results showed that the thermal aging (at 800°C for 16 hours) caused a 30% reduction in the NH3 stored on the catalyst under NH3 saturation conditions and different axial concentration profiles under SCR reaction conditions. The kinetics analysis showed that the thermal aging caused a reduction in total NH3 storage capacity (94.6 compared to 138 gmol/m3), different NH3 adsorption/desorption properties and a decrease in activation energy and the pre-exponential factor for NH3 oxidation, standard and fast SCR reactions. Both reduction in the storage capability and the change in kinetics of the major reactions contributed to the change in the axial storage and concentration profiles observed from the experiments.

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

Concurrent momentum and contrarian strategies in the Australian stock market

We investigate the coexistence of momentum and contrarian strategies in the Australian equity market from 1992 to 2011. We show that contrarian strategies prevail in the short-term investment horizon while momentum strategies dominate in the intermediate- and long-term horizons. However, only short-term contrarian strategies significantly outperform the simple buy-and-hold strategy of investing in the market index over the same period. Further examination of these strategies shows that the Australian mining sector undermines the performance of momentum while enhancing performance of contrarian strategies. Lastly, using both parametric and non-parametric approaches, we show that these strategies’ returns are persistent anomalies and not completely explained by standard return-generating models.

Improving web learning through model optimization using bootstrap for a tour-guide robot

We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.