Explicit Process Model Research Articles

The effect of alcohol hangover on choice response time.

The effect of alcohol hangover on cognitive processing has received little attention. We explored the effect of alcohol hangover on choice response time (RT), a dominant dependent variable (DV) in cognitive research. Prior research of the effect of hangover on RT has produced mixed findings; all studies reviewed relied exclusively on estimates of central tendency (e.g. mean RT), which has limited information value. Here we present novel analytical methods by going beyond mean RT analysis. Specifically, we examined performance in hangover conditions (n=31) across the whole RT distribution by fitting ex-Gaussian models to participant data, providing a formal description of the RT distribution. This analysis showed detriments to performance under hangover conditions at the slower end of the RT distribution and increased RT variance under hangover conditions. We also fitted an explicit mathematical process model of choice RT - the diffusion model - which estimates parameters reflecting psychologically-meaningful processes underlying choice RT. This analysis showed that hangover reduced information processing efficiency during response selection, and increased response caution; changes in these parameters reflect hangover affecting core decisional-components of RT performance. The implications of the data as well as the methods used for hangover research are discussed.

Estimating population density from presence-absence data using a spatially explicit model

Presence–absence (detection/non-detection) data are routinely collected in wildlife studies where identification of individuals is impossible or impractical and where the detection method may be able to detect only the presence of an individual rather than a count (e.g., track or scat surveys). Estimating population density from presence–absence data usually is assumed to be difficult or impossible unless certain restrictive assumptions are made or supplementary information is collected. Recently, Chandler and Royle (2013) presented an extension of a spatially explicit capture–recapture model that estimates population density from spatially replicated counts in unmarked populations. We extended the model of Chandler and Royle (2013) to situations where only presence–absence data can be collected. The model assumes that individuals can be detected at multiple sample units, producing spatially correlated detections. A spatially explicit model of the detection process is then fit to the correlated detection data using Bayesian methods. We report on the performance of the model using simulation and illustrate its use with a practical example estimating the abundance and density of red foxes (Vulpes vulpes) from remote camera surveys in the Grampians National Park in southeastern Australia. Results from simulations suggest the model produces unbiased estimates of density if device spacing is less than the radial length of a typical home range and the number of encounter occasions is high (i.e., at least 10). Application of the model to camera detection data from foxes in the Grampians National Park resulted in an estimated density of 0.22 foxes/km2 (95% CI: 0.16–0.53). For this dataset, precision of the density and detection parameter estimates were increased by the use of an informative prior distribution for the home-range-scale parameter. The current model should apply widely to a range of sampling situations that result in spatially correlated detection/non-detection data such as bait take, scat surveys, tracking stations, and chew cards, to name a few. © 2015 The Wildlife Society.

The Effect of Alcohol Hangover on Choice Response Time

The effect of alcohol hangover on cognitive processing has received little attention. We explored the effect of alcohol hangover on choice response time (RT), a dominant dependent variable in cognitive research. Prior research of the effect of hangover on RT has produced mixed findings; all studies reviewed relied exclusively on estimates of central tendency (e.g., mean RT), which has limited information value. Here we present novel analytical methods by going beyond mean RT analysis. Specifically, we examined performance in hangover conditions across the whole RT distribution by fitting ex-Gaussian models to participant data, providing a formal description of the RT distribution. This analysis showed detriments to performance under hangover conditions at the slower end of the RT distribution; RT variance was also increased under hangover conditions. We also fitted an explicit mathematical process model of choice RT — the diffusion model — to participant data, which estimates parameters reflecting psychologically-meaningful processes underlying choice RT. This analysis showed that hangover reduced information processing efficiency during response selection, and increased response caution; changes in these parameters reflect hangover affecting core decisional-components of RT performance. The implications of the data as well as the methods used for hangover research are discussed.

Modeling Carbon Stocks in a Secondary Tropical Dry Forest in the Yucatan Peninsula, Mexico

The carbon balance of secondary dry tropical forests of Mexico’s Yucatan Peninsula is sensitive to human and natural disturbances and climate change. The spatially explicit process model Forest-DeNitrification-DeComposition (DNDC) was used to estimate forest carbon dynamics in this region, including the effects of disturbance on carbon stocks. Model evaluation using observations from 276 sample plots in a tropical dry forest in the Yucatan Peninsula indicated that Forest-DNDC can be used to simulate carbon stocks for this forest with good model performance efficiency. The simulated spatial variability in carbon stocks was large, ranging from 5 to 115 Mg carbon (C) ha−1, with a mean of 56.6 Mg C ha−1. Carbon stocks in the forest were largely influenced by human disturbances between 1985 and 2010. Based on a comparison of the simulations with and without disturbances, carbon storage in the year 2012 with disturbance was 3.2 Mg C ha−1, lower on average than without disturbance. The difference over the whole study area was 154.7 Gg C, or an 8.5 % decrease. There were substantial differences in carbon stocks simulated at individual sample plots, compared to spatially modeled outputs (200 m2 plots vs. polygon simulation units) at some locations due to differences in vegetation class, stand age, and soil conditions at different resolutions. However, the difference in the regional mean of carbon stocks between plot-level simulation and spatial output was small. Soil CO2 and N2O fluxes varied spatially; both fluxes increased with increasing precipitation, and soil CO2 also increased with an increase in biomass. The modeled spatial variability in CH4 uptake by soils was small, and the flux was not correlated with precipitation. The net ecosystem exchange (NEE) and net primary production (NPP) were nonlinearly correlated with stand age. Similar to the carbon stock simulations, different resolutions resulted in some differences in NEE and NPP, but the spatial means were similar.

MULTISCALE MODELING FOR STOCHASTIC FOREST DYNAMICS

This paper investigates a multiscale characterization of individual-based dynamics. At the fine scale, an individual-based and spatially explicit point process model is used to describe ecological processes relevant to the evolution of individuals and their mutual interactions. At the coarse scale, a stochastic model is synthesized to describe the dynamics of the system on larger spatial scales. The upscaling technique is designed to efficiently coarse-grain very fine-scale variables in order to express new state dynamics that no longer require access to fine details. The coarse dynamics are carefully constructed to preserve fluctuations from localized competition by retaining subgrid information obtained from precomputed Monte Carlo simulations of the fine-scale dynamics. Implications to computational efficiency and cross-scale coupling are significant. The present technique is then described in detail as it is applied to an individual-based and spatially explicit forest dynamics model.

Process Mining Based on Statistic Ordering Relations of Events

An explicit process model is vital in business processes. However, it is complicated and time consuming to create a workflow design. Also discords usually occur between the perceived management processes and the actual workflow processes. Under this condition, the process discovery techniques emerge. The aim is to rebuild a workflow model (e.g. a Petri net) of a business process based on the execution log. The model should give an abstract representation of the system and reproduce the log. This model can be further applied for process redesign/improvement and performance/reliability evaluation. In this paper, we present a new algorithm derived from α-algorithm for process discovery in term of Petri nets, where statistic long distance causal relationship is taken into consideration. Also this algorithm covers some shortages in α-algorithm.

Modeling the carbon dynamics of the dryland ecosystems in Xinjiang, China from 1981 to 2007—The spatiotemporal patterns and climate controls

Dryland ecosystems are particularly sensitive to environmental stresses. Despite their importance to the global carbon (C) cycle, responses of the Central Asian dryland to the rapid climate change in recent decades are still unclear. Using AEM, a newly developed, spatially explicit process model for dryland ecosystems, a case study was conducted in Xinjiang, a 1.66Mkm2 dryland in eastern Central Asia. The goal was to assess the impacts of environmental changes (climate change and elevated CO2) on the regional C dynamics from 1981 to 2007. The results indicated that over the last three decades, Xinjiang acted as a C sink of 138Tg (1T=1012), 78.5% of which was contributed by increased vegetation C. The C dynamic overall was dominated by the CO2 fertilization effect, which resulted in 124Tg C sequestration from 1981 to 2007. Temporal pattern of C dynamic was controlled by the climate change, which resulted in 10Tg C sequestration. The model simulation also indicated that the ecosystem's response to the combined effect by CO2 and climate change together was nonlinear. Among climate factors, temperature change resulted in 12Tg C loss, while precipitation change resulted in 24Tg C sequestration. The rising temperature stimulated heterotrophic respiration, causing 17Tg C loss from the soil. The climate change had complex effects on the regional C dynamics. It caused a 13Tg C loss in southern Xinjiang, while resulting in a 23Tg C sequestration in the north. The region acted as a C source in the 1980s, mainly due to the drought from 1983 to 1986, but has turned into a C sink since 1990. Unlike other dryland plant types, the irrigated crop and the phreatophytic shrub were barely affected by changes in precipitation. The ecosystem complexity in the dryland highlights the importance of addressing environmental heterogeneity with high resolution datasets and considering the characteristic ecophysiology of dryland plants with process-based modeling in climate change studies.

Model Predictive Control Design for Nonlinear Process Control Reactor Case Study: CSTR (Continuous Stirred Tank Reactor)

Continuous Stirred Tank Reactor (CSTR) is an essential focus in process control and offering a diverse range of researches in the area of chemical and control engineering. To ensure the successful operation of a continuous stirred tank reactor (CSTR), it is necessary to understand their dynamic characteristics, which ultimately enable effective control systems design. The problem of controlling the nonlinearity of CSTR is considered as a challenging issue especially for a control engineer corresponding to its non linear dynamics. Most of the traditional controllers are restricted just for linear time invariant system application. But in real world, the non linear characteristics of system and their function parameter changes due to wear and tear, that's why these changes can't be neglected. A simulation on mathematical model has several advantages over the experiment on a real model or system, which is used for steady state analysis and dynamic state analysis. Model predictive control (MPC) has been the most successful advanced control technique applied in the process industries, nowadays recognized as a standard methodology for the control of industrial and process systems. In this approach, a process model is used to predict the effect of a finite number of future moves on the controlled variable. From the transient responses of the controlled variable to the changes in manipulated variable, a dynamic network has been developed for prediction of one time step a forward in future process output with good accuracy. MPC refers to a class of computer controlled algorithms that utilize and explicit process model to predict the future response of the plant. At each control interval, an MPC algorithm attempts to optimize future plant behavior by computing a sequence of future manipulated variable adjustments. The first input in the optimal sequence is then sent into the plant and entire calculation is repeated at subsequent control intervals. The proposed MPC structure is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. The simulation results confirm that the MPC is one of the best possibilities for successful control of nonlinear systems.

All Your Base: a fast and accurate probabilistic approach to base calling.

The accuracy of base calls produced by Illumina sequencers is adversely affected by several processes, with laser cross-talk and cluster phasing being prominent. We introduce an explicit statistical model of the sequencing process that generalizes current models of phasing and cross-talk and forms the basis of a base calling method which improves on the best existing base callers, especially when comparing the number of error-free reads. The novel algorithms implemented in All Your Base (AYB) are comparable in speed to other competitive base-calling methods, do not require training data and are designed to be robust to gross errors, producing sensible results where other techniques struggle. AYB is available at http://www.ebi.ac.uk/goldman-srv/AYB/.

All your base: a fast and accurate probabilistic approach to base calling

The accuracy of base calls produced by Illumina sequencers is adversely affected by several processes, with laser cross-talk and cluster phasing being prominent. We introduce an explicit statistical model of the sequencing process that generalizes current models of phasing and cross-talk and forms the basis of a base calling method which improves on the best existing base callers, especially when comparing the number of error-free reads. The novel algorithms implemented in All Your Base (AYB) are comparable in speed to other competitive base-calling methods, do not require training data and are designed to be robust to gross errors, producing sensible results where other techniques struggle. AYB is available at http://www.ebi.ac.uk/goldman-srv/AYB/

A knowledge acquisition method to model parametric engineering design processes

This paper proposes an interview-style method to create models of engineering design processes intended for design automation. Development of design automation tools often requires a model of the design artefact, design process or design knowledge. However, the knowledge about product development often resides tacitly inside the mind of designers and is difficult to make explicit. The lack of explicit process models creates a challenge for organisations to improve their design processes. A method is proposed to create explicit models of tacit design knowledge of structured design problems. A standardised model of the design process is used to classify sets of information and processes. The content of the model is filled step-by-step through a series of questions. A prescriptive modelling method reduces the required effort for knowledge modelling and allows design processes to be supported more effectively. The method is demonstrated for a design case with tacit knowledge and a further nine cases are used to demonstrate scalability and comparison between design knowledge of mechanical components and complex product systems.

A new method to determine bird and bat fatality at wind energy turbines from carcass searches

AbstractWind energy is of increasing importance for a sustainable energy supply worldwide. At the same time, concerns about the number of birds and bats being killed at wind turbines have been growing. In this situation, methods for a reliable estimation of bird and bat fatality numbers are needed. To obtain an unbiased estimate of the number of fatalities from fatality searches, the probability to detect the carcass of an animal being killed at a turbine has to be assessed by considering carcass persistence rate, searcher efficiency and the probability that a killed animal falls into a searched area. Here, we describe a new formula to determine the detection probability of birds or bats that are killed at wind turbines and which can estimate the number of fatalities from the number of carcasses found. The formula was developed to analyse a large data set of bats killed at wind turbines in Germany. In simulations, we compared it to three other formulas used in this context. Our new formula seems to have unbiased results when searcher efficiency and carcass removal rate are constant over time. When searcher efficiency or carcass removal rate varied with time, all four formulas showed a similar bias. These comparative results can be used to choose between methods depending on the quality of information available. Our estimator can, for instance, be adapted to different situations including temporal changes of searcher efficiency or carcass removal rate because it is based on an explicit process model.

Aggregate and individual replication probability within an explicit model of the research process.

We study a model of the research process in which the true effect size, the replication jitter due to changes in experimental procedure, and the statistical error of effect size measurement are all normally distributed random variables. Within this model, we analyze the probability of successfully replicating an initial experimental result by obtaining either a statistically significant result in the same direction or any effect in that direction. We analyze both the probability of successfully replicating a particular experimental effect (i.e., the individual replication probability) and the average probability of successful replication across different studies within some research context (i.e., the aggregate replication probability), and we identify the conditions under which the latter can be approximated using the formulas of Killeen (2005a, 2007). We show how both of these probabilities depend on parameters of the research context that would rarely be known in practice. In addition, we show that the statistical uncertainty associated with the size of an initial observed effect would often prevent accurate estimation of the desired individual replication probability even if these research context parameters were known exactly. We conclude that accurate estimates of replication probability are generally unattainable.

Adaptive Control in an Axial Turbofan: Model-Free Implementation with Short Response Time

An adaptive control strategy is implemented in a single-input/single-output experiment to improve the aerodynamic performance of an axial turbofan. Pulsed blowing into the blade tip region is used to prevent flow separation. A slope-seeking control approach is shown to be able to extend the operating region of the engine without an explicit process model. By this, flow separation can be mitigated for smaller flow coefficients. The proposed closed-loop control strategy is capable of both driving the system back to stable operating conditions automatically and stabilizing operation in the presence of large-amplitude disturbances. Moreover, it is possible to accelerate the closed-loop control performance by one order of magnitude, compared with classical extremum-seeking control. By this, an extended Kalman filter is applied that enables a rapid determination of gradients of the corresponding characteristic diagram.

Climate, competition and connectivity affect future migration and ranges of European trees

ABSTRACTAim Species ranges have adapted during the Holocene to altering climate conditions, but it remains unclear if species will be able to keep pace with recent and future climate change. The goal of our study is to assess the influence of changing macroclimate, competition and habitat connectivity on the migration rates of 14 tree species. We also compare the projections of range shifts from species distribution models (SDMs) that incorporate realistic migration rates with classical models that assume no or unlimited migration.Location Europe.Methods We calibrated SDMs with species abundance data from 5768 forest plots from ICP Forest Level 1 in relation to climate, topography, soil and land‐use data to predict current and future tree distributions. To predict future species ranges from these models, we applied three migration scenarios: no migration, unlimited migration and realistic migration. The migration rates for the SDMs incorporating realistic migration were estimated according to macroclimate, inter‐specific competition and habitat connectivity from simulation experiments with a spatially explicit process model (TreeMig). From these relationships, we then developed a migration cost surface to constrain the predicted distributions of the SDMs.Results The distributions of early‐successional species during the 21st century predicted by SDMs that incorporate realistic migration matched quite well with the unlimited migration assumption (mean migration rate over Europe for A1fi/GRAS climate and land‐use change scenario 156.7 ± 79.1 m year−1 and for B1/SEDG 164.3 ± 84.2 m year−1). The predicted distributions of mid‐ to late‐successional species matched better with the no migration assumption (A1fi/GRAS, 15.2 ± 24.5 m year−1 and B1/SEDG, 16.0 ± 25.6 m year−1). Inter‐specific competition, which is higher under favourable growing conditions, reduced range shift velocity more than did adverse macroclimatic conditions (i.e. very cold or dry climate). Habitat fragmentation also led to considerable time lags in range shifts.Main conclusions Migration rates depend on species traits, competition, spatial habitat configuration and climatic conditions. As a result, re‐adjustments of species ranges to climate and land‐use change are complex and very individualistic, yet still quite predictable. Early‐successional species track climate change almost instantaneously while mid‐ to late‐ successional species were predicted to migrate very slowly.

Discrete-time model predictive contouring control for biaxial feed drive systems and experimental verification

In this paper we present a novel algorithm to model predictive contouring control for biaxial feed drive systems. model predictive control (MPC) refers to a class of model-based controllers that uses an explicit process model and tracking error dynamics to predict the future behavior of a plant, making it effective for machine tool feed drive systems that must achieve high-precision motion and are severely affected by friction, cutting force and changes in the workpiece mass. To improve contouring performance, we propose a new performance index in which error components orthogonal to the desired contour curve are given more importance than tracking errors with respect to each feed drive axis. Controller parameters are calculated in real time by solving an optimization problem. The parameters depend on the instantaneous slope of the reference trajectory and thus vary with time for curved reference trajectories, resulting in a time-varying controller. Weighting factors for the error components in orthogonal and tangential directions are used to adjust the error importance in each direction. In addition, to consider the required feed drive energy, the control inputs in both directions are included in the performance index. The effectiveness of the proposed control approach is demonstrated with an experimental biaxial feed drive system for circular and non-circular trajectories. The proposed contouring controller allows the feed drive to follow smooth curves and reduces contouring error.

Model Predictive Approach to Precision Contouring Control for Feed Drive Systems

Problem statement: High precision machining requires high capability of multi-axis feed drive systems to follow specified contour accuratel y. Although each feed drive axis is controlled independently in many industrial applications such as X-Y tables and Computer Numerical Control (CNC) machines, machining precision is evaluated by error components orthogonal to desired contour curve. Contouring controller design is required for precision machining, which should consider disturbance and dynamics variation such as friction , cutting force and workpiece mass change. Approach: This study applied model predictive design to cont ouring control systems. Model predictive control utilized an explicit process mod el and tracking error dynamics to predict the futur e behavior of a plant and hence it is effective for p recision machining in machine tool feed drives. To improve the contouring performance, a new performance index was proposed in which error components orthogonal to the desired contour curve are more important than tracking errors with respect to each feed drive axis. Controller paramet ers were calculated in real time by solving an optimization problem. Results: The proposed controller was evaluated by computer simulation for circular and non-circular trajectories. Weighting f actors of performance index terms were used as tuning factors of the proposed controller. Simulati on results showed that a better contouring performance can be obtained by choosing of the weighting factors in performance index items appropriately. Conclusion/Recommendations: A model predictive contouring controller for biaxi al feed drive systems was presented. Simulation result s demonstrated that the proposed approach can significantly improve the contouring accuracy.

Residual Stresses in Cold Spray Process Using Finite Element Analysis

In cold spray process, the simulation of coating deposition and the arising residual stress analysis are critical for an optimisation of process conditions. However, there are not many published literatures on the role of residual stresses in a cold sprayed coating. In addition, the multi-particles deposition behavior is also not well known, especially when coating of pure titanium powder is considered. This paper considers the development of an explicit finite element model of a cold spray process with defined initial and boundary environment. The explicit finite element model is then used to determine the optimum operating parameters to deposit titanium particles. It is also used to predict the residual stress developed in the coating by examining a fluid/structure interaction. The measured velocity and temperature from Computational Fluid Dynamics model are then use as initial input parameters to the explicit dynamic simulation. The predicted results reveal that Finite Element Method can be used to study the development of residual stress in a cold-sprayed coating as well as to find the optimum operating conditions to deposit coating of titanium particles before doing a real time fabrication.

TOWARDS AGENT-BASED MODELING AND VERIFICATION OF COLLABORATIVE BUSINESS PROCESSES: AN APPROACH CENTERED ON INTERACTIONS AND BEHAVIORS

This paper presents the process-oriented aspects of a formal and visual agent-based business process modeling language. The language is of use for (networks of) organizations that elect or envisage multi-agent systems for the support of collaborative business processes. The paper argues that the design of a collaborative business process should start with a proper understanding of the work practice of the agents in the business domain under consideration. The language introduces a novel diagram to represent the wide range of (cross-enterprise) business interactions as a hierarchy of role-based interactions (including their ordering relations) in a tree structure. The behaviors owned by the agents playing the roles in the tree are specified in separate process diagrams. A collaborative business process studied in the context of a case study at a Dutch gas transport company is used to exemplify the modeling approach. Explicit (agent-based) process models can and should be verified using formal methods. In the business process community, design-time verification of a process design is considered vital in order to ensure the correctness and termination of a collaborative business process. The proposed modeling approach is enhanced with a design-time verification method. The direction taken in this research is to combine the interaction tree and the associated agent behaviors into a verifiable hierarchical colored Petri net in order to take advantage of its well-defined (execution) semantics and proven (computerized) verification techniques. The verification method presented in this paper consists of three steps: (1) the translation of the agent-based process design to a hierarchical colored Petri net, (2) the identification of process design errors, and (3) the correction and rollback of process design errors to the agent-based model. The translation technique has been implemented in a software tool that outputs the hierarchical colored Petri net in a format that can be loaded in the widely used CPN Tools software package. Verification results are discussed for the case study model.

The interannual variability of Africa's ecosystem productivity: a multi-model analysis

Abstract. We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are partly corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.