Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Abnormal amygdala-prefrontal effective connectivity to happy faces differentiates bipolar from major depression

Background - Bipolar disorder is frequently misdiagnosed as major depressive disorder, delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation might therefore help discriminate bipolar from major depressive disorder. Methods - Thirty-one depressed individuals—15 bipolar depressed (BD) and 16 major depressed (MDD), DSM-IV diagnostic criteria, ages 18–55 years, matched for age, age of illness onset, illness duration, and depression severity—and 16 age- and gender-matched healthy control subjects performed two event-related paradigms: labeling the emotional intensity of happy and sad faces, respectively. We employed dynamic causal modeling to examine significant among-group alterations in effective connectivity (EC) between right- and left-sided neural regions supporting emotion regulation: amygdala and orbitomedial prefrontal cortex (OMPFC). Results - During classification of happy faces, we found profound and asymmetrical differences in EC between the OMPFC and amygdala. Left-sided differences involved top-down connections and discriminated between depressed and control subjects. Furthermore, greater medication load was associated with an amelioration of this abnormal top-down EC. Conversely, on the right side the abnormality was in bottom-up EC that was specific to bipolar disorder. These effects replicated when we considered only female subjects. Conclusions - Abnormal, left-sided, top-down OMPFC–amygdala and right-sided, bottom-up, amygdala–OMPFC EC during happy labeling distinguish BD and MDD, suggesting different pathophysiological mechanisms associated with the two types of depression.

Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder

Emotional liability and mood dysregulation characterize bipolar disorder (BD), yet no study has examined effective connectivity between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems subserving mood regulation in BD. Participants comprised 46 individuals (age range: 18-56 years): 21 with a DSM-IV diagnosis of BD, type I currently remitted; and 25 age- and gender-matched healthy controls (HC). Participants performed an event-related functional magnetic resonance imaging paradigm, viewing mild and intense happy and neutral faces. We employed dynamic causal modeling (DCM) to identify significant alterations in effective connectivity between BD and HC. Bayes model selection was used to determine the best model. The right parahippocampal gyrus (PHG) and right subgenual cingulate gyrus (sgCG) were included as representative regions of the ventromedial neural system. The right dorsolateral prefrontal cortex (DLPFC) region was included as representative of the dorsal/lateral neural system. Right PHG-sgCG effective connectivity was significantly greater in BD than HC, reflecting more rapid, forward PHG-sgCG signaling in BD than HC. There was no between-group difference in sgCG-DLPFC effective connectivity. In BD, abnormally increased right PHG-sgCG effective connectivity and reduced right PHG activity to emotional stimuli suggest a dysfunctional ventromedial neural system implicated in early stimulus appraisal, encoding and automatic regulation of emotion that may represent a pathophysiological functional neural mechanism for mood dysregulation in BD.

Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

Modeling and validating dynamic adaptation

This paper discusses preliminary work on modeling and validation dynamic adaptation. The proposed approach is on the use of aspect-oriented modeling (AOM) and models at runtime. Our approach covers design and runtime phases. At design-time, a base model and different variant architecture models are designed and the adaptation model is built. Crucially, the adaptation model includes invariant properties and constraints that allow the validation of the adaptation rules before execution. During runtime, the adaptation model is processed to produce a correct system configuration that can be executed.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Business process orientated holonic (PrOH) modeling

Purpose: The purpose of this paper is to describe how the application of systems thinking to designing, managing and improving business processes has resulted in a new and unique holonic-based process modeling methodology know as process orientated holonic modeling. Design/methodology/approach: The paper describes key systems thinking axioms that are built upon in an overview of the methodology; the techniques are described using an example taken from a large organization designing and manufacturing capital goods equipment operating within a complex and dynamic environment. These were produced in an 18 month project, using an action research approach, to improve quality and process efficiency. Findings: The findings of this research show that this new methodology can support process depiction and improvement in industrial sectors which are characterized by environments of high variety and low volume (e.g. projects; such as the design and manufacture of a radar system or a hybrid production process) which do not provide repetitive learning opportunities. In such circumstances, the methodology has not only been able to deliver holonic-based process diagrams but also been able to transfer strategic vision from top management to middle and operational levels without being reductionistic. Originality/value: This paper will be of interest to organizational analysts looking at large complex projects whom require a methodology that does not confine them to thinking reductionistically in "task-breakdown" based approaches. The novel ideas in this paper have great impact on the way analysts should perceive organizational processes. Future research is applying the methodology in similar environments in other industries. © Emerald Group Publishing Limited.

Building a holarchy using business process-oriented holonic (PrOH) modeling

The application of systems thinking to designing, managing, and improving business processes has developed a new "holonic-based" process modeling methodology. The theoretical background and the methodology are described using examples taken from a large organization designing and manufacturing capital goods equipment operating within a complex and dynamic environment. A key point of differentiation attributed to this methodology is that it allows a set of models to be produced without taking a task breakdown approach but instead uses systems thinking and a construct known as the "holon" to build process descriptions as a system of systems (i.e., a holarchy). The process-oriented holonic modeling methodology has been used for total quality management and business process engineering exercises in different industrial sectors and builds models that connect the strategic vision of a company to its operational processes. Exercises have been conducted in response to environmental pressures to make operations align with strategic thinking as well as becoming increasingly agile and efficient. This unique methodology is best applied in environments of high complexity, low volume, and high variety, where repeated learning opportunities are few and far between (e.g., large development projects). © 2007 IEEE.

Hydrodynamic modeling of mineral wool fiber suspensions in a two-dimensional channel flow

A consequence of a loss of coolant accident is the damage of adjacent insulation materials (IM). IM may then be transported to the containment sump strainers where water is drawn into the ECCS (emergency core cooling system). Blockage of the strainers by IM lead to an increased pressure drop acting on the operating ECCS pumps. IM can also penetrate the strainers, enter the reactor coolant system and then accumulate in the reactor pressure vessel. An experimental and theoretical study that concentrates on mineral wool fiber transport in the containment sump and the ECCS is being performed. The study entails fiber generation and the assessment of fiber transport in single and multi-effect experiments. The experiments include measurement of the terminal settling velocity, the strainer pressure drop, fiber sedimentation and resuspension in a channel flow and jet flow in a rectangular tank. An integrated test facility is also operated to assess the compounded effects. Each experimental facility is used to provide data for the validation of equivalent computational fluid dynamic models. The channel flow facility allows the determination of the steady state distribution of the fibers at different flow velocities. The fibers are modeled in the Eulerian-Eulerian reference frame as spherical wetted agglomerates. The fiber agglomerate size, density, the relative viscosity of the fluid-fiber mixture and the turbulent dispersion of the fibers all affect the steady state accumulation of fibers at the channel base. In the current simulations, two fiber phases are separately considered. The particle size is kept constant while the density is modified, which affects both the terminal velocity and volume fraction. The relative viscosity is only significant at higher concentrations. The numerical model finds that the fibers accumulate at the channel base even at high velocities; therefore, modifications to the drag and turbulent dispersion forces can be made to reduce fiber accumulation.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty

Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX specification language can be used to specify more flexible requirements within a goal model to handle the uncertainty. © 2009 Springer Berlin Heidelberg.

Weak form equation–based finite-element modeling of viscoelastic asphalt mixtures

The objective of this study is to demonstrate using weak form partial differential equation (PDE) method for a finite-element (FE) modeling of a new constitutive relation without the need of user subroutine programming. The viscoelastic asphalt mixtures were modeled by the weak form PDE-based FE method as the examples in the paper. A solid-like generalized Maxwell model was used to represent the deforming mechanism of a viscoelastic material, the constitutive relations of which were derived and implemented in the weak form PDE module of Comsol Multiphysics, a commercial FE program. The weak form PDE modeling of viscoelasticity was verified by comparing Comsol and Abaqus simulations, which employed the same loading configurations and material property inputs in virtual laboratory test simulations. Both produced identical results in terms of axial and radial strain responses. The weak form PDE modeling of viscoelasticity was further validated by comparing the weak form PDE predictions with real laboratory test results of six types of asphalt mixtures with two air void contents and three aging periods. The viscoelastic material properties such as the coefficients of a Prony series model for the relaxation modulus were obtained by converting from the master curves of dynamic modulus and phase angle. Strain responses of compressive creep tests at three temperatures and cyclic load tests were predicted using the weak form PDE modeling and found to be comparable with the measurements of the real laboratory tests. It was demonstrated that the weak form PDE-based FE modeling can serve as an efficient method to implement new constitutive models and can free engineers from user subroutine programming.

On modeling and satisfaction of non-functional requirements using cloud computing

Specification of the non-functional requirements of applications and determining the required resources for their execution are activities that demand a great deal of technical knowledge, frequently resulting in an inefficient use of resources. Cloud computing is an alternative for provisioning of resources, which can be done using either the provider's own infrastructure or the infrastructure of one or more public clouds, or even a combination of both. It enables more flexibly/elastic use of resources, but does not solve the specification problem. In this paper we present an approach that uses models at runtime to facilitate the specification of non-functional requirements and resources, aiming to facilitate dynamic support for application execution in cloud computing environments with shared resources. © 2013 IEEE.

Theoretical modeling and experimental validation of fluid-loaded micro rectangular plates

This paper presents a theoretical model on the vibration analysis of micro scale fluid-loaded rectangular isotropic plates, based on the Lamb's assumption of fluid-structure interaction and the Rayleigh-Ritz energy method. An analytical solution for this model is proposed, which can be applied to most cases of boundary conditions. The dynamical experimental data of a series of microfabricated silicon plates are obtained using a base-excitation dynamic testing facility. The natural frequencies and mode shapes in the experimental results are in good agreement with the theoretical simulations for the lower order modes. The presented theoretical and experimental investigations on the vibration characteristics of the micro scale plates are of particular interest in the design of microplate based biosensing devices. Copyright © 2009 by ASME.