Mark Allison

Abnormal left ventricular diastolic filling in eccentric left ventricular hypertrophy of obesity

Left ventricular (LV) diastolic filling pattern of obese subjects with eccentric LV hypertrophy was studied. Findings were compared with those of normal control subjects and hypertensive patients with concentric LV hypertrophy. M-mode, 2-dimensional and Doppler echocardiograms were recorded in 11 obese (body mass index greater than 30 kg/m2) normotensive patients with eccentric LV hypertrophy, 10 normal control subjects, and 18 nonobese, hypertensive patients with concentric LV hypertrophy whose antihypertensive medications were discontinued 2 weeks before study. LV hypertrophy was defined as LV mass/height greater than 143 g/m. Hypertrophy in the obese patients was eccentric: Their LV internal dimension (61 +/- 3 mm) was greater than that of hypertensive patients (55 +/- 5 mm, p less than 0.001) and normal control subjects (55 +/- 2 mm, p less than 0.01); their septal (10.7 +/- 0.7 mm) and posterior (10.9 +/- 0.6 mm) wall thicknesses were smaller than those of the hypertensive patients (12.2 +/- 1.7 mm, p less than 0.05 and 11.7 +/- 1.2 mm, respectively, difference not significant). Pulsed-wave Doppler echocardiographic filling indexes were used to evaluate LV diastolic filling. Obese patients had a higher peak velocity of atrial filling (69 +/- 14 vs 54 +/- 15 cm/s, p less than 0.05), lower early/atrial filling velocity ratio (1.0 +/- 0.26 vs 1.32 +/- 0.21, p less than 0.05), prolonged deceleration half-time (108 +/- 9 vs 86 +/- 15 ms, p less than 0.01) and lower peak filling rate corrected to stroke volume (4.08 +/- 0.68 vs 4.96 +/- 0.88 stroke volume/s, p less than 0.05) than normal control subjects.(ABSTRACT TRUNCATED AT 250 WORDS)

Towards Reliable Smart Microgrid Behavior Using Runtime Model Synthesis

The dominant paradigm of centralized power generation, characterized by heavy transmission losses, is being slowly replaced by the smart micro grid, which promises the proliferation of renewable and distributed energy sources. Micro grid reliability is a well established theme as assurance requirements are inherited from the larger smart grid. In this paper we describe how user defined domain-specific micro grid models can be synthesized using runtime model analysis thereby supporting stability in the micro grid plant. This analysis includes model reconciliation which produces a list of model changes that are then interpreted to control the plant via executable control scripts. To demonstrate the efficacy and applicability of our approach, we apply it to a typical scenario in the energy management domain and prove the concept utilizing a smart micro grid prototype test bed.

Synthesizing interpreted domain-specific models to manage smart microgrids

Abstract The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM). In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.

An adaptive middleware design to support the dynamic interpretation of domain-specific models

ContextAs the use of Domain-Specific Modeling Languages (DSMLs) continues to gain popularity, we have developed new ways to execute DSML models. The most popular approach is to execute code resulting from a model-to-code transformation. An alternative approach is to directly execute these models using a semantic-rich execution engine - Domain-Specific Virtual Machine (DSVM). The DSVM includes a middleware layer responsible for the delivery of services in a given domain. ObjectiveWe will investigate an approach that performs the dynamic combination of constructs in the middleware layer of DSVMs to support the delivery of domain-specific services. This middleware should provide: (a) a model of execution (MoE) that dynamically integrates decoupled domain-specific knowledge (DSK) for service delivery, (b) runtime adaptability based on context and available resources, and (c) the same level of operational assurance as any DSVM middleware. MethodOur approach will involve (1) defining a framework that supports the dynamic combination of MoE and DSK and (2) demonstrating the applicability of our framework in the DSVM middleware for user-centric communication. We will measure the overhead of our approach and provide a cost-benefit analysis factoring in its runtime adaptability using appropriate experimentation. ResultsOur experiments show that combining the DSK and MoE for a DSVM middleware allow us to realize efficient specialization while maintaining the required operability. We also show that the overhead introduced by adaptation is not necessarily deleterious to overall performance in a domain as it may result in more efficient operation selection. ConclusionThe approach defined for the DSVM middleware allows for greater flexibility in service delivery while reducing the complexity of application development for the user. These benefits are achieved at the expense of increased execution times, however this increase may be negligible depending on the domain.

Towards interpreting models to orchestrate IaaS multi-cloud infrastructures

One challenge to the cloud computing paradigm is the task complexity associated with designing and managing multi-cloud solutions based on operational objectives. Heterogeneous vendor interfaces and a lack of standardization compounds this complexity and may eventually lead to vendor lock-in. In this article we present a model driven approach to allowing network administrators to intuitively describe and rapidly realize non-trivial IaaS behavior in realtime. We have developed iCloudML, an interpreted domain-specific modeling language and its interpreter as tooling support for the domain.

Power demand prediction in smart microgrids using interacting multiple model Kalman filtering

Optimized management of energy resources within smart microgrids may require an approximation of near future power demands to institute efficient scheduling of tasks. Since demands are volatile in shorter time spans, localized short-term prediction of demand is non-trivial. Local prediction requires efficiency of calculations to minimize computational resources. To address this concern we present a predictor based on the Interacting Multiple Model Kalman filters. This approach supports effective year round prediction while only storing two model demand profiles. We demonstrate that the approach provides consistency in intra-day demand prediction across an entire year.

A Generic Model of Execution for Synthesizing Interpreted Domain-Specific Models

The prevalent application of domain-specific modeling languages (DSMLs) requires developers to initially specify the requirements for a software product as a domain-specific model then transform that model to a high-level language for subsequent execution. An alternative is to realize behavior directly by executing the models using a specialized interpreter. One category of interpreted domain-specific modeling languages (DSMLs) derives behavior from changes to models at runtime. These are termed interpreted DSMLs or simply i-DSMLs. Existing interpreters for i-DSMLs exhibit tight coupling between the implicit model of execution (MoE) and the semantics of the domain. The interweaving of these two concerns compounds the challenge of developing interpreters for new i-DSMLs without a significant investment in resources. This paper introduces a generalized approach to developing i-DSML interpreters by utilizing a generic framework that is loosely coupled to the domain-specific knowledge as swappable framework extensions. We present a prototype as validation of our approach implemented using a metamodel based architecture to instantiate the interpreter for two distinct cyber-physical domains. c 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of The 2015 International Conference on Soft Computing and Software Engineering (SCSE 2015).

An adaptive delivery strategy for teaching software testing and maintenance

Todays classroom and learner cohorts are supported by new and innovative technology in an unprecedented manner. The pace of technological advancements however does present challenges to educators in keeping abreast of the state of the art while concurrently facilitating a learner-certered approach. Topics within software engineering are especially susceptible to this phenomena and demands a more individualized adaptive model to support significant learning. Although there is an abundance of sound theoretical models which may address the challenge, the literature is sparse as to contextualization, application or concrete operation. Within a blended classroom, we have implemented an adaptive approach to address the pace of technology advancements with consideration for the skillsets and declarative knowledge of the learner. In this paper we present our approach within a software testing and maintenance course and discuss the lessons learnt. We have based our approach on the scientific grounding of Vygotskys zone of proximal development and discuss the necessary scaffolding, inherent challenges and present an evaluation based on pre/post testing.

Towards a flipped cyber classroom to facilitate active learning strategies

Success within the distance learning paradigm typically requires a strong intrinsic motivation on the part of the learner. This motivation needs to be matched with effective delivery of engaging course content. Developing an active engagement environment for deep learning is a nontrivial task as the instructor is not physically present. To address the concern in an online/blended delivery model, we had developed and examined our first cyber classroom, in which lectures are automatically recorded and posted to a website for subsequent online consumption. The observed limitations of this approach were twofold: (1) online student participation in onground classroom activities was unfeasible; and (2) onground student collaborative activities were severely curtailed by the classroom layout. In this work in progress we present our revised concept grounded in active learning scientific principles combined with lessons learned from our prior approach. By leveraging classroom spatial layout best practices combined with the apropos technology, we seek to address the aforementioned two concerns. Specifically we present a description of our revised cyber classroom, along with plans for future application involving integrating online learning with the flipped classroom concept.