Systems Modeling As A Means Of Building Accuate Mental Models Of Physiology Core Concepts In Undergraduate And Graduate Health Sciences Students

Accurate medical and health sciences problem solving relies upon a solid foundation of basic sciences content knowledge, primarily physiology. Yet, due to its nature as a dynamic system of interconnected, networked, concepts, physiology is often difficult for students to master. The three studies in this dissertation explore the use of a cognitive tool, systems modeling, to facilitate the development of an accurate mental model of physiology content knowledge in undergraduate and graduate physiology students. In the first study, undergraduate physiology student participation within online asynchronous peer group systems modeling activities was associated with progressive improvement on multiple choice question answer accuracy in the modeling condition versus the written discussion post condition. In the second study, graduate physician assistant students ranked systems modeling to be the top strategy for learning physiology content in the basic sciences year of study and the second to top strategy for retaining that content into the clinical year. In the third study, graduate physician assistant students demonstrated increased use of integrated core concept terms, after systems modeling activity participation, when describing the pathophysiology threshold concept of inflammation in writing. Together, these three studies provide evidence that the systems modeling strategy is an effective cognitive tool that contributes to improved student learning and retention of physiology content through visualization and subsequent refinement of the learner’s mental model of the problem space

Modeling dynamic reliability using dynamic Bayesian networks

This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this is the dynamic reliability. We consider discrete time case, that is the state of the system as well as the state of process variables are observed or measured at discrete time instants. A mathematical tool that shows interesting properties for modeling and analyzing this problem is the so called Dynamic Bayesian Networks (DBN) that permit graphical representation of stochastic processes. Furthermore their learning and inference capabilities can be exploited to take into account experimental data or expert’s knowledge. We will show that a complex interaction between system and process on one hand and between system, process and exogenous perturbation on the other hand can simply be represented graphically by a dynamic Bayesian network. With their extended tool, known as influence diagrams (ID) that integrate actions or decisions possibilities, one can analyze and optimize a maintenance policy and/or make reactive decision during an accident by simulating different scenarios of its evolution for instance

Computational Modeling of Complex Protein Activity Networks

Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a user-friendly computational tool, Analysis of Networks with Interactive MOdeling (ANIMO). ANIMO is a powerful tool to formalize knowledge on molecular interactions. This formalization entails giving a precise mathematical (formal) description of molecular states and of interactions between molecules. Such a model can be simulated, thereby in silico mimicking the processes that take place in the cell. In sharp contrast to classical graphical representations of molecular interaction networks, formal models allow in silico experiments and functional analysis of the dynamic behavior of the network. In addition, ANIMO was developed specifically for use by biologists who have little or no prior modeling experience. In this chapter, we guide the reader through the ANIMO workflow using osteoarthritis (OA) as a case study. WNT, IL-1β, and BMP signaling and cross talk are used as a concrete and illustrative model

Generating Computational Models for Serious Gaming

Westera, W. (2013, 25 October). Generating computational models for serious gaming. Presentation at the GALA Serious Gaming Conference, Paris, France.Many serious games include computational models that simulate dynamic systems. These models promote enhanced interaction and responsiveness. Under the social web paradigm more and more usable game authoring tools become available that enable prosumers to create their own games, but the inclusion of dynamic simulations remains a specialist’s job involving knowledge of mathematics, numerical modeling and programming. This presentation explains a methodology for specifying and running a specific subset of computational models without the need of bothering with mathematical equations. The methodology comprises a knowledge elicitation procedure for identifying and specifying the required model components, whereupon the mathematical model is automatically generated. The approach is based on the fact that many games focus on optimisation problems that are covered by a general class of linear programming models. The presentation thus sketches the principles of a creativity tool that removes barriers for harvesting the creative potential of teachers and students

Development of Dynamic Modeling Framework Using Convolution Neuron Network for Variable Refrigerant Flow Systems

Modeling the air conditioning system provides an excellent tool for system design, control, operation, and fault diagnosis. Such models were developed as either steady-state and transient models or knowledge-based and physics-based models. Most of the current studies mainly concentrated on physics-based models or steady-state models. Knowledge-based dynamic models were rarely discussed. In this paper, a knowledge-based dynamic model using a Convolutional Neural Network was developed for the air conditioning system. Instead of using operating parameters at a time point, we used the numbers in a time window as input data. We conducted a case study of the variable refrigerant flow system with field tests in an office building to validate this approach. It was found that the new method has a better accuracy within 2% deviation and a faster simulation speed in less than 1 second than the traditional physics-based model. The proposed method, which does not have a convergence problem, is user-friendly for non-experts. This approach also provides a way for existing systems to adjust operation parameters and detect faults. Future work can be making the current model more robust and reliable. In addition, how to combine the strengths of the knowledge-based method and physics-based methods needs to be further studied

Development of an intelligent interface for adding spatial objects to a knowledge-based geographic information system

Earth Scientists lack adequate tools for quantifying complex relationships between existing data layers and studying and modeling the dynamic interactions of these data layers. There is a need for an earth systems tool to manipulate multi-layered, heterogeneous data sets that are spatially indexed, such as sensor imagery and maps, easily and intelligently in a single system. The system can access and manipulate data from multiple sensor sources, maps, and from a learned object hierarchy using an advanced knowledge-based geographical information system. A prototype Knowledge-Based Geographic Information System (KBGIS) was recently constructed. Many of the system internals are well developed, but the system lacks an adequate user interface. A methodology is described for developing an intelligent user interface and extending KBGIS to interconnect with existing NASA systems, such as imagery from the Land Analysis System (LAS), atmospheric data in Common Data Format (CDF), and visualization of complex data with the National Space Science Data Center Graphics System. This would allow NASA to quickly explore the utility of such a system, given the ability to transfer data in and out of KBGIS easily. The use and maintenance of the object hierarchies as polymorphic data types brings, to data management, a while new set of problems and issues, few of which have been explored above the prototype level

OMiLAB: the Role of Model-driven Digital Innovation in Information Systems Development

OMiLAB is a community of practice interested in the value of conceptual models and the role they play in Information Systems development or operation. One key value proposition of the OMiLAB is a Digital Innovation environment having conceptual modeling at its core, as a means for integrating a business-oriented view with a technical view. The business-oriented view is based on a Digital Design Thinking method, whereas the technical view benefits from a diverse set of model-driven IoT devices for cyber-physical experimentation. The semantic and functional integrator between the two views is the BEE-UP modeling tool, together with the Agile Modeling Method Engineering framework - which can be employed to expand the modeling tool\u27s semantic space with domain-specific and technology-specific concepts or functionality. Academic and industry partners are joining the OMiLAB ecosystem as OMiLAB Nodes, sharing knowledge assets and artifacts developed with the help of OMiLAB\u27s Digital Innovation environment. These are disseminated via dedicated research streams and scientific events such as the NEMO summer school (initiated in 2014), the PROSE workshop (initiated in 2017) and a Springer book series on domain-specific conceptual modeling (initiated in 2016). Tool-specific tutorials have been held in recent Business Informatics and Information Systems conferences (e.g. HICSS, BIR, PoEM) to raise awareness on the value of conceptual models for such communities. Recently, the OMiLAB-FSEGA node was established at Babeș-Bolyai University, Faculty of Economics and Business Administration. The thematic focus of the node is Digital Business Models, targeting topics such as semantics of Product-Service Systems, their dynamic pricing, supplying and automated delivery from a design-oriented research perspective. In relation to this thematic specificity, the talk will highlight the value of this node for both research and education

Systems Biology by the Rules: Hybrid Intelligent Systems for Pathway Modeling and Discovery

Background: Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication. Results: A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet. Conclusion: This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer

Evaluación probabilística del peligro por lahares en el flanco NE del Volcán Popocatépetl

This study shows the results of a probabilistic evaluation of laharic hazard to Santiago Xalitzintla, locality in Puebla, at the NE flank of the Popocatépetl volcano in México. The TITAN2F software was used for lahars modeling. The program forecasts were compared with data obtained in the field for the laharic event of 2001 in order to evaluate the reliability of its use on a digital elevation model. The results obtained with TITAN2F are comparable with information reported previously in other studies of this lahar; coming to the conclusion that modeling with TITAN2F is reliable. This investigation provides a useful tool for the knowledge of laharic hazards. Also, it shows the probability of the affected area by inundation as well as the probability distribution of dynamic-pressure levels, which is an important parameter for assessment risk in a lahar flow. To make a probabilistic analysis is required a number of statistically representative hypothetic scenarios, covering all possible cases. According to historical events recorded, two possible sources for a laharic flow were defined, and they correspond to the Huiloac and Alseseca gorges. Based on the geological information, the ranges of initial conditions that TITAN2F requires (velocity, concentration and volume) were defined for each one of the basins. A stratified sampling was carried out using the Latin Hypercube method (LHS). This method generates a representative sample of hundreds of combinations from the initial conditions, in order to modeling laharic events with TITAN2F. The probabilistic analysis was made through Bayesian inference, and programming routines in OCTAVE. The probabilistic distribution indicates that there is a nearly 80 % probability to be reached by lahars at community areas of Santiago Xalitzintla. However it is characterized by low dynamic-pressure levels. The final section of the Huiloac gorge was identified as a critical zone, where the probability that dynamic-pressures surpassing destructive levels is high