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Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Clouds introducing in its computing nodes, processors with different core architectures, accelerators (i.e. GPUs, FPGAs), as well as different memories and storage devices in order to achieve better performance with lower energy consumption. As a consequence of this heterogeneity, programming applications for these distributed heterogeneous platforms becomes a complex task. Additionally to the complexity of developing an application for distributed platforms, developers must also deal now with the complexity of the different computing devices inside the node. In this article, we present a programming model that aims to facilitate the development and execution of applications in current and future distributed heterogeneous parallel architectures. This programming model is based on the hierarchical composition of the COMP Superscalar and Omp Superscalar programming models that allow developers to implement infrastructure-agnostic applications. The underlying runtime enables applications to adapt to the infrastructure without the need of maintaining different versions of the code. Our programming model proposal has been evaluated on real platforms, in terms of heterogeneous resource usage, performance and adaptation. ; This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program under contract 687584 (TANGO project) by the Spanish Government under contract TIN2015-65316 and grant SEV-2015-0493 (Severo Ochoa Program) and by Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272. ; Peer Reviewed ; Postprint (author's final draft)

Modern information technology increases the use of computers in training systems as well as in command-and-control systems in military services and public-safety organizations. This computerization combined with new threats present a challenging complexity. Situational awareness in evolving distributed operations and follow-up in training systems depends on humans in the field reporting observations of events. The use of this observer-reported information can be largely improved by implementation of models supporting both reporting and computer representation of objects and phenomena in operations. This thesis characterises and describes observer model-based data collection in distributed tactical operations, where multiple, dispersed units work to achieve common goals. Reconstruction and exploration of multimedia representations of operations is becoming an established means for supporting taskforce training. We explore how modelling of operational processes and entities can support observer data collection and increase information content in mission histories. We use realistic exercises for testing developed models, methods and tools for observer data collection and transfer results to live operations. The main contribution of this thesis is the systematic description of the model-based approach to using observers for data collection. Methodological aspects in using humans to collect data to be used in information systems, and also modelling aspects for phenomena occurring in emergency response and communication areas contribute to the body of research. We describe a general methodology for using human observers to collect adequate data for use in information systems. In addition, we describe methods and tools to collect data on the chain of medical attendance in emergency response exercises, and on command-and-control processes in several domains.

Situation awareness in teams : Models and measures / Mica Endsley -- Studying team cognition in the C3 fire microworld / BjörnJ. E. Johansson, Rego Granlund, and Peter Berggren -- The dynamical systems approach to team cognition : Theory, models, and measures / Terri A. Dunbar, Jamie C. Gorman, David A. Grimm, and Adam Werner.

Situation awareness in teams : Models and measures / Mica Endsley -- Studying team cognition in the C3 fire microworld / BjörnJ. E. Johansson, Rego Granlund, and Peter Berggren -- The dynamical systems approach to team cognition : Theory, models, and measures / Terri A. Dunbar, Jamie C. Gorman, David A. Grimm, and Adam Werner.

The distributed lag model is a regression model that describes the relationship between the dependent variable of a given period and the independent variables of a certain or previous periods. The model can be used to determine the impact of the independent variable to dependent variables over time and forecast time series data for the next periods. There are two forms of distributed lag model that have been widely proposed in the estimation of distributed lag regression model. The first form is proposed by Koyck and the second form by Almon. This paper aims to apply the Almon model to examine the effect of the ratio of BOPO (Operating Cost and Operating Income) to the ROA (Return on Asset) of a government bank based on quarterly data, to estimate its parameters, to examine the feasibility of the model, and to predict the next quarter. Results shows that distributed lag model is = 10.110 - 0.078 + 0.015 + 0.026 – 0.045 with Yt is ROA, and Xt is the ratio BOPO on the 1st quarter until the previous 3 quarters. The model is quite good according to the determination coefficient that is 0.75, no autocorrelation in the model, t test and F test are also significant. Based on the model, the value of ROA ratio next quarter predicted 4.63%. The decrease in profitability ROA ratio is due to an increase in interest expense while interest income can not compensate