Defining and validating a multimodel approach for product architecture derivation and improvement

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41533-3_24Software architectures are the key to achieving the non-functional requirements (NFRs) in any software project. In software product line (SPL) development, it is crucial to identify whether the NFRs for a specific product can be attained with the built-in architectural variation mechanisms of the product line architecture, or whether additional architectural transformations are required. This paper presents a multimodel approach for quality-driven product architecture derivation and improvement (QuaDAI). A controlled experiment is also presented with the objective of comparing the effectiveness, efficiency, perceived ease of use, intention to use and perceived usefulness with regard to participants using QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). The results show that QuaDAI is more efficient and perceived as easier to use than ATAM, from the perspective of novice software architecture evaluators. However, the other variables were not found to be statistically significant. Further replications are needed to obtain more conclusive results.This research is supported by the MULTIPLE project (MICINN TIN2009-13838) and the Vali+D fellowship program (ACIF/2011/235).González Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM. (2013). Defining and validating a multimodel approach for product architecture derivation and improvement. En Model-Driven Engineering Languages and Systems. Springer. 388-404. https://doi.org/10.1007/978-3-642-41533-3_24S388404Ali-Babar, M., Lago, P., Van Deursen, A.: Empirical research in software architecture: opportunities, challenges, and approaches. 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Microbes as engines of ecosystem function : When does community structure enhance predictions of ecosystem processes?

FUNDING This work was supported by NSF grant DEB-1221215 to DN, as well as grants supporting the generation of our datasets as acknowledged in their original publications and in Supplementary Table S1. ACKNOWLEDGMENT We thank the USGS Powell Center ‘Next Generation Microbes’ working group, anonymous reviews, Brett Melbourne, and Alan Townsend for valuable feedback on this project.Peer reviewedPublisher PD

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

Context: Software architectures should be evaluated during the early stages of software development in order to verify whether the non-functional requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in software product line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development. Objective: We present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI. Method: The family of experiments was carried out by 92 participants: Computer Science Master s and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). Results: The main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results. Conclusions: The results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.The authors would like to thank all the participants in the experiments for their selfless involvement in this research. This research is supported by the MULTIPLE Project (MICINN TIN2009-13838) and the ValI+D Program (ACIF/2011/235).González Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM.; Scanniello, G. (2015). Validating a model-driven software architecture evaluation and improvement method: A family of experiments. Information and Software Technology. 57:405-429. https://doi.org/10.1016/j.infsof.2014.05.018S4054295

The Middeck Active Control Experiment (MACE)

The Middeck Active Control Experiment (MACE) is a NASA In-Step and Control Structure Interaction (CSI) Office funded Shuttle middeck experiment. The objective is to investigate the extent to which closed-loop behavior of flexible spacecraft in zero-gravity (0-g) can be predicted. This prediction becomes particularly difficult when dynamic behavior during ground testing exhibits extensive suspension and direct gravity coupling. On-orbit system identification and control reconfiguration is investigated to improve performance which would otherwise be limited due to errors in prediction. The program is presently in its preliminary design phase with launch expected in the summer of 1994. The MACE test article consists of three attitude control torque wheels, a two axis gimballing payload, inertial sensors and a flexible support structure. With the acquisition of a second payload, this will represent a multiple payload platform with significant structural flexibility. This paper presents on-going work in the areas of modelling and control of the MACE test article in the zero and one-gravity environments. Finite element models, which include suspension and gravity effects, and measurement models, derived from experimental data, are used as the basis for Linear Quadratic Gaussian controller designs. Finite element based controllers are analytically used to study the differences in closed-loop performance as the test article transitions between the 0-g and 1-g environments. Measurement based controllers are experimentally applied to the MACE test article in the 1-g environment and achieve over an order of magnitude improvement in payload pointing accuracy when disturbed by a broadband torque disturbance. The various aspects of the flight portion of the experiment are also discussed

Statistical inference and spatial patterns in correlates of IQ

Cross-national comparisons of IQ have become common since the release of a large dataset of international IQ scores. However, these studies have consistently failed to consider the potential lack of independence of these scores based on spatial proximity. To demonstrate the importance of this omission, we present a re-evaluation of several hypotheses put forward to explain variation in mean IQ among nations namely: (i) distance from central Africa, (ii) temperature, (iii) parasites, (iv) nutrition, (v) education, and (vi) GDP. We quantify the strength of spatial autocorrelation (SAC) in the predictors, response variables and the residuals of multiple regression models explaining national mean IQ. We outline a procedure for the control of SAC in such analyses and highlight the differences in the results before and after control for SAC. We find that incorporating additional terms to control for spatial interdependence increases the fit of models with no loss of parsimony. Support is provided for the finding that a national index of parasite burden and national IQ are strongly linked and temperature also features strongly in the models. However, we tentatively recommend a physiological – via impacts on host–parasite interactions – rather than evolutionary explanation for the effect of temperature. We present this study primarily to highlight the danger of ignoring autocorrelation in spatially extended data, and outline an appropriate approach should a spatially explicit analysis be considered necessary

Used-habitat calibration plots: a new procedure for validating species distribution, resource selection, and step-selection models

“Species distribution modeling” was recently ranked as one of the top five “research fronts” in ecology and the environmental sciences by ISI's Essential Science Indicators (Renner and Warton 2013), reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non-linearity, and multicollinearity. Identifying habitat characteristics that are not well-predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations

Multimodel Operability Framework for Design of Modular and Intensified Energy Systems

In this dissertation, a novel operability framework is introduced for the process design of modular and intensified energy systems that are challenged by complexity and highly constrained environments. Previously developed process operability approaches are reviewed and further developed in terms of theory, application, and software infrastructure. An optimization-based multilayer operability framework is introduced for process design of nonlinear energy systems. In the first layer of this framework, a mixed-integer linear programming (MILP)-based iterative algorithm considers the minimization of footprint and achievement of process intensification targets. Then, in the second layer, an operability analysis is performed to incorporate key features of optimality and feasibility accounting for the system achievability and flexibility. The outcome of this framework consists of a set of modular designs, considering both the aspects of size and process operability. For this study and throughout this dissertation, the nonlinear system is represented by multiple linearized models, which results in lower computational expense and more efficient quantification of operability regions. A systematic techno-economic analysis framework is also proposed for costing intensified modular systems. Conventional costing techniques are extended to allow estimation of capital and operating costs of modular units. Economy of learning concepts are included to consider the effect of experience curves on purchase costs. Profitability measures are scaled with respect to production of a chemical of interest for comparison with plants of traditional scale. Scenarios in which the modular technology presents break-even or further reduction in cost when compared to the traditional process are identified as a result. A framework for the development of process operability algorithms is provided as a software infrastructure outcome. Generated codes from the developed approaches are included in an open-source platform that will give researchers from academia and industry access to the algorithms. This platform has the purpose of dissemination and future improvement of process operability algorithms and methods. To show versatility and efficacy of the developed approaches, a variety of applications are considered as follows: a membrane reactor for direct methane aromatization conversion to hydrogen and benzene (DMA-MR), the classical shower problem in process operability, a power plant cycling application for power generation with penetration of renewable energy sources, and a newly developed modular hydrogen unit. Applications to DMA-MR subsystems demonstrate employment of the multilayer framework to find a region with modular design candidates, which are then ranked according to an operability index. The most operable design is determined and contrasted with the optimal design with respect to process intensification in terms of footprint minimization, showing that optimality at fixed nominal operations does not necessarily ensure the best system operability. For the modular hydrogen unit application, the developed process operability framework provides guidelines for obtaining modular designs that are highly integrated and flexible with respect to disturbances in inlet natural gas composition. The modular hydrogen unit is also used for demonstration of the proposed techno-economic analysis framework. A comparison with a benchmark conventional steam methane reforming plant shows that the modular hydrogen unit can benefit from the economy of learning. An assembled modular steam methane reforming plant is used to map the decrease in natural gas price that must be needed for the plant to break even when compared to traditional technologies. Scenarios in which the natural gas price is low allow break-even cost for both individual hydrogen units and the assembled modular plant. The economy of learning must produce a reduction of 40% or less in capital cost when the natural gas price is under 0.02 US$/Sm3. This result suggests that the synthesized modular hydrogen process has potential to be economically feasible under these conditions. The developed tools can be used to accelerate the deployment and manufacturing of standardized modular energy systems

Big Data Now, 2015 Edition

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data