GMF: A Model Migration Case for the Transformation Tool Contest

Using a real-life evolution taken from the Graphical Modeling Framework, we invite submissions to explore ways in which model transformation and migration tools can be used to migrate models in response to metamodel adaptation.Comment: In Proceedings TTC 2011, arXiv:1111.440

A Solution to the Flowgraphs Case Study using Triple Graph Grammars and eMoflon

After 20 years of Triple Graph Grammars (TGGs) and numerous actively maintained implementations, there is now a need for challenging examples and success stories to show that TGGs can be used for real-world bidirectional model transformations. Our primary goal in recent years has been to increase the expressiveness of TGGs by providing a set of pragmatic features that allow a controlled fallback to programmed graph transformations and Java. Based on the Flowgraphs case study of the Transformation Tool Contest (TTC 2013), we present (i) attribute constraints used to express complex bidirectional attribute manipulation, (ii) binding expressions for specifying arbitrary context relationships, and (iii) post-processing methods as a black box extension for TGG rules. In each case, we discuss the enabled trade-off between guaranteed formal properties and expressiveness. Our solution, implemented with our metamodelling and model transformation tool eMoflon (www.emoflon.org), is available as a virtual machine hosted on Share.Comment: In Proceedings TTC 2013, arXiv:1311.753

Photodegradation disproportionately impacts biodegradation of semi‐labile DOM in streams

Exposure of dissolved organic matter (DOM) to sunlight can increase or decrease the fraction that is biodegradable (BDOM), but conceptual models fail to explain this dichotomy. We investigated the effect of sunlight exposure on BDOM, addressing three knowledge gaps: (1) how fractions of DOM overlap in their susceptibility to degradation by sunlight and microbes, (2) how the net effect of sunlight on BDOM changes with photon dose, and (3) how rates of DOM photodegradation and biodegradation compare in a stream. Stream waters were exposed to sunlight, and then fed through bioreactors designed to separate labile and semi‐labile pools within BDOM. The net effects of photodegradation on DOM biodegradability, while generally positive, represented the balance between photochemical production and removal of BDOM that was mediated by photon dose. By using sunlight exposure times representative of sunlight exposures in a headwater stream and bioreactors colonized with natural communities and scaled to whole‐stream dynamics, we were able to relate our laboratory findings to the stream. The impact of sunlight exposure on rates of DOM biodegradation in streams was calculated using rates of light absorption by chromophoric DOM, apparent quantum yields for photomineralization and photochemical alteration of BDOM, and mass transfer coefficients for labile and semi‐labile DOM. Rates of photochemical alteration of labile DOM were an order of magnitude lower than rates of biodegradation of labile DOM, but for semi‐labile DOM, these rates were similar, suggesting that sunlight plays a substantial role in the fate of semi‐labile DOM in streams.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153758/1/lno11244.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153758/2/lno11244_am.pd

A Systematic Approach for Designing Mutation Operators for MDE languages

Testing is an essential activity in software development, used to increase confidence in the quality of software. One testing approach that is used to evaluate the quality of testing inputs for a particular program is mutation analysis. The most important step in mutation analysis is the process of defining mutation operators that mimic typical errors of the users of a language. There is a wide variety of mutation operators that have been designed for a number of languages including C, Java, and SQL. However, the design of mutation operators is rarely systematic, which may result in passing over crucial operators for specific features of languages. This paper describes a way to apply mutation analysis in the context of Model Driven Engineering (MDE). In particular, the paper proposes a systematic approach for designing mutation operators for MDE languages. The systematic approach is demonstrated for the Atlas Transformation Language (ATL) and the result is a list of mutation operators that includes previously designed ones for ATL from the literature

Saying Hello World with Epsilon - A Solution to the 2011 Instructive Case

Epsilon is an extensible platform of integrated and task-specific languages for model management. With solutions to the 2011 TTC Hello World case, this paper demonstrates some of the key features of the Epsilon Object Language (an extension and reworking of OCL), which is at the core of Epsilon. In addition, the paper introduces several of the task-specific languages provided by Epsilon including the Epsilon Generation Language (for model-to-text transformation), the Epsilon Validation Language (for model validation) and Epsilon Flock (for model migration).Comment: In Proceedings TTC 2011, arXiv:1111.440

A Coupled Geochemical and Biogeochemical Approach to Characterize the Bioreactivity of Dissolved Organic Matter From a Headwater Stream

The bioreactivity or susceptibility of dissolved organic matter (DOM) to microbial degradation in streams and rivers is of critical importance to global change studies, but a comprehensive understanding of DOM bioreactivity has been elusive due, in part, to the stunningly diverse assemblages of organic molecules within DOM. We approach this problem by employing a range of techniques to characterize DOM as it flows through biofilm reactors: dissolved organic carbon (DOC) concentrations, excitation emission matrix spectroscopy (EEMs), and ultrahigh resolution mass spectrometry. The EEMs and mass spectral data were analyzed using a combination of multivariate statistical approaches. We found that 45% of stream water DOC was biodegraded by microorganisms, including 31-45% of the humic DOC. This bioreactive DOM separated into two different groups: (1) H/C centered at 1.5 with O/C 0.1-0.5 or (2) low H/C of 0.5-1.0 spanning O/C 0.2-0.7 that were positively correlated (Spearman ranking) with chromophoric and fluorescent DOM (CDOM and FDOM, respectively). DOM that was more recalcitrant and resistant to microbial degradation aligned tightly in the center of the van Krevelen space (H/C 1.0-1.5, O/C 0.25-0.6) and negatively correlated (Spearman ranking) with CDOM and FDOM. These findings were supported further by principal component analysis and 2-D correlation analysis of the relative magnitudes of the mass spectral peaks assigned to molecular formulas. This study demonstrates that our approach of processing stream water through bioreactors followed by EEMs and FTICR-MS analyses, in combination with multivariate statistical analysis, allows for precise, robust characterization of compound bioreactivity and associated molecular level composition

A coupled geochemical and biogeochemical approach to characterize the bioreactivity of dissolved organic matter from a headwater stream

The bioreactivity or susceptibility of dissolved organic matter (DOM) to microbial degradation in streams and rivers is of critical importance to global change studies, but a comprehensive understanding of DOM bioreactivity has been elusive due, in part, to the stunningly diverse assemblages of organic molecules within DOM. We approach this problem by employing a range of techniques to characterize DOM as it flows through biofilm reactors: dissolved organic carbon (DOC) concentrations, excitation emission matrix spectroscopy (EEMs), and ultrahigh resolution mass spectrometry. The EEMs and mass spectral data were analyzed using a combination of multivariate statistical approaches. We found that 45% of stream water DOC was biodegraded by microorganisms, including 31–45% of the humic DOC. This bioreactive DOM separated into two different groups: (1) H/C centered at 1.5 with O/C 0.1–0.5 or (2) low H/C of 0.5–1.0 spanning O/C 0.2–0.7 that were positively correlated (Spearman ranking) with chromophoric and fluorescent DOM (CDOM and FDOM, respectively). DOM that was more recalcitrant and resistant to microbial degradation aligned tightly in the center of the van Krevelen space (H/C 1.0–1.5, O/C 0.25–0.6) and negatively correlated (Spearman ranking) with CDOM and FDOM. These findings were supported further by principal component analysis and 2‐D correlation analysis of the relative magnitudes of the mass spectral peaks assigned to molecular formulas. This study demonstrates that our approach of processing stream water through bioreactors followed by EEMs and FTICR‐MS analyses, in combination with multivariate statistical analysis, allows for precise, robust characterization of compound bioreactivity and associated molecular level composition. Key Points Humic DOM is susceptible to microbial degradation along with peptide‐like DOM Labile DOM can be distinguished from recalcitrant DOM in van Krevelen space EEMs and FTICR‐MS chemically characterize bioreactive and recalcitrant DOMPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108612/1/jgrg20256.pd

Colored Vertex Models, Colored IRF Models and Invariants of Trivalent Colored Graphs

We present formulas for the Clebsch-Gordan coefficients and the Racah coefficients for the root of unity representations ($N$-dimensional representations with $q^{2N}=1$) of $U_q(sl(2))$. We discuss colored vertex models and colored IRF (Interaction Round a Face) models from the color representations of $U_q(sl(2))$. We construct invariants of trivalent colored oriented framed graphs from color representations of $U_q(sl(2))$.Comment: 39 pages, January 199

Automated multi-objective calibration of biological agent-based simulations

Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions