XRound : A reversible template language and its application in model-based security analysis

Successful analysis of the models used in Model-Driven Development requires the ability to synthesise the results of analysis and automatically integrate these results with the models themselves. This paper presents a reversible template language called XRound which supports round-trip transformations between models and the logic used to encode system properties. A template processor that supports the language is described, and the use of the template language is illustrated by its application in an analysis workbench, designed to support analysis of security properties of UML and MOF-based models. As a result of using reversible templates, it is possible to seamlessly and automatically integrate the results of a security analysis with a model. (C) 2008 Elsevier B.V. All rights reserved

Yield prediction by analysis of multispectral scanner data

A preliminary model describing the growth and grain yield of wheat was developed. The modeled growth characteristics of the wheat crop were used to compute wheat canopy reflectance using a model of vegetation canopy reflectance. The modeled reflectance characteristics were compared with the corresponding growth characteristics and grain yield in order to infer their relationships. It appears that periodic wheat canopy reflectance characteristics potentially derivable from earth satellites will be useful in forecasting wheat grain yield

Chronic Stress, Inflammation, and Colon Cancer: A CRH System-Driven Molecular Crosstalk.

Chronic stress is thought to be involved in the occurrence and progression of multiple diseases, via mechanisms that still remain largely unknown. Interestingly, key regulators of the stress response, such as members of the corticotropin-releasing-hormone (CRH) family of neuropeptides and receptors, are now known to be implicated in the regulation of chronic inflammation, one of the predisposing factors for oncogenesis and disease progression. However, an interrelationship between stress, inflammation, and malignancy, at least at the molecular level, still remains unclear. Here, we attempt to summarize the current knowledge that supports the inseparable link between chronic stress, inflammation, and colorectal cancer (CRC), by modulation of a cascade of molecular signaling pathways, which are under the regulation of CRH-family members expressed in the brain and periphery. The understanding of the molecular basis of the link among these processes may provide a step forward towards personalized medicine in terms of CRC diagnosis, prognosis and therapeutic targeting

Feature Model to Orthogonal Variability Model Transformation Towards Interoperability Between Tools

Feature Model (FM) and Orthogonal Variability Model (OVM) are both modelling approaches employed to represent variability in software product line engineering. The former is the most popular and it is mainly applied to domain engineering. The later is a more recent approach mainly used to document variability in design and realisation artifacts. in the scenario of interest of our research, which focuses on Application Lifecycle Management environment, it would be useful rely on the FM to OVM transformation. To the best of our knowledge, in the literature, there is no proposal for such transformation. in this paper, we propose an algorithm to transform FM into OVM. This algorithm transforms the variable features of a FM into an OVM, thus providing an explicit view of variability of software product line. When working on these transformation, some issues came to light, such as how to preserve semantics. We discuss some of them and suggest a possible solution to transform FM into OVM by extending OVM

Corrective receding horizon EV charge scheduling using short-term solar forecasting

Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution

Interpolation of Spatial Surfaces and Inferring Subsurface Transitions Using Electrical Conductivity

Precision agriculture techniques are becoming more popular within the agriculture community as producers demand more return from an ever-decreasing amount of farmland. Increased environmental regulations are forcing farmers to reduce the input of fertilizers and agrochemicals on their crops. Innovative techniques in precision agriculture are enhancing traditional decision-making processes by offering multiple layers of data for a production field. It is difficult to determine the complex interactions that exist between factors affecting crop growth and the resultant management decisions. Strategies in precision agriculture attempt to modify customary practices in order to address the known variability of field conditions. This case study evaluated some of the tools used to create spatial data maps and the relationship of those maps to various soil properties. Electromagnetic induction (EMI) and ground-penetrating radar (GPR) were used to examine the similarities and differences among spatial and temporal variations of soil water content, soil texture, and bulk soil electrical conductivity (ECa) on a large research watershed in southwestern Tennessee. A protocol was developed that identifies spatial variations in ECa patterns using geographical information system (GIS) maps. Soil cores were collected in areas of contrasting conductivity, which were identified by temporal ECa maps. Repeated spatial measurements of ECa, starting near field capacity and then progressing through the draining and drying process, supplied visually shifting patterns that correspond to dynamic soil moisture variations and subsurface morphology transitions. iv After several seasons of acquiring data for other studies, it was noted that spatial ECa patterns remained somewhat similar across data gathering events, shifting only in relative amplitude in relation to seasonal moisture levels. The overall ECa patterns remained somewhat similar, regardless of field moisture conditions. Soil morphology was considered constant over the data acquisition period, with subsurface moisture variations being the major influence in differing ECa maps during the same period. Follow-up soil coring analysis supported this assumption in this case study. The interpolation of spatial ECa maps creates a continuous surface that contains values at unsampled locations. Inverse distance weighted (IDW), ordinary kriging (OK), and radial basis function (RBF) were examined as potential interpolation algorithms. Data were gathered to investigate the influences of short-term conductivity shifts over the data collection period, as well as from travel route patterns and instrument orientation. Using root-mean-squared error (RMSE) to quantify the transformation accuracy of ECa maps, a data collection method and an appropriate geostatistical model were determined for this particular case study. Analysis showed that a bidirectional travel path produced the highest quality map, as transformation inaccuracies were reduced when measurements were obtained in a manner by which all measurements were temporally contiguous. A skilled application of ordinary kriging (OK) also increased map quality in comparison to the inverse distance weighted (IDW) and radial basis function (RBF) interpolation methods. Due to variability in our data, we are not able to recommend the use of a single interpolation algorithm for all data gathering scenarios

Earth observations from DSCOVR EPIC instrument

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.The NASA GSFC DSCOVR project is funded by NASA Earth Science Division. We gratefully acknowledge the work by S. Taylor and B. Fisher for help with the SO2 retrievals and Marshall Sutton, Carl Hostetter, and the EPIC NISTAR project for help with EPIC data. We also would like to thank the EPIC Cloud Algorithm team, especially Dr. Gala Wind, for the contribution to the EPIC cloud products. (NASA Earth Science Division)Accepted manuscrip

A Comparative Study on Model-Driven Requirements Engineering for Software Product Lines

[EN] Model-Driven Engineering (MDE) and Software Product Lines (SPL) are two software development paradigms that emphasize reusing. The former reuse domain knowledge is represented as models and model transformations for product development, and the latter reuse domain knowledge is represented as core assets to produce a family of products in a given domain. The adequate combination of both paradigms can bring together important advantages to the software development community. However, how to manage requirements during a model-driven product line development remains an open challenge. In particular, the Requirements Engineering (RE) activity must deal with specific properties such as variability and commonality for a whole family of products. This paper presents a comparative study of eleven approaches that perform a MDE strategy in the RE activity for SPL, with the aim of identify ing current practices and research gaps. In summary, most of the approaches are focused on the Domain Engineering phase of the SPL development, giving less attention to the Application Engineering phase. Moreover there is a lack of coverage of the Scoping activity, which defines the SPL boundaries. Several approaches apply some model transformations to obtain architectural and application requirements artifacts. Regarding the tool support for requirements specification and management, we found that most of the approaches use only academic prototypes. Regarding the validation of the approaches, the use of Case Studies as a proof of concept was the most commonly used method; however, there is a lack of well-defined case studies and empirical studies to improve the proposals.This research is part of the MULTIPLE project (with ref. TIN2009-13838).Blanes Domínguez, D.; Insfrán Pelozo, CE. (2012). A Comparative Study on Model-Driven Requirements Engineering for Software Product Lines. Revista de Sistemas e Computação. 2(1):3-13. http://hdl.handle.net/10251/43841S3132

1st doctoral symposium of the international conference on software language engineering (SLE) : collected research abstracts, October 11, 2010, Eindhoven, The Netherlands

The first Doctoral Symposium to be organised by the series of International Conferences on Software Language Engineering (SLE) will be held on October 11, 2010 in Eindhoven, as part of the 3rd instance of SLE. This conference series aims to integrate the different sub-communities of the software-language engineering community to foster cross-fertilisation and strengthen research overall. The Doctoral Symposium at SLE 2010 aims to contribute towards these goals by providing a forum for both early and late-stage Ph.D. students to present their research and get detailed feedback and advice from researchers both in and out of their particular research area. Consequently, the main objectives of this event are: – to give Ph.D. students an opportunity to write about and present their research; – to provide Ph.D. students with constructive feedback from their peers and from established researchers in their own and in different SLE sub-communities; – to build bridges for potential research collaboration; and – to foster integrated thinking about SLE challenges across sub-communities. All Ph.D. students participating in the Doctoral Symposium submitted an extended abstract describing their doctoral research. Based on a good set of submisssions we were able to accept 13 submissions for participation in the Doctoral Symposium. These proceedings present final revised versions of these accepted research abstracts. We are particularly happy to note that submissions to the Doctoral Symposium covered a wide range of SLE topics drawn from all SLE sub-communities. In selecting submissions for the Doctoral Symposium, we were supported by the members of the Doctoral-Symposium Selection Committee (SC), representing senior researchers from all areas of the SLE community.We would like to thank them for their substantial effort, without which this Doctoral Symposium would not have been possible. Throughout, they have provided reviews that go beyond the normal format of a review being extra careful in pointing out potential areas of improvement of the research or its presentation. Hopefully, these reviews themselves will already contribute substantially towards the goals of the symposium and help students improve and advance their work. Furthermore, all submitting students were also asked to provide two reviews for other submissions. The members of the SC went out of their way to comment on the quality of these reviews helping students improve their reviewing skills