Qualifying chains of transformation with coverage based evaluation criteria

Abstract. In Model-Driven Engineering (MDE) the development of complex and large transformations can benefit from the reuse of smaller ones that can be composed according to user requirements. Composing transformations is a complex problem: typically smaller transformations are discovered and selected by developers from different and heterogeneous sources. Then the identified transformations are chained by means of manual and error-prone composition processes. Based on our approach, when we propose one or more transformation chains to the user, it is difficult for him to choose one path instead of another without considering the semantic properties of a transformation. In this paper when multiple chains are proposed to the user, according to his requirements, we propose an approach to classify these suitable chains with respect to the coverage of the metamodels involved in the transformation. Based on coverage value, we are able to qualify the transformation chains with an evaluation criteria which gives as an indication of how much information a transformation chain covers over another

Predictors of weight loss in patients with obesity treated with a Very Low-Calorie Ketogenic Diet

IntroductionThe Very Low-Calorie Ketogenic Diet (VLCKD) has emerged as a safe and effective intervention for the management of metabolic disease. Studies examining weight loss predictors are scarce and none has investigated such factors upon VLCKD treatment. Among the molecules involved in energy homeostasis and, more specifically, in metabolic changes induced by ketogenic diets, Fibroblast Growth Factor 21 (FGF21) is a hepatokine with physiology that is still unclear.MethodsWe evaluated the impact of a VLCKD on weight loss and metabolic parameters and assessed weight loss predictors, including FGF21. VLCKD is a severely restricted diet (<800 Kcal/die), characterized by a very low carbohydrate intake (<50 g/day), 1.2–1.5 g protein/kg of ideal body weight and 15–30 g of fat/day. We treated 34 patients with obesity with a VLCKD for 45 days. Anthropometric parameters, body composition, and blood and urine chemistry were measured before and after treatment.ResultsWe found a significant improvement in body weight and composition and most metabolic parameters. Circulating FGF21 decreased significantly after the VLCKD [194.0 (137.6–284.6) to 167.8 (90.9–281.5) p < 0.001] and greater weight loss was predicted by lower baseline FGF21 (Beta = −0.410; p = 0.012), male sex (Beta = 0.472; p = 0.011), and central obesity (Beta = 0.481; p = 0.005).DiscussionVLCKD is a safe and effective treatment for obesity and obesity related metabolic derangements. Men with central obesity and lower circulating FGF21 may benefit more than others in terms of weight loss obtained following this diet. Further studies investigating whether this is specific to this diet or to any caloric restriction are warranted

A Learning Architecture for Complex Organization

Modern organizations are challenged to understand and put in action latest procedures and rules in order to constantly improve their service quality while coping with quickly changing contexts and decreasing resources. Such are defined by means of several kind of models that are in general quite interrelated. In this paper, we propose a Learning Architecture using Zachman Framework that allows us to define relations among these models and permits us to handle with huge amount of informations and resources in an organized way. Furthermore, the architecture enables process-driven learning and improvement through enriched models with contextual knowledge in terms of documentation and resources

Learning convergence prediction of astrobots in multi-object spectrographs

Astrobot swarms are used to capture astronomical signals to generate the map of the observable universe for the purpose of dark energy studies. The convergence of each swarm in the course of its coordination has to surpass a particular threshold to yield a satisfactory map. The current coordination methods do not always reach desired convergence rates. Moreover, these methods are so complicated that one cannot formally verify their results without resource-demanding simulations. Thus, we use support vector machines to train a model which can predict the convergence of a swarm based on the data of previous coordination of that swarm. Given a fixed parity, i.e., the rotation direction of the outer arm of an astrobot, corresponding to a swarm, our algorithm reaches a better predictive performance compared to the state of the art. Additionally, we revise our algorithm to solve a more generalized convergence prediction problem according to which the parities of astrobots may differ. We present the prediction results of a generalized scenario, associated with a 487-astrobot swarm, which are interestingly efficient and collision-free given the excessive complexity of this scenario compared to the constrained one

A Customizable Approach for the Automated Quality Assessment of Modelling Artefacts

Abstract-In Model-Driven Engineering (MDE) giving a precise defini- tion of quality models, identifying which quality attributes are of interest for specific stakeholders, and how relating and aggregating together quality attributes are still open issues. The main limitations of currently available quality approaches are limited extensibility, artifact specificity, and manual assessment. This paper proposes an approach supporting the definition of custom quality models consisting of hierarchically organized quality attributes whose evaluation depends on metrics specifically conceived and applied on the modeling artifacts to be analysed. A domain specific language is proposed to specify how quality attributes and metrics have to be aggregated. An execution environment is also provided to apply the defined quality models on actual modeling artifacts so to enable their automated quality assessment. Real applications of the approach are presented by defining and applying explanatory quality models suitably conceived to assess the quality of metamodels and transformations retrieved from public repositories

MDEForge: an Extensible Web-Based Modeling Platform

Model-Driven Engineering (MDE) refers to the systematic use of models as first class entities throughout the software development life cycle. Over the last few years, many MDE technologies have been conceived for developing domain specific modeling languages, and for supporting a wide range of model management activities. However, existing modeling platforms neglect a number of important features that if missed reduce the acceptance and the relevance of MDE in industrial contexts, e.g., the possibility to search and reuse already developed modeling artifacts, and to adopt model management tools as a service. In this paper we propose MDEForge a novel extensible Web-based modeling platform specifically conceived to foster a community-based modeling repository, which underpins the development, analysis and reuse of modeling artifacts. Moreover, it enables the adoption of model management tools as software-as-a-service that can be remotely used without overwhelming the users with intricate and error-prone installation and configuration procedures