Generator-Composition for Aspect-Oriented Domain-Specific Languages

Software systems are complex, as they must cover a diverse set of requirements describing functionality and the environment. Software engineering addresses this complexity with Model-Driven Engineering ( MDE ). MDE utilizes different models and metamodels to specify views and aspects of a software system. Subsequently, these models must be transformed into code and other artifacts, which is performed by generators. Information systems and embedded systems are often used over decades. Over time, they must be modified and extended to fulfill new and changed requirements. These alterations can be triggered by the modeling domain and by technology changes in both the platform and programming languages. In MDE these alterations result in changes of syntax and semantics of metamodels, and subsequently of generator implementations. In MDE, generators can become complex software applications. Their complexity depends on the semantics of source and target metamodels, and the number of involved metamodels. Changes to metamodels and their semantics require generator modifications and can cause architecture and code degradation. This can result in errors in the generator, which have a negative effect on development costs and time. Furthermore, these errors can reduce quality and increase costs in projects utilizing the generator. Therefore, we propose the generator construction and evolution approach GECO, which supports decoupling of generator components and their modularization. GECO comprises three contributions: (a) a method for metamodel partitioning into views, aspects, and base models together with partitioning along semantic boundaries, (b) a generator composition approach utilizing megamodel patterns for generator fragments, which are generators depending on only one source and one target metamodel, (c) an approach to modularize fragments along metamodel semantics and fragment functionality. All three contributions together support modularization and evolvability of generators

Circulating Levels of Interleukin-1 Family Cytokines in Overweight Adolescents

Objectives. Obesity and related diseases are dramatically increasing problems, particularly in children and adolescents. We determined circulating levels of different interleukin (IL)-1 family members in normal weight and overweight adolescents. Methods. Seventy male, Caucasian adolescents (13–17 years) were recruited. Thirty-five had a body-mass index (BMI) above the 90th age-specific percentile. IL-1α, IL-1β, IL-1 receptor antagonist (IL-1ra), and IL-18 were determined using multiplex-technology. Results. IL-18 concentrations were higher in the overweight group compared to normal weight (161.6 ± 40.7 pg/ml versus 134.7 ± 43.4 pg/ml, P = .009). Concentrations of circulating IL-1β levels were below the detection threshold. IL-18 (R2:0.355, P < .01) and IL-1ra (R2:0.287, P < .05) correlated with BMI, whereas IL-1α did not. Conclusions. Accumulating data indicate the importance of the endocrine function of adipose tissue for the pathophysiological consequences of obesity-related co-morbidities. Since IL-18 is involved in the pathogenesis of different cardiovascular diseases, we conclude that IL-18 may represent a link between obesity and related co-morbidities in children and adolescents

OceanTEA: A Platform for Sharing Oceanographic Data and Analyses

Ocean observation systems, such as Argo floats or the modular ocean laboratory MoLab, produce an increasing amount of time series data. Both, statistical data mining techniques and manual exploration via visualization are necessary for oceanographers to extract scientific knowledge from such vast datasets. Therefore, scientists require a platform to explore and analyze data visually, supporting their collaboration and research. To deliver results and foster the impact of publications, such platform should facilitate automatic and interactive access to research results for scientists, their peers and the public. Our software platform OceanTEA (Oceanographic TimeSeries Exploration and Analysis) supports oceanographers in their research and publication efforts. The platform leverages modern web technology to support the interactive exploration and analysis of high-dimensional datasets. OceanTEA relies on a microservice architecture which can be deployed on desktops and on cloud computing infrastructure

Hot Electrons Regain Coherence in Semiconducting Nanowires

The higher the energy of a particle is above equilibrium the faster it relaxes due to the growing phase-space of available electronic states it can interact with. In the relaxation process phase coherence is lost, thus limiting high energy quantum control and manipulation. In one-dimensional systems high relaxation rates are expected to destabilize electronic quasiparticles. We show here that the decoherence induced by relaxation of hot electrons in one-dimensional semiconducting nanowires evolves non-monotonically with energy such that above a certain threshold hot-electrons regain stability with increasing energy. We directly observe this phenomenon by visualizing for the first time the interference patterns of the quasi-one-dimensional electrons using scanning tunneling microscopy. We visualize both the phase coherence length of the one-dimensional electrons, as well as their phase coherence time, captured by crystallographic Fabry-Perot resonators. A remarkable agreement with a theoretical model reveals that the non-monotonic behavior is driven by the unique manner in which one dimensional hot-electrons interact with the cold electrons occupying the Fermi-sea. This newly discovered relaxation profile suggests a high-energy regime for operating quantum applications that necessitate extended coherence or long thermalization times, and may stabilize electronic quasiparticles in one dimension

MAMBA: A Measurement Architecture for Model-Based Analysis

Model-based measurement techniques are relevant in the field of software analysis. Several meta models for the specification of quantitative measures have been proposed. However, they often focus either on static or dynamic aspects of a software system. Nevertheless, considering reengineering activities often both dimensions reveal valuable complementary insights. Existing meta models are also frequently bound to specific modeling languages, redefine underlying concepts for any new meta model, or provide only limited tool support for the automated computation of measurements from modeled measures. We present MAMBA, an integrated measurement architecture for model-based analysis---both static and dynamic---of software systems, that can be specified by arbitrary Ecore-based modeling languages. MAMBA extends the Structured Metrics Meta-Model (SMM) by additional modeling features, such as arbitrary statistical aggregate functions and periodic aggregate functions, e.g., for dynamic analysis at runtime. To consider measurements for querying system models, we outline the MAMBA Query Language (MQL) that employs SMM measures. Furthermore, we provide tool support that applies the measures specified in an (extended) SMM model and can integrate raw measurements provided by arbitrary static and dynamic analysis tools to produce the desired measurement model. We demonstrate the applicability of the approach based on three evaluation scenarios from different contexts: migration of software systems into the cloud, model-based engineering of railway control systems, and dynamic analysis for model-driven software modernization

iObserve: Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems

The goal of iObserve is to develop methods and tools to support evolution and adaptation of long-lived software systems. Future long-living software systems will be engineered using third-party software services and infrastructures. Key challenges for such systems will be caused by dynamic changes of deployment options on cloud platforms. Third-party services and infrastructures are neither owned nor controlled by the users and developers of service-based systems. System users and developers are thus only able to observe third-party services and infrastructures via their interface, but are not able to look into the software and infrastructure that provides those services. In this technical report, we summarize our results of four activities to realize a complete tooling around Kieker, Palladio, and MAMBA, supporting performance and cost prediction, and the evaluation of data privacy in context of geo-locations. Furthermore, the report illustrates our efforts to extend Palladio

Elevated Plasma Levels of Interleukin-12p40 and Interleukin-16 in Overweight Adolescents

Introduction. Obesity during adolescence is an increasing problem for both the individual and health care systems alike. In Western world countries, childhood adiposity has reached epidemic proportions. It is known that elevated levels of proinflammatory cytokines can be found in the plasma of obese patients. In this study, we sought to determine the relation between IL-12p40, IL-12p70, and Interleukin-16 (IL-16) in overweight adolescents. Materials and Methods. Seventy-nine male Caucasian adolescents aged 13-17 years were included in this study. Thirty-seven of them had a body mass index (BMI) above the 90th age-specific percentile. Il-12p40, IL-12p70, and IL-16 were measured from plasma using Luminex multiplex technology. Results. Both IL-12p40 and IL- 16 concentrations were significantly increased in overweight subjects compared to normal weight controls (IL-12p40: 1086.6 pg/mL +/- 31.7 pg/mL SEM versus 1228.6 pg/mL +/- 43.5 pg/mL SEM;IL-16 494.0 pg/mL +/- 29.4 pg/mL SEM versus 686.6 pg/mL +/- 52.5 pg/mL SEM, P < 0.05 and P < 0.01, resp.). No differences were found for IL-12p70. Conclusions. Based on these results, we believe that the increased levels of IL-12p40 and IL-16 are associated with an ongoing inflammatory response in obese individuals and could lead to the development of disease conditions related to obesity

Run-time Architecture Models for Dynamic Adaptation and Evolution of Cloud Applications

Cloud applications are subject to continuous change due to modifications of the software application itself and, in particular, its environment. To manage changes, cloud-based systems provide diverse self-adaptation mechanisms based on run-time models. Observed run-time models are means for leveraging self- adaption, however, are hard to apply during software evolution as they are usually too detailed for comprehension by humans.In this paper, we propose iObserve, an approach to cloud-based system adaptation and evolution through run-time observation and continuous quality analysis. With iObserve, run-time adaptation and evolution are two mutual, interwoven activities that influence each other. Central to iObserve is (a) the specification of the correspondence between observation results and design models, and (b) their use in both adaptation and evolution. Run-time observation data is promoted to meaningful values mapped to design models, thereby continuously updating and calibrating those design models during run-time while keeping the models comprehendible by humans. This engineering approach allows for automated adaptation at run-time and simultaneously supports software evolution. Model-driven software engineering is employed for various purposes such as monitoring instrumentation and model transformation. We report on the experimental evaluation of this approach in lab experiments using the CoCoME benchmark deployed on an OpenStack cloud