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In this extended abstract, we will analyze the current challenges for the envisioned Self-Adaptive CPS. In addition, we will outline our results to approach these challenges with SMARTSOS [10] a generic approach based on extensions of graph transformation systems employing open and adaptive collaborations and models at runtime for trustworthy self-adaptation, self-organization, and evolution of the individual systems and the system-of-systems level taking the independent development, operation, management, and evolution of these systems into account.
E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.
Currently available wearables are usually based on a single sensor node with integrated capabilities for classifying different activities. The next generation of cooperative wearables could be able to identify not only activities, but also to evaluate them qualitatively using the data of several sensor nodes attached to the body, to provide detailed feedback for the improvement of the execution. Especially within the application domains of sports and health-care, such immediate feedback to the execution of body movements is crucial for (re-) learning and improving motor skills. To enable such systems for a broad range of activities, generalized approaches for human motion assessment within sensor networks are required. In this paper, we present a generalized trainable activity assessment chain (AAC) for the online assessment of periodic human activity within a wireless body area network. AAC evaluates the execution of separate movements of a prior trained activity on a fine-grained quality scale. We connect qualitative assessment with human knowledge by projecting the AAC on the hierarchical decomposition of motion performed by the human body as well as establishing the assessment on a kinematic evaluation of biomechanically distinct motion fragments. We evaluate AAC in a real-world setting and show that AAC successfully delimits the movements of correctly performed activity from faulty executions and provides detailed reasons for the activity assessment.
We compare Visual Berrypicking, an interactive approach allowing users to explore large and highly faceted information spaces using similarity-based two-dimensional maps, with traditional browsing techniques. For large datasets, current projection methods used to generate maplike overviews suffer from increased computational costs and a loss of accuracy resulting in inconsistent visualizations. We propose to interactively align inexpensive small maps, showing local neighborhoods only, which ideally creates the impression of panning a large map. For evaluation, we designed a web-based prototype for movie exploration and compared it to the web interface of The Movie Database (TMDb) in an online user study. Results suggest that users are able to effectively explore large movie collections by hopping from one neighborhood to the next. Additionally, due to the projection of movie similarities, interesting links between movies can be found more easily, and thus, compared to browsing serendipitous discoveries are more likely.
Tree stands in the boreal treeline ecotone are, in addition to climate change, impacted by disturbances such as fire, water-related disturbances and logging. We aim to understand how these disturbances affect growth, age structure, and spatial patterns of larch stands in the north-eastern Siberian treeline ecotone (lower Kolyma River region), an insufficiently researched region. Stand structure of Larix cajanderi Mayr was studied at seven sites impacted by disturbances. Maximum tree age ranged from 44 to 300 years. Young to medium-aged stands had, independent of disturbance type, the highest stand densities with over 4000 larch trees per ha. These sites also had the highest growth rates for tree height and stem diameter. Overall lowest stand densities were found in a polygonal field at the northern end of the study area, with larches growing in distinct " tree islands". At all sites, saplings are significantly clustered. Differences in fire severity led to contrasting stand structures with respect to tree, recruit, and overall stand densities. While a low severity fire resulted in low-density stands with high proportions of small and young larches, high severity fires resulted in high-density stands with high proportions of big trees. At waterdisturbed sites, stand structure varied between waterlogged and drained sites and latitude. These mixed effects of climate and disturbance make it difficult to predict future stand characteristics and the treeline position.
We develop a simple two-zone interpretation of the broadband baseline Crab nebula spectrum between 10(-5) eV and similar to 100 TeV by using two distinct log-parabola energetic electrons distributions. We determine analytically the very-high energy photon spectrum as originated by inverse-Compton scattering of the far-infrared soft ambient photons within the nebula off a first population of electrons energized at the nebula termination shock. The broad and flat 200 GeV peak jointly observed by Fermi/LAT and MAGIC is naturally reproduced. The synchrotron radiation from a second energetic electron population explains the spectrum from the radio range up to similar to 10 keV. We infer from observations the energy dependence of the microscopic probability of remaining in proximity of the shock of the accelerating electrons.
Objective: We aimed to identify the role of the enzyme acid sphingomyelinase in the aging of stored units of packed red blood cells (pRBCs) and subsequent lung inflammation after transfusion.
Summary Background Data: Large volume pRBC transfusions are associated with multiple adverse clinical sequelae, including lung inflammation. Microparticles are formed in stored pRBCs over time and have been shown to contribute to lung inflammation after transfusion.
Methods: Human and murine pRBCs were stored with or without amitriptyline, a functional inhibitor of acid sphingomyelinase, or obtained from acid sphingomyelinase-deficient mice, and lung inflammation was studied in mice receiving transfusions of pRBCs and microparticles isolated from these units.
Results: Acid sphingomyelinase activity in pRBCs was associated with the formation of ceramide and the release of microparticles. Treatment of pRBCs with amitriptyline inhibited acid sphingomyelinase activity, ceramide accumulation, and microparticle production during pRBC storage. Transfusion of aged pRBCs or microparticles isolated from aged blood into mice caused lung inflammation. This was attenuated after transfusion of pRBCs treated with amitriptyline or from acid sphingomyelinase-deficient mice.
Conclusions: Acid sphingomyelinase inhibition in stored pRBCs offers a novel mechanism for improving the quality of stored blood.
Starch is one of the most popular nutritional sources for both human and animals. Due to the variation of its nutritional traits and biochemical specificities, starch has been classified into rapidly digestible, slowly digestible and resistant starch. Resistant starch has its own unique chemical structure, and various forms of resistant starch are commercially available. It has been found being a multiple-functional regulator for treating metabolic dysfunction. Different functions of resistant starch such as modulation of the gut microbiota, gut peptides, circulating growth factors, circulating inflammatory mediators have been characterized by animal studies and clinical trials. In this mini-review, recent remarkable progress in resistant starch on gut microbiota, particularly the effect of structure, biochemistry and cell signaling on nutrition has been summarized, with highlights on its regulatory effect on gut microbiota.
Background/Aims: Impaired pregnancy outcomes, such as low birth weight are associated with increased disease risk in later life, however little is known about the impact of common infectious diseases during pregnancy on birth weight. The study had two aims: a) to investigate risk factors of influenza virus infection during pregnancy, and b) to analyze the impact of influenza virus infection on pregnancy outcome, especially birth weight.
Methods: Prospective and retrospective observational studies found in PubMed, MEDLINE, Embase, Google Scholar, and WangFang database were included in this meta analysis. Data of included studies was extracted and analyzed by the RevMan software.
Results: Pregnant women with anemia (P=0.004, RR=1.46, 95% CI: 1.13-1.88), obesity (P<0.00001, RR=1.35, 95% CI: 1.25-1.46) and asthma (P<0.00001, RR=1.99, 95% CI: 1.67-2.37) had higher rates of influenza virus infection. Regarding birth outcomes, influenza A virus infection did not affect the likelihood for cesarean section. Mothers with influenza had a higher rate of stillbirth (P=0.04, RR=2.36, 95% CI: 1.05-5.31), and their offspring had low 5-minute APGR Scores (P=0.009, RR=1.39, 95% CI: 1.08-1.79). Furthermore, the rate for birth weight < 2500g (P=0.04, RR=1.71, 95% CI: 1.03-2.84) was increased.
Conclusion: Results of this study showed that anemia, asthma and obesity during pregnancy are risk factors influenza A virus infection during pregnancy. Moreover, gestational influenza A infection impairs pregnancy outcomes and increases the risk for low birth weight, a known risk factor for later life disease susceptibility.
Background/Aims: Contrast induced acute kidney injury (CI-AKI) remains a serious complication of contrast media enhanced procedures like coronary angiography. There is still a lack of established biomarkers that help to identify patients at high risk for short and long-term complications. The aim of the current study was to evaluate plasma kynurenine as a predictive biomarker for CI-AKI and long-term complications, measured by the combined endpoint "major adverse kidney events" (MAKE) up to 120 days after CM application.
Methods: In this prospective cohort study 245 patients undergoing coronary angiography were analyzed. Blood samples were obtained at baseline, 24h and 48h after contrast media (CM) application to diagnose CI-AKI. Patients were followed for 120 days for adverse clinical events including death, the need for dialysis, and a doubling of plasma creatinine. Occurrence of any of these events was summarized in the combined endpoint MAKE.
Results: Preinterventional plasma kynurenine was not associated with CI-AKI. Patients who later developed MAKE displayed significantly increased preinterventional plasma kynurenine levels (p<0.0001). ROC analysis revealed that preinterventional kynurenine is highly predictive for MAKE (AUC=0.838; p<0.0001). The optimal cutoff was found at >= 3.5 mu mol/L. Using this cutoff, the Kaplan-Meier estimator demonstrated that concentrations of plasma kynurenine >= 3.5 mu mol/L were significantly associated with a higher prevalence of MAKE until follow up (p<0.0001). This association remained significant in multivariate Cox regression models adjusted for relevant factors of long-term renal outcome.
Conclusion: Preinterventional plasma kynurenine might serve as a highly predictive biomarker for MAKE up to 120 days after coronary angiography.