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Is part of the Bibliography
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Extracellular acidification is a basic indicator for alterations in two vital metabolic pathways: glycolysis and cellular respiration. Measuring these alterations by monitoring extracellular acidification using cell-based biosensors such as LAPS plays an important role in studying these pathways whose disorders are associated with numerous diseases including cancer. However, the surface of the biosensors must be specially tailored to ensure high cell compatibility so that cells can represent more in vivo-like behavior, which is critical to gain more realistic in vitro results from the analyses, e.g., drug discovery experiments. In this work, O2 plasma patterning on the LAPS surface is studied to enhance surface features of the sensor chip, e.g., wettability and biofunctionality. The surface treated with O2 plasma for 30 s exhibits enhanced cytocompatibility for adherent CHO–K1 cells, which promotes cell spreading and proliferation. The plasma-modified LAPS chip is then integrated into a microfluidic system, which provides two identical channels to facilitate differential measurements of the extracellular acidification of CHO–K1 cells. To the best of our knowledge, it is the first time that extracellular acidification within microfluidic channels is quantitatively visualized as differential (bio-)chemical images.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
There is a very large number of very important situations which can be modeled with nonlinear parabolic partial differential equations (PDEs) in several dimensions. In general, these PDEs can be solved by discretizing in the spatial variables and transforming them into huge systems of ordinary differential equations (ODEs), which are very stiff. Therefore, standard explicit methods require a large number of iterations to solve stiff problems. But implicit schemes are computationally very expensive when solving huge systems of nonlinear ODEs. Several families of Extrapolated Stabilized Explicit Runge-Kutta schemes (ESERK) with different order of accuracy (3 to 6) are derived and analyzed in this work. They are explicit methods, with stability regions extended, along the negative real semi-axis, quadratically with respect to the number of stages s, hence they can be considered to solve stiff problems much faster than traditional explicit schemes. Additionally, they allow the adaptation of the step length easily with a very small cost.
Two new families of ESERK schemes (ESERK3 and ESERK6) are derived, and analyzed, in this work. Each family has more than 50 new schemes, with up to 84.000 stages in the case of ESERK6. For the first time, we also parallelized all these new variable step length and variable number of stages algorithms (ESERK3, ESERK4, ESERK5, and ESERK6). These parallelized strategies allow to decrease times significantly, as it is discussed and also shown numerically in two problems. Thus, the new codes provide very good results compared to other well-known ODE solvers. Finally, a new strategy is proposed to increase the efficiency of these schemes, and it is discussed the idea of combining ESERK families in one code, because typically, stiff problems have different zones and according to them and the requested tolerance the optimum order of convergence is different.
The industrial revolution especially in the IR4.0 era have driven many states of the art technologies to be introduced.
The automotive industry as well as many other key industries have also been greatly influenced. The rapid development of automotive industries in Europe have created wide industry gap between European Union (EU) and developing countries such as in South East Asia (SEA). Indulging this situation, FH JOANNEUM, Austria together with European partners from FH Aachen, Germany and Politecnico di Torino, Italy are taking initiative to close down the gap utilizing the Erasmus+ United Capacity Building in Higher Education grant from EU. A consortium was founded to engage with automotive technology transfer using the European framework to Malaysian, Indonesian and Thailand Higher Education Institutions (HEI) as well as automotive industries in respective countries. This could be achieved by establishing Engineering Knowledge Transfer Unit (EKTU) in respective SEA institutions guided by the industry partners in their respective countries. This EKTU could offer updated, innovative and high-quality training courses to increase graduate’s employability in higher education institutions and strengthen relations between HEI and the wider economic and social environment by addressing University-industry cooperation which is the regional priority for Asia. It is expected that, the Capacity Building Initiative would improve the quality of higher education and enhancing its relevance for the labor market and society in the SEA partners. The outcome of this project would greatly benefit the partners in strong and complementary partnership targeting the automotive industry and enhanced larger scale international cooperation between the European and SEA partners. It would also prepare the SEA HEI in sustainable partnership with Automotive industry in the region as a mean of income generation in the future.
The Rothman–Woodroofe symmetry test statistic is revisited on the basis of independent but not necessarily identically distributed random variables. The distribution-freeness if the underlying distributions are all symmetric and continuous is obtained. The results are applied for testing symmetry in a meta-analysis random effects model. The consistency of the procedure is discussed in this situation as well. A comparison with an alternative proposal from the literature is conducted via simulations. Real data are analyzed to demonstrate how the new approach works in practice.
The Atmospheric Remote-Sensing Infrared Exoplanet Large-survey, ARIEL, has been selected to be the next (M4) medium class space mission in the ESA Cosmic Vision programme. From launch in 2028, and during the following 4 years of operation, ARIEL will perform precise spectroscopy of the atmospheres of ~1000 known transiting exoplanets using its metre-class telescope. A three-band photometer and three spectrometers cover the 0.5 µm to 7.8 µm region of the electromagnetic spectrum. This paper gives an overview of the mission payload, including the telescope assembly, the FGS (Fine Guidance System) - which provides both pointing information to the spacecraft and scientific photometry and low-resolution spectrometer data, the ARIEL InfraRed Spectrometer (AIRS), and other payload infrastructure such as the warm electronics, structures and cryogenic cooling systems.
We discuss the testing problem of homogeneity of the marginal distributions of a continuous bivariate distribution based on a paired sample with possibly missing components (missing completely at random). Applying the well-known two-sample Crámer–von-Mises distance to the remaining data, we determine the limiting null distribution of our test statistic in this situation. It is seen that a new resampling approach is appropriate for the approximation of the unknown null distribution. We prove that the resulting test asymptotically reaches the significance level and is consistent. Properties of the test under local alternatives are pointed out as well. Simulations investigate the quality of the approximation and the power of the new approach in the finite sample case. As an illustration we apply the test to real data sets.
Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem-solving behavior as well as their eye movements to learners. While research on expert-novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem-solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.
Elastic transmission eigenvalues and their computation via the method of fundamental solutions
(2020)
A stabilized version of the fundamental solution method to catch ill-conditioning effects is investigated with focus on the computation of complex-valued elastic interior transmission eigenvalues in two dimensions for homogeneous and isotropic media. Its algorithm can be implemented very shortly and adopts to many similar partial differential equation-based eigenproblems as long as the underlying fundamental solution function can be easily generated. We develop a corroborative approximation analysis which also implicates new basic results for transmission eigenfunctions and present some numerical examples which together prove successful feasibility of our eigenvalue recovery approach.