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The quest for life on other planets is closely connected with the search for water in liquid state. Recent discoveries of deep oceans on icy moons like Europa and Enceladus have spurred an intensive discussion about how these waters can be accessed. The challenge of this endeavor lies in the unforeseeable requirements on instrumental characteristics both with respect to the scientific and technical methods. The TRIPLE/nanoAUV initiative is aiming at developing a mission concept for exploring exo-oceans and demonstrating the achievements in an earth-analogue context, exploring the ocean under the ice shield of Antarctica and lakes like Dome-C on the Antarctic continent.
Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one of the undeformed reference state of a specimen and another of the deformed target state, the relative displacement between those two states is determined. DIC is well known and often used for post-processing analysis of in-plane displacements and deformation of specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and extend the field of use of this technique.
Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether real-time analysis is possible with these methods. To reflect improvements in computing technology different hardware settings were also analysed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm such that it becomes practically slower than a suboptimal algorithm. The Newton-Raphson algorithm in combination with a modified Particle Swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss-Newton algorithm is superior. As expected, the Brute Force Search algorithm is the least effective method. We also found that the correct choice of parallelization tasks is crucial to achieve improvements in computing speed. A poorly chosen parallelisation approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode the correct choice of combinations of integerpixel and sub-pixel search algorithms is decisive for an efficient analysis. Using currently available hardware realtime analysis at high framerates remains an aspiration.
Tribological performance of biodegradable lubricants under different surface roughness of tools
(2019)
A light-addressable potentiometric sensor (LAPS) is a field-effect-based (bio-) chemical sensor, in which a desired sensing area on the sensor surface can be defined by illumination. Light addressability can be used to visualize the concentration and spatial distribution of the target molecules, e.g., H+ ions. This unique feature has great potential for the label-free imaging of the metabolic activity of living organisms. The cultivation of those organisms needs specially tailored surface properties of the sensor. O2 plasma treatment is an attractive and promising tool for rapid surface engineering. However, the potential impacts of the technique are carefully investigated for the sensors that suffer from plasma-induced damage. Herein, a LAPS with a Ta2O5 pH-sensitive surface is successfully patterned by plasma treatment, and its effects are investigated by contact angle and scanning LAPS measurements. The plasma duration of 30 s (30 W) is found to be the threshold value, where excessive wettability begins. Furthermore, this treatment approach causes moderate plasma-induced damage, which can be reduced by thermal annealing (10 min at 300 °C). These findings provide a useful guideline to support future studies, where the LAPS surface is desired to be more hydrophilic by O2 plasma treatment.
Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.