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Structural health monitoring activities are of primal importance for managing transport infrastructure, however most SHM methodologies are based on point-based sensors that have limitations in terms of their spatial positioning requirements, cost of development and measurement range. This paper describes the progress on the SENSKIN EC project whose objective is to develop a dielectric-elastomer and micro-electronics-based sensor, formed from a large highly extensible capacitance sensing membrane supported by advanced microelectronic circuitry, for monitoring transport infrastructure bridges. Such a sensor could provide spatial measurements of strain in excess of 10%. The actual sensor along with the data acquisition module, the communication module and power electronics are all integrated into a compact unit, the SENSKIN device, which is energy-efficient, requires simple signal processing and it is easy to install over various surface types. In terms of communication, SENSKIN devices interact with each other to form the SENSKIN system; a fully distributed and autonomous wireless sensor network that is able to self-monitor. SENSKIN system utilizes Delay-/Disruption-Tolerant Networking technologies to ensure that the strain measurements will be received by the base station even under extreme conditions where normal communications are disrupted. This paper describes the architecture of the SENSKIN system and the development and testing of the first SENSKIN prototype sensor, the data acquisition system, and the communication system.
It has been observationally established that winds of hot massive stars have highly variable characteristics. The variability evident in the winds is believed to be caused by structures on a broad range of spatial scales. Small-scale structures (clumping) in stellar winds of hot stars are possible consequence of an instability appearing in their radiation hydrodynamics. To understand how clumping may influence calculation of theoretical spectra, different clumping properties and their 3D nature have to be taken into account. Properties of clumping have been examined using our 3D radiative transfer calculations. Effects of clumping for the case of the B[e] phenomenon are discussed.
Preclinical assessment of penetration not only in intact, but also in barrier‐disrupted skin is important to explore the surplus value of novel drug delivery systems, which can be specifically designed for diseased skin. Here, we characterized physical and chemical barrier disruption protocols for short‐term ex vivo skin cultures with regard to structural integrity, physiological and biological parameters. Further, we compared the penetration of dexamethasone (Dex) in different nanoparticle‐based formulations in stratum corneum, epidermis and dermis extracts of intact vs. barrier‐disrupted skin as well as by dermal microdialysis at 6, 12 and 24 hours after topical application. Dex was quantified by liquid‐chromatography ‐ tandem‐mass spectrometry (LC‐MS/MS). Simultaneously, we investigated the Dex efficacy by interleukin (IL) analysis. Tape‐stripping (TS) and 4 hours sodium lauryl sulfate 5 % (SLS) exposure were identified as highly effective barrier disruption methods assessed by reproducible transepidermal water loss (TEWL) changes and IL‐6/8 increase which was more pronounced in SLS‐treated skin. The barrier state has also a significant impact on the Dex penetration kinetics: for all formulations, TS highly increased dermal Dex concentration despite the fact that nanocrystals quickly and effectively penetrated both, intact and barrier‐disrupted skin reaching significantly higher dermal Dex concentration after 6 hours compared to Dex cream. The surplus value of encapsulation in ethyl cellulose nanocarriers could mostly be observed when applied on intact skin, in general showing a delayed Dex penetration. Estimation of cytokines was limited due to the trauma caused by probe insertion. In summary, ex vivo human skin is a highly interesting short‐term preclinical model for the analysis of penetration and efficacy of novel drug delivery systems.
Embedded smart home
(2017)
The popularity of MOOCs has increased considerably in the last years. A typical MOOC course consists of video content, self tests after a video and homework, which is normally in multiple choice format. After solving this homeworks for every week of a MOOC, the final exam certificate can be issued when the student has reached a sufficient score. There are also some attempts to include practical tasks, such as programming, in MOOCs for grading. Nevertheless, until now there is no known possibility to teach embedded system programming in a MOOC course where the programming can be done in a remote lab and where grading of the tasks is additionally possible. This embedded programming includes communication over GPIO pins to control LEDs and measure sensor values. We started a MOOC course called "Embedded Smart Home" as a pilot to prove the concept to teach real hardware programming in a MOOC environment under real life MOOC conditions with over 6000 students. Furthermore, also students with real hardware have the possibility to program on their own real hardware and grade their results in the MOOC course. Finally, we evaluate our approach and analyze the student acceptance of this approach to offer a course on embedded programming. We also analyze the hardware usage and working time of students solving tasks to find out if real hardware programming is an advantage and motivating achievement to support students learning success.
Selection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes. In this paper, we suggest genetic algorithm based method which searches several solution spaces simultaneously. The solution spaces are population groups consisting of elements with similar structure. Elements in a group have the same size, while elements in different groups are of different sizes. The proposed algorithm processes the population in groups of chromosomes with one gene, two genes to k genes. These genes hold corresponding information about the cluster centers. In the proposed method, the crossover and mutation operators can accept parents with different sizes; this can lead to versatility in population and information transfer among sub-populations. We implemented the proposed method and evaluated its performance against some random datasets and the Ruspini dataset as well. The experimental results show that the proposed method could effectively determine the appropriate number of clusters and recognize their centers. Overall this research implies that using heterogeneous population in the genetic algorithm can lead to better results.
The identification of vulnerabilities relies on detailed information about the target infrastructure. The gathering of the necessary information is a crucial step that requires an intensive scanning or mature expertise and knowledge about the system even though the information was already available in a different context. In this paper we propose a new method to detect vulnerabilities that reuses the existing information and eliminates the necessity of a comprehensive scan of the target system. Since our approach is able to identify vulnerabilities without the additional effort of a scan, we are able to increase the overall performance of the detection. Because of the reuse and the removal of the active testing procedures, our approach could be classified as a passive vulnerability detection. We will explain the approach and illustrate the additional possibility to increase the security awareness of users. Therefore, we applied the approach on an experimental setup and extracted security relevant information from web logs.