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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.
During their evolution, massive stars are characterized by a significant loss of mass either via spherically symmetric stellar winds or by aspherical mass-loss mechanisms, namely outflowing equatorial disks. However, the scenario that leads to the formation of a disk or rings of gas and dust around these objects is still under debate. Is it a viscous disk or an ouftlowing disk-forming wind or some other mechanism? It is also unclear how various physical mechanisms that act on the circumstellar environment of the stars affect its shape, density, kinematic, and thermal structure. We assume that the disk-forming mechanism is a viscous transport within an equatorial outflowing disk of a rapidly or even critically rotating star. We study the hydrodynamic and thermal structure of optically thick dense parts of outflowing circumstellar disks that may form around,e.g., Be stars, sgB[e] stars, or Pop m stars. We calculate self-consistent time dependent models of the inner dense region of the disk that is strongly affected either by irradiation from the central star and by contributions of viscous heating effects. We also simulate the dynamic effects of collision between expanding ejecta of supernovae and circumstellar disks that may be form in sgB[e] stars and, e.g., LBVs or Pop in stars.
Preclinical studies in cell culture systems as well as in whole animal chronic kidney disease (CKD) models showed that parathyroid hormone (PTH), oxidized at the 2 methionine residues (positions 8 and 18), caused a loss of function. This was so far not considered in the development of PTH assays used in current clinical practice. Patients with advanced CKD are subject to oxidative stress, and plasma proteins (including PTH) are targets for oxidants. In patients with CKD, a considerable but variable fraction (about 70 to 90%) of measured PTH appears to be oxidized. Oxidized PTH (oxPTH) does not interact with the PTH receptor resulting in loss of biological activity. Currently used intact PTH (iPTH) assays detect both oxidized and non-oxPTH (n-oxPTH). Clinical studies demonstrated that bioactive, n-oxPTH, but not iPTH nor oxPTH, is associated with mortality in CKD patients.
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.
As virtualization drives the automation of networking, the validation of security properties becomes more and more challenging eventually ruling out manual inspections. While formal verification in Software Defined Networks is provided by comprehensive tools with high speed reverification capabilities like NetPlumber for instance, the presence of middlebox functionality like firewalls is not considered. Also, they lack the ability to handle dynamic protocol elements like IPv6 extension header chains. In this work, we provide suitable modeling abstractions to enable both - the inclusion of firewalls and dynamic protocol elements. We exemplarily model the Linux ip6tables/netfilter packet filter and also provide abstractions for an application layer gateway. Finally, we present a prototype of our formal verification system FaVe.
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.
Handling manufacturing and aging faults with software-based techniques in tiny embedded systems
(2017)
Non-volatile memory area occupies a large portion of the area of a chip in an embedded system. Such memories are prone to manufacturing faults, retention faults, and aging faults. The paper presents a single software based technique that allows for handling all of these fault types in tiny embedded systems without the need for hardware support. This is beneficial for low-cost embedded systems with simple memory architectures. A software infrastructure and a flow are presented that demonstrate how the presented technique is used in general for fault handling right after manufacturing and in-the-field. Moreover, a full implementation is presented for a MSP430 microcontroller, along with a discussion of the performance, overhead, and reliability impacts.
This paper describes architectural extensions for a dynamically scheduled processor, so that it can be used in three different operation modes, ranging from high-performance, to high-reliability. With minor hardware-extensions of the control path, the resources of the superscalar data-path can be used either for high-performance execution, fail-safe-operation, or fault-tolerant-operation. This makes the processor-architecture a very good candidate for applications with dynamically changing reliability requirements, e.g. for automotive applications. The paper reports the hardware-overhead for the extensions, and investigates the performance penalties introduced by the fail-safe and fault-tolerant mode. Furthermore, a comprehensive fault simulation was carried out in order to investigate the fault-coverage of the proposed approach.
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.