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Titlebook: Modelle der Wissensrepräsentation als Grundlage qualitativer Wissensdiagnostik; Sigmar-Olaf Tergan Book 1986 Springer Fachmedien Wiesbaden

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,Peyronie’s Disease and Penile Curvature,e that reduces the elasticity of tunica albuginea itself leading to penile curvature, shortening or narrowing, painful erection and sexual discomfort. Epidemiological data on Peyronie’s disease are limited, it is not a rare disease; the prevalence in general population is between 0.3% to 13.1%, but
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An Optimal Cryptographic Approach for Addressing Security Breaches to Build Resilient WSN,ce is one of the critical factors affecting survivability service, especially in unattended wireless sensor networks (WSNs). Security attacks to compromise beacon nodes have become common these days. Messages that are exchanged between the sensor nodes have to be protected by implementing optimal cr
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River Shoreline Recognition from High Resolution Remote Sensing Imagery Based on Water Index and Artificial Neural Network,ue to the complexity of river shorelines in high resolution remote sensing images, automatic recognition of the shorelines is still difficult, which needs further development. In this paper, a new intelligent method for river shoreline recognition was proposed, which was based on the integration of
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GDI+ Controls,icated control needs a great deal of basic code just to handle all aspects of its appearance. If you can create a control using composition (i.e., a user control) or by inheriting from a similar control class, as shown in Chapter 7, you’ll save yourself a great deal of effort. On the other hand, if
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What is the Question? (1981),of formulating them. One may define a ‘null hypothesis’ .. which asserts that λ = 0, and ask for its posterior probability; or one may ask simply for an estimate of λ, and from that decide the issue. The procedures ask different questions, yet either should be adequate for the practical purpose.
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Estimation of Vehicle Centroid Side Angle Based on Neural Network,. According to the experimental conditions, vehicle state parameters were obtained based on CarSim, and Matlab was used for network training. Finally, the network is verified, the applicability of the trained network under different working conditions is discussed, and some assumptions about the optimization of this method are put forward.
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