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In our present paper, we approach the mixed problem with initial and boundary conditions, in the context of thermoelasticity without energy dissipation of bodies with a dipolar structure. Our first result is a reciprocal relation for the mixed problem which is reformulated by including the initial data into the field equations. Then, we deduce a generalization of Gurtin’s variational principle, which covers our generalized theory for bodies with a dipolar structure. It is important to emphasize that both results are obtained in a very general context, namely that of anisotropic and inhomogeneous environments, having a center of symmetry at each point.
This study is concerned with the linear elasticity theory for bodies with a dipolar structure. In this context, we approach transient elastic processes and the steady state in a cylinder consisting of such kind of body which is only subjected to some boundary restrictions at a plane end. We will show that at a certain distance d=d(t), which can be calculated, from the loaded plan, the deformation of the body vanishes. For the points of the cylinder located at a distance less than d, we will use an appropriate measure to assess the decreasing of the deformation relative to the distance from the loaded plane end. The fact that the measure, that assess the deformation, decays with respect to the distance at the loaded end is the essence of the principle of Saint-Venant.
A matter of reality
(2018)
Due to the increasing relevance of data, more and more data from various sources is accumulated for a variety of purposes. At the same time, however, there is a shortage of data in areas where it is urgently needed. Particularly in the field of machine learning, there is a lack of good and usable training data. Therefore, this research paper is concerned with the virtual data acquisition for the training of neural networks. For this purpose, first an application was developed that aims to generate virtual, automatically labeled data. Subsequently, a neural network was trained on the generated virtual data and tested on real data.
A matter of reality
(2018)
Due to the increasing relevance of data, more and more data from various sources is accumulated for a variety of purposes. At the same time, however, there is a shortage of data in areas where it is urgently needed. Particularly in the field of machine learning, there is a lack of good and usable training data. Therefore, this research paper is concerned with the virtual data acquisition for the training of neural networks. For this purpose, first an application was developed that aims to generate virtual, automatically labeled data. Subsequently, a neural network was trained on the generated virtual data and tested on real data.
A Review on Dental Materials
(2020)
This book discusses the current biomaterials used for dental applications and the basic sciences underpinning their application. The most critical structures in the oral cavity are the teeth, which play a central role in speaking, biting, chewing, tasting and swallowing. Teeth consist of three types of tissue: the cementum, enamel and dentin, with bone and gingival tissue serving as supporting structures. Caries, tooth wear, trauma and mechanical defects can lead to severe facial conditions; however, correcting these defects remains a challenge for scientists and dentists. Presenting insights form a broad range of disciplines, including materials science, biology, physiology and clinical science, this book provides a timely review of the principles, processing and application of dental materials.