Segmental bridges with unbonded prestressed tendons have some advantages, such as the weather independence and the corrosion protection of prestressing tendons. This paper analyzed the behavior of a prestressed segmen...Segmental bridges with unbonded prestressed tendons have some advantages, such as the weather independence and the corrosion protection of prestressing tendons. This paper analyzed the behavior of a prestressed segmental bridge with unbonded tendons under combined loading of torsion, bending and shear. According to the experiment research, a modified skew bending model was developed to calculate the bearing capacity of segmental bridges subjected to combined bending, shear and torsion. The finite element method was used to investigate the deflection behaviors of such structure, also to check the theoretical model. The theoretical and FEM research results were compared favorably with the test results from Technical University of Braunschweig, Germany. Finally, suggestion for the design and construction of segmental bridges with external prestressing was made.展开更多
Testing rocket and space technology objects in ground conditions for resistance to the impact of meteoroids and fragments of space debris can be carried out using shaped charges. To substantiate the design parameters ...Testing rocket and space technology objects in ground conditions for resistance to the impact of meteoroids and fragments of space debris can be carried out using shaped charges. To substantiate the design parameters of shaped charges that ensure the formation of aluminum particles in a wide velocity range(from 2.5 to 16 km/s), numerical modeling of the formation process was carried out within the framework of a two-dimensional axisymmetric problem of continuum mechanics using three different computing codes to increase the reliability of the results. The calculations consider shaped charges with a diameter of 20-100 mm with aluminum liners of various shapes. It is shown that the formation of particles with velocities close to the lower limit of the considered range is ensured by gently sloping segmental liners of degressive thickness. To form higher-velocity particles with velocities over 5 km/s, it is proposed to use combined liners, the jet-forming part of which has the shape of a hemisphere of constant thickness or the shape of a semi-ellipsoid or semi-superellipsoid of rotation of degressive thickness.展开更多
Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentat...Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.展开更多
文摘Segmental bridges with unbonded prestressed tendons have some advantages, such as the weather independence and the corrosion protection of prestressing tendons. This paper analyzed the behavior of a prestressed segmental bridge with unbonded tendons under combined loading of torsion, bending and shear. According to the experiment research, a modified skew bending model was developed to calculate the bearing capacity of segmental bridges subjected to combined bending, shear and torsion. The finite element method was used to investigate the deflection behaviors of such structure, also to check the theoretical model. The theoretical and FEM research results were compared favorably with the test results from Technical University of Braunschweig, Germany. Finally, suggestion for the design and construction of segmental bridges with external prestressing was made.
文摘Testing rocket and space technology objects in ground conditions for resistance to the impact of meteoroids and fragments of space debris can be carried out using shaped charges. To substantiate the design parameters of shaped charges that ensure the formation of aluminum particles in a wide velocity range(from 2.5 to 16 km/s), numerical modeling of the formation process was carried out within the framework of a two-dimensional axisymmetric problem of continuum mechanics using three different computing codes to increase the reliability of the results. The calculations consider shaped charges with a diameter of 20-100 mm with aluminum liners of various shapes. It is shown that the formation of particles with velocities close to the lower limit of the considered range is ensured by gently sloping segmental liners of degressive thickness. To form higher-velocity particles with velocities over 5 km/s, it is proposed to use combined liners, the jet-forming part of which has the shape of a hemisphere of constant thickness or the shape of a semi-ellipsoid or semi-superellipsoid of rotation of degressive thickness.
文摘Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.
