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In recent years, deep learning has been successfully applied to medical image analysis and provided assistance to medical professionals. Machine learning is being used to offer diagnosis suggestions, identify regions of interest in images, or augment data to remove noise. Training models for such tasks require a large amount of labeled data. It is often difficult to procure such data due to the fact that these requires experts to manually label them, in addition to the privacy and legal concerns that limiting their collection. Due to this, creating self-supervision learning methods and domain-adaptation techniques dedicated to this domain is essential. This paper reviews concepts from the field of deep learning and how they have been applied to medical image analysis. We also review the current state of self-supervised learning methods and their applications to medical images. In doing so, we will also present the resource ecosystem of researchers in this field, such as datasets, evaluation methodologies, and benchmarks. [ABSTRACT FROM AUTHOR]

For braking elements of mechanisms operated by hydraulic cylinders is used hydraulic resistance which controls the working fluid. Determination of these systems is difficult, especially in the design phase. This paper proposes a combined method for determining the functions describing the variation of pressure loss coefficients depending on the parameters of the proposed work. [ABSTRACT FROM AUTHOR]

The present paper is part of a larger project whose purpose is to develop a new hydrokinetic turbine-generator assembly, suitable for very low head water courses. It presents the experimental results of a turbine--generator physical model, installed on the tailrace of Mihailesti Hydropower Plant. In order to carry out the measurements, a floating structure and an anchoring system was installed on the power plant tailrace. This system also allowed the translation of the assembly between the two shores. A first set of measurements followed the water velocity and depth in the channel at various load values of the hydropower plant units, in order to determine the proper conditions for testing the model. The turbine gives the possibility of blades adjustment at three different angles, 0, 9 and 14 degrees respectively. Therefore, a set of measurements has been performed for each position of the blades. The measured parameters were the electric parameters of the generator such as voltage, current and the power variation with rotation speed. For the blades adjustment of 9 degrees and 1.76 m/s water velocity, the generator reached its maximum power of 500 W. The results retrieved will be used for the optimization and design of a new enlarged scale turbine--generator assembly. They will also be used as input data for the numerical modeling of the generator. Also, the exact knowledge of the channel geometry and flow parameters is valuable in studies preceding testing a prototype in the site. [ABSTRACT FROM AUTHOR]