Метод оцінювання дій диспетчерського персоналу в режимному тренажері

Запропоновано метод оцінювання дій диспетчерського персоналу під час тренувань, який ґрунтується на використанні інтегрального показника якості тренувань. Інтегральний показник дозволяє комплексно оцінити дії диспетчера з урахуванням надійності електропостачання, якості електроенергії і економічності режимів ЕЕС

Pre-set extrusion bioprinting for multiscale heterogeneous tissue structure fabrication

Recent advances in three-dimensional bioprinting technology have led to various attempts in fabricating human tissue-like structures. However, current bioprinting technologies have limitations for creating native tissue-like structures. To resolve these issues, we developed a new pre-set extrusion bioprinting technique that can create heterogeneous, multicellular, and multimaterial structures simultaneously. The key to this ability lies in the use of a precursor cartridge that can stably preserve a multimaterial with a pre-defined configuration that can be simply embedded in a syringe-based printer head. The multimaterial can be printed and miniaturized through a micro-nozzle without conspicuous deformation according to the pre-defined configuration of the precursor cartridge. Using this system, we fabricated heterogeneous tissue-like structures such as spinal cords, hepatic lobule, blood vessels, and capillaries. We further obtained a heterogeneous patterned model that embeds HepG2 cells with endothelial cells in a hepatic lobule-like structure. In comparison with homogeneous and heterogeneous cell printing, the heterogeneous patterned model showed a well-organized hepatic lobule structure and higher enzyme activity of CYP3A4. Therefore, this pre-set extrusion bioprinting method could be widely used in the fabrication of a variety of artificial and functional tissues or organs

Review methods for breast cancer detection using artificial intelligence and deep learning methods

Nowadays, there are many related works and methods that use Neural Networks to detect the breast cancer. However, usually they do not take into account the training time and the result of False Negative (FN) while training the model. The main idea of this paper is to compare already existing methods for detecting the breast cancer using Deep Learning Algorithms. Moreover, since the breast cancer is one of the most common lethal cancers and early detection helps prevent complications, we propose a new approach and the use of the convolutional autoencoder. This proposed model has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60% and 93% respectively

Using convolutional neural networks for breast cancer diagnosing

During the last few years, Convolutional Neural Networks (CNN) have been widely used in Computer-Aided Detection and the medical image analysis. The main idea of this paper is to modify CNN’s architectures to achieve the better sensitivity and the precision for detecting breast cancer at an early stage compared to existing methods. For this purpose, several factors were considered before CNN training such as the data processing, model, dataset, etc. In the proposed model the following hyperparameters were the following: the dropout rate 0,2, epoch 38 and batch size 33. Besides the hyperparameters, two fully connected layers in the modified model were used. An average recall (sensitivity) in the recent works was 74%. The precision and recall of proposed model for breast cancer classification were 66,66% and 85,7%, respectively