Ensuring safe and transparent tracking of nuclear materials in the modern era is critical for global security and compliance with international regulations. Blockchain technology, a decentralized and immutable ledger, offers a new approach to recording transactions, increasing trust without intermediaries. In this study, it was investigated whether nuclear material tracking was performed with advanced technology blockchain from past to present; it was seen that there needed to be a study on this subject in the literature, and that there was a gap. Search results proving this are presented. The authors present a model that can enable nuclear material tracking with blockchain technology, which will create a solid structure for recording and verifying every process step in the nuclear supply chain, from the creation of the first product to destruction. This model discusses how nuclear materials, which are very important to track from the beginning until they become waste, can be tracked with blockchain technology, and the contributions they can make nationally and internationally are explained. As a result of the research, it is shown that blockchain technology has the potential to pave the way for more resilient and precise nuclear supply chains by significantly increasing the security and efficiency of nuclear material tracking.

Purpose: Most of the diseases in tomatoes can be determined by observing the leaves. Therefore, the observation of tomato leaves is of critical importance in vegetable cultivation. Since preserving the health of the leaves will increase the productivity of tomatoes, it is a necessity to observe the leaf development and to determine the possible disease risks accurately and quickly in advance. The main purpose of the study is to create a CNN network to automatically determine the diseases in tomatoes using the only leaf images. Theory and Methods: In the proposed study, the "T-LeafNet " Convolutional Neural Network model, which can detect 9 different diseases and healthy leaves in the leaves of tomato plants, is proposed. Classification success was measured by training the proposed network from scratch. In addition, within the scope of transfer learning, MobileNetV2, AlexNet and VGG16 architectures, which are ready-made network models, were also tested on the dataset and the results were compared. The effects of data augmentation and learning coefficient parameters on the achievement were also examined by applying the tests on the original and augmented data with different learning coefficients. Results: The highest classification performances of T-LeafNet, AlexNet, MobileNetV2 and VGG16 networks were measured as 97.32%, 98.32%, 99.1% and 99.21%. Considering the training time and classification performances, it has been observed that the proposed T-LeafNet network gives high performance with shorter time and less resources compared to other tested networks. Conclusion: Contrary to the long training times and high resource usage of pre-trained CNNs, a CNN architecture especially for tomato leafs, which is using low system resources and giving high classification performance in shorter training times is presented. The use of fewer layers in the design of the model made the model applicable for use on mobile platforms. After the implementation of the model in mobile platforms, the proposed model can be used by tomato growers to detect disease more easily.

Cirrhosis is a chronic liver condition characterized by gradual scarring of the tissue in the liver, which then leads to one of the more serious health problems. Early diagnosis and detection of this condition are critical to managing the patient's situation and planning his treatment. Machine learning is a computer science field in which many complex issues have otherwise been successfully resolved, especially in medicine. This work focuses on constructing an artificial intelligence system, assisted by machine learning algorithms, to help professionals diagnose liver cirrhosis at its early stage. In this paper, four different models have been constructed with the aid of clinical parameters of patients and machine learning techniques: Random Forest, KNN, histogram-based Gradient Boosting, and Soft Voting. Two Feature selection methods (Chi-Square and mutual information) have been combined to select the most relevant features in the dataset. Then non-ensemble and ensemble methods are used to detect the condition. The random forest model achieved the highest score among other model with 97.4 % accuracy with a 10-fold Cross-validation method.