Improving tribological properties of cast Al-Si alloys through application of wear-resistant thermal spray coatings

Flame Spray Thermal Spray coatings are low-cost, high-wear surface-treatment technologies. However, little has been reported on their potential effects on cast automotive aluminum alloys. The aim of this research was to investigate the tribological properties of as-sprayed NiCrBSi and WC/12Co Flame Spray coatings applied to two cast aluminum alloys: high-copper LM24 (AlSi8Cu3Fe), and low-copper LM25 (AlSi7Mg). Potential interactions between the mechanical properties of the substrate and the deposited coatings were deemed to be significant. Microstructural, microhardness, friction, and wear (pin-on-disk, microabrasion, Taber abrasion, etc.) results are reported, and the performance differences between coatings on the different substrates were noted. The coefficient of friction was reduced from 0.69-0.72 to 0.12-0.35. Wear (pin-on-disk) was reduced by a factor of 103-104, which was related to the high surface roughness of the coatings. Microabrasion wear was dependent on coating hardness and applied load. Taber abrasion results showed a strong dependency on the substrate, coating morphology, and homogeneity

The practice of using one-time passwords in modern corporate information systems

One-time password (OTP) systems, or one-time passwords, are an authentication method in which the user is provided with a password that is active only for one login session or transaction in an information system. Currently, the systems are not implemented in their pure form, but are one of the components in multi-factor authentication systems. Despite its widespread use in the financial sector, a one-time password does not guarantee secure login authentication

Application of Probability-Entropy Approach to the Selection of Thematically Similar Documents in the Information System Military Administration

The task of finding thematically similar documents, as one of the tasks of text classification, is one of the most important areas of natural language processing. As a result of solving this problem, the data is automatically sorted according to a predefined set of classes. The search for thematically similar documents and text classification is widely used in commercial applications such as spam filtering, decision-making, extracting information from raw data, and many other applications. In special-purpose information systems, automatic text classification is used to process information messages from open sources of information, eliminating the need to use a more expensive and time-consuming manual data classification mechanism. Currently, the best results in automatic text classification are shown by methods based on neural networks. However, it should be taken into account that such results were obtained on test sets containing tens and hundreds of thousands of marked-up documents and under conditions of a constant set of classes. The article offers a method of selection of thematically similar documents, which is based on a reference set of several dozens of documents related to each specific class. The reference set of documents is presented as a ranked list of keywords and phrases (list of key terms). The place of a term in this list (the rank of a term) is determined by calculating several probabilistic-entropy indicators and subsequent summation. Next, proximity to each class is determined based on the number of key terms in each class and the final weight in the document to be classified