Development of technology for the production of a lightweight observation and protective container (LOOK) made of nanostructured ultra-strength steels

The article contains results of research and analyses concerning application of nanostructured bainitic steel in the form of plates for manufacturing of armour components. The presented results of examination of microstructure and properties include a wide range of laboratory experiments and industrial tests, which resulted in the achievement of the assumed functional properties. In the period of 2017-2021, a scientific and industrial consortium consisting of Łukasiewicz – Institute of Ferrous Metallurgy (leader); WITPiS, Tarnów Mechanical Works, Alchemia and Heatmasters Poland carried out a project funded by the POIR 04.01.04 programme aimed to develop the design and to manufacture an observation and protective container with a specified resistance to penetration by armour-piercing projectiles and with a lower mass of steel armouring in relation to that currently produced. The aim of the project was achieved by using armour plates made of nanostructured bainitic steel (nanobainitic), which are characterised by high resistance to high-energy impact concentrated in a small area. The technological tests carried out in the project mainly concerned the development of a new container and industrial technology of armour plates production and their application in the armour of this container. Based on the results of investigation of the semi-industrial scale material, the optimum chemical composition for industrial scale melting and casting was determined. An industrial technology for the production of plates of nano-structured bainitic steel was developed, which includes the following processes: smelting and casting, preliminary heat treatment and ingot hot processing, as well as hot rolling, final heat treatment, and surface treatment. A test batch of the material in the form of 1500×2470 mm armoured plates was fabricated under industrial conditions. The final result of the project is a container armoured with bainitic nanostructured steel plates with implementation documentation and a technology for producing armoured plates from this steel under the technical and technological conditions of domestic steel manufacturers.

Machine Learning Based Assembly of Fragments of Ancient Papyrus

The task of assembling fragments in a puzzle-like manner into a composite picture plays a significant role in the field of archaeology as it supports researchers in their attempt to reconstruct historic artifacts. In this article, we propose a method for matching and assembling pairs of ancient papyrus fragments containing mostly unknown scriptures. Papyrus paper is manufactured from papyrus plants and therefore portrays typical thread patterns resulting from the plant’s stems. The proposed algorithm is founded on the hypothesis that these thread patterns contain unique local attributes such that nearby fragments show similar patterns reflecting the continuations of the threads. We posit that these patterns can be exploited using image processing and machine learning techniques to identify matching fragments. The algorithm and system which we present support the quick and automated classification of matching pairs of papyrus fragments as well as the geometric alignment of the pairs against each other. The algorithm consists of a series of steps and is based on deep-learning and machine learning methods. The first step is to deconstruct the problem of matching fragments into a smaller problem of finding thread continuation matches in local edge areas (squares) between pairs of fragments. This phase is solved using a convolutional neural network ingesting raw images of the edge areas and producing local matching scores. The result of this stage yields very high recall but low precision. Thus, we utilize these scores in order to conclude about the matching of entire fragments pairs by establishing an elaborate voting mechanism. We enhance this voting with geometric alignment techniques from which we extract additional spatial information. Eventually, we feed all the data collected from these steps into a Random Forest classifier in order to produce a higher order classifier capable of predicting whether a pair of fragments is a match. Our algorithm was trained on a batch of fragments which was excavated from the Dead Sea caves and is dated circa the 1st century BCE. The algorithm shows excellent results on a validation set which is of a similar origin and conditions. We then tried to run the algorithm against a real-life set of fragments for which we have no prior knowledge or labeling of matches. This test batch is considered extremely challenging due to its poor condition and the small size of its fragments. Evidently, numerous researchers have tried seeking matches within this batch with very little success. Our algorithm performance on this batch was sub-optimal, returning a relatively large ratio of false positives. However, the algorithm was quite useful by eliminating 98% of the possible matches thus reducing the amount of work needed for manual inspection. Indeed, experts that reviewed the results have identified some positive matches as potentially true and referred them for further investigation.

IMPROVING THE QUALITY OF EXPORT CONSIGNMENTS OF CLASS 4 WHEAT INTENDED TO BE USED AS FOOD

In Ukraine, the gross harvest of grain, including wheat, is growing from year to year. However, along with this, there is a steady tendency towards deterioration in the technological properties of wheat grain: the share of food grain is reduced in comparison with non-food wheat. That is why an important yet little studied issue is how to form export consignments with the use of grain which is substandard by some quality indicators. Primarily, this relates to class 4 non-food wheat grain. In the work, the changes and reproducibility of the quality indicators of consignments formed from different quantities of class 4 wheat of different quality have been studied. It has been shown that export consignments of food wheat can be formed from local batches of non-food wheat. On analysing their class-making characteristics, the quantitative and qualitative parameters have been determined for 11 samples of class 4 soft wheat (harvested in 2019) selected at enterprises of the Odessa Region, and for batches of export wheat formed from these samples by mixing. It has been shown that mixing individual local batches of wheat grain, which belong to class 4 by their quality characteristics, makes it possible to obtain export consignments of wheat conforming to the food class standards. It has been established that the more local batches are mixed, the greater are the differences between the calculated weighed average quality indicators and the experimentally obtained values of the same parameters. The class-making parameters “quantity and quality of gluten” do not always obey the law of mixing 2–4-component mixtures, and can behave in a most unpredictable way. This applies mainly to consignments formed on the basis of local batches where a lot of grains are damaged by the sunn pest. The rest of the quality parameters, though different from the calculated data, are within the tolerance limits for each parameter. It has also been shown that from non-food wheat grains (class 4), by using linear programming methods implemented in the Microsoft Excel spreadsheet, one can obtain the optimal export consignments satisfying all the requirements for food wheat quality (class 3). Thus, exporting enterprises, due to the difference in prices, can receive additional profit. When a consignment is formed, the calculated quality parameters can sometimes differ from the final quality characteristics needed for the intended purpose of the export consignment. Therefore, it is not only necessary to calculate the weighed averages of the consignment quality, but also to form a test batch and experimentally determine its quality indicators in the laboratory, because some of them can deviate towards better quality as well as towards deterioration.

Particle Swarm Optimization of PID Controller under Constraints on Performance and Robustness

This paper presents a design procedure of the PID controller where optimal parameters of controller * * * *p i d f (k ,k ,k ,T ) areobtained by solving the constrained optimization problem. The objective function is given in the form of the Integral of Absolute Error(IAE) under specifications to achieve predictable performance and robustness. The constraints within the optimization problem setupare desired maximum sensitivity, desired maximum complementary sensitivity and maximum sensitivity to measurement noise underhigh frequencies. The optimization problem is transformed to an unconstrained problem using penalty function approach. Solution tothe optimization problem is obtained using Particle Swarm algorithm (PSO) which leads to an efficient suppression of disturbance aswell as an adequate reference tracking performance of the closed-loop system with negligible overshoot. The suggested method isapplicable to the large class of stable, integral and unstable processes, processes with oscillatory dynamics with and without dead-time.Effectiveness of the proposed design procedure is verified via numerical simulations on test batch consisting of processes typicallyencountered in industry. Paper also provides two another solutions of the defined optimization problem using genetic algorithm (GA)and fminunc trust region based approach (TR). Performance of the PSO, GA, and TR based control system is compared with thoseusing recently proposed maximization of proportional gain denoted with max(kp) method. Although the present paper is focused to tunethe PID controller, the same procedure may be used to design PI controller, lead and lag compensators, high-order controllers as well asfractional-order controllers.

Self-propagating high-temperature synthesis of the nitrogen-contain material based on the aluminum and vanadium nitride to prepare the titanium preliminary alloy's

The investigating results are shown on the V‒Al alloy nitriding while burning in the large-scale reactor. The nitriding optimal condition were defned. The phaseforming behavior was investigated while the V‒All alloy nitriding under the burning condition. The processing method was developed for the self-propagating hightemperature nitriding. The test batch of the nitrided V‒ Al‒N alloy was manufactured.Ill.5. Ref. 5.

Brazilian Program For HIV Point-Of-Care Test Evaluation: A Decade’s Experience

The point-of-care tests (POCTs) for HIV diagnosis have been widely used in Brazil in order to expand and to allow HIV diagnosis outside health units including remote areas, such as the Amazon region. In order to guarantee the quality of HIV diagnostics based on rapid tests, the Brazilian Ministry of Health (MoH) implemented the HIV POCT Evaluation Program. This study compiles the Brazilian experience acquired over the last 13 years conducting the HIV POCT Evaluation Program. Methods and Findings The selection of tests was based on the interest of manufacturers to qualify for the MoH tenders. Each round was performed with fresh whole blood and oral fluid samples, always including HIV positive and negative ones. In addition to the POCT, every sample was submitted to a reference testing protocol, based on an immunoassay followed by Western blot. The POCTs were evaluated for clinical sensitivity, clinical specificity, assay operational characteristics, detection of HIV-2 antibodies, sensitivity to subtypes panels; and sensitivity to seroconversion panels. Since its implementation in 2003, the POCT evaluation protocol has undergone some modifications aiming to improve and simplify the evaluation process, to know: (i) for HIV-positive samples, perform EIA and Western blot only if the POCT is non-reactive; (ii) reduction from 800 to 600 HIV negative samples; (iii) increase from one to three subtype panels (including HIV-2 samples); and (iv) inclusion of seroconversion panel. We evaluated six tests, four of which met the sensitivity criteria of 99.5%: BD Chek™ HIV Multi-test (whole blood), HIV 1/2 Colloidal Gold (whole blood), OraQuick ADVANCE® Rapid HIV-1/2 Antibody Test (whole blood and oral fluid) and TR DPP HIV-1/2 (whole blood, plasma and oral fluid). Regarding other evaluated criteria, all assays met the requirements. Conclusions The successful Brazilian policy on POCT use for HIV infection diagnosis includes the evaluation of the POCT itself in addition to appropriate selection of tests to be acquired and nationwide distributed to the public health facilities, control of each test batch distributed by the MoH, proper and easily accessible training to all health professionals involved in rapid testing through distance learning tools, and continued evaluation of POCT use through external quality assessment.

Transductive Methods for the Distributed Ensemble Classification Problem

We consider ensemble classification for the case where there is no common labeled training data for jointly designing the individual classifiers and the function that aggregates their decisions. This problem, which we call distributed ensemble classification, applies when individual classifiers operate (perhaps remotely) on different sensing modalities and when combining proprietary or legacy classifiers. The conventional wisdom in this case is to apply fixed rules of combination such as voting methods or rules for aggregating probabilities. Alternatively, we take a transductive approach, optimizing the combining rule for an objective function measured on the unlabeled batch of test data. We propose maximum likelihood (ML) objectives that are shown to yield well-known forms of probabilistic aggregation, albeit with iterative, expectation-maximization-based adjustment to account for mismatch between class priors used by individual classifiers and those reflected in the new data batch. These methods are extensions, for the ensemble case, of the work of Saerens, Latinne, and Decaestecker (2002). We also propose an information-theoretic method that generally outperforms the ML methods, better handles classifier redundancies, and addresses some scenarios where the ML methods are not applicable. This method also well handles the case of missing classes in the test batch. On UC Irvine benchmark data, all our methods give improvements in classification accuracy over the use of fixed rules when there is prior mismatch.