In this study, hardfacing and a flux-cored/self-shielded powder wire of the FCAW-S-90G13N4 type was employed to produce and investigate the deposits of high-manganese steel. The effects of high-frequency mechanical impact (HFMI) treatment on the microstructure, hardening, and scratch resistance of the deposits were studied to evaluate and predict the impact wear resistance of the hardfacing deposits under controlled impact load conditions. As observed by XRD, SEM, and nanoindentation, the microstructure of deposited metal comprised a soft austenite matrix, dispersed hard carbides, and an epsilon phase (similar to 26 vol.%). The wear resistance is thus not controlled by carbides alone but arises from the synergistic action of a hard carbide network within a ductile matrix. HFMI resulted in twinning, an increase in dislocation density, a grown volume fraction of epsilon (>60%) and alpha '-martensite. The interaction between twins, martensites, and dislocations provides a double/triple increase in microhardness (from HV0.2 = 2.78 GPa to HV0.2 = 6-7.69 GPa). After HFMI, scratch tests showed lower restored depths of scratch tracks and a 36-68% deceleration in the wear rate regarding those of the initial deposit. The underlying wear mechanisms were assessed accounting for the SEM observations of the scratch track morphologies and a 'counterbody penetration vs. shear stresses ratio' map. The initial plastic deformation-related mechanism (wedge/pile-up formation) changed by HFMI to ploughing. The obtained results allow one to evaluate and predict the impact wear resistance of the hardfacing deposits under controlled impact load conditions.
The object of this research is the process of automated testing of application programming interfaces (API) in systems developed following Agile and DevOps approaches, where requirements are heterogeneous, frequently changing, and represented in various formats: from formal OpenAPI specifications to textual documentation (Confluence). The problem addressed is the absence of an architectural mechanism for systematic integration of heterogeneous requirements sources and for decoupling requirements interpretation from test generation. Specification-oriented approaches fail to incorporate business rules from textual sources, covering only 70–80% of specification content. LLM-based approaches are unstable: repeated runs with identical prompts yield test sets differing by 20–40%, with coverage standard deviation reaching ±12%. AI-driven orchestration architecture is proposed, comprising a coordination layer O, requirements-processing agents A, and a protocol-independent unified requirements representation R. The test generation process is formalized as T = G(O(A(S))), where S denotes the set of requirements sources and G the deterministic test generation algorithm. The key property of the architecture is isolation of the stochastic LLM component at the agent level, guaranteeing reproducibility of the test set T for any fixed R. Verification was conducted through a comparative experiment on a REST API service with 5 endpoints and 12 business rules. API coverage: 88–92% vs. 72–78% (specification-based) and 55–82% (LLM-based). Standard deviation: ±2% vs. ±3% and ±12%. Reproducibility: 0.97 vs. 0.95 and 0.62. Maintenance: 15–20% modified tests vs. 60–70% and 40–55%. The proposed architecture targets software development teams practising API-First Development and Documentation-Driven Development. Results are applicable to Agile/DevOps environments with frequently changing requirements.
The rapid development of artificial intelligence is leading to the shortening of the life cycle of digital products and services. Companies are being forced to adapt more quickly to emerging technologies, update their offerings, and implement innovations in order to remain competitive. This is particularly relevant for microstock platforms, where the automation of processes and the use of AI for content generation can significantly transform traditional business models. With the application of machine learning algorithms, it has become possible to automatically evaluate and filter content, which greatly facilitates user interaction but also increases the complexity of managing content usage rights. The aim of this article is to explore the impact of artificial intelligence on the visual digital content market, particularly on the microstock economy, through the implementation of automated systems for generating, monetizing, and promoting digital products. Additionally, the article analyzes the importance of developing effective legal frameworks to ensure copyright protection for market participants. The article presents a detailed analysis of the microstock market as one of the key components of the modern digital technology sector. The main drivers of its development in the near future are identified. The study highlights the pivotal role of artificial intelligence-based technologies in transforming the traditional business models that have shaped the relationships between stakeholders in the microstock market. It examines the direct correlation between the integration of AI-based image generation tools into microstock platforms and the significant decline in sales volumes for traditional producers of commercial visual content. The article also outlines approaches to safeguarding the copyright of image and video creators whose works are used to train AI models on major microstock platforms. Finally, it offers conclusions on alternative pathways for traditional creative industry businesses to adapt to the new rules of the microstock marketplace.
Modern achievements of science allow lignocellulosic biomass to be processed into multifunctional products to meet a variety of needs. The further development of chemical technologies in the field of plant polymer processing should take into account the principles of green chemistry, sustainable development, and circular economy. The use of acidic deep eutectic solvents (DES) as promising and environmentally friendly solvents is attracting particular attention. In this study, acidic eutectic solvents were prepared using acetic acid as a hydrogen bond donor and choline chloride or 1,2 propanediol as hydrogen bond acceptors to pretreat corn stalk and obtain cellulose-rich solid residue. FTIR and 1H NMR spectra confirmed the formation of new hydrogen bonds between the two DES components and the success of their synthesis. The studied DES are electrically conductive solvents, have an acidic pH value (2.65-2.62), and have a sufficiently low surface tension value (35-38 × 10-3 J/m2). The influence of the cо-solvents (water and 1,4-butanediol) on the electrical conductivity and surface tension are discussed. The results showed that under the pretreatment condition of 100 °C for 1 h, DES with a molar ratio of 1:2 had a lignin removal rate less than 5%. The changes in nanopore structure and surface roughness of biomass, after DES-based pretreatment were investigated using SEM. Thus, it has been established that the studied DES act to a greater extent as extractants of organic substances for efficient pretreatment process from raw materials, rather than delignifiers, and will be more useful as a pre-treatment of raw materials before the delignification procedure of corn stalk.
The object of the research is the process of electrochemical anodic dissolution of heat-resistant nickel superalloy as a method of regeneration of alloying components for food equipment materials. Scrap recycling is carried out in an environmentally friendly way, which consists in low-temperature anodic treatment without the use of energy-consuming and harmful metallurgical methods. The starting material was obtained from spent components of high-temperature equipment. The work investigated the anodic dissolution of the specified alloy in environments based on sulfuric and methanesulfonic acids, and also performed a comparative analysis of the results obtained by cyclic voltammetry and galvanostatic measurements. It is shown that in an electrolyte based on H2SO4, anodic processes are characterized by higher current density values, which indicates their greater intensity. At the same time, this method records not only currents associated with metal dissolution, but also the contribution of side reactions, such as oxygen evolution and secondary oxidation of ions in the solution. In contrast, galvanostatic experiments, which allow for direct assessment of the mass loss of the alloy, have shown that the system based on methanesulfonic acid with the addition of sodium chloride provides higher dissolution rates, despite the lower electrical conductivity of the electrolyte. This effect is explained by the increased solubility and stability of methanesulfonate complexes of alloying elements, which reduces the likelihood of passivation of the electrode surface. In a sulfuric acid environment, dissolution occurs more evenly, but with less efficiency in terms of mass, which is associated with the formation of poorly soluble sulfate compounds. It has been established that in the electrolyte CH3SO3H + NaCl in the range of current densities of 1.5–2.5 A·dm-2 the ratio of nickel, chromium, cobalt, tungsten and rhenium in the solution is as close as possible to the composition of the original alloy. This ensures the transition of rhenium into the solution, while in a sulfuric acid environment it is not detected. The results obtained can be used to optimize the initial stage of processing heat-resistant superalloys, as well as in the development of electrochemical technologies for the extraction of strategically important metals used as alloying components in the creation of food equipment.
