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In contemporary manufacturing industries, composite materials such as carbon fiber reinforced plastics (CFRPs) have become indispensable, finding extensive applications in aerospace, automotive, shipbuilding, high-tech sports equipment, and more. The exceptional chemical, physical, and mechanical properties of CFRPs, including high corrosion resistance, superior strength-to-weight ratio, high fatigue strength, and oxidation resistance, make them highly sought after. However, these very advantages pose significant challenges during machining due to the abrasive nature of composites, anisotropic mechanical properties, and poor thermal conductivity, which collectively exacerbate tool wear. This review paper addresses the critical need for innovative solutions to improve the machinability of CFRPs, a subject of paramount importance for advancing manufacturing efficiency and product quality. It comprehensively examines the latest research progress, current practices, and emerging trends in machining techniques such as turning, drilling (including reaming and countersinking), milling and grinding. The review delves into the influence of various cutting conditions, environments, and tool geometries. It also examines tool textures, materials, and coatings. Additionally, advanced machining methods, including vibration, thermal, and hybrid-assisted machining, are discussed. These factors impact key performance metrics such as cutting forces and torques, tool wear and life, chip morphology, surface roughness, and the quality of machined surfaces, including defect analysis. By synthesizing a vast array of literature for the first time, this paper highlights the most effective strategies to enhance machinability while extending tool life and improving surface finish. Notably, it underscores the innovative use of dry and flood lubrication, minimum quantity lubrication, cryogenic lubrication, and high-pressure cooling. Additionally, it emphasizes optimizing cutting tool geometry, employing diamond tools, and coating tools with TiN and TiAlN, alongside the application of heat treatment and hybrid machining methods, particularly those incorporating vibration machining techniques. This review not only identifies the current challenges but also proposes cutting-edge solutions, making it an essential resource for researchers and practitioners aiming to push the boundaries of CFRP machining technology.

The purpose of the research is to apply molecular genetic research methods to identify the taxonomic affiliation of gastrointestinal parasitic sheep nematodes of the family Trichostrongylidae using nested PCR followed by the restriction fragment length polymorphism (RFLP) analysis.Materials and methods. Parasitic nematodes, L3 Strongylata larvae obtained from incubated fecal samples of sheep. The genomic DNA was isolated using a commercial kit for DNA extraction from micro-quantities of tissues (Synthol, Moscow) as per the manufacturer’s guidelines. For DNA amplification, a T-100 Bio-Rad thermal cycler and a commercial Eurogen Master Mix reagent kit were used. The PCR regime was performed according to the WAAVP guidelines, 2006. The restriction endonuclease Rsa I of amplified Trichostrongylidae fragments was performed according to guidelines of the enzyme manufacturer (Sibenzyme, Novosibirsk).Results and discussion. To determine the taxonomic affiliation of Strongylata larvae isolated after incubation of feces from sheep, molecular genetic studies were performed using nested PCR followed by the restriction fragment length polymorphism (RFLP) analysis. This method makes it possible to identify, with the least effort, the genotypes of three species of Strongylata Haemonchus contortus, Trichostrongylus colubriformis, and Teladorsagia circumcincta at the larval stage.

Aim. To analyze dynamic of incidence and mortality of COVID-19 and clinical and epidemiological characteristics of adult patients with a new coronavirus infection during the early period of the Omicron SARS-COV-2 distribution in Russia. Materials and Methods. We conducted a retrospective analysis of the dynamics of COVID-19 incidence and mortality in Russia until 2023. Study included patients aged ≥18 years with a laboratory-confirmed diagnosis of COVID-19, detected in the period from 01/02/2022 to 14/03/2022 (n = 3 582 688) in 85 regions of Russia. Participants were included regardless their COVID-19 vaccination history. Results. We identified 6 periods of rise and decline in the COVID-19 incidence until 2023 in Russia. The 5th (January–July 2022) and the 6th (August-November 2022) periods were associated with the spread of the Omicron SARS-CoV-2. The median age of patients in the early period of Omicron spreading was 49 (36–62) years, 62.7% were women. The largest proportion of patients were represented by the age groups 30–39 and 40–49 years (19.2% each), the lowest – 18–29 years (12.3%). Proportion of patients with mild disease was 90.0%, moderate – 8.5%, severe – 0.9%, extremely severe – 0.6%. Hospitalization rate, proportion of patients treated in the intensive care unit and rate of invasive mechanical ventilation were 7.6%, 9.5% and 6.7% respectively. The median period from the onset of symptoms to the diagnosis was 2 (1–3) days, median of duration of the disease was 8 (6-10) days and median duration of hospitalization was 10 (7–14) days. The median age of the deceased patients was 77 (69–84) years, of which 50.8% were women, 72.6% were persons ≥70 years old. One or more concomitant diseases were detected among 8.7% of patients who became ill and 75.8% of those who died. The probability of hospitalization, admission to the ICU, IMV and death in patients with one or more concomitant diseases were 24.5, 3.2, 3.5 and 35.8 times higher, respectively, compared with patients without concomitant diseases. Conclusion. In the early period of the spread of the Omicron variant in Russia, among adult patients with COVID-19, excluding their vaccine history, the frequency of severe and extremely severe forms of infection was 1.5%. The elderly age and the presence of concomitant diseases remained key risk factors for the development of adverse outcomes of the COVID-19

Aim: To study the manifestations of the COVID-19 epidemic and the clinical and epidemiological characteristics of adult patients with a new coronavirus infection during the dominance of the Delta SARS-COV-2 in the Russian Federation.Materials and Methods: Study included patients aged ≥18 years with a laboratory-confirmed diagnosis of COVID-19, detected in the period from 09/01/2021 to 11/30/2021 (n= 2 619 298) in 85 Russian regions.Results: There were 2 periods of rise and decline in the COVID-19 incidence and mortality during the spread of the Delta SARS-CoV-2 in Russia: June-September and OctoberDecember 2021 (Wald – Wolfowitz criterion, p

Low-density polymer foams pre-ionized by a well-controlled nanosecond pulse are excellent plasma targets to trigger direct laser acceleration (DLA) of electrons by sub-picosecond relativistic laser pulses. In this work, the influence of the nanosecond pulse on the DLA process is investigated. The density profile of plasma generated after irradiating foam with a nanosecond pulse was simulated with a two-dimensional hydrodynamic code, which takes into account the high aspect ratio of interaction and the microstructure of polymer foams. The obtained plasma density profile was used as input to the three-dimensional particle-in-cell code to simulate energy, angular distributions and charge carried by the directional fraction of DLA electrons. The modelling shows good agreement with the experiment and in general a weak dependence of the electron spectra on the plasma profiles, which contain a density up-ramp and a region of near-critical electron density. This explains the high DLA stability in pre-ionized foams, which is important for applications.

The purpose of the research is to monitor farms located in the European part of the Russian Federation to identify resistance to effects of benzimidazole anthelmintics in populations of nematodes Haemonchus contortus dwelling in the gastrointestinal tract of small cattle.Materials and methods. The studies were conducted in slaughterhouses located in the Moscow Region in 2023–2024. At the first stage, taxonomic identification of parasitic nematodes and larvae (L3) was made, and Strongylata species was determined from sheep. The study material was the abomasum with duodenum fragments and a distal rectum fragment with feces. For molecular studies, we used mature nematodes and H. contortus L3 larvae isolated from the abomasum and feces of small cattle brought to slaughterhouses in the Moscow Region from 8 regions of the European part of the Russian Federation: Moscow, Astrakhan, Oryol, Lipetsk, Tula, Bryansk regions, Stavropol and Dagestan. The studies were conducted at the premises of the Laboratory of Molecular Biology, the VNIIP – FSC VIEV. Statistical processing of the obtained data was made, and mean infection rates of parasitic nematodes (infection intensity and prevalence) were determined. Fifty-six DNA samples of nematodes H. contortus were examined using nested isothermal amplification (PCR) to identify gene alleles that determine resistance to benzimidazole drugs.Results and discussion. Molecular genetic studies of H. contortus DNA sampled from sheep brought from different Regions only detected homozygous individuals (100%) resistant to benzimidazole in the parasitic nematode population from the Oryol Region. Other regions identified only homozygous and heterozygous individuals susceptible to benzimidazole.

An incredibly wide range of plastic products and enormous volumes of generated plastic wastes makes sorting technologies highly important. In order to enhance the efficiency and accuracy of sorting process, this article proposes an Internet of Video Things solution based on the deep learning algorithms for image recognition of plastic waste on a moving conveyor belt and embedded intelligence. In particular, the state-of-the-art object detection models, including Faster R-CNN, RetinaNet and YOLOv8 are used. Target categories of plastic are Polyethylene Terephthalate (PET) and Polypropylene (PP). Furthermore, we implemented quantization techniques for trained models on a commercial off-the-shelf embedded system for fast processing time. We achieved a high mean Average Precision (mAP) metric of 77.74% and accuracy of 95.67% on a test set and are fine-tuned and optimized for the deployment on an Nano embedded system providing 20 frames per second. This research contributes to the field of application of Internet of Things by demonstrating the efficacy of deep learning algorithms run on the embedded system in the industrial plastic waste sorting process. The findings highlight the practical applicability of these algorithms and offer insights into resource management and recycling practices.