Search

Abstract Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder characterized by brain aggregation of β-amyloid (Aβ) peptides and phosphorylated tau (P-tau) proteins. Extracellular vesicles (EVs) can be isolated and studied for potential roles in disease. While several studies have tested plasma-derived EVs in AD, few have analyzed EVs from cerebrospinal fluid (CSF), which are potentially more closely related to brain changes. This study included 20 AD patients and 20 cognitively unimpaired (CU) participants. Using a novel EV isolation method based on acoustic trapping, we isolated and purified EVs from minimal CSF volumes. EVs were lysed and analyzed by immunoassays for P-tau217 and P-tau181. Isolation was confirmed through transmission electron microscopy and the presence of EV-specific markers (CD9, CD63, CD81, ATP1A3). Nanoparticle tracking analysis revealed a high variance in EV distribution. AD patients exhibited increased P-tau181 and decreased P-tau217 in EVs, leading to a higher EV P-tau181/P-tau217 ratio compared to CU. No significant differences in EV counts or sizes were observed between AD and CU groups. This study is the first to use acoustic trapping to isolate EVs from CSF and demonstrates differential P-tau content in AD-derived EVs, warranting further research to understand the relationship between these EV changes and brain pathology.

The purpose of this study was to analyse the political and economic factors that led to the aggravation of the global food crisis in 2022 and to assess the effectiveness of international mechanisms to overcome it. The study found that the Russian-Ukrainian war, the energy crisis, and macroeconomic instability were the key factors behind the crisis. It was found that the blocking of Ukrainian ports and restrictions on grain exports from Ukraine and Russia led to a significant shortage on the world market and a rapid rise in food prices. The analysis showed that rising energy prices led to an in- crease in production costs in the agricultural sector, especially for fertiliser production, which had a negative impact on the cost of agricultural production. High global inflation and exchange rate fluctuations were found to have put additional pressure on food markets, especially in developing countries. The study found that high market uncertainty and increased financial speculation in agricultural markets led to increased price volatility and a disconnect between stock prices and fundamental supply and demand factors. It was found that the introduction of export restrictions by many agricultural producing countries further increased market uncertainty and led to further price increases on the world market. The analysis of the effectiveness of policy decisions and international initiatives showed that national measures, such as increased public food purchases and expanded social support programmes, helped stabilise internal markets, but their effectiveness varied depending on the economic capacity of each country. It was found that international organisations played a key role in coordinating global efforts to overcome the crisis, but the effectiveness of their actions was often lim- ited by the difficulty of access to conflict zones and insufficient funding. The practical significance of the obtained findings lies in the possibility of their use for forecasting the political and economic situation in the context of future food crises

The conductive properties of CNT/polymer composites have been extensively studied. However, the impact of CNT distribution in the matrix on the composite polarization remains underexplored and poorly understood. Since it is difficult to achieve a uniform distribution of CNTs in polymers, most researchers have focused only on indiscriminately aggregated states. In this article, a new blending method was suggested to prepare a series of epoxy resin-based composite samples with varying levels of CNT uniformity/aggregation and the same filling fractions. Notably, the permittivity values turned out to be inversely related to the composite uniformity: the lowest permittivity values were obtained in the most uniform formulation, and vice versa. With 0.1% CNT, the real part values of the most uniform and aggregated samples were 6.6 and 16.2 at 107 Hz and 11.6 and 370.5 at 101 Hz, respectively. For the filler content of 0.1–0.5%, the conductive properties were largely determined by the distribution of CNTs and not their content. Within the entire frequency range, the uniform sample with 0.2% CNT exhibited significantly lower permittivity than the aggregated sample with 0.1% CNT. These findings emphasize the importance of the aggregation factor and underscore the non-universality and limitations of the percolation theory and power laws. The observed phenomenon is best explained by the micro-capacitor model, or the Maxwell–Wagner polarization, and suggests that a significant portion of the literature in the field needs to be reconsidered.

The fat production industry is one of the key and fast-growing components of the food sector of Uzbekistan, and therefore, it is relevant to analyse the economic part of its crucial elements. The purpose of this study was to examine the hydrogenation process from the standpoint of production cost features and pricing issues. The methods employed in the study were descriptive, systematisation, and dialectical. The study showed that the production and utilisation of targeted edible fats play a vital role in the food industry by achieving certain characteristics of other products. However, a hydrogenation procedure is often used to make the fats more efficient. The study described its essence in greater detail and evaluated the economic feasibility of the hydrogenation process. The significance of choosing the correct reactor and catalysts to maximise yield was highlighted, as well as the influence of some other characteristics on it. The study also addressed the disadvantages of the fat hydrogenation process, which has a negative impact on public health. Therefore, activities aimed at mitigating negative impacts in this industry should be carried out more actively. Furthermore, attention has also been paid to assessing the state of the edible fats market in Uzbekistan: the rapid growth rate of the industry has been noted due to the increasing revenues of enterprises engaged in the production of these products. The obtained findings can be applied by enterprises to change the principles of hydrogenation process, as well as by the state authorities to formulate a long-term policy for the development of the industry

The majority of reserves of small oil companies of the Republic of Tatarstan are represented by carbonate reservoirs with low filtration and capacitance properties, low reservoir pressures and high oil viscosity. All this leads to a rapid decrease and a low level of current oil flows. The Akan oil field of Predpriyatie Kara Altyn CJSC is one of the examples. Thermal methods are considered as the most effective technologies for deposits with high-viscosity oil. According to the results of laboratory study of various variants of thermal methods and geological and hydrodynamic modeling, the steam-cyclic method of development was determined as the most effective at the Akan field. Two producing directional wells and two horizontal wells have been selected for experimental work on steam injection. The article considers the downhole equipment used for injection heat carrier, describes the process of steam injection with the dynamics of controlled parameters, production indicators and efficiency after heat treatment of wells. Conclusions are presented for the completed works on wells.

The article deals with arrowheads from sites of the Piany Bor culture with various marks applied to the items in the process of manufacture and use. In the course of work with museum collections, we managed to identify 38 arrowheads with marks, which come from 15 burials of Piany Bor burial grounds and one settlement monument. The purpose of markings can be different: from casual to purposeful. The analysis has shown that some of the marks are only traces of marking of the material during the manufacture of the item, the other part can be specially applied signs in the process of manufacture or while using. There is no reason to consider the marks as elements of writing systems. Chronologically the considered finds correspond to the I century BC – II century AD. The spread of marks on arrowheads from Piany Bor sites is probably an epochal phenomenon characteristic of the Middle Sarmatian time. The marks are not a direct copying and exact borrowing, but a reworking of the idea of marking of objects, using their own traditions of inscription of marks.

The article publishes preliminary results of archaeological research on mound No. 45, the Bustanaevsky burial mound, located on the territory of the Buraevsky district of the Republic of Bashkortostan. The monument was discovered in 2011, archaeological research began in 2015 under the leadership of A.G. Kolonskikh. Currently, excavations of the necropolis continue in the course of joint research by the Institute of Ethnological Research named after. R.G. Kuzeev and the Institute of Archaeology named after A. Kh. Khalikov Tatarstan Academy of Sciences. During excavations, one single burial was discovered under the mound. Along with the poorly preserved skeleton, the burial contained archaeological material represented by two vessels, arrowheads and elements of a belt set made in the heraldic style, horse bones and iron bits. The data obtained allow us to assume that the necropolis was left by the bearers of the Kushnarenkovo archaeological culture. The preliminary dating of the burial is the end of the 6th - beginning of the 7th century AD.

In 2023, the research activities of the Institute of Archaeology named after A.Kh. Khalikov of the Tatarstan Academy of Sciences were carried out in three scientific fields. In 2023 the work on preparation and publication of the collective monograph "Archaeology of the Volga-Urals" in 7 volumes was completed. As part of the implementation of the state program of the Republic of Tatarstan, comprehensive scientific and safety and rescue studies were conducted in the regions of the Russian Federation, as well as in Mongolia, Kyrgyzstan and Uzbekistan. The formation of an information geographic system has been continued. The work with collections of the Museum of Archaeology of the Republic of Tatarstan is being successfully carried out. An important part of the Institute's activity is the training of highly qualified personnel under postgraduate programs with further defense of theses, advanced training within the international archaeological schools. The Institute carries out expert international activities on the inclusion of archaeological sites in the UNESCO World Cultural Heritage List, in particular such archaeological heritage sites as Denisova Cave (Altai region), Tanais (Rostov region).

The rapid evolution of cryptocurrency markets and the increasing complexity of trading strategies necessitate a comprehensive understanding of price-prediction models and their direct impact on trading efficacy. While extensive research has been conducted separately on price prediction methods and trading strategies, there remains a significant gap in studies explicitly correlating precise price forecasts with successful trading outcomes. This review paper addresses this gap by critically examining the role of accurate cryptocurrency price predictions in enhancing trading strategies. We conducted a systematic review of sufficient scholarly articles and web resources, focusing on the methodologies and effectiveness of various predictive models and their integration into cryptocurrency trading strategies. Our selection criteria ensured the inclusion of papers that demonstrate methodological rigor, relevance, and recent contributions to the field, spanning from economic theories and statistical models to advanced machine learning techniques. The findings reveal that precise price predictions significantly contribute to the development of adaptive and risk-managed trading strategies, which are crucial in the highly volatile cryptocurrency market. The review also identifies current challenges and proposes directions for future research, emphasizing the need for interdisciplinary approaches and ethical considerations in predictive modeling. This synthesis aims to bridge the existing research gap and guide future studies, thereby fostering more sophisticated and profitable trading strategies in the cryptocurrency domain.

In the Bitcoin trading landscape, predicting price movements is paramount. Our study focuses on identifying the key factors influencing these price fluctuations. Utilizing the Pearson correlation method, we extract essential data points from a comprehensive set of 14 data features. We consider historical Bitcoin prices, representing past market behavior; trading volumes, which highlight the level of trading activity; network metrics that provide insights into Bitcoin’s blockchain operations; and social indicators: analyzed sentiments from Twitter, tracked Bitcoin-related search trends on Google and on Twitter. These social indicators give us a more nuanced understanding of the digital community’s sentiment and interest levels. With this curated data, we forge ahead in developing a predictive model using Deep Q-Network (DQN). A defining aspect of our model is its innovative reward function, tailored for enhancing predicting Bitcoin price direction, distinguished by its multi-faceted reward function. This function is a blend of several critical factors: it rewards prediction accuracy, incorporates confidence scaling, applies an escalating penalty for consecutive incorrect predictions, and includes a time-based discounting to prioritize recent market trends. This composite approach ensures that the model’s performance is not only precise in its immediate predictions but also adaptable and responsive to the evolving patterns of the cryptocurrency market. Notably, in our tests, our model achieved an impressive F1-score of 95%, offering substantial promise for traders and investors.

Abstract The Bitcoin market has experienced unprecedented growth, attracting financial traders seeking to capitalize on its potential. As the most widely recognized digital currency, Bitcoin holds a crucial position in the global financial landscape, shaping the overall cryptocurrency ecosystem and driving innovation in financial technology. Despite the use of technical analysis and machine learning, devising successful Bitcoin trading strategies remains a challenge. Recently, deep reinforcement learning algorithms have shown promise in tackling complex problems, including profitable trading strategy development. However, existing studies have not adequately addressed the simultaneous consideration of three critical factors: gaining high profits, lowering the level of risk, and maintaining a high number of active trades. In this study, we propose a multi-level deep Q-network (M-DQN) that leverages historical Bitcoin price data and Twitter sentiment analysis. In addition, an innovative preprocessing pipeline is introduced to extract valuable insights from the data, which are then input into the M-DQN model. A novel reward function is further developed to encourage the M-DQN model to focus on these three factors, thereby filling the gap left by previous studies. By integrating the proposed preprocessing technique with the novel reward function and DQN, we aim to optimize trading decisions in the Bitcoin market. In the experiments, this integration led to a noteworthy 29.93% increase in investment value from the initial amount and a Sharpe Ratio in excess of 2.7 in measuring risk-adjusted return. This performance significantly surpasses that of the state-of-the-art studies aiming to develop an efficient Bitcoin trading strategy. Therefore, the proposed method makes a valuable contribution to the field of Bitcoin trading and financial technology.

Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.

Background. Improving pediatric observation globally and in Kyrgyzstan is essential for early detection of developmental issues and monitoring normal physical parameters. The purpose of the study was to investigate the indicators of bone composition in children of Kyrgyz nationality of both sexes in the early childhood period. Materials and methods. To achieve the goal, 800 children from 4 to 7 years old were examined using anatomical-anthropometric and bioimpedance methods. Results. It was revealed that the absolute content of bone component in male respondents at the age of 4 years ranges from 3.24 to 5.17 (4.10 ± 0.01) kg, 5 years — from 3.27 to 5.81 (4.26 ± 0.01) kg, 6 years — from 3.34 to 5.87 (4.39 ± 0.01) kg and 7 years — from 3.23 up to 6.02 (4.59 ± 0.01) kg. The absolute index of the bone component in 4-year-old girls has an individual variability of 3.04–5.17 (3.97 ± 0.01) kg, 5-year-old — 3.27–5.61 (4.20 ± 0.01) kg, 6-year-old — 3.34–5.77 (4.34 ± 0.01) kg and 7-year-old — 3.43–6.02 (4.40 ± 0.01) kg. Conclusions. The highest bone content was found in individuals with muscular somatotype, while the lowest values were in asthenoid and thoracic somatotypes for both males and females. Boys had higher bone component index at ages of 4 and 7.

Generating a realistic three-dimensional model of the human body is a very time-consuming task. Even with the necessary computing resources, generation errors occur on the figures of people who differ from the average physique. In this paper, an experimental algorithm for reading anthropometric data from only two full-face and profile photographs is proposed. The proposed solution to the problem of generation using the selection of anthropometric points involves setting the constraints of the SMPL (Skinned Multi-Person Linear Model) model. For segmentation of the human body based on empirical studies, a modification of the Fully Connected Convolutional Neural Network (FCN) ResNet101, trained on the COCO Segmentation 2017 dataset, was used. With its help, the basis for the detection of anthropometric points in full-face and profile photos was obtained. The error in determining anthropometric points ranges from 2 to 5 % depending on their location. The constraints for the SMPL rendering model are calculated using the Levenberg- Marquardt algorithm. For its correct operation, a special cost function is proposed, taking into account the features of this task. The dataset collected by the authors of the article (117 people of different physiques and height) shows that the proposed method allows you to obtain a small average absolute error (MAE = 0.0395 m) and a high coefficient of determination (R2 = 0.913). The graph of anthropometric points sets stricter conditions for generating a figure and any deviation from the graph is a consequence of a large generation error. The proposed solution allows you to accurately generate a model of the human body. At the same time, low requirements for computing resources and the quality of users’ initial photos remain. The proposed solution can be used in online fitting rooms, which adds additional complexity to the task due to the requirements to restore the figure from only two pictures as well as the need to accurately reproduce the features of male and female figures.

The lands of the forest fund on the territory of the steppe Don region are distinguished by a significant spread over the area of the main melliferous plant Robinia pseudoacacia (R. Pseudoacaci L.), which has a high bioresource potential for the honey collection of the Rostov region. The objectives of the research included identifying the features of the honey productivity of R. pseudoacacia plantations, depending on the dynamics of taxation indicators and forest conditions in the steppe Don region. In order to study changes in the honey productivity of Robinia plantations, accounting work was carried out in pure and mixed stands in each age group. The relationship of the studied indicator of Robinia forest stand with taxation characteristics and growing conditions was revealed. The highest honey productivity (347.5 kg/ha) is observed in pure Robinia forest stands in the forest growing conditions of fresh glague - sedge oak groves, where at the age of 50 there is an intensive flowering of trees (80.060 thousand pieces) and active release of sugar in nectar (2.48 mg). According to the observations, the density of the forest stand decreases with age, and the number of flowers on the trees increases. The results of the research showed that at the age of 40 years, the honey productivity of a hectare of Robin plantations reaches its maximum (586.3 kg/ha). The equations for the dependence of honey productivity on the taxation characteristics of R. pseudoacacia plantations and the obtained high correlation coefficients indicate a close relationship between the studied indicators. The data obtained will make it possible in the future to calculate the honey productivity of Robinia forest stands more accurately, taking into account the dynamics of their taxation indicators.