Traditional Framework Research Articles

Estimating and investigating multiple constructs multiple indicators social relations models with and without roles within the traditional structural equation modeling framework: A tutorial.

The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials, we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison with alternative modeling options are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

“Civilization State” Concept in the Works of Non-Western Scholars

The article discusses the main ideas and theories of non-Western countries researchers that reveal the concept of “civilization state”. As a methodological basis for the study of ideas developed within the framework of a non-Western tradition, the author uses the provisions of decolonial theory, allowing to overcome the clear and implicit dominance of attitudes and epistemological foundations inherent in European-American social science. The article draws attention to the critical perception of decolonial optics, which insists on the need to revise the epistemological foundations of Western thinking and the world order as a whole. The author analyzes the features of distribution and development of “civilization state” concept in a number of non-Western countries in the first quarter of the 21st century; the article presents a brief overview of emergence and evolution of the “civilization” concept, the diversity of semantic and conceptual variations of this concept is shown, the features of this process in the conditions of decolonization of knowledge were identified, an analysis of modern ideas and justification of the “civilization state” concept in China, India, Turkey, Egypt and in a number of African countries was carried out. The author draws conclusions that the concept of “civilization state”, being the Western origin concept, nevertheless, is perceived and develops within the framework of overcoming the West-centric theories of social development. In the conclusion the main advantages, threats and risks are presented that are associated with the adoption of the “civilization state” concept. Among the advantages are such as cultural and value consolidation of society; political mobilization; the basis for sovereignty and an independent policy; preservation and development of civilizational identity; the uniqueness of the civilization country as a source of further development. Challenges and risks include the following: the ambiguity of the new world order; contradictions between the concept of civilization and the concept of the national state; civilizational populism; ideas of isolationism; the risks of becoming the failed civilizational state. The article also recognizes the need for consolidated research work for the further theoretical development of the “civilization state” concept.

SPACES OF ART CULTURE ORGANIZATION CREATED BY WOMEN INTELLECTUALS OF THE 17TH CENTURY

Background. The concepts of cultural space and cultural space were analyzed by A.Ya. Flier, L.P. Repina and others. In Western European culture of the 17th century, the problem of the place and role of a woman creator became more acute. The beginning of the present of women’s creativity originates in those times. Women's intellectual culture is a mental and verbal foundation created by creative women of a specific historical era and a specific country. The relevance of the study of women's intellectual culture of the 17th century is associated with the revival of the awareness of women creators of the past and the destruction of stereotypes in relation to women's creativity. The article analyzes the aspect of the organization of women's intellectual culture of the 17th century in order to identify the spaces in which women creators created works of art. Methods. While analyzing cultural institutions, an axiological approach is used with the inclusion of an anthropological component. While analyzing the space of art culture organization, methods such as analysis of the physical characteristics of the space, its internal structure, functions, values, place and role of creators in it and the results of their creative activity are used. Results. Creative women in the 17th century create their own spaces for the production of artistic cultural objects. Such spaces are monastery, female academy, salon, and Women’s Republic of Letters. Conclusion. The closed spaces for the organization of women's intellectual culture were monastery and female academy, where works of art were created within the framework of tradition. The open space was the salon, which created favorable conditions for creative innovation. In the space of Women’s Republic of Letters the new principles of upbringing and education were worked out.

CRITICISMS ON ONTOLOGICAL RELATIVISM OF MODERN SCIENCE BY SYED MUHAMMAD NAQUIB AL-ATTAS AND SEYYED HOSSEIN NASR

Postmodern approaches to science introduce various methods, and result in multiple explanations and competing theories. This has exacerbated the science war in philosophy of science. The ongoing disputes known as ‘science war’ is a consequence of ontological disputes within the philosophical branch of metaphysics. Even though science had denied the importance of metaphysics in its practice, this paper argues that the recognition of philosophy specifically that in regards to metaphysics is a solution to the confusion arising from relativism of knowledge. As part of a preliminary attempt to confront this problem, this paper focuses on the criticisms of Syed Muhammad Naquib al-Attas and Seyyed Hossein Nasr on the ontology of modern science, which issues from the denial of metaphysics by modern scientists. Their views on ontology are part of both scholars’ Islamic philosophy of science. This is a qualitative research based on historical, doctrinal and comparative analysis of the primary works by the two scholars. The arguments are presented within the scope of philosophy of science based on the framework of the Islamic intellectual tradition. Thus, we find that a wider and transcendental epistemology is possible for understanding the multiple levels of reality subsumed within the metaphysics of Islam, which opens the door to reality and truth.

AIfES: A Next-Generation Edge AI Framework.

Edge Artificial Intelligence (AI) relies on the integration of Machine Learning (ML) into even the smallest embedded devices, thus enabling local intelligence in real-world applications, e.g. for image or speech processing. Traditional Edge AI frameworks lack important aspects required to keep up with recent and upcoming ML innovations. These aspects include low flexibility concerning the target hardware and limited support for custom hardware accelerator integration. Artificial Intelligence for Embedded Systems Framework (AIfES) has the goal to overcome these challenges faced by traditional edge AI frameworks. In this paper, we give a detailed overview of the architecture of AIfES and the applied design principles. Finally, we compare AIfES with TensorFlow Lite for Microcontrollers (TFLM) on an ARM Cortex-M4-based System-on-Chip (SoC) using fully connected neural networks (FCNNs) and convolutional neural networks (CNNs). AIfES outperforms TFLM in both execution time and memory consumption for the FCNNs. Additionally, using AIfES reduces memory consumption by up to 54% when using CNNs. Furthermore, we show the performance of AIfES during the training of FCNN as well as CNN and demonstrate the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM.

Entrepreneurship as career choice of Russian students: the role of university education, cultural traditions, and gender stereotypes

In this paper, based on our own recent sociological survey of more than 800 students of 15 Russian universities, we explore the views of Russian students regarding entrepreneurship as their possible choice of a future career. We are interested above all in the role of university education in shaping these views. However, in order to scrutinize this role to full extent, we place our study in the broader framework of national cultural traditions which constitute an integral contributing factor and underlying influence not only in educational, but also in the general existential realm. In this context, we argue that a substantial impact on the students’ future career choice is made by existing gender differences based on the stereotypes of the ‘typically male’ and ‘female’ employment fields. Our study demonstrates, in particular, that Russian students display—notably, just as the older generation of educated Russians too—a noticeable conservatism in their career choices (including entrepreneurial aspirations), by adhering to the traditional cultural values and gender perceptions. We also provide a nuanced analysis of the motivations and expectations associated with choosing an entrepreneurial career by our respondents. Our method of collecting information is chosen to be questionnaire-based—as most effective for descriptive research—which is then complemented by further qualitative analytics intended to reveal hidden causal connections. Our approach is interdisciplinary, as it combines sociological research methods with socio-economic and culturological analysis.

Candl: cosmic microwave background analysis with a differentiable likelihood

We present candl, an automatically differentiable python likelihood for analysing cosmic microwave background power spectrum measurements. candl is powered by JAX, which makes it fast and easy to calculate derivatives of the likelihood. This facilitates, for example, robust Fisher matrices without finite-difference methods. We show the benefits of candl through a series of example calculations, covering forecasting, robustness tests, and gradient-based Markov chain Monte Carlo sampling. These also include optimising the band power bin width to minimise parameter errors of a realistic mock data set. Moreover, we calculate the correlation of parameter constraints from correlated and partially overlapping subsets of the SPT-3G 2018 TT/TE/EE data release. In a traditional analysis framework, these tasks are slow and require careful fine-tuning to obtain stable results. As such, a fully differentiable pipeline allows for a higher level of scrutiny; we argue that this is the paradigm shift required to leverage incoming data from ground-based experiments, which will significantly improve the cosmological parameter constraints from the Planck mission. candl comes with the latest primary and lensing power spectrum data from the South Pole Telescope and Atacama Cosmology Telescope collaborations and will be used as part of the upcoming SPT-3G TT/TE/EE and ϕϕ data releases. Along with the core code, we release a series of auxiliary tools, which simplify common analysis tasks and interface the likelihood with other cosmological software. candl is pip-installable and publicly available on Github.

Bayesian Estimation of Inverted Beta Mixture Models With Extended Stochastic Variational Inference for Positive Vector Classification.

The finite inverted beta mixture model (IBMM) has been proven to be efficient in modeling positive vectors. Under the traditional variational inference framework, the critical challenge in Bayesian estimation of the IBMM is that the computational cost of performing inference with large datasets is prohibitively expensive, which often limits the use of Bayesian approaches to small datasets. An efficient alternative provided by the recently proposed stochastic variational inference (SVI) framework allows for efficient inference on large datasets. Nevertheless, when using the SVI framework to address the non-Gaussian statistical models, the evidence lower bound (ELBO) cannot be explicitly calculated due to the intractable moment computation. Therefore, the algorithm under the SVI framework cannot directly use stochastic optimization to optimize the ELBO, and an analytically tractable solution cannot be derived. To address this problem, we propose an extended version of the SVI framework with more flexibility, namely, the extended SVI (ESVI) framework. This framework can be used in many non-Gaussian statistical models. First, some approximation strategies are applied to further lower the ELBO to avoid intractable moment calculations. Then, stochastic optimization with noisy natural gradients is used to optimize the lower bound. The excellent performance and effectiveness of the proposed method are verified in real data evaluation.

Digital Emporium

Abstract: E-commerce has become an indispensable part of contemporary business, revolutionizing the way goods and services are bought and sold by utilizing electronic networks, particularly the internet. This shift in commerce has significantly altered traditional business frameworks, witnessing remarkable global expansion. In India, e-commerce has experienced substantial growth alongside the widespread adoption of internet usage, particularly among startups, which have adopted it as a distinctive business model. Despite its widespread acceptance, e-commerce remains a field that warrants further exploration. This research aims to provide a comprehensive overview of the e-commerce landscape by analyzing trends and crucial factors essential for success. Furthermore, the study investigates consumer preferences and purchasing behaviors across various e-commerce platforms, including JD, Taobao, Pinduoduo, and Tmall, with the goal of enhancing marketing effectiveness through a deep understanding of consumer inclinations and motivations. Through empirical analysis and gathering feedback, this study aims to illuminate the varied preferences of consumers across different platforms, shedding light on factors that influence platform selection, such as product categories, age, and gender. Ultimately, the objective of this research is to contribute to a more profound understanding of e-commerce dynamics and their implications for both businesses and consumers.

Творчество Хусея Будаева в контексте развития балкарской литературы в первой трети ХХ века

The scientific work examines the work of the poet Budaev Husey Aslanovich (1880-1973), his contribution to the development of spiritual poetry, his place in settlement poetry, and works aimed at harmony in family and social life are also analyzed. The article establishes the insepara-ble connection of the works of H. Budaev with the semantics and poetics of folklore, which is the specificity of the author's poetic world. The depth of philosophical thought and the acuteness of social analysis permeating the work of Budaev Husey are noted. The analysis of the author's crea-tive heritage ("Altyn tau" - "Golden Mountain", "Khak Zholunda" - "Dear truth") allowed us to determine his creative individuality and uniqueness. Poems like "Din ieleny kuyu" ("Crying for the Lights of Faith, defending theologians, express protest against the violence committed during the era of Stalinism. The work establishes that the work of Husey Budaev was formed within the framework of the poetic traditions of the pre-Soviet period, laid down by the classics of Karachay-Balkarian literature Semenov Kaltur, Dzhanibekov Appa, Kochkarov Kaspot. The presentation of their ideas in clear words and understandable images is a tradition of Karachay-Balkarian oral folk poetry. The work notes the direct realization of folklore artistic installations in the poetry of Husey Budaev. To complete the tasks, the author employed comparative and contextual method-ologies, which are coupled in the paper with the concept of consistency and integrity of analysis. Historicism is the primary methodological and systematic premise guiding the inquiry.

Multi-Instance Learning with One Side Label Noise

Multi-instance Learning (MIL) is a popular learning paradigm arising from many real applications. It assigns a label to a set of instances, which is called a bag, and the bag’s label is determined by the instances within it. A bag is positive if and only if it has at least one positive instance. Since labeling bags is more complicated than labeling each instance, we will often face the mislabeling problem in MIL. Furthermore, it is more common that a negative bag has been mislabeled to a positive one, since one mislabeled instance will lead to the change of the whole bag label. This is an important problem that originated from real applications, e.g., web mining and image classification, but little research has concentrated on it as far as we know. In this article, we focus on this MIL problem with one side label noise that the negative bags are mislabeled as positive ones. To address this challenging problem, we propose, to the best our our knowledge, a novel multi-instance learning method with one side label noise. We design a new double weighting approach under traditional framework to characterize the “faithfulness” of each instance and each bag in learning the classifier. Briefly, on the instance level, we employ a sparse weighting method to select the key instances, and the MIL problem with one size label noise is converted to a mislabeled supervised learning scenario. On the bag level, the weights of bags, together with the selected key instances, will be utilized to identify the real positive bags. In addition, we have solved our proposed model by an alternative iteration method with proved convergence behavior. Empirical studies on various datasets have validated the effectiveness of our method.

Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks

In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework’s performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework’s 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework’s advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field.

Legal regulation of cryptocurrency and cryptocurrency operations in the European Union

The article “Legal Regulation of Cryptocurrency and Cryptocurrency Operations in the European Union” offers a comprehensive examination of the evolving legal landscape surrounding cryptocurrencies within the European Union (EU). It begins by defining cryptocurrencies, highlighting their unique characteristics such as decentralization, volatility, and potential for misuse. These features pose significant regulatory challenges, as traditional legal frameworks may not be fully equipped to address them.
 The article delves into the EU’s response to these challenges. It outlines the various legislative measures that have been implemented to regulate cryptocurrencies and related operations. These measures aim to strike a balance between fostering innovation in the digital economy and ensuring consumer protection and financial stability. The article discusses the implications of these regulations, noting that while they have brought some clarity and security to the sector, they also risk stifling innovation if not carefully calibrated.
 The article explores the ongoing debates within the EU regarding the appropriate regulatory approach to cryptocurrencies. It underscores the need for a nuanced understanding of the technology and its potential impacts. The article argues that regulation should not merely react to the challenges posed by cryptocurrencies but should also anticipate future developments to remain effective and relevant.
 The article concludes by emphasizing the importance of dialogue and collaboration among regulators, industry stakeholders, and the public in shaping the regulatory approach to cryptocurrencies. It suggests that such engagement can help ensure that regulations are not only responsive to current issues but also adaptable to future changes.
 The article provides a thorough and insightful analysis of the legal regulation of cryptocurrency and cryptocurrency operations in the European Union. It underscores the complexity of the issue and the need for a dynamic and forward-looking regulatory approach. The article serves as a valuable resource for anyone interested in understanding the intricacies of cryptocurrency regulation in the EU.

Macroeconomic drivers effect on housing sale prices in China

PurposeThe purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay.Design/methodology/approachUsing monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices.FindingsThe empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability.Research limitations/implicationsAlthough the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities.Practical implicationsTo ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market.Originality/valueFirst, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.

Towards the development of legislative framework for solar neighborhoods

The growing implementation of sustainable urban infrastructure, utilizing solar energy for heat and power generation, daylighting, and thermal comfort, has intensified the focus on sustainability standards and guidelines. Nevertheless, a noticeable deficiency persists in regulations that specifically address solar energy access and protection, posing a barrier to the diffusion of solar-centric neighborhoods. This paper examines the traditional urban regulatory frameworks and the state of solar energy regulations and practices within five countries (i.e., Canada, Italy, Norway, Sweden, and Switzerland). The aim of the study is to (i) identify gaps in existing regulations, standards, and codes, (ii) highlight the need for future regulations to protect solar access and rights, and (iii) support the deployment of solar technologies on a large scale. The results underline that climate-related regulations often fall short of specificity tailored to regional and local climates, relying on generalized climate considerations. Solar energy legislation is generally scarce and lacks comprehensive planning. Finally, despite various financial incentives for the installation of active solar strategies, their impact remains limited, impeding the wide spread of solar technology as a primary source of energy production in urban environment.

DEVELOPMENT OF A MATHEMATICAL MODEL FOR THE ENERGY BALANCE OF THE LASER CLADDING PROCESS OF METALLIC POWDERS WITH A COAXIAL NOZZLE

Modern technologies require innovative solutions for producing complex structures from various materials. Additive manufacturing (AM) or 3D printing presents such a challenge and has significantly impacted the environment across manufacturing sectors, regardless of size. Researchers and technicians have developed new design approaches by exploring fundamental components, materials, and manufacturing processes beyond traditional frameworks, addressing global complex material and design challenges. Laser cladding of metal powders with a coaxial nozzle is one such technology, offering highly productive processes for large-scale component production. Fiber lasers are optimal for providing laser radiation, ensuring required emission quality and stability. The authors seek to develop a mathematical model for the energy balance of coaxial laser cladding of metal powders, aiming to provide an overview of energy consumption levels and unit characteristics for powder delivery, inert gas, and laser radiation sources in direct metal powder coating setups.

BitTrace

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.

A novel framework for predicting active flow control by combining deep reinforcement learning and masked deep neural network

Active flow control (AFC) through deep reinforcement learning (DRL) is computationally demanding. To address this, a masked deep neural network (MDNN), aiming to replace the computational fluid dynamics (CFD) environment, is developed to predict unsteady flow fields under the influence of arbitrary object motion. Then, a novel DRL-MDNN framework that combines the MDNN-based environment with the DRL algorithm is proposed. To validate the reliability of the framework, a blind test in a pulsating baffle system is designed. Vibration damping is considered to be the objective, and a traditional DRL-CFD framework is constructed for comparison. After training, a spatiotemporal evolution of 200 time steps under the influence of arbitrary object motion is predicted by the MDNN. The details of the flow field are compared with the CFD results, and a relative error within 5% is achieved, which satisfies the accuracy of serving as an interactive environment for DRL algorithms. The DRL-MDNN and traditional DRL-CFD frameworks are then applied to the pulsating baffle system to find the optimal control strategy. The results indicate that both frameworks achieve similar control performance, reducing vibration by 90%. Considering the resources expended in establishing the database, the computational resource consumption of the DRL-MDNN framework is reduced by 95%, and the interactive response time during each episode is decreased by 98.84% compared to the traditional DRL-CFD framework.

Discursive analysis of nursing care toward childhood fever and its contextual differences: An ethnomethodological study.

Fever is the most common clinical sign during infancy. Nurses deal with fever in children most directly and it is an integral aspect of their role as pediatric nurses. The objective of this study is to analyze the perceptions, knowledge and attitudes toward childhood fever of nurses in three health contexts: pediatric hospitalization, pediatric emergency and primary care. To respond this objective, a qualitative study with ethnomethodological approach has been carried out. In-depth interviews were conducted and theoretical clinical cases were presented to nurses working in pediatrics in the three settings studied. After the analysis of the discourses, the codes were classified into three categories: static and number-centric knowledge, dependent nursing attitude, and unconscious model nurses. On the one hand, when we analyze and compare the perceptions, attitudes and knowledge of the nurses between the different contexts, we find differences that consist mainly of what the context requires of them. On the other hand, in general and regardless of the context, the nurses interviewed place themselves in a traditional framework when faced with the phenomenon of fever in children.

Смерть А. Чехова как финал эпохи: между журналистикой, литературой и философией (на материале публикаций в газете «Новое время»)

The purpose of the article is to analyze publications in “Novoye Vremya” devoted to the death of A. Chekhov. During the viewing of the newspaper for 1904–1905 it was possible to find more than 50 relevant materials of different genres. These are, in particular, notes on the writer's last days; the meeting of the coffin in St. Petersburg at the Nikolaevsky railway station; the arrangement of the grave at the cemetery of the Novodevichy Monastery; memorial meetings and lectures. The most interesting are the obituaries and memoirs of the leading journalists of “Novoye Vremya”. N. Engelhardt, V. Rozanov and V. Burenin, recognizing Chekhov’s outstanding talent, nevertheless assess him as a minor author and refuse to put him on a par with the great novelists — Goncharov, Dostoevsky, Tolstoy. M. Menshikov and A. Suvorin, in turn, talk about the end of the “golden age” of Russian literature. At the origins of this era stood Pushkin, who overcame romanticism and turned to the poetry of reality, and its finale is associated with the death of Chekhov, who always remained within the framework of the realistic tradition, but mastered techniques that were soon adopted by modernists — new romantics. In a sense, Chekhov mirrored Pushkin’s creative path.