Dominant Feature Research Articles

Lexical features and persuasion techniques in the selected Philippine television advertisements

The language of advertising is powerful in persuading consumers to purchase the product. It is how the advertiser uses language to attract and grab the interest of the people. This research aims to identify and reveal the lexical features and persuasion techniques used in the selected Philippine television advertisements. This will determine how the lexical features are interrelated to the persuasion techniques that the advertiser uses in promoting their products. To obtain the data needed for these objectives, qualitative and content analysis were employed in this research method. Specifically, categorical data were used to categorize data based on each advertisement (Personal Hygiene, Medicine and Vitamins, and Food Products). Based on the findings, personal hygiene advertisements dominantly use hyperbole, while medicine and vitamin and food advertisements both dominantly use familiar language; personal hygiene advertisements belong to the competitive stage as manifested by the dominant lexical feature used which is hyperbole, while medicine and vitamin, and food belong to the pioneering stage as reflected by the dominant lexical feature used which is familiar language. Hence, the output of this study will serve as new learning instructional materials for English teachers in determining the usage of the language in an advertisement. In addition, the researchers recommend finding another framework for lexical features and persuasion techniques in advertisements and creating another material in the language in advertisements.

Where the small things are: Modelling edge effects on mouse lemur population density and distribution in northwestern Madagascar.

Edge effects result from the penetration to varying depths and intensities, of abiotic and biotic conditions from the surrounding non-forest matrix into the forest interior. Although 70% of the world's forests are within 1 km of a forest edge, making edge effects a dominant feature of most forest habitats, there are few empirical data on inter-site differences in edge responses in primates. We used spatially explicit capture-recapture (SECR) models to determine spatial patterns of density for two species of mouse lemurs (Microcebus murinus and Microcebus ravelobensis) in two forest landscapes in northwestern Madagascar. The goal of our study was to determine if mouse lemurs displayed spatially variable responses to edge effects. We trapped animals using Sherman live traps in the Mariarano Classified Forest (MCF) and in the Ambanjabe Forest Fragment Site (AFFS) site within Ankarafantsika National Park. We trapped 126 M. murinus and 79 M. ravelobensis at MCF and 78 M. murinus and 308 M. ravelobensis at AFFS. For M. murinus, our top model predicted a positive edge response, where density increased towards edge habitats. In M. ravelobensis, our top model predicted a negative edge response, where density was lower near the forest edges and increased towards the forest interior. At regional and landscape-specific scales, SECR models estimated different density patterns between M. murinus and M. ravelobensis as a result of variation in edge distance. The spatial variability of our results using SECR models indicate the importance of studying the population ecology of primates at varying scales that are appropriate to the processes of interest. Our results lend further support to the theory that some lemurs exhibit a form of ecological flexibility in their responses to forest loss, forest fragmentation, and associated edge effects.

Exploring the potential of SrTi0.7Fe0.3O3 perovskite/Chitosan nanosheets for the development of a label-free electrochemical sensing assay for determination of naproxen in human plasma samples

Naproxen is a nonsteroidal anti-inflammatory drug used to treat nonrheumatic inflammation, migraine, and gout. Therefore, the determination of naproxen in pharmaceutical and biological samples is of particular importance. In the present work, SrTi0.7Fe0.3O3 perovskite/Chitosan nanosheets were used to modify the surface of a glassy carbon electrode (GCE) for highly sensitive determination of naproxen. To ensure the successful synthesis of the perovskite nanosheets, morphological studies including scanning electron microscopy (SEM), Energy-dispersive X-ray (EDX), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and X-ray photoelectron spectroscopy (XPS) were carried out. The electrochemical investigations of naproxen on the modified surface of GCE were investigated and the limit of detection (LOD) and limit of quantification (LOQ) were acquired 0.50 and 1.67 μM, respectively. Additionally, the linear range (LR) of 1.99–130.84 μM was obtained for the oxidation of naproxen. The obtained results have been proved that the mentioned method is simple, sensitive, and specific with a short analysis time. The dominant analytical features of the designed sensor are possessing a low detection limit, excellent stability, repeatability, and high selectivity in the presence of naproxen. For investigation of the applicability of the designed assay in real sample analysis, human plasma samples have been examined and a recovery index was acquired 95%.

Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior

Recent vision-language pre-trained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a method to improve the pre-trained VLP model by adding a learnable context vector to the inputs of the text encoder. In a few-shot learning scenario of the downstream task, MLE training can lead the context vector to over-fit dominant image features in the training data. This overfitting can potentially harm the generalization ability, especially in the presence of a distribution shift between the training and test dataset. This paper presents a Bayesian-based framework of prompt tuning, which could alleviate the over-fitting issues on few-shot learning application and increase the adaptability of prompts on unobserved instances. Specifically, modeling data-dependent prior enhances the adaptability of text features for both seen and unseen image features without the trade-off of performance between them. Based on the Bayesian framework, we utilize the Wasserstein gradient flow in the estimation of our target posterior distribution, which enables our prompt to be flexible in capturing the complex modes of image features. We demonstrate the effectiveness of our method on benchmark datasets for several experiments by showing statistically significant improvements on performance compared to existing methods.

Clinical and microbiological characteristics of onychomycosis in a tertiary hospital: a cross-sectional study

BACKGROUND Onychomycosis is a common fungal nail infection with a low cure rate. While dermatophytes are the most common causal agent for onychomycosis, the incidence of Candida and nondermatophyte mold (NDM) onychomycosis is increasing. This study aimed to analyze the clinical and microbiological characteristics of patients with onychomycosis. METHODS Patients who visited the Department of Dermatology and Venereology, Cipto Mangunkusumo Hospital, and were diagnosed with onychomycosis from 2017 to 2022 were included. Diagnosis was established through clinical examination, supported by the result of direct microscopic examination with potassium hydroxide. RESULTS Of 171 patients, 93.6% had onychodystrophy, 65.5% were females, and 62.0% were aged 19–59 years. Most patients had onychodystrophy in more than three nails, affecting fingernails (31.6%) and toenails (34.5%). Interestingly, 84.8% of patients had no history of nail diseases. The median onset of disease was 24.0 (1–1,040) weeks, while the median onychomycosis severity index was 10.0 (2–40). Most cases were caused by Candida albicans (48.3%). Fusarium was the only NDM documented (2.3%). Some patients were resistant to itraconazole (11.4%) and miconazole (4.5%). Overall, 49.1% of the patients were declared not cured. CONCLUSIONS Candida was the predominant cause of onychomycosis, and onychodystrophy was the dominant feature. Current treatment regimens with systemic or topical antifungal agents did not yield satisfactory results, with more than half of the patients deemed not cured.

Языковые средства создания образа страны в англоязычном научно-популярном дискурсе (на материале издания “Gerald & Lee Durrell in Russia”)

The paper studies the means of linguistic representation of Russia in the book “Gerald & Lee Durrell in Russia” by the famed naturalist G. Durrell. The paper aims at analyzing the position of the author towards Russia and the realia described on the level of content and linguistic means employed in the text. Scientific originality of the study involves identification of the linguistic mechanisms of positive image construction and its dominant features in popular scientific discourse. The analysis has demonstrated that the material under consideration presents a highly positive appraisal of what Gerald & Lee Durrell explored in the vast Russian nature reserves. Evaluative vocabulary is concentrated in such conceptual blocks as “characteristics of the country, flora and fauna”, “profiles of the staff of natural reserves”, “environmental protection”. The material contains the frequently used lexical units that verbalize the semantics of love for the nature and animals, commitment to the cause, the importance of environmental protection, which contributes to the humanization and positivization of the perception of Russia and its people.

Excess glucocorticoids inhibit murine bone turnover via modulating the immunometabolism of the skeletal microenvironment.

Elevated bone resorption and diminished bone formation have been recognized as the primary features of glucocorticoid-associated skeletal disorders. However, the direct effects of excess glucocorticoids on bone turnover remain unclear. Here, we explored the outcomes of exogenous glucocorticoid treatment on bone loss and delayed fracture healing in mice and found that reduced bone turnover was a dominant feature, resulting in a net loss of bone mass. The primary effect of glucocorticoids on osteogenic differentiation was not inhibitory; instead, they cooperated with macrophages to facilitate osteogenesis. Impaired local nutrient status - notably, obstructed fatty acid transportation - was a key factor contributing to glucocorticoid-induced impairment of bone turnover in vivo. Furthermore, fatty acid oxidation in macrophages fueled the ability of glucocorticoid-liganded receptors to enter the nucleus and then promoted the expression of BMP2, a key cytokine that facilitates osteogenesis. Metabolic reprogramming by localized fatty acid delivery partly rescued glucocorticoid-induced pathology by restoring a healthier immune-metabolic milieu. These data provide insights into the multifactorial metabolic mechanisms by which glucocorticoids generate skeletal disorders, thus suggesting possible therapeutic avenues.

Fast dominant feature selection with compensation for efficient image steganalysis

Image steganalysis has witnessed significant development but still encounters challenges in detection speed and accuracy. Based on this consideration, this paper proposes a simple yet efficient dominant feature selection method. First, a separability measurement is designed utilizing the principles of “intraclass aggregation and interclass dispersion” and “maximum interclass disparity”, by which the contribution of each feature is better evaluated. Second, a separability measurement is presented considering the “holistic interclass disparity”, and thus dominant features are directly determined. Moreover, a compensation strategy is proposed to reduce the possibility of missing dominant features, thereby further enhancing the accuracy of the selected features. The effectiveness of the proposed method is empirically verified. Extensive experiments are conducted on the BOSSbase 1.01 and ALASKA2 datasets. The results show that, compared to some state-of-the-art works, the proposed method achieves better performance in terms of computational cost, feature dimension, and detection accuracy. In particular, the computational cost of the proposed method is extremely low.

Structural semantic features dominant in the poetics of Yu.V. Bondarev’s works about the Great Patriotic War (to the 100th anniversary of the birth)

The article aims to show the evolution of the significant structural semantic features dominant in the poetics of Yu.V. Bondarev’s works about the Great Patriotic War. The principal research methods are structural semantic and comparative-contrastive analysis. To achieve the goal, the study consistently reveals the specific features of the poetics of the writer’s early novellas "The Battalions Request Fire" and "The Last Shot", as well as the novel "The Hot Snow". Based on the comparison of the obtained results, the paper concludes that the poetics of the writer’s works about the Great Patriotic War evolved in the course of time. Moreover, specific forms of such changes are indicated. The dominant features of Yu.V. Bondarev’s poetics are the protagonist’s specific confessional discourse, the chronotope locality, the leitmotif opposition between the images of lyrical and tragic planes, the expressiveness of the eclectic detail, cinematographicity, sensory language, the semantic significance of the finales. These features are manifested in the writer’s early works about the Great Patriotic War, as well as in his novel "The Hot Snow". The novellas "The Battalions Request Fire" and "The Last Shots" are considered within the framework of the leading genre model of "lieutenant prose", i. e. the genre of the frontline lyric novella. "The Hot Snow" is studied as a lyric-epic novel about the Great Patriotic War. The novel retains some features of the frontline lyrical novella, while others are taken to a new level. In "The Hot Snow" novel, the confessional discourse is enhanced with a rational-analytical component; the chronotope of the work, while retaining its locality, acquires the specificity of a documentary one. Details (object, portrait, landscape, interior, psychological) are actively used, but at the same time they are generalised to a symbolic level. Yu.V. Bondarev’s cinematographic vision is enriched with new forms and techniques; the linguo-sensory system becomes intricate and includes components previously not used by the writer. The novel finale is not abstract, but conceptual. These dominant structural semantic features of the poetics of Yu. V. Bondarev’s works make it possible to determine the genesis of his novelistic creativity and conclude that the writer’s artistic thinking evolved.

Design of experiment approach to identify the dominant geometrical feature of left coronary artery influencing atherosclerosis

Background and Objective. Coronary artery geometry heavily influences local hemodynamics, potentially leading to atherosclerosis. Consequently, the unique geometrical configuration of an individual by birth can be associated with future risk of atherosclerosis. Although current researches focus on exploring the relationship between local hemodynamics and coronary artery geometry, this study aims to identify the order of influence of the geometrical features through systematic experiments, which can reveal the dominant geometrical feature for future risk assessment. Methods. According to Taguchi’s method of design of experiment (DoE), the left main stem (LMS) length (l LMS), curvature (k LMS), diameter (d LMS) and the bifurcation angle between left anterior descending (LAD) and left circumflex (LCx) artery (α LAD-LCx) of two reconstructed patient-specific left coronary arteries (LCA) were varied in three levels to create L9 orthogonal array. Computational fluid dynamic (CFD) simulations with physiological boundary conditions were performed on the resulting eighteen LCA models. Average helicity intensity (h 2) and relative atheroprone area (RAA) of near-wall hemodynamic descriptors were analyzed. Results. The proximal LAD (LAD proximal) was identified to be the most atheroprone region of the left coronary artery due to higher h 2, large RAA of time averaged wall shear stress (TAWSS < 0.4 Pa), oscillatory shear index (OSI ∼ 0.5) and relative residence time (RRT > 4.17 Pa−1). In both patient-specific cases, based on h 2 and TAWSS, d lms is the dominant geometric parameter while based on OSI and RRT, α LAD-LCx is the dominant one influencing hemodynamic condition in proximal LAD (p < 0.05). Based on RRT, the rank of the geometrical factors is: α LAD-LCx > d LMS > l LMS > k LMS, indicating that α LAD-LCx is the most dominant geometrical factor affecting hemodynamics at proximal LAD which may influence atherosclerosis. Conclusion. The proposed identification of the rank of geometrical features of LCA and the dominant feature may assist clinicians in predicting the possibility of atherosclerosis, of an individual, long before it will occur. This study can further be translated to be used to rank the influence of several arterial geometrical features at different arterial locations to explore detailed relationships between the arterial geometrical features and local hemodynamics.

Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures

The generation of chatter during machining operations is extremely detrimental to the cutting tool life and the surface quality of the workpiece. The present study aims to identify chatter conditions during the end milling of Ti6Al4V alloy. Experimental modal analysis is carried out, and stability lobe diagrams (SLDs) are developed to identify machining parameters under stable and chatter conditions. Experiments are conducted to acquire cutting force and vibration signatures corresponding to machining conditions selected from the SLD. Non-linear chatter features, such as Approximate Entropy, Holder Exponent, and Lyapunov Exponent extracted from the sensor signatures, are used to build Machine Learning (ML) models to identify chatter using Decision Trees (DTs), Support Vector Machines (SVMs) and DT-based Ensembles. A feature-level fusion approach is adopted to improve the classification performance of the ML models. The DT-based Adaboost model trained using dominant non-linear features classifies chatter with an accuracy of 96.8%. The non-linear features extracted from the sensor signatures offer a direct indication of the chatter and are found to be effective in identifying the machining chatter with good accuracy.

Machine Learning Based Open Switch Fault Detection and Localization of Inverters

The location and detection of switch faults is a crucial step in improving the dependability of inverters, which is a requirement for critical applications. This paper focuses on a machine learning-based approach for the open-switch fault detection system for cascaded H-bridge (CHB) multilevel inverters (MLI) used in high- and medium-power applications. Each switch failure is taken into account in this analysis, and the problem is used to model logically different machine learning algorithms, namely Gaussian Naive Bayes (GNB), support vector machines (SVM) and multinomial logistic regression (MLR) models for fault identification and localization based on multi-class classification (MCC) methods. This work includes the simulation and hardware-based data collection from five-level CHB-MLI and correlation-based selection of prominent features in the final dataset. The results of three MCC models for all possible combinations of predictors (features) prove that the mean voltage of bridges is the dominant feature for fault detection in the CHB inverter. Other major findings are machine learning-assisted fault identification and diagnosis is undoubtedly accurate and significantly more promising in condition monitoring and fault diagnosis in CHB-MLIs. The results reveal 100% accuracy in fault diagnosis. Therefore, the presented work confirms switch fault detection in CHB-MLI with mean value as dominant fault feature along with machine learning techniques simple, robust and accurate fault detection.

East Barents Basin and Admiralty High Composite Tectono-Sedimentary Elements, Barents Sea

Abstract The East Barents Sea (E Barents Sea) includes two major tectonic elements, the East Barents Basin (EB Basin) and the Admiralty High, which are, in the context of the volume, characterized as composite tectono-sedimentary elements. Whereas they share some stratigraphy, their early history may differ. The EB Basin is a dominant structural feature of the Barents Shelf, and one of the largest and deepest sedimentary basins in the Arctic. Admiralty High rims the EB Basin in the east. The advance in understanding the E Barents Sea geology was achieved during 1980s and 1990s mainly due to a significant amount of multichannel reflection seismic data and a number of drilled deep exploration wells. These wells tested many large anticlinal structures and made several large gas discoveries including a giant Shtokman gas and gas condensate accumulation. In the following decades, many important geological results were published in the international and Russian literature. In this chapter we provide a summary of the geology and petroleum geology of the E Barents Sea based on published results and public-domain geological reports.

Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection

Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection

Leveraging Spatial Metadata in Machine Learning for Improved Objective Quantification of Geological Drill Core

AbstractHere we present a method for using the spatial x–y coordinate of an image cropped from the cylindrical surface of digital 3D drill core images and demonstrate how this spatial metadata can be used to improve unsupervised machine learning performance. This approach is applicable to any data set with known spatial context, however, here it is used to classify 400 m of drillcore imagery into 12 distinct classes reflecting the dominant rock types and alteration features in the core. We modified two unsupervised learning models to incorporate spatial metadata and an average improvement of 25% was achieved over equivalent models that did not utilize metadata. Our semi‐supervised workflow involves unsupervised network training followed by semi‐supervised clustering where a support vector machine uses a subset of M expert labeled images to assign a pseudolabel to the entire data set. Fine‐tuning of the best performing model showed an f1 (macro average) of 90%, and its classifications were used to estimate bulk fresh and altered rock abundance downhole. Validation against the same information gathered manually by experts when the core was recovered during the Oman Drilling Project revealed that our automatically generated data sets have a significant positive correlation (Pearson's r of 0.65–0.72) to the expert generated equivalent, demonstrating that valuable geological information can be generated automatically for 400 m of core with only ∼24 hr of domain expert effort.

Global patterns of tree wood density.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%-63% of spatial variations, followed by vegetation characteristics (25%-31%) and edaphic properties (11%-16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Energetic and dynamic characterization of pollutant dispersion in varied building layouts through an augmented analysis procedure

This work presents a post-data analysis procedure, namely, proper orthogonal decomposition (POD)–dynamic mode decomposition (DMD)–discrete Fourier transform analysis, for evaluating the dominant features of the flow fields from both energetic and dynamic perspectives. The large-eddy simulation (LES) was first employed to reproduce the flow field surrounding three types of building layouts. Subsequently, both POD and DMD were conducted according to LES simulation results. The extracted modes were classified into three types based on the POD and DMD: Type-1 mode: energetically and dynamically significant mode, Type-2 mode: energetically significant and dynamically insignificant mode, and Type-3 mode: energetically insignificant and dynamically significant mode. The findings indicate that Type-1 mode governs the primary velocity field and the predominant vortex patterns observed at the rear of the building arrays, as the reduction of inter-building widths leads to a shorter flow separation region. Type-2 mode is characterized by the presence of small-scale vortices and the high turbulent kinetic energy region, which periodically triggers pollutant increase in the vicinity of structures. Type-3 mode demonstrates a minimal energetic influence on the flow field; nevertheless, it significantly contributes to the consistent build-up of pollutants within the far-wake region. The present study also investigates the predominant coherent structures of flow fields concerning various building layouts and highlights the influence of passage widths on the efficiency of pollutant removal. This comprehensive analysis enables a systematic exploration of flow patterns within various building layouts, offering potential solutions for pollutant dispersion challenges in metropolitan areas.

Summer Russian Heat Waves Linked to Arctic Sea Ice Anomalies in 2010 and 2016

Abstract This study investigates dominant features of the atmospheric circulation evolution associated with extreme heat waves (HWs) in Russia during the summers of 2010 and 2016, respectively, and their possible association with Arctic sea ice loss. Results show that a region of Russia (20°–70°E, 45°–65°N) experienced a lasting 44-day HW event from 4 July to 16 August 2010 and a 26-day HW event from 2 to 27 August 2016. The associated atmospheric circulation anomalies are characterized by the summer Arctic cold anomaly in the mid- to low troposphere and an anticyclonic circulation anomaly over the Ural Mountains. Simulation experiments forced by summer Arctic sea ice anomalies reproduce the major characteristics of observational associations. Observations and numerical simulations indicate that summer Arctic sea ice anomaly is conducive to the formation of the summer Arctic cold anomaly, which is often accompanied by the enhanced baroclinicity in most of the Arctic troposphere and increased and decreased meridional temperature gradient in the high and midlatitudes, respectively. Such a configuration strengthens westerly winds over most of the Arctic and weakens zonal westerly over the southern Ural Mountains. This anomalous zonal wind pattern establishes the background conditions for the sustained positive geopotential height anomaly in the mid- to low troposphere that dynamically facilitates the prevalence of Russian HW events. Moreover, when compared with 2016, the weaker meridional potential vorticity gradient anomaly in the summer of 2010 prolonged the persistence of Ural blocking, which may lead to longer HW events in Russia.

Multi-omics analysis elucidates the host-microbiome interplay in severe udder cleft dermatitis lesions in dairy cows

Udder cleft dermatitis is a skin disease in dairy cattle that is characterized by painful, large open wounds between the udder halves or at the front udder attachment. Its impact on animal welfare and production warrants an in-depth investigation of its pathogenesis. The present study delves into the pathophysiology of severe udder cleft dermatitis, employing a multi-omics approach by integrating transcriptomic and metagenomic data obtained from samples of severe udder cleft dermatitis lesions and healthy udder skin of dairy cattle. All dominant features selected from the virulence factor, taxonomic and transcriptomic data sets, except for the facultative pathogen Streptococcus pyogenes, form a network which could be associated with the healthy udder skin. The severe udder cleft dermatitis-associated Streptococcus pyogenes exhibited a negative correlation with these virulence factors and genes, but was not correlated to the other commensal bacteria in the analysis. Examining the different components interacting with each other could advance our understanding of the pathogenesis of severe udder cleft dermatitis.

Constancy and Breakthrough as Opposing Vectors of Empire Construction

In the present article the author addresses the issue of the correlation of the principles of constancy and breakthrough as dominant temperamental features of Russian pre-revolutionary and Soviet societies. Stating the formation in society of the attitude to the perception of national history in the unity of all its constituent historical periods, he draws attention to the need to realize their distinctive singularity, which makes it possible to understand the patterns of their change and comprehension of the qualities that make up their historical advantages and successes. From this point of view, the main difference between the Soviet society and the pre-revolutionary one is seen in the fact that the pre-revolutionary represented a kind of civilization and an empire of Permanence, whereas the Soviet, in contrast to it, was a civilization and an empire of Breakthrough. Attention is drawn to the fact that the preservation of the achievements accumulated during the Soviet period should be considered, fi rst of all, not as the preservation of its “Treasures” and riches, but as the preservation of the “Capital” of the Soviet era, in other words, such qualities of society that are capable of producing wealth, and in their functioning constantly expanded and reproduce them