U-shaped Model Research Articles

Exploring the Nexus between Greenhouse Emissions, Environmental Degradation and Green Energy in Europe: A Critique of the Environmental Kuznets Curve

This study examines the intricate relationship between economic growth and European environmental degradation via the Environmental Kuznets Curve (EKC). Our results contest the traditional inverted U-shape model of the Environmental Kuznets Curve, indicating that the theory may not be consistently applicable across European countries. Utilizing CS-ARDL and MMQR modelling, we reveal substantial regional disparities. Western European nations demonstrate a typical Environmental Kuznets Curve (EKC) pattern in the short term, characterized by an initial increase in emissions alongside GDP development, followed by a subsequent fall. Conversely, Eastern and Balkan nations exhibit a U-shaped connection, described by an early decline in emissions followed by a subsequent increase as their development levels increase. The influence of renewable energy differs, as it decreases emissions in the short term in Western Europe. However, its long-term impacts are variable, especially when contrasted with its more pronounced effect on emissions in Eastern and Balkan countries. Furthermore, trade openness intensifies environmental degradation in the short-term across all regions, although its long-term impact diminishes, particularly concerning greenhouse gases (GHG). The relationship between renewable energy and trade openness is substantial for the short-term reduction of carbon dioxide emissions, but this effect declines with time. The results indicate that a uniform environmental policy throughout Europe may lack efficacy. Customized strategies to expedite the transition in Western Europe and more specific interventions in Eastern Europe are essential to harmonize economic progress with environmental sustainability. Future research should examine the determinants of the diminishing long-term effects of renewable energy and the interplay between trade and environmental policies.

Becoming a co-designer: the change in participants’ perceived self-efficacy during a co-design process

ABSTRACT Perceived self-efficacy is an integral part of a capability set that allows participants to transform design capabilities into the functioning of doing design. However, existing research investigating the change in participants’ perceived self-efficacy during a co-design process is inadequate. Therefore, this paper aims to demonstrate the changeability of participants’ perceived self-efficacy and its changing trajectory in a co-design process. The findings are extracted from a remote co-design project with older people, employing generative toolkits and culture probes, to address loneliness in later life. It has been discovered that the participants who successfully completed the co-design process experienced an increase in their perceived self-efficacy, following a U-shaped model. Furthermore, the depth of the U-shaped curve is contingent on how well the co-design process is designed to align with participants’ embodied knowledge and skills. The findings of this paper address the inadequate discussion of the change in participants’ perceived self-efficacy in co-design methodological literature. The introduced U-shaped model can equip co-design researchers and practitioners with the ability to anticipate and prepare for potential situations arising from changes in participants’ perceived self-efficacy during co-design processes.

SaraNet: Semantic aggregation reverse attention network for pulmonary nodule segmentation

Accurate segmentation of pulmonary nodule is essential for subsequent pathological analysis and diagnosis. However, current U-Net architectures often rely on a simple skip connection scheme, leading to the fusion of feature maps with different semantic information, which can have a negative impact on the segmentation model. In response to this challenge, this study introduces a novel U-shaped model specifically designed for pulmonary nodule segmentation. The proposed model incorporates features such as the U-Net backbone, semantic aggregation feature pyramid module, and reverse attention module. The semantic aggregation module combines semantic information with multi-scale features, addressing the semantic gap between the encoder and decoder. The reverse attention module explores missing object parts and captures intricate details by erasing the currently predicted salient regions from side-output features. The proposed model is evaluated using the LIDC-IDRI dataset. Experimental results reveal that the proposed method achieves a dice similarity coefficient of 89.11%and a sensitivity of 90.73 %, outperforming state-of-the-art approaches comprehensively.

A conditional crowd out/in model of government funding on giving of money and time: evidence from social service organizations in China

Existing literature has studied the impact of government funding on donations to nonprofits, proposing and testing different theoretical models, including crowd-out, crowd-in, and inverted U-shaped models. However, there is no consensus regarding whether or under what conditions government funding crowds out or crowds in donations to nonprofits, and little is known regarding whether and how government funding influences time donations. Existing evidence focuses on Western contexts and may not be applicable to non-Western contexts. This study used a random sample of Chinese social service organizations and proposed a more nuanced and complex conditional crowd-out/in model of government funding on donations to nonprofits, extending the testing to both financial and time donations in China. The results from extended regression models with instrumental variables indicate that government funding levels crowd out money donations, while dependence on government funding has an inverted U-shaped effect on money donations. Both the level of and dependence on government funding have curvilinear crowding-out effects on time donations. More importantly, the level of and dependence on government funding have significant interactive effects on both money and time donations. These novel findings make significant theoretical contributions to the literature and have important practical implications for policymakers and nonprofit managers.

CNN-Transformer hybrid network for concrete dam crack patrol inspection

Regular patrol inspection of concrete dams can detect cracks at an early stage. However, conventional crack segmentation models based on deep learning (DL) are difficult to be deployed in resource-constrained mobile devices due to the large number of parameters. This paper describes a lightweight semantic segmentation model, termed as CrackTrNet, for images of concrete dam cracks. CrackTrNet is a hybrid U-shaped model based on convolutional neural network (CNN) and Vision Transformer. The CNN is adopted to extract low-level visual features and the Transformer focuses on learning the global contextual information. The results demonstrate that its segmentation accuracy can reach 97.60%, while the model size is only 34.86 MB, which is 66.12%–87.85% lower than that of current mainstream DL-based models. To make the model more practical, a crack inspection mobile application (APP) is developed using Android Studio. The integration of lightweight CrackTrNet and APP can effectively assist the intelligent inspection of dam cracks to ensure structural safety.

GREEN ECONOMY WITH ECONOMIC GROWTH AND PER CAPITA INCOME IN VIETNAM

Objective: The objective of this study is to investigate the green economy with economic growth and per capita income in Vietnam, to analyze the quantitative relationship among those factors, and to improve long-term green economic development in Vietnam towards sustainable national economic growth. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. The inverted U-shaped model of Kuznets; Green economy concept and regression model stand out, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprises desk research methods to clarify the theoretical basis of green economy, economic growth, and per capita income; qualitative research methods; and quantitative research methods. Data collection was carried out through the collection of time-series data: Real Gross Domestic Product (GDP), Gross Domestic Product per capita (GDPP), and CO2 emissions from 1995 to 2022 from the World Bank website. Results and Discussion: The results obtained revealed the positive relationship between economic growth, CO2 emissions, and per capita income in the short term; however, in the long term, when GDP as well as per capita income reach a certain threshold. The level of waste gradually decreases and environmental quality improves. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of development economics. These implications could encompass many sectors, especially the processing industry. Originality/Value: This study contributes to the literature by Simon Kuznets - economic growth is not a threat, but rather a means to improve the environment in the future. The relevance and value of this research are evidenced by contributing one more case in Vietnam which approves Kuznets curve on the environment and thus is the basis for future research on this urgent problem all over the world.

FTUNet: A Feature-Enhanced Network for Medical Image Segmentation Based on the Combination of U-Shaped Network and Vision Transformer

Semantic Segmentation has been widely used in a variety of clinical images, which greatly assists medical diagnosis and other work. To address the challenge of reduced semantic inference accuracy caused by feature weakening, a pioneering network called FTUNet (Feature-enhanced Transformer UNet) was introduced, leveraging the classical Encoder-Decoder architecture. Firstly, a dual-branch Encoder is proposed based on the U-shaped structure. In addition to employing convolution for feature extraction, a Layer Transformer structure (LTrans) is established to capture long-range dependencies and global context information. Then, an Inception structural module focusing on local features is proposed at the Bottleneck, which adopts the dilated convolution to amplify the receptive field to achieve deeper semantic mining based on the comprehensive information brought by the dual Encoder. Finally, in order to amplify feature differences, a lightweight attention mechanism of feature polarization is proposed at Skip Connection, which can strengthen or suppress feature channels by reallocating weights. The experiment is conducted on 3 different medical datasets. A comprehensive and detailed comparison was conducted with 6 non-U-shaped models, 5 U-shaped models, and 3 Transformer models in 8 categories of indicators. Meanwhile, 9 kinds of layer-by-layer ablation and 4 kinds of other embedding attempts are implemented to demonstrate the optimal structure of the current FTUNet.

Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems

The development of technology accelerates the upgrade of products, which results in a significant number of obsolete products. This research aims to solve the Multi-robotic Multi-product U-shaped Disassembly-line-balancing Problem (M2UDP), in which different products are disassembled on a U-shaped model in a pre-set cycle time and assigned tasks to robots in each workstation reasonably. A linear mixed-integer model is established to maximize disassembly profit and minimize carbon emissions. An improved multi-objective multi-verse optimizer (IMMO) that utilizes the sigmoid activation function in neural networks is proposed to find the optimal plan for the model. The improved algorithm is verified via a set of real-life instances and compared with three classical multi-objective optimization algorithms. The experimental results show that the proposed IMMO performs better than those peers in solving the M2UDP problems.

HSH-UNet: Hybrid selective high order interactive U-shaped model for automated skin lesion segmentation

The use of computer-assisted clinical dermatologists to diagnose skin diseases is an important aid. And computer-assisted techniques mainly use deep neural networks. Recently, the proposal of higher-order spatial interaction operations in deep neural networks has attracted a lot of attention. It has the advantages of both convolution and transformers, and additionally has the advantages of efficient, extensible and translation-equivariant. However, the selection of the interaction order in higher-order interaction operations requires tedious manual selection of a suitable interaction order. In this paper, a hybrid selective higher-order interaction U-shaped model HSH-UNet is proposed to solve the problem that requires manual selection of the order. Specifically, we design a hybrid selective high-order interaction module HSHB embedded in the U-shaped model. The HSHB adaptively selects the appropriate order for the interaction operation channel-by-channel under the computationally obtained guiding features. The hybrid order interaction also solves the problem of fixed order of interaction at each level. We performed extensive experiments on three public skin lesion datasets and our own dataset to validate the effectiveness of our proposed method. The ablation experiments demonstrate the effectiveness of our hybrid selective higher order interaction module. The comparison with state-of-the-art methods also demonstrates the superiority of our proposed HSH-UNet performance. The code is available at https://github.com/wurenkai/HSH-UNet.

A quantitative exploration of the interactions and synergistic driving mechanisms between factors affecting regional air quality based on deep learning

Air pollution represents a significant environmental challenge on a global scale. The exploration of factors and mechanisms influencing air quality has garnered considerable attention and produced a substantial body of research. Nonetheless, the absence of effective research tools has resulted in a limited understanding of the nonlinear interactive effects of multiple factors on air quality, as well as their patterns and spatiotemporal variability characteristics. Furthermore, the efficacy of multifactor joint control strategies in practical air pollution management remains inadequate. A novel regional air environmental quality prediction model was developed utilizing a neural network with long short-term memory (LSTM) in deep learning based on an analysis of the spatiotemporal heterogeneity of regional air environmental quality in the Yangtze River Economic Belt (YEB). The model results were attributed and interpreted by integrating the SHapley Additive explanation (SHAP) method, focusing on the possible interactive effects and synergistic driving mechanisms of PM2.5 and O3 as the main pollutants and other factors on the air quality index (AQI) in a quantitative way, respectively. The SHAP interaction values of five factor pairs, namely PM2.5 and temperature, PM2.5 and precipitation, O3 and temperature, O3 and NO2, and O3 and PM2.5, are depicted in dependence scatter plots that exhibit three distinct trajectory shapes, namely linear, U-shaped, and irregular. The linear pattern of the dependence scatter plots for PM2.5 and precipitation in the YEB, midstream, and downstream regions, as well as for O3 and temperature in the upstream region, is evident. The irregular patterns of PM2.5 and precipitation, O3 and NO2, O3 and PM2.5 in the upstream, and PM2.5 and temperature in the downstream are observed to be correspondingly related. Additionally, a U-shaped pattern is exhibited by O3 and temperature in the YEB, midstream and downstream, PM2.5 and temperature in the upstream and midstream, O3 and NO2 in the YEB, midstream and downstream, and O3 and PM2.5 in the YEB. Of the three models, the U-shaped model's identification of inflection points and corresponding quantitative determination of factor thresholds hold greater practical implications for decision-making in air environment management. This pattern suggests that the synergistic effect of factor pairs initially decreases with changes in each factor but subsequently begins to increase after reaching inflection points. It is anticipated that this research will contribute to the advancement of theoretical inquiry into the nonlinear multifactor interaction effect and synergistic driving model of regional air quality. Additionally, it is expected to furnish a valuable foundation for decision-making in the development of joint air pollution prevention and control strategies aimed at addressing compound air pollution issues.

Hybrid CNN-Transformer model for medical image segmentation with pyramid convolution and multi-layer perceptron

Vision Transformer (ViT) has emerged as a potential alternative to convolutional neural networks for large datasets. However, applying ViT directly to medical image segmentation is challenging due to its lack of induction bias, which requires a large number of high-quality annotated medical images for effective model training. Recent studies have discovered that, in addition to the increased model capacity and generalization resulting from the lack of induction bias, the excellent performance of Transformer can also be attributed to its large receptive field. In this paper, we propose a U-shaped medical image segmentation model that combines large kernel convolutions with Transformers. Specifically, we construct a basic Transformer unit using pyramidal convolution modules with multi-scale kernels and multi-layer perceptron. In the pyramid convolution module, we employ grouped convolution to reduce parameter and computational complexity while utilizing multi-scale large kernel attention as a foundation for more efficient feature extraction. For different types of grouping, different sizes of convolutions are used to enhance the extraction of features with multiple receptive fields. To optimize the extracted features from the encoder, the U-shaped model integrates a variant of the pyramidal convolutional module into the skip connections. This variant utilizes multi-scale large kernel convolutional attention based on channel splitting. The incorporation of this variant enables efficient refinement of the feature representations within the skip connections. Through extensive comparisons on multi-modal medical image datasets, our model outperforms state-of-the-art methods across various evaluation metrics, with notable superiority observed on small-scale medical datasets. Our research findings suggest that the combination of large kernel convolutions and Transformer models introduces an advantageous inductive bias, resulting in enhanced performance specifically for small-scale medical image datasets. To facilitate accessibility, we have made our code openly accessible on our GitHub repository, which can be found at https://github.com/medical-images-process/CNN-Transformer.

FEGNet: A Feedback Enhancement Gate Network for Automatic Polyp Segmentation

Regular colonoscopy is an effective way to prevent colorectal cancer by detecting colorectal polyps. Automatic polyp segmentation significantly aids clinicians in precisely locating polyp areas for further diagnosis. However, polyp segmentation is a challenge problem, since polyps appear in a variety of shapes, sizes and textures, and they tend to have ambiguous boundaries. In this paper, we propose a U-shaped model named Feedback Enhancement Gate Network (FEGNet) for accurate polyp segmentation to overcome these difficulties. Specifically, for the high-level features, we design a novel Recurrent Gate Module (RGM) based on the feedback mechanism, which can refine attention maps without any additional parameters. RGM consists of Feature Aggregation Attention Gate (FAAG) and Multi-Scale Module (MSM). FAAG can aggregate context and feedback information, and MSM is applied for capturing multi-scale information, which is critical for the segmentation task. In addition, we propose a straightforward but effective edge extraction module to detect boundaries of polyps for low-level features, which is used to guide the training of early features. In our experiments, quantitative and qualitative evaluations show that the proposed FEGNet has achieved the best results in polyp segmentation compared to other state-of-the-art models on five colonoscopy datasets.

TPFR-Net: U-shaped model for lung nodule segmentation based on transformer pooling and dual-attention feature reorganization.

Accurate segmentation of lung nodules is the key to diagnosing the lesion type of lung nodule. The complex boundaries of lung nodules and the visual similarity to surrounding tissues make precise segmentation of lung nodules challenging. Traditional CNN based lung nodule segmentation models focus on extracting local features from neighboring pixels and ignore global contextual information, which is prone to incomplete segmentation of lung nodule boundaries. In the U-shaped encoder-decoder structure, variations of image resolution caused by up-sampling and down-sampling result in the loss of feature information, which reduces the reliability of output features. This paper proposes transformer pooling module and dual-attention feature reorganization module to effectively improve the above two defects. Transformer pooling module innovatively fuses the self-attention layer and pooling layer in the transformer, which compensates for the limitation of convolution operation, reduces the loss of feature information in the pooling process, and decreases the computational complexity of the Transformer significantly. Dual-attention feature reorganization module innovatively employs the dual-attention mechanism of channel and spatial to improve the sub-pixel convolution, minimizing the loss of feature information during up-sampling. In addition, two convolutional modules are proposed in this paper, which together with transformer pooling module form an encoder that can adequately extract local features and global dependencies. We use the fusion loss function and deep supervision strategy in the decoder to train the model. The proposed model has been extensively experimented and evaluated on the LIDC-IDRI dataset, the highest Dice Similarity Coefficient is 91.84 and the highest sensitivity is 92.66, indicating the model's comprehensive capability has surpassed state-of-the-art UTNet. The model proposed in this paper has superior segmentation performance for lung nodules and can provide a more in-depth assessment of lung nodules' shape, size, and other characteristics, which is of important clinical significance and application value to assist physicians in the early diagnosis of lung nodules.

Dynamic-Pix2Pix: Medical image segmentation by injecting noise to cGAN for modeling input and target domain joint distributions with limited training data

Learning to translate images from a source to a target domain with applications such as converting simple line drawing to oil painting has attracted significant attention. The quality of translated images is directly related to two crucial issues. First, the consistency of the output distribution with that of the target is essential. Second, the generated output should have a high correlation with the input. Conditional Generative Adversarial Networks, cGANs, are the most common models for translating images. The performance of a cGAN drops when we use a limited training dataset. In this work, we study the obstacles that prevent a U-shaped model from learning the target domain distribution from limited data by using noise as input. This study helps to increase the Pix2Pix (a form of cGAN) target distribution modeling ability from limited data with the help of dynamic neural network theory. Our model has two learning cycles. The model learns the correlation between input and ground truth in the first cycle. Then, the model's architecture is refined in the second cycle to learn the target distribution from noise input. These processes are executed in each iteration of the training procedure. Helping the cGAN learn the target distribution from noise input results in a better model generalization during the test time and allows the model to fit almost perfectly to the target domain distribution. As a result, our model surpasses the Pix2Pix model in segmenting HC18 and Montgomery's chest x-ray images. Both qualitative and Dice scores show the superiority of our model. Although our proposed method does not use thousand of additional data for pretraining, it produces comparable results for the in and out-domain generalization compared to the state-of-the-art methods.

Visual Geometry Group based on U-Shaped Model for Liver/Liver Tumor Segmentation

Liver cancer is the primary reason of death around the globe. Manually detecting the infected tissues is a challenging and time-consuming task. The computerized methods help make accurate decisions and therapy processes. The segmentation accuracy might be increased to reduce the loss rate. Semantic segmentation performs a vital role in infected liver region segmentation. This article proposes a method that consists of two major steps; first, the local Laplacian filter is applied to improve the image quality. The second is the proposed semantic segmentation model in which features are extracted to the pre-trained VGG16 model and passed to the U-shaped network. This model consists of 51 layers: input, 23 convolutional, 4 max pooling, 4 transpose convolutional, 4 concatenated, 8 activation, and 7 batch-normalization. The proposed segmentation framework is trained on the selected hyperparameters that reduce the loss rate and increase the segmentation accuracy. The proposed approach more precisely segments the infected liver region. The proposed approach performance is accessed on two datasets such as 3DIRCADB and LiTS17. The proposed framework provides an average dice score of 0.98, which is far better compared to the existing works.

Perancangan Tata Letak Gudang Tipe Aktiva Tetap Tidak Beroperasi

The Non-Operating Fixed Assets I Warehouse of Menes Baru substation used randomized storage that caused a lot of different types materials were stacked on top of each other and placed in sparsed random locations created inefficient order picking process. To solve the problem, the facility layout design using dedicated storage concept is created to minimize the order picking distance at the ATTB I Warehouse of Menes Baru substation. This research was conducted in several stages. There were (1) identifying materials and the flow patterns, including Materials Handling Equipment , (2) determining storage methods and options, and (3) analyzing activities using Activity Relationship Chart and Activity Relationship Diagram. The result shows that the recommended flow pattern material for the ATTB I Warehouse of Menes Baru substation is a U-shaped model. In addition, based on the results of the numerical scenario comparison test of order picking distances, the proposed layout using the dedicated storage concept of the ATTB I Warehouse of Menes Baru substation, could decrease the distances of order picking activity by 34% compared to the initial layout usin the randomized storage concept.

Effect of dimensional variations on the manufacturing process and the 3D shrinkage ratio of stereolithographic dental alumina ceramics.

The purpose of the present study was to evaluate the influence of height and length variations of alumina ceramics manufactured by stereolithography on deformations caused by the manufacturing process and on the 3D shrinkage ratio to control the final dimensions and improve the adaptation of stereolithographic ceramic dental prostheses. Two different U-shaped models were designed with variable heights or lengths. The specimens were manufactured by stereolithography and were scanned using a microtomographic device before and after the heat treatment. Dimensional variations were measured using inspection software. The number and surface of layers of alumina ceramic influenced the reliability of the stereolithography manufacturing but did not influence the 3D shrinkage ratio. The larger the layer surface, the larger the deformation of the ceramic. Dental ceramics manufactured by stereolithography with smallest layer surface are the most reliable. This helps in the selection of the build orientation.

SDSC-UNet: Dual Skip Connection ViT-Based U-Shaped Model for Building Extraction

Benefiting from effective global information interaction, vision-transformers (ViTs) have been widely used in the building extraction task. However, buildings in remote sensing (RS) images usually differ greatly in size. Mainstream ViT-based segmentation models for RS images are based on Swin Transformer, which lacks multi-scale information inside the ViT block. In addition, they only connect the output of the entire ViT encoder block to the decoder, which ignore the similarity information of the attention maps inside the ViT encoder block, and are unable to provide better global dependencies for the decoder. To solve above problems, we introduce a novel Shunted Transformer, which enables the model to capture multi-scale information internally while fully establishing global dependencies, to build a pure ViT-based U-shaped model for building extraction. Furthermore, unlike the previous single-skip-connection structure of U-shaped methods, we build a novel dual skip connection structure inside the model. It simultaneously transmits the attention maps inside the ViT encoder block and its entire output to the decoder, thereby fully mining the information of the ViT encoder block and providing better global information guidance for the decoder. Thus, our model is named Shunted Dual Skip Connection UNet (SDSC-UNet). We also design a feature fusion module called Dual Skip Upsample Fusion Module (DSUFM) to aggregate the information. Our model has yields state-of-the-art (SOTA) performance (83.02%IoU) on the Inria Aerial Image Labeling Dataset. Code will be available.

Seismic Facies Segmentation via a Segformer-Based Specific Encoder–Decoder–Hypercolumns Scheme

Seismic facies classification plays an important role in oil and gas reservoir interpretation. In the past few years, convolution neural network (CNN)-based models have been widely used in supervised seismic facies classification. However, to improve some inherent limitations of CNNs, it is significant to explore alternative architectures, such as the Transformer with the self-attention mechanism. In this study, based on the Segformer, a cutting-edge semantic segmentation Transformer, we propose a U-shaped model of joint the Segformer and the Hypercolumn representation for seismic facies classification, named U-Segformer-Hyper. As the emerging lightweight variant of the Transformer for seismic facies segmentation, the proposed U-Segformer-Hyper consists of a specific encoder-decoder-hypercolumns scheme, which can extract various features in different layers and fuse the output features of different layers with different scales. In the application of the public F3 seismic data, compared with the CNN Benchmark model, the U-Segformer-Hyper model has fewer parameters, fewer floating-point operations per second, and higher accuracy of classification in both the section-based and patch-based training modes. Moreover, the proposed U-Segformer-Hyper is open-source, which benefits to further explore alternative deep models in seismic interpretation.

Are Russian Capitals Most Upgraded? (Paradoxes of Settlement Differentiation)

Differentiation of access security of Russians to information-cultural content and cultural characteristics of residents of different types of settlements is considered. Statistics 1970–2020s shows that the differences in the information and cultural infrastructure of various types of settlements were largely smoothed out (although not completely eliminated) in the late Soviet era. However, the absence of qualitative differences between different types of settlements in access to the main channels of constant broadcasting of information and cultural content is combined, according to sociological surveys organized by the Institute of Sociology of the Federal Scientific Research Center of the Russian Academy of Sciences, with the preservation of culturally determined significant differences in the consumption of these contents. This is a factor in the reproduction of the differences between modern Russians living in different types of settlements in terms of adherence to different cultural values and attitudes. As expected, in accordance with the theories of urbanization and modernization, the urban population in Russia in the 21st century is somewhat more committed to the values of modern society (freedom, innovation, etc.) than the rural population. At the same time, the data of the all- Russian sociological survey conducted in 2021 showed strong differences depending on the type of urban settlement. The most advanced in terms of modernization are residents of cities with a population of 0.5–1.0 million people, i.e. far from the most urbanized settlements. Unexpectedly, the cultural values of the inhabitants of the two Russian capitals turned out, in terms of compliance with the norms of modern society, by no means the most advanced. An inverted U-shaped model of sociologically identifiable cultural settlement differences shows the incompleteness of cultural modernization in post-Soviet Russia, which forms the most important components of social capital.