Hierarchical Characteristics Research Articles

Soft sensor model for endpoint carbon content and temperature in BOF steelmaking based on just-in-time learning with multilayer structure preservation and sparse information enhancement

Ensuring precise prediction of the endpoint carbon content and temperature is paramount in controlling the endpoint of the basic oxygen furnace (BOF) steelmaking process. However, due to the frequent fluctuations in real-time blowing conditions, the conventional just-in-time learning soft sensor approach encounters challenges in effectively anticipating the blowing endpoint. To address these aforementioned issues, this research introduces a novel approach known as the pattern-oriented multilayer structure preservation and sparse information enhancement model (POMLSP-SIE). This approach involves dynamically generating a dataset with the same distribution based on the characteristics of the query sample pattern. It is combined with a multi-pathway dimension reduction model to preserve multi-view spatial geometry information while reducing data dimension. The low-dimensional embedding serves as dictionaries containing various hierarchical structural characteristics. Significant information within these dictionaries is sparsely represented to amplify its influence during the regression prediction process while diminishing the impact of less relevant information. This refinement aims to rectify the problem of static regression models being weak to adapt to changing working conditions, ultimately enhancing prediction performance. The effectiveness of the proposed method is substantiated through an experimental study utilising real converter steelmaking process data, thereby confirming its practical applicability.

Research on Dynamic Evaluation Method of Tourist Destination Accessibility Based on Interpretive Structure Model

Because of its core role in the process of tourism, tourism destination has always been the focus of discussion among the industry, but the comprehensive evaluation of tourism destination is rarely mentioned. This paper selects tourism destination as the focus of investigation, gives the evaluation method of tourism destination, adapts to the trend of tourism modernization, and discusses the quantifiable model of tourism destination evaluation. This paper first selects several tourism destinations as samples, collects and combs the information related to spatial equity of tourism destinations, and determines the main problem categories of spatial equity of tourism destinations. Then, through the summary of relevant data and case analysis, a framework of tourism target accessibility risk factors is formed. At the same time, considering the interaction between risk factors, we then give a method to determine the weight that can improve granular computing. First, we use this method to obtain the quotient space family with hierarchical characteristics through the similarity relationship between data, and then we can calculate the importance of each feature in the quotient space family, so as to obtain the corresponding weight between different influence indexes. Finally, the paper takes the tourism destination spatial equity problem as the main research goal, and through the dynamic evaluation of the tourism destination spatial equity risk impact structure using the interpretive structure model, and describes the schematic diagram of its impact structure, so as to find out the direct, source, process and other factors that cause the problem.

About the characteristic features of Russians (territorial and settlement aspect)

Relevance. In the post-Soviet era, changes in the consciousness and behavior of the masses of people became inevitable. The study of the structure of characteristic features (consciousness and behavior) of Russians (both positive and negative) in modern conditions is of great scientific and practical interest.The purpose is to use sociological materials to identify the hierarchical structure of characteristic positive and negative traits (consciousness and behavior) of Russians, including by territorial settlement groups.Objectives: to analyze the assessments of positive and negative traits inherent, in the opinion of respondents, to people of their generation; to identify their hierarchical structure and characteristics by territorial settlement groups.Methodology. The sociological survey and the sampling method, methods of grouping data, their analysis and generalization, and correlation analysis were used.Results. The leading features (both positive and negative) inherent, in the opinion of respondents, to people of their generation are revealed, as well as the relationships between them by territorial settlement groups are revealed.Conclusions. The positive features that have taken the leading places indicate the high potential of humanism and harmonious personality development among the majority of Russians. The negative traits that took the leading places indicate a relatively low level of sociality, social empathy and social responsibility of significant masses of people (according to respondents' estimates). However, the respondents' overall assessment of people of their generation is predominantly positive, which gives grounds for social optimism. At the same time, although the differences between territorial settlement groups in both positive and negative features are not antagonistic, they can be the basis for elements of contradictions and conflicts. Consistent active work of advanced social forces is necessary, first of all, by means of education to strengthen the positive traits of people as a "situationally dominant factor" and a center of attraction, a positive pole, especially for the younger generations.

ACU-TransNet: Attention and convolution-augmented UNet-transformer network for polyp segmentation.

UNet has achieved great success in medical image segmentation. However, due to the inherent locality of convolution operations, UNet is deficient in capturing global features and long-range dependencies of polyps, resulting in less accurate polyp recognition for complex morphologies and backgrounds. Transformers, with their sequential operations, are better at perceiving global features but lack low-level details, leading to limited localization ability. If the advantages of both architectures can be effectively combined, the accuracy of polyp segmentation can be further improved. In this paper, we propose an attention and convolution-augmented UNet-Transformer Network (ACU-TransNet) for polyp segmentation. This network is composed of the comprehensive attention UNet and the Transformer head, sequentially connected by the bridge layer. On the one hand, the comprehensive attention UNet enhances specific feature extraction through deformable convolution and channel attention in the first layer of the encoder and achieves more accurate shape extraction through spatial attention and channel attention in the decoder. On the other hand, the Transformer head supplements fine-grained information through convolutional attention and acquires hierarchical global characteristics from the feature maps. mcU-TransNet could comprehensively learn dataset features and enhance colonoscopy interpretability for polyp detection. Experimental results on the CVC-ClinicDB and Kvasir-SEG datasets demonstrate that mcU-TransNet outperforms existing state-of-the-art methods, showcasing its robustness.

FCGNet: Foreground and Class Guided Network for human parsing

Understanding the inherent hierarchical human structure is key to human parsing. To capture the human-specific characteristic, it is necessary to focus on the spatial and class information corresponding to the foreground (i.e., human) in an image. Inspired by these insights, we introduce two supervision signals, spatial foreground information and existent class information in the image. By utilizing foreground information as guidance, the network is guided to generate a human-focused feature map and capture the pixel-wise hierarchical characteristics by computing correlations between pixels. Furthermore, we guide the network to consider class information in the image at the feature level and capture the class-wise relationship by calculating correlations between channels. Moreover, during the training phase, we prevent the network from misclassifying pixels into confusing classes by providing the existent class information in the image to the network at the prediction level. Our model achieves state-of-the-art performance with significantly reduced parameters and Multiply-Accumulate Operations (MACs) in three public benchmarks.

Comprehensive evaluation of leaching toxicity of tailings in non-resource-based cities in Anhui Province based on entropy weight-clustering method

<p>Toxic components in tailings pose a severe threat to the ecosystem and human health and negatively affect tailings’ resource utilization. Therefore, using the entropy weight-clustering method, we evaluated the leaching toxicity based on tailings samples from non-resource cities in Anhui Province. The results show that, all the extracted tailings samples first passed the leaching toxicity test, but there is still a large room for improvement. Secondly, there are complex interrelationships among the 15 indicators measuring leaching toxicity. Thirdly, in the process of entropy weight method calculation, the top three indicators in terms of weight are Cu (17.65%), Ag (16.52%), and Be (8.66%). Finally, the Ward clustering results show that the toxicity scores of tailings in each mining area exhibit distinct hierarchical characteristics, and they can be divided into two major categories and three subcategories (Ⅰ-1, Ⅰ-2, Ⅱ), with the toxicity scores of category Ⅰ-1 significantly higher than the rest of the mining areas. These findings provide empirical evidence for targeted treatment of tailings resources.</p>

Explicit phase-field material point method for thermally induced fractures

Thermally induced fractures in solids pose significant challenges in various fields, such as aerospace, deep underground, and civil engineering structures. Numerical methods are effective for addressing such problems, with recent advancements in phase-field and material point methods demonstrating notable advantages in crack simulation. This paper presents a computational approach within an explicit material point method framework for coupling the solutions of temperature, displacement, and phase (damage) fields. The displacement and phase fields are intricately linked through a history-dependent strain field and degradation function. The model incorporates temperature-induced strains from temperature gradients for coupling the temperature and displacement fields. In addition, the model accounts for the detrimental effects of cracks on heat conduction, ensuring a comprehensive representation of the coupled system. Two numerical examples involving thermomechanical coupling and dynamic crack branching were used to validate the effectiveness of the proposed method. Finally, the proposed method was applied to simulate the thermal shock and large deformations of thin circular ceramic specimens, successfully replicating the initiation and propagation of cracks observed during the experiment. The simulated thermally induced fractures exhibited periodic and hierarchical characteristics consistent with the experimental findings.

Heterogeneous feature learning network for multimodal remote sensing image collaborative classification

ABSTRACT While multimodal remote sensing data processing has garnered growing interests in the geoscience field, how to effectively extract, represent, and fuse heterogeneous features remains challenging. In this article, a novel heterogeneous feature learning network (HFLN) architecture is investigated for multimodal remote sensing collaborative classification, which takes both advantages of long-range feature modelling capability of transformer as well as local feature extraction of convolutional neural network (CNN). First, the global spectral dependencies are extracted from hyperspectral images using the spectral transformer structure. Then, local spatial invariant characteristics are extracted from multimodal remote sensing images by convolutional operation. Next, heterogeneous spectral and spatial characteristics, having significant structural and semantic differences, are dynamically integrated through the feature coupling module in an interactive manner. Finally, the multi-stage network architecture is utilized for extracting hierarchical characteristics, where the numbers of feature maps in CNN and heads in transformer are gradually increased to represent more complex features. Four benchmark remote sensing datasets (i.e. hyperspectral images and multimodal remote sensing datasets) are utilized for experimental analysis, and extensive experimental results have certified the progressiveness of the investigated approach.

Research on Digital Literacy of Humanities and Social Science Students

[Purpose / Significance] In the info society, digital education is vital for digital society building. College students, as digital transformation helpers, should have basic digital literacy. [Method / process] This study targets humanities and social science students, integrating national digital literacy initiatives and domestic/international frameworks. A digital literacy framework was built using Delphi and AHP. 402 valid questionnaires were analyzed with descriptive stats & cluster analysis. [Results / Conclusion]Humanities students' digital literacy includes: info/data literacy, learning/thinking ability, digital citizenship, and tech application. Self-reported literacy is generally good, but tech innovation is a weakness. All dimensions show hierarchical characteristics. This study proposes: integrating digital literacy with professional training, building educational programs, and providing diverse training scenarios for comprehensive digital literacy development.

Microstructure and tensile properties of Ti6Al4V with layered structures fabricated by electron beam directed energy deposition

Adding modified elements to regulate the microstructure of titanium alloys prepared by additive manufacturing and enhance the mechanical properties of as-deposited titanium alloys has become a research focus. However, this approach also brings the risk of causing the materials with high strength-low plasticity characteristics. In this study, a macroscopic layered structure titanium alloy with hierarchical heterogeneous characteristics was constructed by alternately melting Ti6Al4V wire and Cu-modified Ti6Al4V wire using electron beam as a heat source. The tensile test results demonstrated that the layered structure titanium alloy exhibiting great strength and plasticity matching, which proved that the layered structure constructed via additive manufacturing could effectively alleviate the strength-ductility trade-off. The mechanism of synergistical improvement of the fracture strength and plastic was attributed to the deformation-induced strengthening as well as multiple crack-deflection and tip-passivation occurring during the plastic deformation of layered Ti6Al4V alloy.

Development and Application of a New Open-Source Integrated Surface–Subsurface Flow Model in Plain Farmland

Accurately characterizing rainfall runoff processes in plain farmland, especially at the plot scale with significant micro-topographic features, has presented challenges. Integrated surface–subsurface flow models with high-precision surface flow modules are appropriate tools, yet open-source versions are rare. To address this gap, we proposed an open-source integrated surface–subsurface flow model called the FullSWOF-Plain model, in which the one-dimensional subsurface module Hydrus-1D was integrated with a modified two-dimensional surface water flow module (FullSWOF-2D) using the sequential head method. Various experimental scenarios were simulated to validate the model’s performance, including two outflow cases (i.e., 1D and 2D) without infiltration, a classical one-dimensional infiltration case, and two typical rainfall events at the experimental field. The results demonstrate the accuracy of this proposed model, with the Nash–Sutcliffe efficiency (NSE) of the simulated discharge exceeding 0.90 in the experimental field case and the root mean squared error (RMSE) values for soil moisture at five depths consistently below 0.03 cm3/cm3. However, we observed a lag in the simulated response time of soil moisture due to the neglect of preferential flow. The micro-topography significantly influenced ponding time and ponding areas. Lower local terrain normally experienced earlier surface ponding. Scattered surface ponding water first occurred in the ditch and followed in the relatively low areas in the main field. The concentration process of surface runoff exhibited hierarchical characteristics, with the drainage ditch contributing the most discharge initially, followed by the connection of scattered puddles in the main field, draining excess surface water to the ditch through rills. This quantitative study sheds light on the impact of micro-topography on surface runoff in plain farmland areas.

Enhanced collapsible linear blocks for arbitrary sized image super-resolution

AbstractImage up-scaling and super-resolution (SR) techniques have been a hot research topic for many years due to its large impact in the field of medical imaging, surveillance etc. Especially single image super-resolution (SISR) become very popular because of the fast development of deep convolution neural network (DCNN) and the low requirement on the input. They are achieving outstanding performance. However, there are still problems in the state-of-the-art works, especially from two perspectives: 1. failed at exploiting the hierarchical characteristics from the input, resulting in loss of information and artifacts in the final high resolution (HR) image; 2. failed to handle arbitrary-sized images; the existing research works are focused on fixed size input images. To address these challenges, this paper proposed a residual dense network (RDN) and multi-scale sub-pixel convolution network (MSSPCN) which are integrated into a Collapsible Linear Block Super Efficient Super-Resolution (SESR) network. The RDNs aims to tackle the first challenge, carrying the hierarchical features from end-to-end. An adaptive cropping strategy (ACS) technique is introduced before feature extraction targeting at the image size challenge. The novelty of this work is extracting the hierarchical features and integrating RDNs with MSSPCNs. The proposed network can upscale any arbitrary-sized image (1080p) to ×2 (4K) and ×4 (8K). To secure ground truth for evaluation, this paper follows the opposite flow, generating the input LR images by down-sampling the given HR images (ground truth). To evaluate the performance, the proposed algorithm is compared with eight state-of-the-art algorithms, both quantitatively and qualitatively. The results are verified on six benchmark datasets. The extensive experiments justify that the proposed architecture performs better than other methods and upscales the images satisfactorily.

Urban Texture Identification and Characteristic Analysis Based on Percolation Theory—A Case Study of the Second Ring Road Area in Wuhan City

The urban texture is the physical manifestation of the urban form’s evolution. In the rapid process of urbanization, protecting and reshaping the urban texture has become an essential means to sustain the overall form and vitality of cities. Previous studies in this field have primarily relied on image analysis or typological methods, lacking a quantitative approach to identify and analyze the urban texture on a large scale. Moreover, the hierarchical structure and networked organization of the urban texture are gradually being elucidated and emphasized. This study takes a complex network perspective and applies percolation theory to identify and analyze the urban texture. The hierarchical evolutionary characteristics of the urban fabric and the underlying mechanisms driving the scale-dependent differences are analyzed, and the toughness of the urban texture is evaluated based on hierarchical connections. The research findings reveal the key scale in the cross-scale evolution of urban textures, with variations in scale dependence and hierarchical evolution characteristics among different types of urban texture. The traditional urban texture displays sensitivity to scale changes, maintaining its integrity and internal connectivity at small scales. On the other hand, the texture characteristics of modern and contemporary urban areas are only discernible at larger scales. The emergence of large-scale texture clusters at specific scale levels can be explained using multiple index systems. This study has reference significance for the preservation and renewal of the urban fabric in urban renewal processes.

Handwritten Digit Recognition of MNIST dataset using Deep Learning Convolutional Neural Network (CNN)

With applications ranging from postal services to digitized document processing, handwritten digit identification is a key problem in the fields of machine learning and computer vision. By utilizing their capacity to automatically extract hierarchical characteristics from unprocessed pixel data, Convolutional Neural Networks (CNNs) have become effective instruments for addressing this job. This study presents a thorough investigation of a CNN-based method that uses the MNIST dataset to recognize handwritten numbers. We explore the design, implementation, and performance assessment of the CNN model, demonstrating its ability to achieve high accuracy on tasks involving the recognition of numbers. We also go over the significance of our results for the larger picture of image categorization and suggest directions for further investigation and advancement. Key Words: CNN, MNIST, Convolutional Layer, Pooling layer, Max Pooling, Neural Networks, Preprocessing, Dropout Layer, Activation layer, Rectified Linear Unit (ReLU), Epochs, MNIST dataset

Deep graph layer information mining convolutional network

Graph convolution network is a powerful method of deep learning of graph structure data. Existing methods usually adjust the neighborhood information aggregation mode or optimize the graph topology layer by layer for improving the graph convolution network. However, these methods seldom consider the discriminative information about hierarchical characteristics nodes (some special nodes only can be correctly classified in one layer and are the misclassification nodes in the other layers of deep graph convolutional networks) in the different layers for complementing the neighborhood topology information. To further find these information, a deep graph layer information mining convolutional network (GLIM) can alternately measure the neighborhood ranking information on topology structure and update the residual identity mapping node information on the different layers for enhancing the model classification performance. Moreover, GLIM can construct a unified framework with the various hyper-parameters for the different graph learning method based on graph convolution network. Experiments show GLIM outperforms the state-of-the-art methods for semi-supervised node classification in three cite datasets (Cora, CiteSeer,and PubMed) and three image datasets (MNIST, Cifar10 and Cifar100).

Heterogeneous Ni-Boride/Phosphide Anchored Amorphous B-C Layer for Overall Water Electrocatalysis.

The rational design of efficient and economical bifunctional electrocatalysts remained a challenge for overall water electrolysis. In this work, the Ni-boride/ phosphide particles anchored amorphous B-doped carbon layer with hierarchical porous characteristics in Ni foam (Ni3P/Ni3B/B-C/NF) was fabricated for overall water splitting. The Boroncarbide (B4C) power was filled and fixed in the NF interspace through the electroplating and electroless plating, and then annealed in vacuum high temperature. The amorphous B-C layer derived from the B4 C not only speeded up the electron transport, but also cooperate with Ni-boride/phosphide to enhance the electrocatalytic activity for HER and OER synergistically. Furthermore, the hierarchical porous architecture of Ni3P/Ni3B/B-C/NF increased space utilization to load more active materials. The self-supported Ni3P/Ni3B/B-C/NF electrode possessed a low overpotential of 212 and 280 mV to deliver 100 mA cm-2 for HER and OER, respectively, and high stability for 48 h. In particular, the electrolyzer constituted with the Ni3P/Ni3B/B-C/NF bifunctional electrocatalyst only required a voltage of 1.59 V at 50 mA cm-2 for water electrocatalysis under alkaline medium, and demonstrated long-term stability for 48 h. This study provides a new technical path for the development of bifunctional of transition metal borides to promote the application of hydrogen production from water splitting.

Additive manufacturing of high entropy shape memory alloy with outstanding properties through multi-remelting in-situ alloying

With the introduction of a fourth dimension, time, into 3D printing, 4D printing enables unparalleled control over spatial and temporal aspects of 3D products in respond to external stimuli. However, the recovery performance of the 4D printed shape memory alloys still falls short of expectations. In this study, a novel high entropy shape memory alloy (HESMEA) was developed using in-situ alloying laser powder bed fusion (LPBF) by introducing a moderate amount of silicon (10 % atomic percent) to a metastable Fe50Mn30Co10Cr10 HEA matrix. Multi-remelting strategy was employed to improve the chemical homogeneity of the printed HESMA with enhanced diffusion occurring during the remelt scan. The 3-remelting HESMA exhibited a near uniform chemical distribution, showcasing an excellent combination of shape memory performance and mechanical properties, with an admirable recoverable strain of 5.2 % and a high yield strength of about 500 MPa. It was found that the high recovery strain was attributed to low stacking fault energy of the HESMA matrix, while combination of refined grain size, high density dislocations and nano precipitation contributed to high yield strength of the HESMA. Overall, the solid solution matrix and hierarchical strengthening characteristics of the newly developed HESMA makes it particularly suitable for 4D printing with excellent comprehensive properties.

Manipulation of Nonradiative Process Based on the Aggregation Microenvironment to Customize Excited-State Energy Conversion.

ConspectusNonradiative processes with the determined role in excited-state energy conversion, such as internal conversion (IC), vibrational relaxation (VR), intersystem crossing (ISC), and energy or electron transfer (ET or eT), have exerted a crucial effect on biological functions in nature. Inspired by these, nonradiative process manipulation has been extensively utilized to develop organic functional materials in the fields of energy and biomedicine. Therefore, comprehensive knowledge and effective manipulation of sophisticated nonradiative processes for achieving high-efficiency excited-state energy conversion are quintessential. So far, many strategies focused on molecular engineering have demonstrated tremendous potential in manipulating nonradiative processes to tailor excited-state energy conversion. Besides, molecular aggregation considerably affects nonradiative processes due to their ultrasensitivity, thus providing us with another essential approach to manipulating nonradiative processes, such as the famous aggregation-induced emission. However, the weak interactions established upon aggregation, namely, the aggregation microenvironment (AME), possess hierarchical, dynamic, and systemic characteristics and are extremely complicated to elucidate. Revealing the relationship between the AME and nonradiative process and employing it to customize excited-state energy conversion would greatly promote advanced materials in energy utilization, biomedicine, etc., but remain a huge challenge. Our group has devoted much effort to achieving this goal.In this Account, we focus on our recent developments in nonradiative process manipulation based on AME. First, we provide insight into the effect of the AME on nonradiative process in terms of its steric effect and electronic regulation, illustrating the possibility of nonradiative process manipulation through AME modulation. Second, the distinct enhanced steric effect is established by crystallization and heterogeneous polymerization to conduct crystallization-induced reversal from dark to bright excited states and dynamic hardening-triggered nonradiative process suppression for highly efficient luminescence. Meanwhile, promoting the ISC process and stabilizing the triplet state are also manipulated by the crystal and polymer matrix to induce room-temperature phosphorescence. Furthermore, the strategies employed to exploit nonradiative processes for photothermy and photosensitization are reviewed. For photothermal conversion, besides the weakened steric effect with promoted molecular motions, a new strategy involving the introduction of diradicals upon aggregation to narrow the energy band gap and enhance intermolecular interactions is put forward to facilitate IC and VR for high-efficiency photothermal conversion. For photosensitization, both the enhanced steric effect from the rigid matrix and the effective electronic regulation from the electron-rich microenvironment are demonstrated to facilitate ISC, ET, and eT for superior photosensitization. Finally, we explore the existing challenges and future directions of nonradiative process manipulation by AME modulation for customized excited-state energy conversion. We hope that this Account will be of wide interest to readers from different disciplines.

Short carbon nanorod-grafted N-doped carbon nanocages derived from LDH/MOFs for efficient electromagnetic wave absorption

The systematic design of morphology and structure plays a significant role in enhancing the physicochemical properties of functional materials. In this work, short carbon nanorod-grafted magnetic N-doped carbon nanocage (FeCoNi@NC) was prepared using FeCoNi layered double hydroxide (LDH)/metal organic frameworks (MOFs) as precursor. Herein, a multi-level hollow nanocage was synthesized by hydrothermal method, in which nanorods were formed on the surface of the hierarchical structure, and after annealing, an N-doped hollow nanocage with short carbon nanorod-grafted was formed. Due to its unique hollow and hierarchical interfaces, the optimal FeCoNi@NC exhibits highly efficient electromagnetic wave absorption (EWA). At 14.32 GHz, the minimum reflection loss (RLmin) reach −54.4 dB, while the effective absorption bandwidth (EAB) is over a wide range of 5.28 GHz, corresponding to a thickness of 1.5 mm. The outstanding ability to absorb electromagnetic waves is due to the hierarchical and hollow characteristics, which increase dielectric and conductive losses, while also optimizing impedance matching. The study presents a highly effective approach to fabricate hierarchical and hollow nanostructures with short nanorods, aiming to enhance the efficiency of EWA.

The angiogenic growth of cities.

Describing the space-time evolution of urban population is a fundamental challenge in the science of cities, yet a complete theoretical treatment of the underlying dynamics is still missing. Here, we first reconstruct the evolution of London (UK) over 180 years and show that urban growth consists of an initial phase of diffusion-limited growth, followed by the development of the railway transport network and a consequential shift from central to suburban living. Such dynamics-which are analogous to angiogenesis in biological systems-can be described by a minimalist reaction-diffusion model coupled with economic constraints and an adaptive transport network. We then test the generality of our approach by reproducing the evolution of Sydney, Australia, from 1851 to 2011. We show that the rail system coevolves with urban population, displaying hierarchical characteristics that remain constant over time unless large-scale interventions are put in place to alter the modes of transport. These results demonstrate that transport schemes are first-order controls of long-term urbanization patterns and efforts aimed at creating more sustainable and healthier cities require careful consideration of population-transport feedbacks.