Network Structure Research Articles

Method for analyzing urban waterlogging mechanisms based on a 1D-2D water environment dynamic bidirectional coupling model

Urban waterlogging is a significant global issue. To achieve precisely control urban waterlogging and enhance our understanding of its causes, a novel study method was introduced. This method is based on a dynamic bidirectional coupling model that combines 1D-2D hydrodynamic and water quality simulations. The waterlogging phenomenon in densely populated metropolitan areas of Changzhi city, China, was studied. This study focused on investigating the process involved in waterlogging formation, particularly overflow at nodes induced by the design of the topological structure of the pipe network, constraints on the capacity of the underground drainage system, and the surface runoff accumulation. The complex interplay among these elements and their possible influences on waterlogging formation were clarified. The results indicated notable spatial and temporal variation in the waterlogging formation process in densely populated urban areas. Node overflow in the drainage system emerged as the key influencing factor in the waterlogging formation process, accounting for up to 71% of the total water accumulation at the peak time. The peak lag time of waterlogging during events with short return periods was primarily determined by the rainfall peak moment. In contrast, the peak time of waterlogging during events with long return periods was influenced by the rainfall peak moment, drainage capacity and topological structure of the pipe network. Notably, the access of inflow from both upstream and downstream segments of the pipe network drainage system significantly impacted the peak time of waterlogging, with upstream water potentially delaying the peak time substantially. This study not only provides new insights into urban waterlogging mechanisms but also provides practical guidance for optimizing urban drainage systems, urban planning, and disaster risk management.

Learning From Human Educational Wisdom: A Student-Centered Knowledge Distillation Method.

Existing studies on knowledge distillation typically focus on teacher-centered methods, in which the teacher network is trained according to its own standards before transferring the learned knowledge to a student one. However, due to differences in network structure between the teacher and the student, the knowledge learned by the former may not be desired by the latter. Inspired by human educational wisdom, this paper proposes a Student-Centered Distillation (SCD) method that enables the teacher network to adjust its knowledge transfer according to the student network's needs. We implemented SCD based on various human educational wisdom, e.g., the teacher network identified and learned the knowledge desired by the student network on the validation set, and then transferred it to the latter through the training set. To address the problems of current deficiency knowledge, hard sample learning and knowledge forgetting faced by a student network in the learning process, we introduce and improve Proportional-Integral-Derivative (PID) algorithms from automation fields to make them effective in identifying the current knowledge required by the student network. Furthermore, we propose a curriculum learning-based fuzzy strategy and apply it to the proposed PID control algorithm, such that the student network in SCD can actively pay attention to the learning of challenging samples after with certain knowledge. The overall performance of SCD is verified in multiple tasks by comparing it with state-of-the-art ones. Experimental results show that our student-centered distillation method outperforms existing teacher-centered ones.

Information and analytical activity in the system of economic security of the enterprise

This article is devoted to understanding the importance of information and analytical support for management decisions in modern conditions, when there are threats of various nature. It has been proven that the selection of reliable necessary information, scientifically based methods of its analysis provide optimal solutions in these conditions, aimed at ensuring the effective operation of the enterprise. Analysis occupies an intermediate place between the selection of information and decision-making. It has been studied that depending on business conditions, different decisions are made, different techniques and methods of analysis are used. The main features that determine the content of analytical activity are highlighted, such as: the study of economic phenomena, objective assessment of the efficiency of the enterprise, scientific substantiation of business plans and identification of reserves at the enterprise. The main tasks of the information and analytical system of enterprises of network structures in modern conditions are highlighted. It is considered that the object of analysis is the economic activity of the enterprise, which can be divided into economic processes and financial results formed under the influence of objective and subjective factors.

Environmental heterogeneity caused by large-scale cultivation of Pyropia haitanensis shapes multi-group biodiversity distribution in coastal areas

The response of marine biodiversity to mariculture has long been a research focus in marine ecology. However, the effects of seaweed cultivation on biological community assembly are poorly understood, especially in diverse communities with distinct ecological characteristics. In this study, we used environmental DNA metabarcoding to investigate the spatial distribution patterns of bacterial, protistan, and metazoan diversity, aiming to reveal the mechanisms of community assembly in the Pyropia haitanensis cultivation zone along the Fujian coast, China. We found that, compared with the biological communities in control zones, those in P. haitanensis cultivation zones exhibited stronger geographic distance-decay patterns and displayed more complex and stable network structures. Deterministic processes (environmental selection) played a more important role in the assembly of bacterial, protistan, and metazoan communities in P. haitanensis cultivation zones, especially metazoan communities. Variance partitioning analysis showed that environmental variables made greater contributions to the diversity of the three types of communities within the P. haitanensis cultivation zones than in the control zones. Partial least squares path modeling analysis identified nitrate‑nitrogen (NO3-N), pH, particulate organic carbon (POC), and dissolved organic carbon (DOC) as the key environmental variables affecting biodiversity. Overall, the environmental heterogeneity caused by the large-scale cultivation of P. haitanensis could be the crucial factor influencing the composition and structure of various biological communities. Our results highlight the importance of the responses of multi-group organisms to the cultivation of seaweed, and provide insights into the coexistence patterns of biodiversity at the spatial scale.

Liner shipping network vulnerability to component disruptions: A China-Europe container flow analysis

This study conducts a vulnerability analysis of the China-Europe liner shipping network, including all relevant liner shipping services and China Railway Express services. The standard multicommodity network flow model is extended to assess network vulnerability and the relative importance of ports and services by simulating container flow redistribution in various disruption scenarios. The results show the network is highly resilient to both individual port disruptions and individual service disruptions. Additionally, the distinct shipping network structures of the three shipping alliances result in varying degrees of vulnerability, highlighting the significance of diversified market suppliers in enhancing the overall network resilience. Specifically, the Ocean Alliance is vital in facilitating China-Europe trade, especially along the Mediterranean route, and is more resilient to individual port disruptions.

Effects of wheat flour levels on the thermal curing behavior of Urea–Formaldehyde and Phenol–Formaldehyde resins

Urea–formaldehyde (UF) and phenol–formaldehyde (PF) resins are the most prevalent adhesives in the manufacturing of wood composites, and they are cured by hot pressing to build a three-dimensional network structure in the composites. UF and PF resins are typically formulated with organic fillers like wheat flour to control viscosity and reduce costs in the commercial production of plywood. Although this formulation is well established, research on the effect of filler on the curing behavior of UF and PF resins has been insufficient. Therefore, this study used differential scanning calorimetry (DSC) to examine the influence of wheat flour as a filler in varying amounts on the thermal curing behavior of UF and PF resins. The results demonstrated that the viscosity and solid content of the UF and PF resins increased as the filler amount increased. In contrast, the gelation time, which reflected the curing speed of the resins, decreased as the filler content increased. Similarly, DSC results demonstrated that with increased filler amounts, the curing reaction of UF resins accelerated, as evidenced by a decrease in the apparent activation energy (Ea) determined using the Kissinger–Akahira–Sunose and Vyazovkin methods. However, in the case of PF resins, the average Ea increased and subsequently declined when 10% and 20% fillers were added. This demonstrated the complicated interaction between wheat flour and resins, particularly PF resins. Furthermore, the isothermal DSC curves reveal that the highest exothermic peaks of UF resins are observed at different isothermal temperatures as the filler amount increases. In contrast, PF resins maintain an isothermal temperature for the highest exothermic peaks across all filler levels. In addition, FTIR analysis suggested that wheat flour might contribute to the curing reaction of UF and PF resins.

Survey of Techniques and Applications in Object Recognition

Abstract: Object detection, which is closely related to video analysis and image comprehension, has received attention in years from researchers. Traditional approaches to object detection rely on crafted features and shallow trainable architectures. However, these methods face challenges. Often reach a plateau, in performance improvement. To overcome these limitations complex ensembles are often created by combining low-level image features with high-level context from object detectors and scene classifiers. The emergence of learning has introduced powerful tools that can learn semantics high level and deeper features. These tools address the limitations of architectures by introducing network structures training strategies and optimization functions. This paper provides a review of learning-based frameworks for object detection. The review starts with an overview of the history of learning with a particular emphasis on the Convolutional Neural Network (CNN) as an exemplary tool. It then delves into generic object detection architectures exploring modifications and effective strategies for improving detection performance. Recognizing the characteristics of detection tasks, the paper also briefly surveys several tasks such, as salient object detection, face detection, crowd analysis, and pedestrian detection.

Spanish Students' Categorical Perceptions of Feminist Movements.

Identifying a society's perceptions and, by extension, opinions of a certain social movement can help to understand to what extent the movement has been successful in effecting change. When working to gain such an understanding, a focus on the student population is essential, as their opinions provide insight into the future conditions of society and, thus, into whether the movement has been successful in effecting lasting social change. The present work focusses on the feminist movements and, in line with the above, analyses the perceptions held by a sample of 600 Spanish students enrolled in compulsory secondary, pre-university, and university education. The method employed begins with the use of association tests to extract lexical networks. Then, following a theoretical transformation, the traditional lexical availability index is applied in combination with fuzzy set theory to the sample of lists obtained so as to map the structure of the collective network, a novel approach that results in different levels of compatibility. The highest levels of compatibility reveal the prototypical conceptualisation as well as the sample's shared cognitive perception. The results suggest that although the population under study may have absorbed the feminist movements' messages of equality and respect, distorted perceptions could still remain in certain groups analysed. This work therefore recommends that education centres may wish to consider communicating objective information on the feminist movements specifically to women, as this could ultimately lead to all students fully embracing a feminist awareness distanced from extreme ideologies.

Physics-guided neural network for grinding temperature prediction

Creep-feed grinding is a high-efficiency, high-precision grinding process widely used in the manufacturing of aviation engines. However, the workpiece burn and other quality issues caused by high processing temperature limit the yield of grinding. Therefore, the accurate model of grinding temperature has become the key to improving processing efficiency and quality. Different from the traditional physical or data-driven models, this paper attempts to combine both perspectives based on Physics-Guided Neural Networks (PGNN) to accurately predict grinding temperature with a small number of experiments. At the level of data acquisition, real grinding experiment data was obtained and a data augmentation method had been proposed. At the level of neural network structure, optimisation processes were implemented to enhance prediction performance, and a physics-guided loss function was inserted to guide network training. The experiment results shows that PGNN had better prediction accuracy than the physical model, while also mitigating the limitations of data-driven models on small sample sets. PGNN also performed better with noisy data and predictions out of the training data range, this reveals the benefits of PGNN for small sample problems in processing scenarios.

A networked iron and nitrogen-doped ZIF-8/MWCNTs heterostructure for oxygen reduction reaction.

Zeolitic Imidazolate Frameworks-8 (ZIF-8) is commonly used as an ideal precursor for non-noble metal catalysts because of its high specific surface area, ultra-high porosity, and N-rich content. Upon pyrolyzing ZIF-8 at 900 °C in Ar, the resulting material, referred to as Z8, displayed good activity toward the oxygen reduction reaction (ORR). Then the ZIF-8 was mixed with various conductive carbon materials, such as multiwall carbon nanotubes (MWCNTs), Acetylene black (ACET), Vulcan XC-72R (XC-72R), and Ketjenblack EC-600JD (EC-600JD), to form Z8 composites. The Z8/MWCNTs composite exhibited enhanced ORR activity owing to its network structure, meso-/microporous hierarchical porous structure, improved electrical conductivity, and graphitization. Subsequently, iron and nitrogen co-doping is achieved through the pyrolysis of a mixture comprising Fe, N precursor, and ZIF-8/MWCNTs, which is denoted as FeN-Z8/MWCNTs. The intrinsically high electrical conductivity of MWCNTs facilitated efficient electron transfer during the ORR, while the meso-/microporous hierarchical porous structure and network structure of Fe, N co-doped ZIF-8/MWCNTs promoted oxygen transport. The presence of Fe-containing species in the catalyst acted as activity centers for ORR. This strategy of preparing Z8 composites and modifying them with Fe, N co-doping offers an insightful approach to designing cost-effective electrocatalysts.

Transmission Mechanism of Influencing Factors in the Promotion and Application of Whole-Process Engineering Consulting

As a project construction organization model, whole-process engineering consulting (WPEC) is currently being promoted in China’s engineering consulting industry. However, various factors create numerous obstacles that hinder this model’s effective promotion and application. One essential task is to deeply analyze the transmission mechanism of the influencing factors in the promotion and application of WPEC, and explore effective pathways for promoting and applying this model. This paper systematically organizes and summarizes 10 primary influencing factors and 38 secondary influencing factors for promoting WPEC. Using the complex network analysis method, a network of influencing factors for promoting WPEC, a network structure, and motif analyses are conducted. The results show that the influencing factors have a complex transmission mechanism, and complex network analysis can reveal the laws of their construction and their causes. This paper provides a systematic basis and theoretical support for the effective formulation of policies related to the promotion and application of WPEC.

The influence of network structure on spreading dynamics via tie range

Abstract There are various phenomena of malicious information spreading in the real society, which cause many negative impacts on the society. In order to better control the spreading, it is crucial to reveal the influence of network structure on network spreading. Motifs, as fundamental structures within a network, play a significant role in spreading. Therefore, it is of interest to investigate the influence of the structural characteristics of basic network motifs on spreading dynamics. Considering the edges of the basic network motifs in an undirected network correspond to different tie ranges, two edge removal strategies are proposed, short ties priority removal strategy and long ties priority removal strategy. The tie range represents the second shortest path length between two connected nodes. The study focuses on analyzing how the proposed strategies impact network spreading and network structure, as well as examining the influence of network structure on network spreading. Our findings indicate that the long ties priority removal strategy is most effective in controlling network spreading, especially in terms of spread range and spread velocity. In terms of network structure, the clustering coefficient and the diameter of network also have an effect on the network spreading, and the triangular structure as an important motif structure effectively inhibits the spreading.

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

Urban spatial cluster structure in metro travel networks: An explorative study of Wuhan using big and open data

River valley urban network and morphology: A study on the urban morphology evolution of Lanzhou.

The present study investigates the dynamic evolution characteristics of urban spatial morphology by analyzing real road network data from 2000, 2010, and 2020, along with nighttime lighting data employing spatial analysis methods and spatial syntax models. Accordingly, two separate dimensions of urban morphology: internal and external, are covered. First, the integration and synergy of interior morphology features are analyzed using spatial syntactic modeling. Subsequently, the spatial compactness, fractal dimension, and level of center of gravity shift of the city are assessed by combining the nighttime lighting data with the earlier dataset. This analysis facilitated the deep exploration of the spatiotemporal evolution of the city's external morphology. Building upon this foundation, the interaction between the "internal and external" domains was analyzed further. The main findings of the study reveal a synchronous pattern of urban expansion throughout the evolution of urban spatial morphology. Furthermore, the urban form was observed to undergo a progressive transformation, transitioning from a "single core" morphology to a "primary and secondary double core" morphology. Over time, this development progressed and evolved into a "belt-like multi-core" structure. Additionally, the coupling characteristics further validate the relationship between the structure of the road network and the urban morphology in river valley-type cities. In particular, accessibility of dense and horizontally distributed transportation network was found to significantly influence the spatial development of these cities. As observed, the findings provides valuable insights into understanding the characteristics of internal and external associations regarding urban spatial patterns.

Analysing the drivers of smart city implementation using DEMATEL method

<p>Indian cities are becoming more and more populated, and the technological, environmental, social, political and economic infrastructure of those cities is deteriorating, necessitating the development of more innovative methods to enhance public utilities and services. The growth of smart cities encourages the creation of a stable, networked, and sustainable metropolitan structure. The “100 Smart Cities Mission” was started by the Indian government to encourage planned development. However, there is also a literature of criticisms on the used indicators by the smart city mission. A thorough investigation is necessary to identify the critical infrastructure, important resources, and development patterns for smart city planning. The purpose of my study is to achieve and move towards Smart City Mission goals in a holistic framework. This research paper proposes a weighted criteria to assess the eligibility factors identified from the literature studies of Smart City Mission, to analyze the complex relationships among the indicators, to develop a holistic approach and classify factors based on the drivers of dependence and implementation. The findings of this study shed light on the key drivers and barriers to the implementation of smart city initiatives. The DEMATEL method provided a valuable tool for analysing the complex interrelationships between different factors and identifying the most influential drivers. The results can guide policymakers, city managers, and other stakeholders in developing more effective strategies for smart city implementation. However, the study also highlights the need for further research to explore the context-specific factors that may affect the drivers of smart city implementation in different regions and cities. Overall, this study contributes to the growing body of literature on smart cities and provides practical insights for decision-makers seeking to promote sustainable and inclusive urban development.</p>

Community-based person-centred integrated care (PIC) networks for healthy ageing in place: a scoping review protocol

IntroductionThe economic case for preventive care delivered in or near citizens’ homes is strong, and there is growing evidence of the role of local-level support in supporting people’s health and...

Rationality, inequality, and the output gap: evidence from a disaggregated Keynesian cross diagram

AbstractThis paper examines the conditions under which a representative agent (RA) model can accurately approximate the output of a multi-agent model that assumes many interacting agents. The study compares the widely used Keynesian cross diagram, which employs a representative agent, to an extended model that explicitly considers multiple interacting households and firms. The extended model reduces to the original RA model when there is one agent of each type. The findings suggest that the RA Keynesian cross diagram model does not accurately approximate the extended multi-agent model when: (1) the network structure of the economy is asymmetric (e.g., firms have different sizes), or (2) the rationality of agents is too low. Additionally, when income inequality is considered by introducing capitalists, the RA model is no longer a good approximation, even if agents are rational. However, fiscal policies that redistribute income can improve the accuracy of the RA model’s predictions. In general, features that increase the overall rationality of the economy and decrease heterogeneity tend to improve the performance of the RA approximation.

3D Printed Multifunctional Biomimetic Bone Scaffold Combined with TP-Mg Nanoparticles for the Infectious Bone Defects Repair.

Infected bone defects are one of the most challenging problems in the treatment of bone defects due to the high antibiotic failure rate and the lack of ideal bone grafts. In this paper, inspired by clinical bone cement filling treatment, α-c phosphate (α-TCP) with self-curing properties is composited with β-tricalcium phosphate (β-TCP) and constructed a bionic cancellous bone scaffolding system α/β-tricalcium phosphate (α/β-TCP) by low-temperature 3D printing, and gelatin is preserved inside the scaffolds as an organic phase, and later loaded with a metal-polyphenol network structure of tea polyphenol-magnesium (TP-Mg) nanoparticles. The scaffolds mimic the structure and components of cancellous bone with high mechanical strength (>100MPa) based on α-TCP self-curing properties through low-temperature 3D printing. Meanwhile, the scaffolds loaded with TP-Mg exhibit significant inhibition of Staphylococcus aureus (S.aureus) and promote the transition of macrophages from M1 pro-inflammatory to M2 anti-inflammatory phenotype. In addition, the composite scaffold also exhibits excellent bone-enhancing effects based on the synergistic effect of Mg2+ and Ca2+. In this study, a multifunctional ceramic scaffold (α/β-TCP@TP-Mg) that integrates anti-inflammatory, antibacterial, and osteoinduction is constructed, which promotes late bone regenerative healing while modulating the early microenvironment of infected bone defects, has a promising application in the treatment of infected bone defects.

Network structure indicators predict ecological robustness in food webs

AbstractFood web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs.

Mapping out the interpersonal boundary stones in contemporary China: Guanxi network structure and its association with traditional culture endorsement.

Guanxi research would benefit from an empirical description of holistic guanxi network structures and consideration of sociologically meaningful antecedents such as one's cultural value endorsement. This study, inspired by the relational sociology and drawing on the reported trustworthiness of a rich array of referees in one's guanxi network collected from the Traditional Culture and Cognitive Pattern Survey, identifies two types of guanxi network structures in contemporary China: one is featured by the binary distinction between family and non-family referees, and the other displays a fourfold classification scheme, respectively concerning parents, nuclear family members (children and spouse), other relatives and close friends, and acquaintances. Furthermore, traditional culture endorsement is positively correlated with the likelihood of being subject to the binary classification scheme, while some counter social forces, such as the establishment of quasi-kinship relationships, encourage one to lean toward the more fine-grained fourfold guanxi network partitioning.