General Network Analysis Research Articles

An ecological input-output CGE model for unveiling CO2 emission metabolism under China's dual carbon goals

Carbon mitigation is essential for combating climate change and its impacts, safeguarding ecosystems, and protecting human health and livelihoods. This study develops an ecological input-output CGE (EIOC) model that integrates computable general equilibrium (CGE) model, input-output analysis (IOA), and ecological network analysis (ENA) to explore the evolution of the CO2 emission metabolism under long-term carbon tax policies. The EIOC model excels not only in intimately depicting economic relationships and evaluating the economic impacts of policy interventions but also in unveiling the complex internal interactions within the context of emission dynamics through multidimensional network perspectives. The EIOC model is employed to analyze CO2 emission flows under carbon tax policies aimed at fulfilling China's dual carbon goals, thereby validating its applicability. It is found that (i) the implementation of carbon tax policies (P25 and P30 scenarios) would lead to economic decline; however, there would emerge potential for recovery in investment and international trade; (ii) adopting an earlier carbon peak target (P25 scenario) would yield more pronounced emission reductions in the early to mid-term (in 2035, reductions in steel, construction, and manufacturing sectors would exceed those under the P30 scenario by 15.74%, 11.89%, and 11.59% respectively); (iii) long-term policies would significantly impact the dynamics of CO2 emission metabolism; chemical sector would display an increased reliance on the system, indicating a heightened need for inputs from the system; transactions exhibiting increased centrality (between chemical and other mining, and between chemical and manufacturing) reveal their elevated significance as transfer pathways of CO2 flows. Identifying significant changes in CO2 emission metabolism can inform incentivizing or constraining measures aimed at further adjusting emissions.

Research collaboration networks in Latin American geography education

The current development of science is characterized by a global trend of scientific collaboration (SC). However, this growth has been mostly linked to intra-national cooperation. Several factors including geographic, institutional, and social proximities have been identified as key for promoting or hinder collaboration among scholars. Based on these premises, the present study analyses the co-authorship and affiliation relationships among geography education researchers in Latin America, exploring through a general trend and social network analysis, the characteristics of SC in geography education. For this purpose, 1774 articles (922 co-authored) published within the region were included as part of the data used for understanding the structure of SC. The results showed an increasing and dominant pattern of co-authoring in the region, but mostly developed at the local and national scale, with scarce intra-regional research. Moreover, the network analysis showed that there are some institutions with a dominant role in the production of research, and a loose network structure of research that could foster diverse perspectives on the field. These findings offer opportunities for thinking about how to enhance future SC processes in the region to strengthen the development of geography education research based on a regional collaborative approach.

Informative core identification in complex networks

AbstractIn a complex network, the core component with interesting structures is usually hidden within noninformative connections. The noises and bias introduced by the noninformative component can obscure the salient structure and limit many network modeling procedures’ effectiveness. This paper introduces a novel core–periphery model for the noninformative periphery structure of networks without imposing a specific form of the core. We propose spectral algorithms for core identification for general downstream network analysis tasks under the model. The algorithms enjoy strong performance guarantees and are scalable for large networks. We evaluate the methods by extensive simulation studies demonstrating advantages over multiple traditional core–periphery methods. The methods are also used to extract the core structure from a citation network, which results in a more interpretable hierarchical community detection.

IGraph/M: graph theory and network analysis for Mathematica

IGraph/M is an efficient general purpose graph theory and network analysis package for Mathematica. IGraph/M serves as the Wolfram Language interfaces to the igraph C library, and also provides several unique pieces of functionality not yet present in igraph, but made possible by combining its capabilities with Mathematica's. The package is designed to support both graph theoretical research as well as the analysis of large-scale empirical networks.

The inter-connections between self-harm and aggressive behaviours: A general network analysis study of dual harm.

Dual harm is the co-occurrence of self-harm and aggression during an individual’s lifetime. This behaviour is especially prevalent within criminal justice and forensic settings. The forms of aggression that should be included in the definition of dual harm have not yet been established. This study aimed to use network analysis to inform an evidence-based definition of dual harm by assessing the relationship between self-harm and different forms of aggressive behaviour in young people (N = 3,579). We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Results revealed low correlations between the variables, leading to sparse network models with weak connections. We found that when separated into their distinct forms, aggressive acts and self-harm are only weakly correlated. Our work provides preliminary evidence to assist in understanding and managing dual harm within clinical and forensic settings and informs recommendations for future research.

Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network.

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

Ten years of Journal of Accounting in Emerging Economies: a review and bibliometric analysis

PurposeThe Journal of Accounting in Emerging Economies (JAEE), established in 2011, aims to publish research on contemporary accounting issues in emerging economies. This study used the bibliometric and scientometric approaches to provide deeper insights into the journal performance, prominent topics, author's contributions and citation structure. Content analysis was conducted to provide insights on the major themes addressed in JAEE.Design/methodology/approachThis study analyses data from the Scopus database, Google Scholar and Journal website. The total number of documents analysed are 190. This study employs VOSviewer and RStudio to conduct the analysis which is categorised into four major parts: General performance indicators, citation structure, network analysis and content analysis.FindingsSince JAEE commenced publication in 2011 and indexed in the Scopus in 2018, it achieved a 14.47% annual growth rate in document publication. It is encouraging to note that 88.4% of published documents were cited. In terms of total publication, the top contributing country is Malaysia; the USA is the primary contributor in citations. Five key themes emerged from the content analysis namely, international standards and earnings quality; audit quality and IFRS practices in emerging economies; corporate governance; financial reporting and earnings management; corruption and accounting disclosure; and ownership structure and firm performance.Originality/valueThis study offers a comprehensive assessment to the journal stakeholders about the past and current journal performance besides future trends and perspectives. Additionally, JAEE readers can gain insight into the nature of academic contributions in JAEE from 299 authors of 273 affiliated institutions in 67 countries.

A Precondition of Sustainability: Industry 4.0 Readiness

To achieve sustainability and further develop their businesses, manufacturing companies need to find an appropriate response to the frequent changes in customer demands, development of new technologies, and dynamic competition. One of the possible requirements for sustainability is the adoption of the Industry 4.0 paradigm. This paper aims to apply Industry 4.0 general readiness assessment methodology and social network analysis to find out the readiness level of Serbian manufacturing and how digital technologies interplay during the time. The results of this study show that wireless human-machine communication technologies initiated the digital transformation of Serbian manufacturing from non-users to basic readiness level. Secondly, manufacturing companies significantly invested in the Cyber-Physical Production System to increase the level towards high readiness. Finally, manufacturing companies in Serbia should consider investing in capabilities to adapt the Near real-time production control system if aiming to compete with competitors from developed countries. The main contribution of this paper is to show the general readiness level of manufacturing companies for digital transformation in transition economies.

Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis.

With the remarkable improvement in people’s socioeconomic living standards around the world, adolescent obesity has increasingly become an important public health issue that cannot be ignored. Thus, we have implemented its use in an attempt to explore the viability of scenario-based simulations through the use of a data mining approach. In doing so, we wanted to explore the merits of using a General Bayesian Network (GBN) with What-If analysis while exploring how it can be utilized in other areas of public health. We analyzed data from the 2017 Korean Youth Health Behavior Survey conducted directly by the Korea Centers for Disease Control & Prevention, including 19 attributes and 11,206 individual data points. Our simulations found that by manipulating the amount of pocket money-between $60 and $80-coupled with a low-income background, it has a high potential to increase obesity compared with other simulated factors. Additionally, when we manipulated an increase in studying time with a mediocre academic performance, it was found to potentially increase pressure on adolescents, which subsequently led to an increased obesity outcome. Lastly, we found that when we manipulated an increase in a father’s education level while manipulating a decrease in mother’s education level, this had a large effect on the potential adolescent obesity level. Although obesity was the chosen case, this paper acts more as a proof of concept in analyzing public health through GBN and What-If analysis. Therefore, it aims to guide health professionals into potentially expanding their ability to simulate certain outcomes based on predicted changes in certain factors concerning future public health issues.

Analytical probability propagation method for reliability analysis of general complex networks

Reliability analysis of complex networks is often limited by large and exponentially increasing computational requirements with system size. In this paper, a new approximated analytical method the authors call the probability propagation method (PrPm) is proposed to calculate the reliability of general complex networks. The proposed method originates from the idea of belief propagation for inference in network graphs to pass a joint probability distribution between nodes in the network. At each step, the distribution is updated and passed as the message to its direct neighbors. After the message passes to the terminal node, an approximation of the network reliability is found. In this paper, the derived updating rules for message passing are provided, as well as a precise formulation of the error compared to the exact solution. The method is applied to three test applications: an example from a previous study on network reliability, a power distribution network, and a general grid network. Results from these applications show high accuracy for the proposed method compared to exact solutions where possible for comparison. In addition, the authors show orders of magnitude increases in computational efficiency of PrPm compared to existing approaches. This includes reducing the computational cost for analyses from an exponential increase in computation time with the size of the system to a quartic increase. The proposed PrPm enables accurate and computationally tractable reliability assessments of larger, complex networks.

211 - A Generalized Image Workflow for Quantitative Analysis of Mitochondrial Networks

Mitochondria undergo constant fusion, fission, and morphological change in response to environmental stressors and a cell's energy demands. The ability of a mitochondrial network to undergo such changes plays a vital role in maintaining cellular physiological homeostasis. Disruptions have been linked to cellular dysfunction, disease onset, and developmental abnormalities. To effectively investigate such processes, it is important to quantitatively describe structural features of the mitochondrial network. Methods by others have been developed for analysis of single images, high content images, and 3-dimensional stacks acquired through fluorescence microscopy. These efforts have provided a foundation for our development of a generalized, simple workflow using ImageJ, encapsulated into a plugin for convenience and portability. The plugin, called GeNA (Generalized Network Analysis) is an offshoot of the MiNA macro (Valente et al. 2017; Acta Histol). GeNA provides significant improvements on MiNA in terms of performance and accuracy, and it is flexible enough to be used with many different applications. Here, we demonstrate its usage for investigating mitochondrial networks in 2D, 3D, and time series data sets. The GeNA plugin pack provides a free cross-platform solution that is easy to understand and implement. It conveniently quantifies key mitochondrial network morphology features, such as the mitochondrial area or volume, the number of branches in network-like structures, and the lengths of rod-like features and network branches.

A Systems Theory Approach to the District Central Office's Role in School-Level Improvement

This qualitative case study used General Systems Theory and social network analysis to explore the relationships between the members of a district central office and principals of elementary schools within an urban school district in the Midwest. Findings revealed sparse relationships between members of the district central office and principals, more opportunities for higher performing schools to participate in decision making, and few opportunities for all schools to provide feedback to the district, resulting in a centralized network structure that may inhibit the transfer of knowledge and communication, and ultimately, hinder school-level improvement efforts. Keywords: systems theory, social network analysis, district central office, reform

Performance analysis of open general queuing networks with blocking and feedback

Queuing network models have been extensively used for performance evaluation in many modern manufacturing and communication systems. The phenomenon of feedback reflects many practical situations, e.g. reworking in the production systems. However, existing research on open queuing network with feedback mainly concentrates on the models with infinite buffers or the models with finite buffers but exponentially distributed inter-arrival and service times. Research on open queuing networks with finite buffers, feedback and general inter-arrival and service times has not been reported. In this paper, a Rate Iterative Method embedded with the Generalised Expansion Method, is proposed for modelling this type of queuing network. System performance measures include the mean throughput, work-in-process and sojourn time all calculated by the proposed method. The accuracy and the efficiency of the proposed method are tested by comparing the results with other methods or simulation results from the experiments. Finally, a case study of a practical production system used in the manufacturing industry is studied and illustrates the applications of the proposed method. The results in this paper can be used as a basis for system design analysis and resource planning.

Lag synchronization analysis of general complex networks with multiple time-varying delays via pinning control strategy

This paper focuses on the lag synchronization issue for a kind of general complex networks with multiple time-varying delays via the pinning control strategy. By applying the Lyaponov functional theory and mathematical analysis techniques, sufficient verifiable criteria that depend on both intrinsic time-varying delay and coupled time-varying delay are obtained to achieve lag synchronization of the networks. Moreover, the coupling configuration matrices are not required to be symmetric or irreducible, and the minimum number of pinned nodes is determined by node dynamics, coupling matrices, and the designed parameter matrices. Finally, a numerical example is given to illustrate the feasibility of the theoretical results.

Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

Compact rf polarizer and its application to pulse compression systems

We present a novel method of reducing the footprint and increasing the efficiency of the modern multi-MW rf pulse compressor. This system utilizes a high power rf polarizer to couple two circular waveguide modes in quadrature to a single resonant cavity in order to replicate the response of a traditional two cavity configuration using a 4-port hybrid. The 11.424 GHz, high-Q, spherical cavity has a 5.875 cm radius and is fed by the circularly polarized signal to simultaneously excite the degenerate $T{E}_{114}$ modes. The overcoupled spherical cavity has a ${Q}_{0}$ of $9.4\ifmmode\times\else\texttimes\fi{}{10}^{4}$ and coupling factor ($\ensuremath{\beta}$) of 7.69 thus providing a loaded quality factor ${Q}_{L}$ of $1.06\ifmmode\times\else\texttimes\fi{}{10}^{4}$ with a fill time of 150 ns. Cold tests of the polarizer demonstrated good agreement with the numerical design, showing transmission of $\ensuremath{-}0.05\text{ }\text{ }\mathrm{dB}$ and reflection back to the input rectangular WR 90 waveguide less than $\ensuremath{-}40\text{ }\text{ }\mathrm{dB}$ over a 100 MHz bandwidth. This novel rf pulse compressor was tested at SLAC using XL-4 Klystron that provided rf power up to 32 MW and generated peak output power of 205 MW and an average of 135 MW over the discharged signal. A general network analysis of the polarizer is discussed as well as the design and high power test of the rf pulse compressor.

Uncertainty quantification for quantum chemical models of complex reaction networks.

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

General network analysis of national soccer teams in FIFA World Cup 2014

This study analyzed the network characteristics of successful and unsuccessful national teams that participated in FIFA World Cup 2014. The relationship between the variables of overall team performance and the network characteristics measured on the basis of the passes between teammates was also investigated. A dataset of 37,864 passes between teammates in 64 soccer matches enabled the study on network structure and team performance of 32 national soccer teams. Our results showed significant differences in the dependent variables of network density (F4,123 = 2.72; p = 0.03; η2p = 0.04; small effect size) and total links (F4,123 = 2.73; p = 0.03; η2p = 0.04; small effect size) between the teams that reached the later stages of the tournament. Goals scored presented a small positive correlation with total links (r = 0.24; p = 0.001), network density (r = 0.24; p = 0.001), and clustering coefficient (r = 0.17; p > 0.050). High levels of goals scored were associated with high levels of total links, network density, and clustering coefficient. This study showed that successful teams have a high level of network density, total links, and clustering coefficient. Thus, large values of connectivity between teammates are associated with better overall team performance.

A Novel Ranking Model for a Large-Scale Scientific Publication

With a large number of scientific literature, it has been difficult to search for a set of relevant articles and to rank them. In this work, we propose a generalized network analysis approach (called N-star ranking model) for sorting them based on . The ranking of the result is considered in the mutual relationships between another classes: keyword, publication, citation. From the model, we propose two ranks for this problem: the Universal-Publication rank - (UP rank) and Topic-Publication rank (TP rank). We also study two simple ranks based on citation counting (RCC rank) and content matching (RCM rank). We propose the metrics for ranking comparison and analysis on two criteria value and order. We have conducted the experimentations for confirming the predictions and studying the features of the ranks. The results show that the proposed ranks are very impressive for the given problem since they consider the query/topic, the content of publication and the citations in the ranking model.

Coupled protein–ligand dynamics in truncated hemoglobin N from atomistic simulations and transition networks

BackgroundThe nature of ligand motion in proteins is difficult to characterize directly usingexperiment. Specifically, it is unclear to what degree these motions are coupled. MethodsAll-atom simulations are used to sample ligand motion in truncated Hemoglobin N. A transition network analysis including ligand- and protein-degrees of freedom is used to analyze the microscopic dynamics. ResultsClustering of two different subsets of MD trajectories highlights the importance of a diverse and exhaustive description to define the macrostates for a ligand-migration network. Monte Carlo simulations on the transition matrices from one particular clustering are able to faithfully capture the atomistic simulations. Contrary to clustering by ligand positions only, including a protein degree of freedom yields considerably improved coarse grained dynamics. Analysis with and without imposing detailed balance agree closely which suggests that the underlying atomistic simulations are converged with respect to sampling transitions between neighboring sites. ConclusionsProtein and ligand dynamics are not independent from each other and ligand migration through globular proteins is not passive diffusion. General significanceTransition network analysis is a powerful tool to analyze and characterize the microscopic dynamics in complex systems. This article is part of a Special Issue entitled Recent developments of molecular dynamics.