Average Shortest Path Research Articles

Talking water: interplay of gender, trust and expertise in agricultural extension groups in Mendoza, Argentina

Abstract Stakeholder adaptation is a critical strategy to overcome changing climate patterns worldwide. Still it relies on the speed and effectiveness of information flow to end-users. Research shows that the loss of information in several stages of its spread and learning from peers is more important than the knowledge circulated by extension services. Women’s participation and contribution are supportive and strategic, depending on the level of agreement and the interplay of trust variables within the network. In the arid Andes, agriculture is central and dependent on water management and macroeconomic conditions that shape market prospects, irrigation practices, and stakeholder behavior. Data were collected using the platform of a capacity-building program for organisations of water users in the Diamante and Atuel River basins in Mendoza, Argentina. Social Network Analysis (SNA) contributes to unveiling the cornerstones of information flow by identifying group structures, strong bonds, and bottlenecks in water management systems. In the first step, we evaluated the characteristics (density, centrality, average shortest path, and degree) of the pre-existing relationships and five sub-topic networks. Second, we compare networks containing pre-existing links only with those formed during the lecture. Emphasizing adaptation practices to cope with climate change impacts, the results provide valuable insights into the intricate interplay of gender dynamics, trust, expertise recognition, and discussion patterns within water and agricultural extension groups in Argentina. These insights highlight the ongoing need to promote gender equity, address biases in expertise recognition, and leverage trust for meaningful knowledge exchanges within evolving social contexts. It also reveals the alignment of Argentina’s gender performance with similar production setups in Southern America or the Global North, highlighting the universality of challenges and opportunities in fostering inclusive and equitable participation. Our findings indicate that each group within the two river basins exhibits numerous pre-existing links and tends to be less accessible to newcomers, resulting in a shorter average path. Thus, information can spread faster. Trust is an underlying facilitator for sensible topics and a catalyzer for communication.

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

Design, setup and routine operation of a water treatment system for the monitoring of low activities of tritium in water

In the TRITIUM project, an on-site monitoring system is being developed to measure tritium (3H) levels in water near nuclear power plants. The quite low-energy betas emitted by 3H have a very short average path in water (5 μm as shown by simulations for 18 keV electrons). This path would be further reduced by impurities present in the water, resulting in a significant reduction of the detection efficiency. Therefore, one of the essential requirements of the project is the elimination of these impurities through a filtration process and the removal of salts in solution. This paper describes a water treatment system developed for the project that meets the following requirements: the water produced should be of near-pure water quality according to ISO 3696 grade 3 standard (conductivity < 10 μS/cm); the system should operate autonomously and be remotely monitored.

Analysis of the characteristics and evolution of knowledge label networks in the Q&A community: taking the Zhihu platform as an example

PurposeIn the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.Design/methodology/approachThis paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.FindingsThe research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.Originality/valueThe Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

Evolutionary Algorithm Simulation of Japanese to Chinese Buzzwords Based on Quantum Social Network Modeling

Based on the quantum social network theory, this paper conducts an evolutionary algorithm research on the law of Japanese catchphrase introduced into China and its changes in China. The network generated by this modeling method is comprehensively analyzed from the aspects of network degree, degree distribution, average shortest path, node aggregation coefficient, etc., which verifies the effectiveness of the modeling method. Starting from the language itself, from both internal and external perspectives, this paper explores the reasons for the popularity of Riyuan's animation language in China. One is to study from the internal causes of language; the other is to study from the outside of society.

Analysis of co-authoring and board of examiners networks of graduate programs: Evidence on the performance of teachers in the interdisciplinary area for the 2013-2016 quadrennium

Scientific collaboration is a common practice for the construction and dissemination of knowledge and one of the products is the publication of co-authored scientific articles. These publications form a network of co-authors, and the study of these networks helps to understand aspects of collaboration and construction of knowledge. This co-authorship network can be analyzed using complex network measures to make the comparison easier between programs, such as the shortest average path, clustering coefficient, density, among other measures. Other types of scientific collaboration also generate a network of contacts, such as the network of mutual participating in board of examiners. In Brazil, science is mainly produced in the stricto sensu postgraduate programs of universities, which are evaluated and classified by Coordination for the Improvement of Higher Education Personnel(CAPES – directly translation of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). As each area of ​​knowledge is evaluated differently, this work makes a study of the stricto sensu programs of the Interdisciplinary area, subarea of ​​engineering, technology and management for the 2013-2016 quadrennium. The objective is to evaluate the scientific collaboration between professors of the same program from the network of co-authorship of articles. The conclusion is that networks have different characteristics according to the type of management (public or private), and if a program has a network that indicates greater internal collaboration among researchers for a type of scientific production (co-authorship or boards), this is reflects on the other network

A Mathematical Kinetic Model and Network Analysis for Multicomponent Dissolution Relationships during the Extraction of Natural Products

Traditional Chinese Medicine (TCM) has a long history and typical ethnic traits. Astragalus and Angelica are used in a natural product called a buyang huanwu decoctionand are considered to function as both food and medicine; such products are called a “homology of medicine and food”. In this study, we examined the complex extraction kinetics that occur during the preparation of the natural product BYHWD. Mathematical tools, including the Laplace transformation and Fick’s law, were used to set up kinetic equations for different components in a model of the decoction. We selected the five most important bioactive ingredients of the BYHWD to find the most important speed control component. The intensity and capacity process parameters of the model were determined. A kinetic model was used to quantitatively analyze the dissolution restriction mechanism among the major components. Further, a component–effect network relationship was established to study the interactions of different components during extraction, considering the integrative effect of TCM compositions. Finally, using network pharmacology, certain network parameters were determined through topological analysis. The results indicate that Astragaloside IV exerts the strongest control over the dissolution rates of other components. The BYHWD has a short average path and a high clustering coefficient. The theoretical and experimental results can be used to quantitatively simulate and optimize TCM extraction processes.

Network structure from a characterization of interactions in complex systems

Many natural and man-made complex dynamical systems can be represented by networks with vertices representing system units and edges the coupling between vertices. If edges of such a structural network are inaccessible, a widely used approach is to identify them with interactions between vertices, thereby setting up a functional network. However, it is an unsolved issue if and to what extent important properties of a functional network on the global and the local scale match those of the corresponding structural network. We address this issue by deriving functional networks from characterizing interactions in paradigmatic oscillator networks with widely-used time-series-analysis techniques for various factors that alter the collective network dynamics. Surprisingly, we find that particularly key constituents of functional networks—as identified with betweenness and eigenvector centrality—coincide with ground truth to a high degree, while global topological and spectral properties—clustering coefficient, average shortest path length, assortativity, and synchronizability—clearly deviate. We obtain similar concurrences for an empirical network. Our findings are of relevance for various scientific fields and call for conceptual and methodological refinements to further our understanding of the relationship between structure and function of complex dynamical systems.

Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry.

Storm disasters are the most common cause of accidents in offshore oil and gas industries. To prevent accidents resulting from storms, it is vital to analyze accident propagation and to learn about accident mechanism from previous accidents. In this paper, a novel risk analysis framework is proposed for systematically identifying and analyzing the evolution of accident causes. First, accident causal factors are identified and coded based on grounded theory (GT). Then, decision making trial and evaluation laboratory (DEMATEL) is integrated with interpretative structural modeling (ISM) to establish accident evolution hierarchy. Finally, complex networks (CN) are developed to analyze the evolution process of accidents. Compared to reported works, the contribution is threefold: (1) the demand for expert knowledge and personnel subjective influence are reduced through the data induction of accident cases; (2) the method of establishing influence matrix and interaction matrix is improved according to the accident frequency analysis; (3) a hybrid algorithm that can calculate multiple shortest paths of accident evolution under the same node pair is proposed. This method provides a new idea for step-by-step assessment of the accident evolution process, which weakens the subjectivity of traditional methods and achieves quantitative assessment of the importance of accident evolution nodes. The proposed method is demonstrated and validated by a case study of major offshore oil and gas industry accidents caused by storm disasters. Results show that there are five key nodes and five critical paths in the process of accident evolution. Through targeted prevention and control of these nodes and paths, the average shortest path length of the accident evolution network is increased by 35.19%, and the maximum global efficiency decreases by 20.12%. This indicates that the proposed method has broad applicability and can effectively reduce operational risk, so that it can guide actual offshore oil and gas operations during storm disasters.

Complex Network Study of Solar Magnetograms.

In this paper, we study solar magnetic activity by means of a complex network approach. A complex network was built based on information on the space and time evolution of sunspots provided by image recognition algorithms on solar magnetograms taken during the complete 23rd solar cycle. Both directed and undirected networks were built, and various measures such as degree distributions, clustering coefficient, average shortest path, various centrality measures, and Gini coefficients calculated for all them. We find that certain measures are correlated with solar activity and others are anticorrelated, while several measures are essentially constant along the solar cycle. Thus, we show that complex network analysis can yield useful information on the evolution of solar activity and reveal universal features valid at any stage of the solar cycle; the implications of this research for the prediction of solar maxima are discussed as well.

An Adaptive Designer Network Model and Its Robustness Research

The designer network is the carrier for designers to carry out product design tasks, and the study of its evolution helps to accurately identify the designer team. The uncertainty of whether the designer chooses to keep in touch with other members of the network, its nonlinearity on the overall performance of the network and the external interference of the network, etc., increase the complexity of the designer network, which is not conducive to the development of product design tasks. Therefore, it is very interesting and important to study the co-evolution mechanism of the designer’s network structure and the designer’s opinion. This paper analyzes the Deffuant opinion dynamics model, considers the asymmetry of nodes in the network caused by the designer’s unequal relationship (the number of node links is different), and the difference in the degree of acceptance of other people’s opinions by different individuals, and improves the Deffuant model. Then combining the improved Deffuant model with the BA (Barabasi–Albert) model, a DBA (Deffuant and BA Adaptive) model was proposed that integrates opinion update, broken edge and reconnection, and opinion changes. On this basis, this paper designs a virtual network through the crowdsourcing of a certain product. The adjacency matrix of this network is symmetric, and corresponding comparative experiments are carried out on this network. The analysis of test results shows that under six different deliberate attacks, the DBA model is more robust than the BA model. In addition, the average shortest path of the DBA network will vary with the parameters. The proposed integrated DBA model has important guiding significance for building a robust designer network.

Microbiome network traits in the rumen predict average daily gain in beef cattle under different backgrounding systems

BackgroundBackgrounding (BKG), the stage between weaning and finishing, significantly impacts feedlot performance in beef cattle; however, the contributions of the rumen microbiome to this growth stage remain unexplored. A longitudinal study was designed to assess how BKG affects rumen bacterial communities and average daily gain (ADG) in beef cattle. At weaning, 38 calves were randomly assigned to three BKG systems for 55 days (d): a high roughage diet within a dry lot (DL, n = 13); annual cover crop within a strip plot (CC, n = 13); and perennial pasture vegetation within rotational paddocks (PP, n = 12), as before weaning. After BKG, all calves were placed in a feedlot for 142 d and finished with a high energy ration. Calves were weighed periodically from weaning to finishing to determine ADG. Rumen bacterial communities were profiled by collecting fluid samples via oral probe and sequencing the V4 region of the 16S rRNA bacterial gene, at weaning, during BKG and finishing.ResultsRumen bacterial communities diverged drastically among calves once they were placed in each BKG system, including sharp decreases in alpha diversity for CC and DL calves only (P < 0.001). During BKG, DL calves showed a substantial increase of Proteobacteria (Succinivibrionaceae family) (P < 0.001), which also corresponded with greater ADG (P < 0.05). At the finishing stage, Proteobacteria bloomed for all calves, with no previous alpha or beta diversity differences being retained between groups. However, at finishing, PP calves showed a compensatory ADG, particularly greater than that in calves coming from DL BKG (P = 0.02). Microbiome network traits such as lower average shortest path length, and increased neighbor connectivity, degree, number and strength of bacterial interactions between rumen bacteria better predicted ADG during BKG and finishing than variation in specific taxonomic profiles.ConclusionsBacterial co-abundance interactions, as measured by network theory approaches, better predicted growth performance in beef cattle during BKG and finishing, than the abundance of specific taxa. These findings underscore the importance of early post weaning stages as potential targets for feeding interventions that can enhance metabolic interactions between rumen bacteria, to increase productive performance in beef cattle.

Design and Analysis of Fibonacci Based TGO Compared with Real-time Mesh using Graph Invariant Technique

Background: Graph analytics is one of the foremost established and unique strategies utilized in taking care of present-day designing issues. In this study, this procedure was applied to networks. The connectivity of gadgets is one of the intense issues distinguished in wireless systems. To deal with this issue, a unique Fibonacci-based TGO was proposed for a superior network. Methods: The proposed model attempts to construct a trimet graph based on the Fibonacci arrangement, implying that a cluster is formed with 3, 5, 8, 13, 21... hubs. To frame Fibonacci-based TGO, each of these hubs is recursively connected with a trimet diagram. For the random regular graph, the practical mesh is invariant. Edges, diameter, average degree, average clustering, density, and average shortest path are currently being compared for both meshes. Results: Fibonacci TGO has approximately 50 edges at 100 nodes and a constant diameter of 4. The average degree of Fibonacci TGO is less, which is approximately 3, having 0.7 high average clustering over random regular. As the number of nodes increases, the density decreases.TGO is having a better path than the random regular model. Finally, Fibonacci TGO mesh has better performance and connectivity over real-time meshes in wireless networks. Conclusion: We have proposed Fibonacci-based TGO mesh in the following steps. This formation is split into two stages. Fibonacci-dependent trimets based on the input nodes are created in the first stage, with 3, 5, 8, 13, and 21... nodes. These trimets will be connected in the second step to create a Fibonacci-based TGO. Both meshes are now being studied using network science parameters. In any scenario, Fibonacci-based TGO has better connectivity over the real-time random mesh. The NetworkX package in the Python language is used to produce the results automatically.

Disrupting networks of hate: characterising hateful networks and removing critical nodes

Hateful individuals and groups have increasingly been using the Internet to express their ideas, spread their beliefs and recruit new members. Understanding the network characteristics of these hateful groups could help understand individuals’ exposure to hate and derive intervention strategies to mitigate the dangers of such networks by disrupting communications. This article analyses two hateful followers’ networks and three hateful retweet networks of Twitter users who post content subsequently classified by human annotators as containing hateful content. Our analysis shows similar connectivity characteristics between the hateful followers networks and likewise between the hateful retweet networks. The study shows that the hateful networks exhibit higher connectivity characteristics when compared to other “risky” networks, which can be seen as a risk in terms of the likelihood of exposure to, and propagation of, online hate. Three network performance metrics are used to quantify the hateful content exposure and contagion: giant component (GC) size, density and average shortest path. In order to efficiently identify nodes whose removal reduced the flow of hate in a network, we propose a range of structured node-removal strategies and test their effectiveness. Results show that removing users with a high degree is most effective in reducing the hateful followers network connectivity (GC, size and density), and therefore reducing the risk of exposure to cyberhate and stemming its propagation.

Graph Analysis of Verbal Fluency Tests in Schizophrenia and Bipolar Disorder.

Verbal Fluency Tests (VFT) are one of the most common neuropsychological tasks used in bipolar disorder (BD) and schizophrenia (SZ) research. Recently, a new VFT analysis method based on graph theory was developed. Interpreting spoken words as nodes and every temporal connection between consecutive words as edges, researchers created graph structures, allowing the extraction of more data from participants’ speech, called Speech Graph Attributes (SGA). The aim of our study was to compare speech graphs, derived from Phonemic and Semantic VFT, between SZ, BD, and healthy controls (HC). Twenty-nine SZ patients, twenty-nine BD patients, and twenty-nine HC performed Semantic and Phonemic VFT. Standard measures (SM) and 13 SGA were analyzed. SZ patients’ Semantic VFT graphs showed lower total word count and correct responses. Their graphs presented less nodes and edges, higher density, smaller diameter, average shortest path (ASP), and largest strongly connected component than the HC group. SM did not differentiate BD and HC groups, and patients’ Semantic VFT graphs presented smaller diameter and ASP than HC. None of the parameters differentiated BD and SZ patients. Our results encourage the use of speech graph analysis, as it reveals verbal fluency alterations that remained unnoticed in the routine comparisons of groups with the use SM.

The Study of the Pore Structure Properties of Rocks Based on Complex Network Theory: Taking an Example of the Sandstone in the Tongnan Area in China

The pores in rocks are multi-scale and highly complex. Based on complex network theory, the topological properties of a sandstone flow network are studied. The results show that the sandstone pore scale network is a small-world network with an average shortest path of 5.720, and 80% of the network node degree is less than 3. This network structure can improve the security of the network and ensure the permeability of the pores under the action of external forces. In addition, nodes with appropriate degree distribution have a significant influence on permeability. Nodes with a larger degree will significantly reduce the average shortest path of the network, while nodes with a smaller degree will expand the coverage of the seepage network.

Опыт сетевого моделирования и картографирования на основе пространственно-временной базы данных по магистральным электрическим сетям

A unique spatio-temporal database of the backbone electric networks of the Moscow power system was previously based on various information sources and published as a cartographic web service. In this study, we consider some mapping possibilities based on calculated parameters, including network analysis methods. To represent the data correctly for each studied year from 1936 to 2020, we have developed algorithms for verifying data integrity, as well as for automated creation of a topologically correct network model. Bringing the network to a topologically correct form implies the snapping of the end vertices of the lines to the point objects of the power system, the elimination of hanging dangles, as well as the elimination of self-intersections. The integrity check is carried out in three stages: 1) coordination of the time frame for the existence of network segments; 2) checking the connectivity of each power line for each time slice; 3) checking the connectivity of the entire network as a whole for each year. The age of the network, betweenness centrality, electric grid centrality, closeness centrality in this paper are taken as an example of local parameters, i. e. indicators confined to specific elements of the network (edges or vertices). In addition, we have considered a global indicator characterizing the network as a whole—the average shortest path in the network, which can be calculated in three ways: without taking into account the weight, taking into account the length of the lines or taking into account its capacitance characteristics, depending on voltage.

Dynamics of Multiagent Reinforcement Learning Compared to Synchronisation Dynamics of Kuramoto Oscillators

Multiagent reinforcement learning is a widespread approach to optimisation problem on systems with possibility of feedback links. Multiple intelligent agents interact with each other to enhance search of the optimal behavioural patterns. Structure of interactions was shown to affect system dynamics, in particular, on learning dynamics of intelligent agents, resulting in emergent behaviour, which make topicality of optimal topology exploration for different task formulations. To effectively solve certain problems, the ability of agents to cooperate in the frame of environments is extremely important. The main idea of coordinated reinforcement learning is that agents, when performing actions, take into account the actions of their neighbors, and this is taken into account when updating their parameters. However, due to the fact that multi-agent environments are very dynamic, a large amount of computation is required to effectively train MARL system. We believe the development of graph formation method for multiagent reinforcement learning systems is able to decrease resource consumption. In order to contribute to this problem, we explore effects of complete, star, path, and ring graphs on learning dynamics in a predator-prey environment, which is compared to synchronisation dynamics on graphs. In contrast to synchronisation case, showing dependence on average shortest paths, predator-prey system has two types of agents, which results in different patterns of influence.

Information Flow Network of International Exchange Rates and Influence of Currencies.

The main purpose of the study is to investigate how price fluctuations of a sovereign currency are transmitted among currencies and what network traits and currency relationships are formed in this process under the background of economic globalization. As a universal equivalent, currency with naturally owned network attributes has not been paid enough attention by the traditional exchange rate determination theories because of their overemphasis of the attribute of value measurement. Considering the network attribute of currency, the characteristics of the information flow network of exchange rate are extracted and analyzed in order to research the impact they have on each other among currencies. The information flow correlation network between currencies is researched from 2007 to 2019 based on data from 30 currencies. A transfer entropy is used to measure the nonlinear information flow between currencies, and complex network indexes such as average shortest path and aggregation coefficient are used to analyze the macroscopic topology characteristics and key nodes of information flow-associated network. It was found that there may be strong information exchange between currencies when the overall market price fluctuates violently. Commodity currencies and currencies of major countries have great influence in the network, and local fluctuations may result in increased risks in the overall exchange rate market. Therefore, it is necessary to monitor exchange rate fluctuations of relevant currencies in order to prevent risks in advance. The network characteristics and evolution of major currencies are revealed, and the influence of a currency in the international money market can be evaluated based on the characteristics of the network. The world monetary system is developing towards diversification, and the currency of developing countries is becoming more and more important. Taking CNY as an example, it was found that the international influence of CNY has increased, although without advantage over other major international currencies since 2015, and this trend continues even if there are trade frictions between China and the United States.

Evolving complex networks with logistic property: Global versus local growth

Considering the retarding effect of natural resources, environmental conditions, and other factors on network growth, the capacity of network nodes to connect to new edges is generally limited. Inspired by this hindered growth of many real-world networks, two types of evolving network models are suggested with different logistic growth schemes. In the global and local logistic network, the total number of network edges and the number of edges added into the network at each step are in line with the Logistic growth, respectively. The most exciting feature of the Logistic growth network is that the growth rule of network edges is first fast, then slow and finally reaches the saturation value [Formula: see text]. Theoretical analysis and numerical simulation reveal that the node degrees of two new networks converge to the same results of the BA scale-free network, [Formula: see text], as the growth rate [Formula: see text] approaches to 0. The local logistic network follows a bilateral power-law degree distribution with a given value of [Formula: see text]. Meanwhile, for these two networks, it is found that the greater [Formula: see text] and [Formula: see text], the smaller the average shortest paths, the greater the clustering coefficients, and the weaker the disassortativity. Additionally, compared to the local logistic growth network, the clustering feature of the global logistic network is more obvious.