Quantitative Network Analysis Research Articles

Landscape evolution in orogenic plateaus: Insights from quantitative morphotectonic analysis of the Turkish–Iranian Plateau and Caucasus regions

AbstractTo investigate the recent landscape evolution of the orogenic Turkish–Iranian Plateau (TIP) and the Lesser/Greater Caucasus ranges, we perform a quantitative analysis of topography and drainage networks at a regional scale. This analysis focuses on climatic–topographic swath profiles, local relief distribution, longitudinal river profiles, normalized channel steepness index (ksn), paleo‐river profile, and chi analysis (χ). In general, we find that the evolution of topographic relief and river networks are locally controlled by Late Cenozoic volcanism, contrasts in rock strength, and neotectonic activity along regional tectonic structures. However, our results show that the influence of these factors on the landscape evolution of the Greater Caucasus is significantly different from the Lesser Caucasus and TIP. This study shows that a combination of enhanced aridity, rapid uplift of orographic barriers along the TIP margins, and exposure of resistant rock types associated with the regional volcanic activity resulted in low‐relief plateau and the Lesser Caucasus surfaces that are preserved in the upper/middle Kura–Arax and Qezel‐Owzan river catchments. High local relief in the flanks of the Greater Caucasus and west Alborz, downstream increase in channel steepness, and perturbations in the χ plots of the three main rivers indicate that the topography is in a transient state.We reconstruct the paleo‐river profiles from the relict section of the upper Kura–Arax and Qezel‐Owzan rivers and show that the last drastic fall of the Caspian base‐level, which occurred during the Late Miocene–Early Pliocene, can explain the incision of ~500–1000 m gorges (i.e., Amardos) below major knickpoints. Furthermore, local cross‐divide differences of the χ parameter reveal that disequilibrium in the plan‐form geometry of the drainage networks, attributed to the upstream migration of the regional knickpoints towards the plateau interior and integration of plateau areas into the external fluvial system.

CLOUD TECHNOLOGIES IN EDUCATION: THE BIBLIOGRAPHIC REVIEW

The paper considers the use of cloud technologies in education through the prism of bibliographic analysis. The article characterizes the current state of cloud technologies in education, summarizes the trends, and forecasts the directions of recent scientific research. The leading research methods were bibliographic (visual and quantitative) analysis of keyword networks and qualitative discussion. The bibliographic analysis is based on publications indexed by the scientometric database Web Of Science over the past 20 years. The sample for analysis was formed by searching for the words cloud technology, education, learning, and teaching. The results of the study showed: a significant increase in the popularity of cloud technologies in education in recent years; an increase in the number of studies related to various aspects of educational activities under the influence of Industry 4.0; a gradual increase in the number of studies on the virtualization of the educational process and the use of artificial intelligence in education; dissemination of research on the effectiveness of various types of training using cloud services and teaching methods based on artificial intelligence; the relevance of the trend of visualization of educational material and visual analysis in education. The qualitative discussion provided grounds to identify general trends regarding future research directions.: development of mass online courses and learning technologies (immersive, the use of virtual, augmented, and mixed reality, gaming learning technologies, BYOD approach); further virtualization of universities; development of inclusive education, educational analytics, and assessment (formative and adaptive computer assessment); early training of teachers to use cloud technologies and specialized services in subject learning; research related to visualization (big data, design, simulation, simulation of various processes, etc.) and the designing of relevant new academic disciplines; research of STEM and STEAM education.

The impact of digital technologies on knowledge networks in two engineering organisations.

The management and exploitation of knowledge can contribute to the competitive advantage of an organisation and hence can be a driver of its value. This paper examines knowledge management practices within two engineering organisations with an emphasis on barriers to its effectiveness and the influence of current and anticipated deployment of digital technologies. Two methods were used to gather research data across both organisations by combining a series of semi-structured interviews with a quantitative social network analysis. Examination of the acquired data provided insights into the relationship between the knowledge management culture in both organisations and their adoption of digital tools. Hudson's evolutionary model of safety culture has been modified to describe the culture of knowledge management in an organisation and the acquired data used to locate the two organisations on their knowledge management journey. It is proposed that social networks could be used as an indicator of the stage of evolution of knowledge management in engineering organisations more generally with low network densities and dispersed networks representing higher stages of evolution.

Scaling up One Health: A network analysis in Lao PDR

BackgroundOne Health focuses on sustainable health for humans, animals, and ecosystems. The approach has been well demonstrated, yet most efforts have not been scaled up. Understanding the organisations involved in scaling up processes is critical to translating research into practice. The Lao People's Democratic Republic has successfully implemented One Health projects for multiple decades; however, the organisational network has not been described and scaling up efforts have been limited. MethodsData from organisations involved in One Health projects over the past five years were collected by key-informant interview or workshop. The network was investigated using a mixture of quantitative network analysis and qualitative thematic analysis. ResultsThe organisational network was quantitatively described as sparse and centralised. Organisations were required to harness pre-existing relationships to maximise scarce resources and make co-ordination and alignment of priorities more efficient. A lack of international organisations in the top 10% of resource sharing metrics suggests a potential disconnect between donors. This was reflected in the challenges faced by national organisations and a feeling of being stretched thin over numerous externally funded projects with donor-driven priorities. ConclusionsIt appears that high-level political support for country ownership of development and aid priorities remains unrealised. Developing network capacity and capability may assist scaling up efforts and build resilience in the network and its core organisations. This may allow for the inclusion of more development, education, environment, and water, sanitation, and hygiene organisations that were perceived to be lacking. Future One Health programmes should focus on practical activities that do not overload staff capacity. There is much for One Health to learn about the art of scaling up and organisations are encouraged to include implementation science in their research to inform future scaling up efforts.

Recommender systems for teachers: The relation between social ties and the effectiveness of socially-based features

Open Educational Resources (OER) repositories provide teachers with a wide range of learning resources (LRs), enabling them to design various learning sequences. However, search & select in large OER repositories can be a daunting task for teachers. Incorporating peer recommendations, as is common in online marketplaces, is becoming a popular solution that seeks to exploit the wisdom of the crowd for this task. However, teachers are often reluctant to take a contributory role and provide social recommendations. In addition, little is known about the actual value of social recommendations as a search aid. In this research, we implemented a “light-weight” socially-based recommender system (RS) within a large OER repository that includes social network features. We examined two aspects of the socially-based recommendation mechanisms. First, their utility as search aids that assist teachers in searching and selecting suitable LRs, and second, their impact on teachers' incentives to share recommendations that can assist fellow teachers. To study these two aspects, we examined two science teacher communities using this repository. The results demonstrated the incentivising power of social rewards, and the value of social recommendations as means for search & select. However, we also observed a heterogeneous effect of social features on teachers' behaviour. To explore the factors that may explain these differences, we employed a mixed-method approach, combining qualitative, quantitative, and Social Network Analysis methods. Triangulation of the findings underline the relation between the strength of the social ties within the teachers’ community and the effectiveness of socially-based features.

Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach.

Expression of quantitative disease resistance in many host-pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean-Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant-pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant-pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

Slovak MPs’ response to the 2022 Russian invasion of Ukraine in light of conspiracy theories and the polarization of political discourse

The 2022 Russian invasion of Ukraine received widespread international condemnation. In Slovakia, the invasion became a subject of much political discussion with large number of MPs openly advocating Russian interests. This study investigates Slovak political discourse on Facebook in the weeks before and after the invasion began. We examine the discourse through the lens of Discourse Network Analysis, combining qualitative content analysis of MPs’ Facebook posts with quantitative bipartite social network analysis. During the two weeks, we retrieved all (n = 1880) posts from all (n = 117) MPs who had an active public Facebook page. We manually coded all posts and created a bipartite discourse network consisting of MPs connected to each other via shared discourse codes in two timepoints. We performed a series of exploratory analyses that identified the content of the political discourse, the structure of the political discourse network, and the mechanisms driving the change of the political discourse network. Our results show that the invasion dramatically changed political discourse in Slovakia, with the domestic coalition-opposition struggles losing prominence among the invasion-related topics. The structure of the political discourse network showed a strong coalition-opposition split. While coalition MPs had largely pro-Ukrainian sentiments, opposition MPs largely communicated pro-Russian propaganda. A cluster of opposition MPs consistently spread conspiracy theories both before and after the invasion began, supporting a “conspiracy singularity” theory—the tendency of actors to spread multiple different conspiracy theories and interconnect various conspiracy theories into one overarching narrative. The change of the discourse network at the beginning of the invasion was largely driven by the agenda setting of several parties, agenda reinforcement, and increasing political polarization. We discuss our findings in relation to the previous research on the spread of conspiracy theories among politicians and the polarization of political discourse during the invasion, and we suggest implications for future research.

A Quantitative Social Network Analysis of the Character Relationships in the Mahabharata

Despite the advances in computational literary analysis of Western literature, in-depth analysis of the South Asian literature has been lacking. Thus, social network analysis of the main characters in the Indian epic Mahabharata was performed, in which it was prepossessed into verses, followed by a term frequency–inverse document frequency (TF-IDF) transformation. Then, Latent Semantic Analysis (LSA) word vectors were obtained by applying compact Singular Value Decomposition (SVD) on the term–document matrix. As a novel innovation to this study, these word vectors were adaptively converted into a fully connected similarity matrix and transformed, using a novel locally weighted K-Nearest Neighbors (KNN) algorithm, into a social network. The viability of the social networks was assessed by their ability to (i) recover individual character-to-character relationships; (ii) embed the overall network structure (verified with centrality measures and correlations); and (iii) detect communities of the Pandavas (protagonist) and Kauravas (antagonist) using spectral clustering. Thus, the proposed scheme successfully (i) predicted the character-to-character connections of the most important and second most important characters at an F-score of 0.812 and 0.785, respectively, (ii) recovered the overall structure of the ground-truth networks by matching the original centralities (corr. > 0.5, p < 0.05), and (iii) differentiated the Pandavas from the Kauravas with an F-score of 0.749.

Utilising qualitative data for social network analysis in disaster research: opportunities, challenges, and an illustration.

An abundance of unstructured and loosely structured data on disasters exists and can be analysed using network methods. This paper overviews the use of qualitative data in quantitative social network analysis in disaster research. It discusses two types of networks, each with a relevant major topic in disaster research-that is, (i) whole network approaches to emergency management networks and (ii) personal network approaches to the social support of survivors-and four usable forms of qualitative data. This paper explains five opportunities afforded by these approaches, revolving around their flexibility and ability to account for complex network structures. Next, it presents an empirical illustration that extends the authors' previous work examining the sources and the types of support and barrier experienced by households during long-term recovery from Hurricane (Superstorm) Sandy (2012), wherein quantitative social network analysis was applied to two qualitative datasets. The paper discusses three challenges associated with these approaches, related to the samples, coding, and bias.

Threat, fixable or opportunity? Contested smart home futures in the German social media debate

The idea of the ‘smart home’ envisions futures of a transformed day-to-day life. However, contrary to the glossy visions of the smart home industry, the public discourse in Germany is marked by skepticism due to concerns about data protection, security risks and state surveillance. This paper seeks to illustrate how certain social groups imagine the smart home and how fears and promises related to the digitalized home shape the public social media debate in Germany. We advance the analysis of smart energy futures with a novel methodological approach which combines quantitative network analysis with qualitative discourse analysis. We apply our approach to the online discourse around ‘#smarthome’ on Twitter and related media spaces in Germany in 2018 and 2019, which unveils the contested German smart home imaginaries.We identify five discourse coalitions that constitute shared understandings and perceptions of smart home futures in the social media sphere in Germany: Threat, Hackable, Useless, Fixable and Opportunity. Those discourse coalitions form around specific storylines that reveal partly opposing perspectives and competing ideas on what smart home systems might or should entail for society. Our empirical analyses reveal that in Germany, the envisioned smart home is highly contested and public discourse on social media is dominated by actors who critically interrogate the promises and prospects of smart home futures.

Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population.

Amyloid-beta (Aβ) is a pathological hallmark of Alzheimer's disease (AD). We aimed to identify genes related to Aβ uptake in the Korean population and investigate the effects of these novel genes on clinical outcomes, including neurodegeneration and cognitive impairments. We recruited a total of 759 Korean participants who underwent neuropsychological tests, brain magnetic resonance imaging, 18F-flutemetamol positron emission tomography, and microarray genotyping data. We performed gene-based association analysis, and also performed expression quantitative trait loci and network analysis. In genome-wide association studies, no single nucleotide polymorphism (SNP) passed the genome-wide significance threshold. In gene-based association analysis, six genes (LCMT1, SCRN2, LRRC46, MRPL10, SP6, and OSBPL7) were significantly associated with Aβ standardised uptake value ratio in the brain. The three most significant SNPs (rs4787307, rs9903904, and rs11079797) on these genes are associated with the regulation of the LCMT1, OSBPL7, and SCRN2 genes, respectively. These SNPs are involved in decreasing hippocampal volume and cognitive scores by mediating Aβ uptake. The 19 enriched gene sets identified by pathway analysis included axon and chemokine activity. Our findings suggest novel susceptibility genes associated with the uptake of Aβ, which in turn leads to worse clinical outcomes. Our findings might lead to the discovery of new AD treatment targets.

Methodological Approach to Study Relationships in Network: Qualitative Social Network Analysis

The purpose of this article is to substantiate qualitative network analysis as a separate methodological approach to the study of relationships in the network. In this paper, I define the theoretical framework of the network approach as a whole, then I give a theoretical basis for qualitative network analysis. I designate theoretical concepts related to the study of the deep meanings of relationships in the network. The place of qualitative network analysis in network approaches is determined, among which two approaches to the division of methodologies are distinguished: the division into quantitative and qualitative methods and the division into quantitative, ego-network and qualitative network analysis. Next, I propose a systematization of network approaches at different levels, where network analysis of structures at the macro level, network analysis of subgroups at the meso level and ego-network analysis are distinguished, which can be both in the design of quantitative and qualitative research. The definition of qualitative network analysis is given, the main tasks of this methodology and examples of empirical research are proposed.

Genome-Wide Analysis of the bHLH Gene Family in Loropetalum chinense var. rubrum: Identification, Classification, Evolution, and Diversity of Expression Patterns under Cultivation.

The basic helix-loop-helix (bHLH) transcription factor family is the second-largest transcription factor family in plants. Members of this family are involved in the processes of growth and development, secondary metabolic biosynthesis, signal transduction, and plant resistance. Loropetalum chinense var. rubrum is a critical woody plant with higher ornamental and economic values, which has been used as ornamental architecture and traditional Chinese herbal medicine plants. However, the bHLH transcription factors in Loropetalum chinense var. rubrum (L. chinense var. rubrum) have not yet been systematically demonstrated, and their role in the biosynthesis of anthocyanin is still unclear. Here, we identified 165 potential LcbHLHs genes by using two methods, and they were unequally distributed on chromosomes 1 to 12 of the genome of L. chinense var. rubrum. Based on an evolutionary comparison with proteins from Arabidopsis and Oryza sativa, these bHLH proteins were categorized into 21 subfamilies. Most LcbHLHs in a particular subfamily had similar gene structures and conserved motifs. The Gene Ontology annotation and Cis-elements predicted that LcbHLHs had many molecular functions and were involved in processes of plant growth, including the biosynthesis of flavonoids and anthocyanins. Transcriptomic analysis revealed different expression patterns among different tissues and cultivars of L. chinense var. rubrum. Many LcbHLHs were expressed in the leaves, and only a few genes were highly expressed in the flowers. Six LcbHLHs candidate genes were identified by bioinformatics analysis and expression analysis. Further Real-time quantitative PCR analysis and protein interaction network analysis showed that LcbHLH156, which is one of the candidate proteins belonging to the IIIf subfamily, could interact with proteins related to anthocyanin synthesis. Therefore, LcbHLH156 was transiently expressed in L. chinense var. rubrum to verify its function in regulating anthocyanin synthesis. Compared with the control group, red pigment accumulation appeared at the wound after injection, and the total anthocyanin content increased at the wound of leaves. These results lay a foundation for the research of the regulation mechanism of leaf colors in L. chinense var. rubrum and also provide a basis for the function of the LcbHLH family.

Visual Representation for Patterned Proliferation of Social Media Addiction: Quantitative Model and Network Analysis

Visual Representation for Patterned Proliferation of Social Media Addiction: Quantitative Model and Network Analysis

Quantitative EEG and brain network analysis: predicting awakening from early coma after cardiopulmonary resuscitation

ABSTRACT Objective For patients in early coma after cardiopulmonary resuscitation (CPR), quantitative electroencephalogram (EEG) and brain network analysis was performed to identify relevant indicators of awakening. Methods A prospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit. The included patients received clinical evaluation. The bedside high-density (64-lead) EEG monitoring was performed for visual grading and calculation of power spectrum and brain network parameters. A 3-month prognostic assessment was performed and the patients were dichotomized into the awakening group and the unawakening group. Results A total of 25 patients were included. The awakening group had higher GCS score, more slow wave pattern and reactive EEG than the unawakening group (P = 0.003, P < 0.001, P < 0.001, respectively). Compared with the unawakening group, (1) the awakening group had significantly higher absolute and relative θ power and slow/fast band ratio of the whole brain (P < 0.05), (2) the awakening group had stronger connection based on coherence, phase synchronization, phase lag index and cross-correlation (P < 0.05), (3) the awakening group had higher small-worldness, clustering coefficient and average path length based on graph theory (P < 0.05). Conclusions The power spectrum and brain network characteristics in patients in early coma after CPR have predictive value for recovery.

Linking the potential activities of methanogens and methanotrophs to their communities under different fertilization regimes in coastal saline paddy soils

Methane (CH4) emissions are primarily dependent on the net balance between the activities of methanogens and methanotrophs. Straw returning (straw residue incorporation) and nitrogen (N) fertilizer applications are two of the most frequent agronomic practices applied to ensure rice yield, and they also profoundly affect the behavior of methanogens and methanotrophs. However, the effect of rice straw or its biochar combined with N fertilizer on the activities and compositions of methanogens and methanotrophs is still not well understood, especially in rice root zone soil in coastal saline paddy fields. Moreover, only few studies linking communities and functions of methanogens and methanotrophs are available, resulting in a lack of adequate indicators to predict methane production potential (MPP) or methane oxidation potential (MOP), for improving fertilizer strategies. Here, through quantitative PCR (qPCR), high-throughput sequencing, network analysis and linear regression model, we investigated the response of the abundances, compositions and activities of methanogens and methanotrophs to rice straw or its biochar combined with N fertilizer in rice root zone soil in coastal regions, and explored the relationships between MPP and methanogenic compositions as well as MOP between methanotrophic compositions, respectively. Therefore, a combination of pot and mesocosm experiment was conducted including seven fertilizer treatments: CK, without N fertilizer and rice straw return; SN100, 100 % N plus straw return; SN80, 80 % N plus straw return; SN60, 60 % N plus straw return; BN100, 100 % N plus rice biochar return; BN80, 80 % N plus biochar return; and BN60, 60 % N plus biochar return. Results showed that irrespective of MPP, MOP, or methanogenic and methanotrophic abundance, they all decreased significantly (P < 0.05), with the reduction of N application under the condition of combined straw or biochar with N fertilizer in coastal saline paddy soils. Moreover, compared with straw, biochar could significantly (P < 0.05) reduce MPP and methanogenic abundances, whereas improve MOP and methanotrophic abundances under the same N application rate. The models incorporating mcrA gene copy and soil dissolved organic carbon (DOC) could accurately predict MPP, while MOP was best predicted by a model incorporating pmoA gene copy and NO3−-N. The family Methanobacteriaceae and Methanosarcinaceae were dominant in the methanogenic co-occurrence and keystone patterns. Correspondingly, the genera Methylocystis and Metilomonas were dominant in the methanotrophic co-occurrence pattern, and genus Methylocystis was also the keystone members in coastal saline root zone soil of rice. Soil DOC and salinity were the key factors in shaping the methanogenic co-occurrence and keystone patterns, whereas the methanotrophic co-occurrence and keystone patterns were mainly regulated by NO3−-N. Furthermore, methanogenic keystone taxa were more suitable for predicting MPP than their co-occurrence pattern. Our findings provide insights into the possibility of key microbial taxa for predicting MPP or MOP for low-carbon production fertilizer application strategies in coastal saline paddy soils.

Global Research Trends of Neurosurgery: A Comprehensive Bibliometric and Visualized Analysis of Systematic Reviews

This study aimed to determine the bibliometric characteristics of scientific articles on systematic reviews of neurosurgery worldwide. Bibliographic searches were performed in journals indexed by the Web of Science database until 2022, with no language restrictions. A total of 771 articles were ultimately included on the basis of predefined inclusion criteria, which were manually reviewed. The bibliometric analysis involved the use of quantitative bibliometric indicators and network analysis, which were performed using the bibliometrix package in R and VOSviewer, respectively. The first publication was in 2002, and the number of publications increased over time, with the largest number of publications in 2021 (156 articles). The average number of citations per document was 17.36, and the annual growth rate was 6.82%. Nathan A. Shlobin was the author with the most published articles (19 articles). The most cited study was published by Jobst BC (2015). WORLD NEUROSURGERY was the journal with the most publications (51 articles). The country of corresponding authors with the most publications and the greatest number of total citations was the United States. The affiliations with the most articles were University of Toronto (67 articles) and Harvard Medical School (54 articles). The increasing trend during the past 20 years, and especially in the past 2 years, highlights the advancements in various subspecialities in the field. Our analysis revealed that North American and Western European countries are at the forefront of the field. There is a poor output of publications, authors, and affiliations in Latin-American and African countries.

HeMGNN: Heterogeneous Network Embedding Based on a Mixed Graph Neural Network

Network embedding is an effective way to realize the quantitative analysis of large-scale networks. However, mainstream network embedding models are limited by the manually pre-set metapaths, which leads to the unstable performance of the model. At the same time, the information from homogeneous neighbors is mostly focused in encoding the target node, while ignoring the role of heterogeneous neighbors in the node embedding. This paper proposes a new embedding model, HeMGNN, for heterogeneous networks. The framework of the HeMGNN model is divided into two modules: the metapath subgraph extraction module and the node embedding mixing module. In the metapath subgraph extraction module, HeMGNN automatically generates and filters out the metapaths related to domain mining tasks, so as to effectively avoid the excessive dependence of network embedding on artificial prior knowledge. In the node embedding mixing module, HeMGNN integrates the information of homogeneous and heterogeneous neighbors when learning the embedding of the target nodes. This makes the node vectors generated according to the HeMGNN model contain more abundant topological and semantic information provided by the heterogeneous networks. The Rich semantic information makes the node vectors achieve good performance in downstream domain mining tasks. The experimental results show that, compared to the baseline models, the average classification and clustering performance of HeMGNN has improved by up to 0.3141 and 0.2235, respectively.

Study on Structural Properties of Brain Networks Based on Independent Set Indices

Studies of brain network organisation have swiftly adopted graph theory-based quantitative analysis of complicated networks. Small-world topology, densely connected hubs, and modularity characterise the brain’s structural and functional systems. Many measures quantify graph topology. It has not yet been determined which measurements are most appropriate for brain network analysis. This work introduces a new parameter applicable to brain network analysis. This parameter may help in the identification of symmetry and the study of symmetry breakdown in the brain. This is important because decreased symmetry in the brain is associated with a decreased chance of developing neurodevelopmental and psychiatric disorders. This work is to study brain networks using maximal independent set-based topological indices. These indices seem to depict significant properties of brain networks, such as clustering, small-worldness, etc. One new parameter introduced in this paper for brain network analysis depends on Zagreb topological indices and independence degree. This parameter is useful for analyzing clusters, rich clubs, small-worldness, and connectivity in modules.

Quantitative evaluation of emergency shelters in mountainous areas among multiple scenarios: Evidence from Biancheng, China

Accurate evaluation of urban emergency shelters is key to improving disaster response capacity and government emergency management ability, especially in mountainous areas where disasters are common. Most existing studies focus on evaluating material aspects, such as the capacity and accessibility of emergency shelters, ignoring the impact of different scenarios in the same area such as topographic, demographic, and population spatial–temporal distribution differences. Considering the multiple scenarios factors influencing emergency shelter practice, this study constructed a validity, efficiency, and security evaluation framework which was tested in Biancheng, Hunan, China. A combination of qualitative multiple scenario analysis and quantitative Network Analysis method were used to portray existing emergency shelters during emergency evacuations. The important conclusions are as follows: (1) there are significant differences in the evaluation of emergency shelters in the different scenarios. Scenario I (considering terrain) and Scenario II (considering demographics) decreased the evaluation results, while Scenario III (considering time–space) increased the evaluation results. (2) All three scenarios could cause variation in the validity indicators (A) and efficiency indicators (B), but A1 were unchanged in Scenario II and B2 remained constant in Scenario III, while none of the security indicators (C) were affected except in Scenario I. (3) While different emergency shelters performed differently in different scenarios, overall ID2 performed best and ID5 performed worst. Consideration of all scenarios was clearly necessary for us to scientifically evaluate the real use of emergency shelters, which can effectively guide emergency shelter planning and thus improve the abilities of urban disaster relief.