Steady Flow Calculations Using Transient Analysis Combined With Network Analysis

2003 ◽  
Author(s):  
Masashi Shimada ◽  
Ryota Kurisu
Keyword(s):  
Network Analysis ◽  
Transient Analysis ◽  
Steady Flows ◽  
Speed Of Convergence ◽  
Wave Speeds ◽  
Network Analyses ◽  
Cpu Time ◽  
Friction Factors ◽  
Optimization Parameter ◽  
Time Marching

This paper proposes a method of solving steady flows in large pipelines with the transient analysis (MOC) combined with the network analysis. The existing methods of accelerating the speed of convergence to steady flows in pipelines, i.e., the time marching approach (TMA) replaces the system dimensions (lengths of pipes, friction factors, wave speeds) by not actual ones and dynamically controls one optimization parameter to reduce the spectral radius. That method will be applied to two pipeline systems having a few thousand of pipes. To accelerate much more the convergence the graph-theoretical information used in the network analyses is implemented. From the discharges computed with TMA the heads at each node are adequately modified using the information of “Tree” of the directed-graph defined for pipelines. Two variations of the method are also proposed. They reduces much the Cpu time to solve steady flows in large pipelines.

scholarly journals Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256696
Author(s):  
Anna Keuchenius ◽  
Petter Törnberg ◽  
Justus Uitermark
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Language Processing ◽  
User Interaction ◽  
User Interactions ◽  
Online Interactions ◽  
Network Analyses ◽  
Social Media Platforms ◽  
Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

scholarly journals Studying Positive and Negative Direct and Extended Contact: Complementing Self-Reports With Social Network Analysis

2017 ◽  
Vol 43 (11) ◽  
pp. 1566-1581 ◽  
Author(s):  
Ralf Wölfer ◽  
Eva Jaspers ◽  
Danielle Blaylock ◽  
Clarissa Wigoder ◽  
Joanne Hughes ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Intergroup Relations ◽  
Large Scale ◽  
Intergroup Contact ◽  
Network Analyses ◽  
Self Reports ◽  
Extended Contact ◽  
Contact Parameters

Traditionally, studies of intergroup contact have primarily relied on self-reports, which constitute a valid method for studying intergroup contact, but has limitations, especially if researchers are interested in negative or extended contact. In three studies, we apply social network analyses to generate alternative contact parameters. Studies 1 and 2 examine self-reported and network-based parameters of positive and negative contact using cross-sectional datasets ( N = 291, N = 258), indicating that both methods help explain intergroup relations. Study 3 examines positive and negative direct and extended contact using the previously validated network-based contact parameters in a large-scale, international, and longitudinal dataset ( N = 12,988), demonstrating that positive and negative direct and extended contact all uniquely predict intergroup relations (i.e., intergroup attitudes and future outgroup contact). Findings highlight the value of social network analysis for examining the full complexity of contact including positive and negative forms of direct and extended contact.

scholarly journals Information sharing in supply chains: a review of risks and opportunities using the systematic literature network analysis (SLNA)

2019 ◽  
Vol 24 (1) ◽  
pp. 5-21 ◽  
Author(s):  
Claudia Colicchia ◽  
Alessandro Creazza ◽  
Carlo Noè ◽  
Fernanda Strozzi
Keyword(s):  
Supply Chain ◽  
Network Analysis ◽  
Literature Review ◽  
Supply Chains ◽  
Information Sharing ◽  
Content Type ◽  
Network Analyses ◽  
Dynamic Representation ◽  
Research Areas ◽  
Over Time

Purpose The purpose of this paper is to identify and discuss the most important research areas on information sharing in supply chains and related risks, taking into account their evolution over time. This paper sheds light on what is happening today and what the trajectories for the future are, with particular respect to the implications for supply chain management. Design/methodology/approach The dynamic literature review method called Systematic Literature Network Analysis (SLNA) was adopted. It combines the Systematic Literature Review approach and bibliographic network analyses, and it relies on objective measures and algorithms to perform quantitative literature-based detection of emerging topics. Findings The focus of the literature seems to be on threats that are internal to the extended supply chain rather than on external attacks, such as viruses, traditionally related to information technology (IT). The main arising risk appears to be the intentional or non-intentional leakage of information. Also, papers analyze the implications for information sharing coming from “soft” factors such as trust and collaboration among supply chain partners. Opportunities are also highlighted and include how information sharing can be leveraged to confront disruptions and increase resilience. Research limitations/implications The adopted methodology allows for providing an original perspective on the investigated topic, that is, how information sharing in supply chains and related risks are evolving over time because of the turbulent advances in technology. Practical implications Emergent and highly critical risks related to information sharing are highlighted to support the design of supply chain risks strategies. Also, critical areas to the development of “beyond-the-dyad” initiatives to manage information sharing risks emerge. Opportunities coming from information sharing that are less known and exploited by companies are provided. Originality/value This paper focuses on the supply chain perspective rather than the traditional IT-based view of information sharing. According to this perspective, this paper provides a dynamic representation of the literature on the investigated topic. This is an important contribution to the topic of information sharing in supply chains is continuously evolving and shaping new supply chain models.

scholarly journals The association between insight and depressive symptoms in schizophrenia: Undirected and Bayesian network analyses

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Mario Amore ◽  
Martino Belvederi Murri ◽  
Pietro Calcagno ◽  
Paola Rocca ◽  
Alessandro Rossi ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Network Analysis ◽  
Bayesian Network ◽  
Depression Scale ◽  
Community Dwelling ◽  
Global Network ◽  
Network Analyses ◽  
Causal Direction ◽  
Service Engagement ◽  
Symptom Level

Abstract Background. Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions. Methods. Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression. Results. After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms. Conclusions. In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem.

Evaluation of molecular pathogenesis of colon cancer in Kerala using high-throughput expression, signaling pathways, and network analyses.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16065-e16065
Author(s):  
Prasanth Ariyannur ◽  
Pavithran Keechilat ◽  
Roopa Paulose ◽  
Damodaran M Vasudevan
Keyword(s):  
Network Analysis ◽  
Signaling Pathways ◽  
High Throughput ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Fold Change ◽  
Molecular Pathogenesis ◽  
Pathway Enrichment Analysis ◽  
Network Analyses ◽  
Significant Difference

e16065 Background: The molecular pathogenesis of colon adenocarcinoma (COAD) is attributed to large molecular changes such as Chromosomal Instability and high somatic copy number variation or defective DNA mismatch repair and consequent microsatellite instability (MSI). The prevalence of this type of cancer is increasing in the state of Kerala, India. A detailed study of the signaling pathways could lead to a better understanding of the central molecular pathological changes and potential biomarkers particular to this population. Methods: High throughput somatic expression analysis using Nanostring PanCancer pathway panel assay was performed. Differentially expressed (DE) genes were selected and KEGG pathway enrichment analysis was performed for assessment of canonical signaling pathways. Expression profiles were compared against the public database, Colon Adenocarcinoma cohort of the Cancer Genome Atlas (TCGA-COAD), using the Genome Expression Profiling Interactive Analysis (GEPIA). Protein-protein interaction network analysis was done using Cytoscape. DE genes were compared against immune cell infiltration in the TCGA-COAD from Tumor Immune Estimation Resource (TIMER) databases. Results: Nanostring assay was performed in 10 Tumor-Normal paired FFPE samples of stage II/III colon adenocarcinoma. Out of > 700 genes, significant difference in expression was found in 83 genes (FDR adjusted p -value < 0.01) with fold-change |fc(log2)| of at least one. Fold-change |fc(log2)| ≥ 2 was found in 19/83 genes. Network analysis clustered 13 out of 17 upregulated genes. Superimposition of the current data on TCGA study showed that among the three genes, four (MET, MCM2, ETV4 and MMP7), were common. DE Genes which were not significant in TCGA COAD study such as INHBA, COL1A1, COL11A1, COMP, SFRP4, SPP1, IL11, LIF, WT1 and DDIT4, were found to be significant in the current study. A few of the unique genes identified in the current study were found to have significant correlation with antigen presenting cells infiltration in the TCGA-COAD studies. Conclusions: Many DE genes from the study population were found to be different from previous large cohort studies in other population. This may suggest a different pathogenesis of COAD in this population, warranting a detailed study on the pathogenesis.

Network analysis reveals cryptic seasonal patterns of association in Gould’s wattled bats (Chalinolobus gouldii) roosting in bat-boxes

Behaviour ◽  
2015 ◽  
Vol 152 (15) ◽  
pp. 2079-2105 ◽  
Author(s):  
Lisa N. Godinho ◽  
Linda F. Lumsden ◽  
Graeme Coulson ◽  
Stephen R. Griffiths
Keyword(s):  
Network Analysis ◽  
Social Structure ◽  
Temperate Zone ◽  
Seasonal Patterns ◽  
Fine Scale ◽  
Peak Activity ◽  
Network Analyses ◽  
The Social ◽  
Scale Data

Tree-roosting bats are highly social mammals, which often form fission–fusion societies. However, extensive, fine scale data is required to detect and interpret these patterns. We investigated the social structure of Gould’s wattled bats, Chalinolobus gouldii, roosting in artificial roosts (bat-boxes) over a continuous 18-month period. Network analyses revealed non-random associations among individuals in the roosting population consistent with a temperate zone fission–fusion social structure. Females generally showed stronger associations with roost-mates than did males. Two distinct sub-groups within the larger roosting population were detected. There was also evidence of smaller subunits within these larger roosting groups in spring and summer, with broader mixing at other times of the year. The extensive roost occupancy data collected across all seasons was critical in defining this fine scale, and otherwise cryptic, social structure, and in particular indicating that associations observed during peak activity periods may not be maintained across the year.

scholarly journals Bioinformatics and Gene Network Analyses of the Swine Mammary Gland Transcriptome during Late Gestation

2013 ◽  
Vol 7 ◽  
pp. BBI.S12205 ◽  
Author(s):  
Wangsheng Zhao ◽  
Khuram Shahzad ◽  
Mingfeng Jiang ◽  
Daniel E. Graugnard ◽  
Sandra L. Rodriguez-Zas ◽  
...  
Keyword(s):  
Network Analysis ◽  
Receptor Binding ◽  
Gene Network ◽  
Fatty Acid Biosynthesis ◽  
Mammary Development ◽  
Late Gestation ◽  
Interleukin 4 ◽  
Receptor Interaction ◽  
Bioinformatics Analyses ◽  
Network Analyses

We used the newly-developed Dynamic Impact Approach (DIA) and gene network analysis to study the sow mammary transcriptome at 80, 100, and 110 days of pregnancy. A swine oligoarray with 13,290 inserts was used for transcriptome profiling. An ANOVA with false discovery rate (FDR < 0.15) correction resulted in 1,409 genes with a significant time effect across time comparisons. The DIA uncovered that Fatty acid biosynthesis, Interleukin-4 receptor binding, Galactose metabolism, and mTOR signaling were among the most-impacted pathways. IL-4 receptor binding, ABC transporters, cytokine-cytokine receptor interaction, and Jak-STAT signaling were markedly activated at 110 days compared with 80 and 100 days. Epigenetic and transcription factor regulatory mechanisms appear important in coordinating the final stages of mammary development during pregnancy. Network analysis revealed a crucial role for TP53, ARNT2, E2F4, and PPARG. The bioinformatics analyses revealed a number of pathways and functions that perform an irreplaceable role during late gestation to farrowing.

scholarly journals Hospital-Nursing Home Transfer Patterns and Influence on Nursing Home Clostridium difficile Infection Rates

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S402-S403
Author(s):  
Lauren Campbell ◽  
Kristen Bush ◽  
Ghinwa Dumyati
Keyword(s):  
Nursing Home ◽  
Network Analysis ◽  
Online Survey ◽  
Negative Binomial ◽  
Network Clustering ◽  
Emerging Infections ◽  
Clustering Methods ◽  
Data Set ◽  
Patient Transfers ◽  
Network Analyses

Abstract Background Little is known as to how hospital C. difficile infection (CDI) may impact nursing home (NH) CDI, or how patient transfers may modify this relationship. This study aims to examine a possible association between hospital and NH CDI rates, and whether NH CDI rates are influenced by patient transfers from hospital to NH. Methods Patient transfers among the 5 hospitals and 34 NHs in Monroe County, NY were identified from the Minimum Data Set (MDS) 3.0 and Medicare Provider Analysis and Review files for 2011–13, and aggregated to the NH level. NH and hospital CDI rates were obtained from Emerging Infections Program CDI population surveillance and National Healthcare Safety Network data, respectively. Multivariate negative binomial regression modeled the association between hospital CDI rate (weighted by hospital-to-NH transfers/overall transfers among hospitals and NHs) and NH CDI rate, controlling for NH covariates from NH Compare and the Online Survey, Certification, and Reporting files. Patient transfer networks between hospitals and NHs were constructed, and basic network analysis of transfer patterns was conducted to confirm contributing factors to NH CDI rates from the multivariate model. Results When weighted hospital CDI rate increased by 1%, NH CDI rate increased by 18% (P = 0.016). Antibiotic and feeding tube prevalence were associated with a 4% and 8% increase in NH CDI rate, respectively (P≤0.014). Network analysis confirmed multivariate results and detected hospital-NH pairs with high edge weights (number of transfers) where NHs receiving patients from hospitals with high CDI rates had higher CDI rates. Network clustering methods were used to identify 2 sub-networks within overall annual networks and clusters of hospital-NH pairs for targeted intervention. Conclusion Hospital CDI rate, adjusting for patient transfers, is associated with higher NH CDI rates in multivariate and network analyses, suggesting that NHs with a large inflow of patients from hospitals may need to implement stricter infection prevention practices to reduce transmission among residents. By identifying regional sub-networks, network analysis can also be used to actively manage facility CDI and prevent spread to other healthcare facilities. Disclosures All authors: No reported disclosures.

scholarly journals Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Leonie Neuhäuser ◽  
Felix I. Stamm ◽  
Florian Lemmerich ◽  
Michael T. Schaub ◽  
Markus Strohmaier
Keyword(s):  
Network Analysis ◽  
Measurement Errors ◽  
Social Systems ◽  
Systematic Bias ◽  
Model Framework ◽  
Network Analyses ◽  
External Node ◽  
Systematic Biases ◽  
Node Attributes ◽  
Attributed Networks

AbstractNetwork analysis provides powerful tools to learn about a variety of social systems. However, most analyses implicitly assume that the considered relational data is error-free, and reliable and accurately reflects the system to be analysed. Especially if the network consists of multiple groups (e.g., genders, races), this assumption conflicts with a range of systematic biases, measurement errors and other inaccuracies that are well documented in the literature. To investigate the effects of such errors we introduce a framework for simulating systematic bias in attributed networks. Our framework enables us to model erroneous edge observations that are driven by external node attributes or errors arising from the (hidden) network structure itself. We exemplify how systematic inaccuracies distort conclusions drawn from network analyses on the task of minority representations in degree-based rankings. By analysing synthetic and real networks with varying homophily levels and group sizes, we find that the effect of introducing systematic edge errors depends on both the type of edge error and the level of homophily in the system: in heterophilic networks, minority representations in rankings are very sensitive to the type of systematic edge error. In contrast, in homophilic networks we find that minorities are at a disadvantage regardless of the type of error present. We thus conclude that the implications of systematic bias in edge data depend on an interplay between network topology and type of systematic error. This emphasises the need for an error model framework as developed here, which provides a first step towards studying the effects of systematic edge-uncertainty for various network analysis tasks.

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