association graphs
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2021 ◽  
Vol 29 (1) ◽  
pp. 145-163
Author(s):  
Krzysztof Borodako ◽  
Jadwiga Berbeka ◽  
Michał Rudnicki

Professional service providers, due to their use of information and communication technology (ICT), could be global players. The market for congresses, conferences, trade shows, and business events attract clients and contractors from around the world. Competition between firms motivates them to apply advance technologies that enable faster and easier cooperation. The aim of this exploratory study was to identify and classify ICT used in knowledge management among professional event service providers. By applying method triangulation (interviews, meta-linguistic coding, analysis of association graphs, and netnography), the authors identified key terms related to knowledge management and technology. Firms differed by type and length of market presence. The technologies used by firms were grouped into five types. The analysis of competition in search engines confirm high scores for technology service providers (i.e., cloud data and beacon).


Author(s):  
B. Sushma ◽  
R. Gayatri Devi ◽  
A. Jothi Priya

Obesity is a medical condition in which excess body fat has accumulated to the extent that it may have an adverse effect on health. It may even affect mental health also. Obesity also plays a role in academics; it may be disadvantageous to some of the students. Previous studies have found that obesity could influence academic performance. So this study concerned preparing questions and circulating among the students (school and college). The aim of this study is to determine the awareness level of association between obesity and academic performance. The purpose of the study was clearly explained to the students. A total of 100 responses were received. All the responses were taken into consideration and the statistical analysis paired t test was made using SPSS software for the representation of pie charts and bar graphs (association graphs). According to the results, 67% students answered that the rough weight of the topper of their class would be 45-65 kgs, 25% of them said that it would be 65-80 kgs and the rest 8% said that it would be 80-90 kgs. 37% of students responded that obesity is disadvantageous towards academics, another 63% disagree with it. Most of the students of this survey responded that obesity does not cause any adverse effects on academics. But the care should be taken by the obese students towards their health and academics.


2020 ◽  
Author(s):  
Weishen Pan ◽  
Chang Su ◽  
Kun Chen ◽  
Claire Henchcliffe ◽  
Fei Wang

AbstractBackgroundParkinson’s disease (PD) is associated with multiple clinical manifestations including motor and non-motor symptoms, and understanding of its etiologies has been informed by a growing number of genetic mutations, and various fluid-based and brain imaging biomarkers. However, the precise mechanisms by which these phenotypic features interact remain elusive. Therefore, we aimed to generate the phenotypic association graph of multiple heterogeneous features within PD to reveal pathological pathways of the complex disease.MethodsA data-driven approach was introduced to generate the phenotypic association graphs using data from the Parkinson’s Progression Markers Initiative (PPMI) and Fox Investigation for New Discovery of Biomarkers (BioFIND) studies. We grouped features based on the structure of the learned graphs in both cohorts, and investigated their dynamic patterns in the longitudinal PPMI cohort.Findings424 patients with PD from the PPMI study and 126 patients with PD from the BioFIND study were available for analysis. For PPMI, the phenotypic association graphs were generated at different time points of the disease, including baseline (without any PD treatments), and 1-, 2-, 3-, 4-, and 5-year follow-up time points. Based on topological structure of the learned graph, clinical features were classified into homogeneous groups, that were densely intra-connected while sparsely inter-connected. Importantly, we observed both stable and longitudinally changing relations in the graphs generated, likely reflecting the dynamic pathologies of PD. By cross-cohort comparison, we observed very similar structure for graphs constructed from BioFIND (in which patients have a much longer duration of PD at enrollment than PPMI) and later-period (4- and 5-year follow-up) data from PPMI. This consistency demonstrates the effectiveness of our method.InterpretationWe analyzed the heterogeneous features of PD by generating the phenotypic association graphs. By analyzing the structural relationships among the features over time, our findings could improve the understanding of the pathologies of PD.FundingMichael J Fox Foundation for Parkinson’s Research.


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