Conference Paper

1998 ◽  
Vol 11 (3) ◽  
pp. 188-189
Keyword(s):  
2020 ◽  
Vol 16 ◽  
Author(s):  
Md Mahfujul Islam ◽  
Md Al Amin Molla

: The aim to examine the signs for the potency of interventions to raise cancer awareness along with promote early demonstration in most cancers to share with future and policy investigation. Several peer-reviewed journals as well as books, conference paper and authentic website (like as NCBI, PubMed, CRI, CRU, Google Scholar, MEDLINE, Web of Science, etc.) Also, we looked for bibliographic databases and reference lists Such as randomized controlled trials of interventions brought to Individuals and commanded or uncontrolled reports of interventions brought to communities. The results found the signs that interventions reach to people subconsciously increase the cancer awareness from the short term and inadequate signs they advertise premature demonstration. This analysis helps to find appropriate info about categorizing the early indicators of many kinds of cancer cells on addition to appropriate guidelines to conquer cancer in low-Mid center income areas. Whereas contained the structured screening program, which helps detect cancer early and having a heightened chance of therapeutic and treatment content, taking essential measures to elevate the consciousness of the detectable symptoms.


1998 ◽  
Vol 59 ◽  
pp. 82-83
Author(s):  
Unknown
Keyword(s):  

2016 ◽  
Vol 11 (1) ◽  
pp. 40-57 ◽  
Author(s):  
Hend Alrasheed ◽  
Feodor F Dragan

Hyperbolicity is a global property of graphs that measures how close their structures are to trees in terms of their distances. It embeds multiple properties that facilitate solving several problems that found to be hard in the general graph form. In this paper, we investigate the hyperbolicity of graphs not only by considering Gromov’s notion of δ-hyperbolicity but also by analyzing its relationship to other graph’s parameters. This new perspective allows us to classify graphs with respect to their hyperbolicity and to show that many biological networks are hyperbolic. Then we introduce the eccentricity-based bending property which we exploit to identify the core vertices of a graph by proposing two models: the maximum-peak model and the minimum cover set model. In this extended version of the paper, we include some new theorems, as well as proofs of the theorems proposed in the conference paper. Also, we present the algorithms we used for each of the proposed core identification models, and we provide more analysis, explanations, and examples.


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