Enhancing the blockchain voting process in IoT using a novel blockchain Weighted Majority Consensus Algorithm (WMCA)

Author(s):  
Manal Mohamed Alhejazi ◽  
Rami Mustafa A. Mohammad
1986 ◽  
Vol 38 (3) ◽  
pp. 213-227
Author(s):  
A. Ravichandran ◽  
R.K. Shyamasundar

2019 ◽  
Vol 65 (1) ◽  
pp. 56-62
Author(s):  
Alisa Villert ◽  
Larisa Kolomiets ◽  
Natalya Yunusova ◽  
Yevgeniya Fesik

High-grade ovarian carcinoma is a histopathological diagnosis, however, at the molecular level, ovarian cancer represents a heterogeneous group of diseases. Studies aimed at identifying molecular genetic subtypes of ovarian cancer are conducted in order to find the answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements in this trend is the recognition of the dualistic model that categorizes various types of ovarian cancer into two groups designated high-grade (HG) and low-grade (LG) tumors. However, the tumor genome sequencing data suggest the existence of 6 ovarian carcinoma subtypes, including two LG and four HG subtypes. Subtype C1 exhibits a high stromal response and the lowest survival. Subtypes C2 and C4 demonstrate higher number of intratumoral CD3 + cells, lower stromal gene expression and better survival than sybtype C1. Subtype C5 (mesenchymal) is characterized by mesenchymal cells, over-expression of N-cadherin and P-cadherin, low expression of differentiation markers, and lower survival rates than C2 and C4. The use of a consensus algorithm to determine the subtype allows identification of only a minority of ovarian carcinomas (approximately 25%) therefore, the practical importance of this classification requires additional research. There is evidence that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and groups with overexpression of the immune response genes, as in the angiogenic group there is a comparative superiority in terms of survival. The administration of bevacizumab in the angiogenic group improves survival, while the administration of bevacizumab in the immune group even worsens the outcome. Molecular subtypes with worse survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. This review focuses on some of the advances in understanding molecular, cellular, and genetic changes in ovarian carcinomas with the results achieved so far regarding the formulation of molecular subtypes of ovarian cancer, however further studies are needed.


2009 ◽  
Vol 19 (1) ◽  
pp. 41-42
Author(s):  
Andreas Dielacher ◽  
Thomas Handl ◽  
Christian Widtmann

Author(s):  
Sascha Meyen ◽  
Dorothee M. B. Sigg ◽  
Ulrike von Luxburg ◽  
Volker H. Franz

Abstract Background It has repeatedly been reported that, when making decisions under uncertainty, groups outperform individuals. Real groups are often replaced by simulated groups: Instead of performing an actual group discussion, individual responses are aggregated by a numerical computation. While studies have typically used unweighted majority voting (MV) for this aggregation, the theoretically optimal method is confidence weighted majority voting (CWMV)—if independent and accurate confidence ratings from the individual group members are available. To determine which simulations (MV vs. CWMV) reflect real group processes better, we applied formal cognitive modeling and compared simulated group responses to real group responses. Results Simulated group decisions based on CWMV matched the accuracy of real group decisions, while simulated group decisions based on MV showed lower accuracy. CWMV predicted the confidence that groups put into their group decisions well. However, real groups treated individual votes to some extent more equally weighted than suggested by CWMV. Additionally, real groups tend to put lower confidence into their decisions compared to CWMV simulations. Conclusion Our results highlight the importance of taking individual confidences into account when simulating group decisions: We found that real groups can aggregate individual confidences in a way that matches statistical aggregations given by CWMV to some extent. This implies that research using simulated group decisions should use CWMV instead of MV as a benchmark to compare real groups to.


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