Authors: Ilija Barukčić
Objective: Cervical cancer is the second most prevalent cancer in females worldwide. Infection with human papillomavirus (HPV) is regarded as the main risk factor of cervical cancer. Our objective was to conduct a qualitative systematic review of some case-control studies to examine the role of human papillomavirus (HPV) in the development of human cervical cancer beyond any reasonable doubt.
Methods: We conducted a systematic review and re-analysis of some impressive key studies aimed to answer the following question. Is there a cause effect relationship between human papillomavirus (HPV) and cervical cancer? The method of the conditio sine qua non relationship was used to proof the hypothesis whether the presence of human papillomavirus (HPV) guarantees the presence of cervical carcinoma. In other words, if human papillomavirus (HPV) is present, then cervical carcinoma is present too. The mathematical formula of the causal relationship k was used to proof the hypothesis, whether there is a cause effect relationship between human papillomavirus (HPV) and cervical carcinoma. Significance was indicated by a p-value of less than 0.05.
Result: One study was able to provide strict evidence that human papillomavirus (HPV) is a conditio sine qua non (a necessary condition) of cervical carcinoma while the other studies analyzed failed on this point. The studies analyzed provide impressive evidence of a cause-effect relationship between human papillomavirus (HPV) and cervical carcinoma.
Conclusion: Human papillomavirus (HPV) is the cause of cervical carcinoma.
Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.
In this research investigation, the author has detailed a novel definition of Standard Deviation.
Authors: Russell Leidich
Comments: 11 Pages.
Herein we present the “surround” function, which is intended to produce a set of “surround codes” which enhance the sparsity of integer sets which have discrete derivatives of lesser Shannon entropy than the sets themselves. In various cases, the surround function is expected to provide further entropy reduction beyond that provided by straightforward delta (difference) encoding alone.
We then present the simple concept of “densification”, which facilitates the elimination of entropy overhead due to masks (symbols) which were considered possible but do not actually occur in a given mask list (set of symbols).
Finally we discuss the ramifications of these techniques for the sake of enhancing the speed and sensitivity of various entropy scans.
Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.
In this research investigation, the author has detailed a novel method of finding the ‘Total Intra Similarity And Dissimilarity Measure For The Values Taken By A Parameter Of Concern’. The advantage of such a measure is that using this measure we can clearly distinguish the contribution of Intra aspect variation and Inter aspect variation when both are bound to occur in a given phenomenon of concern. This measure provides the same advantages as that provided by the popular F-Statistic measure.