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. One objective of this study was to...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. One objective of this study was to conduct a qualitative systematic review of some case-control studies and to examine the role of human papillomavirus (HPV) in the development of human cervical cancer (CC) 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 and cervical cancer? The method of the conditio sine qua non relationship was used to proof the hypothesis whether the presence of human papillomavirus guarantees the presence of cervical carcinoma. In other words, if human cervical cancer is present, then human papillomavirus 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 and cervical carcinoma. Significance was indicated by a p-value of less than 0.05. Result: The studies analyzed (sample size N = 7657) were able to provide strict evidence that human papillomavirus is a necessary condition (a conditio sine qua non) of cervical carcinoma. Furthermore, the studies analyzed provide impressive evidence of a cause-effect relationship (k = +0.723669245, p value < 0.00001) between human papillomavirus and cervical carcinoma. Conclusion: Human papillomavirus is the cause of human cervical carcinoma.展开更多
随着电力系统数据采集手段的不断完善,基于数据的分析方法在电力系统运行分析中扮演着日益重要的角色。现有的数据分析方法主要分析数据之间的相关关系。事实上,两个强相关变量间通常呈现出不对称的因果关系。若能揭示电力系统运行变量...随着电力系统数据采集手段的不断完善,基于数据的分析方法在电力系统运行分析中扮演着日益重要的角色。现有的数据分析方法主要分析数据之间的相关关系。事实上,两个强相关变量间通常呈现出不对称的因果关系。若能揭示电力系统运行变量间的因果关系,必将有助于深刻地洞察电力系统运行的内在规律性。近年来,因果推断的研究取得很大进展,使得基于数据的因果分析成为可能。该文从物理机制上揭示电力系统中强相关变量之间因果关系的不对称属性;提出一种逆信息熵因果推理(reciprocal information entropy causal inference,RIECI)方法,所构建的指标不仅可以有效判别相关变量间的因果方向,还能正确反映因果强度。在电力系统算例中的验证表明,RIECI方法能有效揭示电力系统运行数据中的因果关系。对电力系统运行数据中因果关系的分析对于认知电力系统运行机理和正确调控电力系统运行状态有重要意义。展开更多
文摘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. One objective of this study was to conduct a qualitative systematic review of some case-control studies and to examine the role of human papillomavirus (HPV) in the development of human cervical cancer (CC) 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 and cervical cancer? The method of the conditio sine qua non relationship was used to proof the hypothesis whether the presence of human papillomavirus guarantees the presence of cervical carcinoma. In other words, if human cervical cancer is present, then human papillomavirus 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 and cervical carcinoma. Significance was indicated by a p-value of less than 0.05. Result: The studies analyzed (sample size N = 7657) were able to provide strict evidence that human papillomavirus is a necessary condition (a conditio sine qua non) of cervical carcinoma. Furthermore, the studies analyzed provide impressive evidence of a cause-effect relationship (k = +0.723669245, p value < 0.00001) between human papillomavirus and cervical carcinoma. Conclusion: Human papillomavirus is the cause of human cervical carcinoma.
文摘随着电力系统数据采集手段的不断完善,基于数据的分析方法在电力系统运行分析中扮演着日益重要的角色。现有的数据分析方法主要分析数据之间的相关关系。事实上,两个强相关变量间通常呈现出不对称的因果关系。若能揭示电力系统运行变量间的因果关系,必将有助于深刻地洞察电力系统运行的内在规律性。近年来,因果推断的研究取得很大进展,使得基于数据的因果分析成为可能。该文从物理机制上揭示电力系统中强相关变量之间因果关系的不对称属性;提出一种逆信息熵因果推理(reciprocal information entropy causal inference,RIECI)方法,所构建的指标不仅可以有效判别相关变量间的因果方向,还能正确反映因果强度。在电力系统算例中的验证表明,RIECI方法能有效揭示电力系统运行数据中的因果关系。对电力系统运行数据中因果关系的分析对于认知电力系统运行机理和正确调控电力系统运行状态有重要意义。
