The quantum effect plays an important role in quantum thermodynamics,and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention.Based on two trapped ions,we propo...The quantum effect plays an important role in quantum thermodynamics,and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention.Based on two trapped ions,we propose a scheme to add an indefinite causal order to the isochoric cooling stroke of an Otto engine through reservoir engineering.Then,we observe that the quasi-static efficiency of this heat engine is far beyond the efficiency of a normal Otto heat engine and may reach one.When the power is its maximum,the efficiency is also much higher than that of a normal Otto heat engine.This enhancement may originate from the nonequilibrium of the reservoir and the measurement on the control qubit.展开更多
Academic biology-medicine refers to a couple of philosophies, Organicism and Mechanism, which translates into an association of Cybernetic diagrams and molecular Reductionism. This association presents logical difficu...Academic biology-medicine refers to a couple of philosophies, Organicism and Mechanism, which translates into an association of Cybernetic diagrams and molecular Reductionism. This association presents logical difficulties which make it unsuitable to describe correctly biological effects of electromagnetic fields, EMF. But these logical difficulties may be overcome when renewing the organic cell idea by means of a Philosophy of Nature which juxtaposes causality order and sense order in the cell. The signalsome, the set of descriptive components resulting from the genome, is constantly reorganized. This remodeling may become epigenetic when the phenotype becomes transformed by experience of perceptions in a given medium, because the perception of overall information coming from the extracellular medium becomes functional within the system. In that cellular perception, it is stated that the significance base which contributes to the sense order results from the qualitative topological structure of the extracellular medium. Therefore the EMF interactions target is not only the membrane and its molecules;it is also the structure of the extracellular medium which bathes the membrane. Knowing that the sense order modulation constitutes the global soil of the (localized) causality order, it is possible to obtain a same EMF bioeffect on a membrane molecule by treating a culture of cells in its bath or by treating only the extracellular aqueous medium. Consequently, the double bioeffect resulting from EMF exposure is described simply, because the sense order, such as it results from the qualitative structuring of the medium, forms the significance base which directs the causal mechanics of the cellular answer.展开更多
为有效预防民用航空产品制造过程中的人因差错事件,以人的因素分析与分类系统(Human Factors Analysis and Classification System,HFACS)为理论基础,揭示各致因因素间的复杂耦合机制。首先,基于HFACS-ME简化模型,通过整合98个四级指标...为有效预防民用航空产品制造过程中的人因差错事件,以人的因素分析与分类系统(Human Factors Analysis and Classification System,HFACS)为理论基础,揭示各致因因素间的复杂耦合机制。首先,基于HFACS-ME简化模型,通过整合98个四级指标构建了适用于航空产品的产线分析模型,并利用该模型对某航空维修(Maintenance,Repair&Overhaul,MRO)企业民用航空产品生产线中的人因差错事件进行分类分析;其次,通过卡方检验识别出具有统计学显著性的强相关致因组,结合让步比分析与基于条件概率的权重分配,定量划分指标组间的影响程度;再次,应用决策试验与评估实验法(Decision-Making Trial and Evaluation Laboratory,DEMATEL)确定指标组间的因果关系与依赖程度,并采用逼近理想解排序法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS)对关键指标进行排序,识别出引发人因差错事件的高重要度因素,进一步分析揭示了潜在的事故致因路径,并提出基于切断致因路径的风险预防建议;最后,通过某MRO企业民用航空产品制造产线的225例人因差错事件对方法进行验证。结果表明:此分析方法共识别出11对强相关致因组、2项高中心度致因因素及5项高原因度因素,总结出6条易导致人因差错的事故致因路径;可以证明人因差错事件并非孤立存在,管理层面因素、不安全行为前提条件与不安全行为之间存在显著因果关系。展开更多
Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two ...Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models(DirectLiNGAM) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning.展开更多
幸存者平均因果效应(Survivor Average Causal Effect,SACE)可以用来度量任何处理下都能存活的受试者接受不同处理的影响差异,是因果推断中的一个重要研究方向。由于处理组和对照组中总是存活的受试者样本不能直接观测,SACE通常是不可...幸存者平均因果效应(Survivor Average Causal Effect,SACE)可以用来度量任何处理下都能存活的受试者接受不同处理的影响差异,是因果推断中的一个重要研究方向。由于处理组和对照组中总是存活的受试者样本不能直接观测,SACE通常是不可识别的,只能得到SACE的边界。已有文献中SACE尖锐边界的主流求解方法依赖于多参数线性规划,通过枚举对偶问题的约束多边形的所有顶点来产生封闭形式的解。如果单调性和随机占优等条件不成立,则无法采用枚举法求解该多参数线性规划问题。文章基于主分层框架考虑了“死亡截断”、稳定个体处理效应和可忽略性假设下SACE的尖锐边界问题,其中,优化问题的求解是基于一阶KKT(Kraush-Kuhn-Tucker)条件所对应的多项式方程组。实证选取美国国家支持工作示范项目(National Supported Work Demonstration,NSW)中的Lalonde数据集,计算了“永远幸存者”(always-survivor)在完整协变量情形下的SACE尖锐边界。展开更多
基金supported by National Natural Science Foundation of China under Grant No.11965012Yunan Province’s Hi-tech Talents Recruitment Plan No.YNWR-QNBJ-2019-245。
文摘The quantum effect plays an important role in quantum thermodynamics,and recently the application of an indefinite causal order to quantum thermodynamics has attracted much attention.Based on two trapped ions,we propose a scheme to add an indefinite causal order to the isochoric cooling stroke of an Otto engine through reservoir engineering.Then,we observe that the quasi-static efficiency of this heat engine is far beyond the efficiency of a normal Otto heat engine and may reach one.When the power is its maximum,the efficiency is also much higher than that of a normal Otto heat engine.This enhancement may originate from the nonequilibrium of the reservoir and the measurement on the control qubit.
文摘Academic biology-medicine refers to a couple of philosophies, Organicism and Mechanism, which translates into an association of Cybernetic diagrams and molecular Reductionism. This association presents logical difficulties which make it unsuitable to describe correctly biological effects of electromagnetic fields, EMF. But these logical difficulties may be overcome when renewing the organic cell idea by means of a Philosophy of Nature which juxtaposes causality order and sense order in the cell. The signalsome, the set of descriptive components resulting from the genome, is constantly reorganized. This remodeling may become epigenetic when the phenotype becomes transformed by experience of perceptions in a given medium, because the perception of overall information coming from the extracellular medium becomes functional within the system. In that cellular perception, it is stated that the significance base which contributes to the sense order results from the qualitative topological structure of the extracellular medium. Therefore the EMF interactions target is not only the membrane and its molecules;it is also the structure of the extracellular medium which bathes the membrane. Knowing that the sense order modulation constitutes the global soil of the (localized) causality order, it is possible to obtain a same EMF bioeffect on a membrane molecule by treating a culture of cells in its bath or by treating only the extracellular aqueous medium. Consequently, the double bioeffect resulting from EMF exposure is described simply, because the sense order, such as it results from the qualitative structuring of the medium, forms the significance base which directs the causal mechanics of the cellular answer.
文摘为有效预防民用航空产品制造过程中的人因差错事件,以人的因素分析与分类系统(Human Factors Analysis and Classification System,HFACS)为理论基础,揭示各致因因素间的复杂耦合机制。首先,基于HFACS-ME简化模型,通过整合98个四级指标构建了适用于航空产品的产线分析模型,并利用该模型对某航空维修(Maintenance,Repair&Overhaul,MRO)企业民用航空产品生产线中的人因差错事件进行分类分析;其次,通过卡方检验识别出具有统计学显著性的强相关致因组,结合让步比分析与基于条件概率的权重分配,定量划分指标组间的影响程度;再次,应用决策试验与评估实验法(Decision-Making Trial and Evaluation Laboratory,DEMATEL)确定指标组间的因果关系与依赖程度,并采用逼近理想解排序法(Technique for Order Preference by Similarity to Ideal Solution,TOPSIS)对关键指标进行排序,识别出引发人因差错事件的高重要度因素,进一步分析揭示了潜在的事故致因路径,并提出基于切断致因路径的风险预防建议;最后,通过某MRO企业民用航空产品制造产线的225例人因差错事件对方法进行验证。结果表明:此分析方法共识别出11对强相关致因组、2项高中心度致因因素及5项高原因度因素,总结出6条易导致人因差错的事故致因路径;可以证明人因差错事件并非孤立存在,管理层面因素、不安全行为前提条件与不安全行为之间存在显著因果关系。
基金Supported by the National Natural Science Foundation of China(61573266)
文摘Finding causality merely from observed data is a fundamental problem in science. The most basic form of this causal problem is to determine whether X leads to Y or Y leads to X in the case of joint observation of two variables X, Y. In statistics, path analysis is used to describe the direct dependence between a set of variables. But in fact, we usually do not know the causal order between variables. However, ignoring the direction of the causal path will prevent researchers from analyzing or using causal models. In this study, we propose a method for estimating causality based on observed data. First, observed variables are cleaned and valid variables are retained. Then, a direct linear non-Gaussian acyclic graph models(DirectLiNGAM) estimates the causal order K between variables. The third step is to estimate the adjacency matrix B of the causal relationship based on K. Next, since B is not convenient for model interpretation, we use adaptive lasso to prune the causal path and variables. Further, a causal path graph and a recursive model are established. Finally, we test and debug the recursive model, obtain a causal model with good fit, and estimate the direct, indirect and total effects between causal variables. This paper overcomes the randomness assigning causal order to variables. This study is different from the researcher’s understanding of his own model by generating some form of simulation data. The simplest and relatively unsmooth statistical learning method used in this study has obvious advantages in the field of interpretable machine learning.
文摘幸存者平均因果效应(Survivor Average Causal Effect,SACE)可以用来度量任何处理下都能存活的受试者接受不同处理的影响差异,是因果推断中的一个重要研究方向。由于处理组和对照组中总是存活的受试者样本不能直接观测,SACE通常是不可识别的,只能得到SACE的边界。已有文献中SACE尖锐边界的主流求解方法依赖于多参数线性规划,通过枚举对偶问题的约束多边形的所有顶点来产生封闭形式的解。如果单调性和随机占优等条件不成立,则无法采用枚举法求解该多参数线性规划问题。文章基于主分层框架考虑了“死亡截断”、稳定个体处理效应和可忽略性假设下SACE的尖锐边界问题,其中,优化问题的求解是基于一阶KKT(Kraush-Kuhn-Tucker)条件所对应的多项式方程组。实证选取美国国家支持工作示范项目(National Supported Work Demonstration,NSW)中的Lalonde数据集,计算了“永远幸存者”(always-survivor)在完整协变量情形下的SACE尖锐边界。
