强点火作用下C3HF7对甲烷-空气爆炸的抑制

为解决瓦斯输送过程中的爆炸安全问题,探索寻找绿色环保且阻火性能优越的新型抑爆剂,开展了当量比下甲烷-空气预混气体爆炸传播过程中的七氟丙烷抑爆效果研究。实验采用长径比L/D=108的水平管道爆炸特性测试系统,研究了在强点火作用下不同体积分数的七氟丙烷对9.5%甲烷-空气预混气体最大爆炸压力、最大压力上升速率和火焰传播速度的影响。实验结果显示:将2.5 m长的管段作为七氟丙烷抑爆区时,七氟丙烷阻断9.5%甲烷-空气预混气体爆炸火焰传播的最小体积分数为5%;当七氟丙烷的体积分数为1%~4%时,不仅无法阻断爆炸火焰的传播,而且与对照组相比,会使火焰传播速度加快;当七氟丙烷的体积分数为1%~6%时,爆炸源及管道末端处的爆炸压力峰值随着七氟丙烷体积分数的增加而逐渐减小;当七氟丙烷的体积分数为3%时,抑爆区处的爆炸压力峰值与对照组相比增幅为10.9%。     

Properties of bounded stochastic processes employed in biophysics

Abstract

Realistic stochastic modeling is increasingly requiring the use of bounded noises. In this work, properties and relationships of commonly employed bounded stochastic processes are investigated within a solid mathematical ground. Four families are object of investigation: the Sine-Wiener (SW), the Doering–Cai–Lin (DCL), the Tsallis–Stariolo–Borland (TSB), and the Kessler–Sørensen (KS) families. We address mathematical questions on existence and uniqueness of the processes defined through Stochastic Differential Equations, which often conceal non-obvious behavior, and we explore the behavior of the solutions near the boundaries of the state space. The expression of the time-dependent probability density of the Sine-Wiener noise is provided in closed form, and a close connection with the Doering–Cai–Lin noise is shown. Further relationships among the different families are explored, pathwise and in distribution. Finally, we illustrate an analogy between the Kessler–Sørensen family and Bessel processes, which allows to relate the respective local times at the boundaries.     

Neural network representations of quantum many-body states

Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.     

n次积分C半群与抽象柯西问题的强解

给出了n次积分C半群的几个性质及其证明,讨论了它与一类抽象柯西问题存在强解的关系及强解的表示式.