应用EMD方法分析交通流量的动态特性

经验模态分解(EMD)方法是一种新型的数据分析方法.将EMD方法应用于交通网络流量分析,从空间上对网络交通流量进行研究,通过数学分析来探讨交通流量在网络条件下的动态特性,初步分析了在复杂的交通分布情况下不同路线长度的交通情况所产生的交通流量所占的比例,并寻找网络中的交通流量产生的主要线路以及关键节点.结果表明,在交通网络中,以相邻城市作为起始点的短距离交通所产生的交通流量占据了整个网络交通流量的主要部分.     

基于pi-sigma神经网络的模糊建模与控制及其仿真研究

提出了一种基于高木-关野模糊系统的pi-sigma神经网络结构、学习算法,并分析了学习算法的收敛性.用这种混合型pi-sigma神经网络系统去实现模糊规则及其隶属函数的修正,从而得到模糊推理的自适应性.在设计过程中,引入Zadel模糊取乘算子,使之适合基于梯度的学习算法.最后的仿真结果表明此种网络的有效性、优越性并且在非线性建模、控制等方面有重要的应用价值.     

新增股民数量增长模式分析

考虑到传染病的传播与股民数量的增长之间的类似性,建立了刻画新增股民数量变化规律的数学模型,并进行了相应的数学实验.建立的模型较好地反映了短期内新增股民数量的变化规律.     

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

A Pressure-Induced Inverse Order–Disorder Transition in Double Perovskites

Given the consensus that pressure improves cation ordering in most of known materials, a discovery of pressure-induced disordering could require recognition of an order–disorder transition in solid-state physics/chemistry and geophysics. Double perovskites Y₂CoIrO₆ and Y₂CoRuO₆ polymorphs synthesized at 0, 6, and 15 GPa show B-site ordering, partial ordering, and disordering, respectively, accompanied by lattice compression and crystal structure alteration from monoclinic to orthorhombic symmetry. Correspondingly, the long-range ferrimagnetic ordering in the B-site ordered samples are gradually overwhelmed by B-site disorder. Theoretical calculations suggest that unusual unit-cell compressions under external pressures unexpectedly stabilize the disordered phases of Y₂CoIrO₆ and Y₂CoRuO₆.