Optimization Model of Cold-Chain Logistics Network for Fresh Agricultural Products —Taking Guangdong Province as an Example

Cold-chain demand of fresh agricultural products is increasing in China, while network layout of cold-chain logistics is in disorder and its cost is huge. To address this problem, this paper casts an optimal model of cold-chain logistics network and tackles it with genetic algorithms. This optimal model takes running total cost of logistics network as the objective, and embeds a nonlinear mixed integer programming including two assignment issues. The model determines optimal layout and logistics management for pre-cooling stations and logistics center for fresh agricultural products. Our main contribution is to consider construction cost and operation cost of cold chain logistics simultaneously. Case study illustrates the effectiveness of the model.

THE GROWTH OF DIFFERENCE EQUATIONS AND DIFFERENTIAL EQUATIONS

In this paper,we mainly apply a new,asymptotic method to investigate the growth of meromorphic solutions of linear higher order difference equations and differential equations.We delete the condition(1.6)of Theorems E and F,yet obtain the same results for Theorems E and F.We also weaken the condition(1.4)of Theorems C and D.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

Large magnetoelectric effect in BaFe_(12)O_(19)-(Ba_(0.85)Ca_(0.15))(Zr_(0.1)Ti_(0.9))O_(3)particulate composite

Multiferroic composites with a large magnetoelectric response are gaining attractive interests for the design of magnetoelectric(ME)functional devices.In this work,the particulate ME composites(Ba_(0.85)Ca_(0.15))(Zr_(0.1)Ti_(0.9))O_(3)-xBaFe_(12)O_(19)(x=0,0.1,0.2,0.3,0.4 and 1)were prepared,and their structural,dielectric,magnetic,ferroelectric,piezoelectric properties and magnetoelectric coupling were systematically investigated.The composites consisted of only two chemically separated phases with well-bonded interface.Dielectric and impedance analyses indicated the co-contribution of grain and grain boundary to polarization.Well-saturated ferroelectric and magnetic hysteresis loops demonstrated multiferroic nature.ME response was investigated elaborately by employing magnetically induced polarization,together with measuring ME voltage coefficient and magnetodielectric value.Specifically,a large ME coefficient of 26.78 mV/cm·Oe was achieved for x=0.3,which is higher than that in single-phase BaFe_(12)O_(19)and its coupled composites.

Dynamical analysis of bursting oscillations in the Chay-Keizer model with three time scales

Based on the Chay-Keizer model with three time scales, we investigate the role of the slowest variable in generating bursting oscillations in pancreaticcells. It is shown that both of the two slow processes can interact to drive fast, medium and slow bursting oscillations typically observed in pancreaticcells. Moreover, diverse patterns of electrical bursting are presented, including the "fold/fold" bursting, "fold/homoclinic" bursting, "fold/Hopf" bursting via "fold/fold" hysteresis loop, and the "fold/fold" bursting via point-point hysteresis loop. Fast-slow dynamics is used to analyze the types and generation mechanisms of these bursting oscillations. The results can be instructive for understanding the role of the slow variables and the current conductance in pancreaticcells activities.