The sugar manufacturing enterprises use traditional algorithm to forcast the raw sugar demand, which ignors the influence of time factors and the industry characteristics, resulting in low accuracy. To address this problem, combining with the periodic characteristics of the supply and demand of raw materials of refining sugar,a temporal feature-correlated raw sugar demand forecast model based on improved Elman Neural Network with Modified Cuckoo Search(MCS) optimization was proposed, namely TMCS-ENN. Firstly, an adaptive learning rate formula was proposed to optimize Elman Neural Network (ENN). Secondly, the adaptive parasitic failure probability and adaptive step-length control variable formula were introduced to obtain MCS algorithm to optimize the weight and threshold of ENN, which effectively improved the local search ability of the model and avoided local optimum. Finally, combining time correlation and hysteresis of raw material purchase of sugar manufacturing enterprise, the data slices were designed based on week granularity, and the ENN was trained with festivals and holidays as important features to obtain TMCS-ENN. Experimental results show that, with week as time granularity, the forecasting accuracy of the proposed TMCS-ENN forecasting model reaches 93. 89%. It can be seen that TMCS-ENN can meet the forecast accuracy demand of sugar manufacturing enterprises and effectively improve their production efficiency.

In order to mitigate the network performance reduction by congestion problem, a new Passive Queue Management (PQM) algorithm named Drop Front n based on Cloud Model (DFCM) was proposed with drop front. At first, the drop packet strategy and drop packet probability were presented by considering network queue length and arrival rate in this algorithm, and the actual queue length was computed with cloud model. Finally, a simulation with NS2 and Matlab was conducted to study the key influencing factors of this algorithm. The results show that, compared with Drop Tail and DFSQ (Drop Front n based on Synchronized Queue) algorithms, DFCM has better suitability in instantaneous queue length variance and effective packet transmission.