Researches on Classification Features of Rural and Urban Domestic Waste in Tianjin City Under Secondary Classification Mode

In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Village of Xiqing District into 3 groups： compostable materials, recyclable materials and toxics on the basis of the constructed secondary classification mode of domestic waste. The study focused on waste generation strength and classification features, compared the waste generation features between rural and urban residents, and analyzed the re- lation between waste generation strength and economic and cultural factors. The re- sults indicated that the average generation speed of urban domestic waste was 423.08 g/（d.capita）, and that of rural domestic waste was 629.89 g/（d.capita）, there was significant difference between rural and urban compost generation strength （P= 0.00002）, while the generation strength of recyclable materials and toxics between rural and urban areas had no significant difference （P=0.471 and P=0.099, respec- tively）. Secondary classification mode is an effective source classification mode for domestic wastes and has positive effects on waste reduction and treatment.

New Thinking of Traditional Industry Development under the New Economic Condition——Classification of the Industry by the Management Mode of Nike

This paper has announced the arrival of new economic era through an analysis of Nike＇s management mode. The traditional industry classification can＇t meet demands of industry development. We should inherit and improve traditional economy in order to adapt to the development demand of new economy.

Overall Evaluation of the Effect of Residual Stress Induced by Shot Peening in the Improvement of Fatigue Fracture Resistance for Metallic Materials

Before 1980s,the circular suspension spring in automobile subjected to torsion fatigue load,under the cyclic normal tensile stresses,the majority of fatigue fracture occurred was in normal tensile fracture mode（NTFM）and the fracture surface was under 45°diagonal.Because there exists the interaction between the residual stresses induced by shot peening and the applied cyclic normal tensile stresses in NTFM,which represents as＂stress strengthening mechanism＂,shot peening technology could be used for improving the fatigue fracture resistance（FFR）of springs.However,since 1990s up to date,in addition to regular NTFM,the fatigue fractures occurred of peened springs from time to time are in longitudinal shear fracture mode（LSFM）or transverse shear fracture mode（TSFM）with the increase of applied cyclic shear stresses,which leads to a remarkable decrease of FFR.However,LSFM/TSFM can be avoided effectively by means of shot peening treatment again on the peened springs.The phenomena have been rarely happened before.At present there are few literatures concerning this problem.Based upon the results of force analysis of a spring,there is no interaction between the residual stresses by shot peening and the applied cyclic shear stresses in shear fracture.This;means that the effect of＂stress strengthening mechanism＂for improving the FFR of LSFM/TSFM is disappeared basically.During shot peening,however,both of residual stress and cyclic plastic deformed microstructure are induced synchronously like＂twins＂in the surface layer of a spring.It has been found for the first time by means of force analysis and experimental results that the modified microstructure in the＂twins＂as a＂structure strengthening mechanism＂can improve the FFR of LSFM/TSFM.At the same time,it is;also shown that the optimum technology of shot peening strengthening must have both＂stress strengthening mechanism＂and＂structure strengthening mechanism＂simultaneously so that the FFR of both NTFM and LSFM/TSFM can be improved by shot peening.

An Optimization System for Intent Recognition Based on an Improved KNN Algorithm with Minimal Feature Set for Powered Knee Prosthesis

In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event.Then,the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization(BGWOPSO)algorithm is used to select features.And,the selected features are optimized and assigned different weights by the Biogeography-Based Optimization(BBO)algorithm.Finally,an improved K-Nearest Neighbor(KNN)classifier is employed for intention recognition.This classifier has the advantages of high accuracy,few parameters as well as low memory burden.Based on data from eight patients with transfemoral amputations,the optimization system is evaluated.The numerical results indicate that the proposed model can recognize nine daily locomotion modes(i.e.,low-,mid-,and fast-speed level-ground walking,ramp ascent/decent,stair ascent/descent,and sit/stand)by only seven features,with an accuracy of 96.66%±0.68%.As for real-time prediction on a powered knee prosthesis,the shortest prediction time is only 9.8 ms.These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.