Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Effect of surfactant on styrene removal from waste gas streams in biotrickling filters

Abstract: BACKGROUND: The performance of two biotrickling filters (BTFs) was evaluated for styrene removal from gas streams at the start-up period and at a pseudo-steady-state under various operating conditions. RESULTS: The BTFs exceeded 99% removal efficiency within 19 days when the average inlet styrene concentration was 250 mg m −3 and gas empty bed retention time (EBCT) was 30.0 s. The effect of a surfactant, Triton X-100, on styrene removal was examined by comparative experiments in which one biofilter was fed with nutrient solution with the surfactant while the other without the surfactant, and the average organic loading rate of styrene was set at 65.3, 100.9 and 201.7 g styrene m −3 h −1 , respectively. Results showed that the corresponding average removal efficiency was 87%, 70% and 50% for the BTF withoutsurfactant,whiletheaverageremovalefficiencyfortheBTFwithsurfactantwas96%,92%and82%.Excessivebiomass accumulation was observed in the medium when the styrene loading rate was high. However, the biomass density within the BTF medium when the surfactant was added remained stable during the whole period of the operation. CONCLUSION: These results demonstrated that the use of Triton X-100 can improve the degradation of styrene and control the excessive biomass accumulation. c � 2012 Society of Chemical Industry

Auto-classification of insect images based on color histogram and GLCM

Abstract: Aims to provide general technicians who manage pects in production with a convenient way to recognize them, a novel method to classify insects by analyzing color histogram and GLCM (Gray-Level Co-occurrence Matrices) of wing images is proposed. The wing image of lepidopteran insect is preprocessed to get the ROI (Region of Interest); then the color image is first converted from RGB(Red-Green-Blue) to HSV (Hue-Saturation-Value) space, and the 1D color histograms of ROI are generated from hue and saturation distributions. Afterward, the color image is converted to grayscale one, rotated and transformed to a standard position, and their GLCM features are extracted. Matching is first undergone by computing the correlation of the histogram vectors between testing and template images; if the correlation is higher than certain threshold, then their GLCM features are further matched. The winner-take-all policy is adopted in deciding most matched species in k nearest neighbors. The method is tested at the lepidopteran insect database with 100 species. The recognition rate is as high as 71.1%. An ideal time performance is also achieved. The experimental results testify the efficiency of proposed method.

Preference heterogeneity in mode choice for car-sharing and shared automated vehicles

Abstract: Membership-based short-term car rental–more popularly referred to in the marketplace and popular media as “car-sharing”–has been expanding rapidly worldwide. The advent of self-driving vehicles is likely to facilitate the growth of car-sharing by addressing a couple of the current barriers, including limited dedicated parking and non-competitive access times. Despite the growing importance of this topic, however, the patterns emerging in the market penetration of automated vehicles in car-sharing programs have not received much attention in the literature because of the relative newness of the automated vehicle technologies enabling them. Towards addressing this research gap in this increasingly important area, this paper presents the results of an Australian survey with a focus on consumer preferences towards car-sharing. A stated preference (SP) methodology was adopted to elicit consumers’ valuation of specified mode-choice related factors. In particular, vehicle self-driving capability, a factor rarely examined in the literature, was provided as an option to participants in the SP survey. To increase the realness of the experiment, SP tasks were pivoted from respondents’ most recent trips. The travel preferences data were analyzed using a random parameter (mixed) logit model. To explore preference heterogeneity, socio-demographic and other factors were interacted with alternative-specific attributes, and their influences on marginal utilities were extracted using simulation analysis. Preference heterogeneity across individuals towards shared automated vehicles (SAVs) was identified. Consistent with the literature, consumers’ experience of using car-sharing appears to have significant influence on household mode choices, increasing the probability of using a diversified mode tools (e.g., TWS and Taxi) and decreasing the likelihood of choosing to use privately owned travel tools, such as private car. Although it has been argued in the literature that females, non-drivers, and the elderly are the most likely to benefit from SAVs, the results of this study reveal that these user groups in fact hold negative opinions about SAVs. The findings highlight the challenges policymakers may encounter in maximizing the social and individual benefits of SAVs.