Influencing factors of urban residents' willingness to classify waste during COVID-19: The case of Hangzhou.

During COVID-19, the urban environment has faced more challenges, and household waste classification has become increasingly important. Based on the theory of planned behavior (TPB), this paper studies the key influencing factors and influence paths of urban residents' willingness to perform waste classification using a structural equation model. Based on the timing of two questionnaires, one before and one after the COVID-19 outbreak, we apply multigroup analysis to test the moderating role of the pandemic. We find that 1) social norms are the primary factor that directly affects residents' willingness to classify waste, followed by perceived behavior costs and behavior attitude. All factors show a positive effect, except for perceived behavior costs. We also find that 2) the results of multigroup analysis indicate that before and after the epidemic there are significant differences in the effect from three influencing paths, which verifies that during the epidemic, the influence paths of behavior attitude and perceived behavior costs on waste classification willingness have been strengthened, but the influence from social norms is weakened. Finally, we suggest that the government should keep playing an important role in waste classification in terms of promotion, reward and penalty, as well as improvement in laws, rules and waste classification facilities.

A Convolutional Neural Network Based on Transfer Learning for Medical Waste Classification During Pandemic Covid-19

Globally, the pandemic of the coronavirus disease (COVID-19) is spreading quickly. Inadequate handling of contaminated garbage and waste management can unintentionally transmit the virus within the company. the complete spectrum from waste generation to treatment must be re-evaluated to scale back the socio-economic and environmental impacts of waste and help achieve a sustainable society. In the area of computer vision, deep learning is beginning to demonstrate high efficiency and minimal complexity. However, the problem now is the performance of the various CNN architectures with transfer learning compared to the classification of medical waste images. Using data augmentation, and preprocessing before performing the two-stage classification of medical waste classification. The research obtained an accuracy of 99.40%, a sensitivity of 98.18%, and a specificity of 100% without overfitting. © 2022 IEEE.

Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer.

The sorting of Construction and Demolition (C&D) waste is a critical step to linking the recycling system and to the macro prediction, which helps to promote the development of the circular economy. Moreover, the effective classification and automated separation process will also help to stop the spreading of pathogenic organisms, such as virus and bacteria, by minimizing human intervention in the sorting process, while also helping to prevent further contamination by COVID-19 virus. This study aims to develop an efficient method to sort C&D waste through deep learning combined with knowledge transfer approach. In this paper, CVGGNet models, that is four VGG structures (VGGNet-11, VGGNet-13, VGGNet-16, and VGGNet-19), based on knowledge transfer combined with the technology of data augmentation and cyclical learning rate, are proposed to classify ten types of C&D waste images. Results show that 2.5 × 10-4, 1.8 × 10-4, 0.8 × 10-4, and 1.0 × 10-4 are the optimum learning rate for CVGGNet-11, CVGGNet-13, CVGGNet-16, and CVGGNet-19, respectively. Knowledge transfer helped shorten the training time from 1039.45 s to 991.05 s, and while it improved the performance of the CVGGNet-11 model in training, validation, and test datasets. The average training time increases as the number of the layers in the CVGGNet architecture rises: CVGGNet-11 (991.05 s) ˂ CVGGNet-13 (1025.76 s) ˂ CVGGNet-16 (1090.48 s) ˂ CVGGNet-19 (1337.81 s). Compared to other CVGGNet models, CVGGNet-16 showed an excellent performance in various C&D waste types, in terms of accuracy (76.6%), weighted average precision (76.8%), weighted average recall (76.6%), weighted average F1-score (76.6%) and micro average ROC (87.0%). In addition, the t-distributed Stochastic Neighbor Embedding (t-SNE) approach can reduce the dataset to a lower dimension and distinctly separate each type of C&D waste. This study demonstrates the good performance of CVGGNet models that can be used to automatically sort most of the C&D waste, paving the way for better C&D waste management.