Survey on Disposal Behaviour and Awareness of Mobile Phones in Chinese University Students

AbstractRetired mobile phones represent the most valuable electrical and electronic equipment in the main waste stream because of such characteristics as large quantity, high reuse/recovery value and fast replacement frequency. An online survey was conducted in the university students in China to identify the disposal behaviour and awareness of mobile phones, which will promote sustainable management of retired mobile phones. The results show that about 22% of the respondents replace their mobile phones annually, while most respondents replace their phones in 2-3 years. The most common reason for mobile phones replacement is the physical broken. 64% of the respondents stockpile their most recently retired phones mainly due to lack of formal management system. The survey results on mobile phones consumers’ environmental awareness also can help improve the policy-making. Nearly 50% of the respondents believe the recycling cost of the retired phones should be shared by all the stakeholders. The incentives with cash or voucher will be the efficient take-back approach. Some suggestions for constructing efficient management system for retired mobile phones are given based on the results and discussions, in which the important effects of the monetary incentives and targeted publicity are emphasized

Run for the Group: The Impacts of Offline Teambuilding, Social Comparison and Competitive Climate on Group Physical Activity - Evidence from Mobile Fitness Apps

To encourage users to exercise more and to improve the retention, mobile fitness app developers build apps with more social interaction features on the collective level, such as allowing users to join groups to work out and holding offline group meetup events. However, literature has not provided a clear theory on the impacts of the within-group social comparison and between-group competitive climate on the participation in group exercises. Motivated by this gap, we build a conceptual framework to explain the empirical effects based on the Social Comparison theory. Based on the Teamwork theory, we also propose that offline group team building activities moderate the above relationships. We collect usage data from a mobile fitness app and conduct a series of comprehensive empirical analyses to test and validate the main and moderating effects. Our results show that both the within-group social comparison and the between- group competitive climate can improve group exercise participation. Additionally, the amount of offline activities moderates the main effects in opposite directions. Our findings help fitness app developers to better understand the impacts of offline team building activities on the participation of the online virtual groups, and further, we provide implications regarding how to make online community policies and design gamification incentive mechanism to stimulate and promote offline team building activities

Spin-dependent Andreev reflection tunneling through a quantum dot with intradot spin-flip scattering

We study Andreev reflection (AR) tunneling through a quantum dot (QD) connected to a ferromagnet and a superconductor, in which the intradot spin-flip interaction is included. By using the nonequibrium-Green-function method, the formula of the linear AR conductance is derived at zero temperature. It is found that competition between the intradot spin-flip scattering and the tunneling coupling to the leads dominantes resonant behaviours of the AR conductance versus the gate voltage.A weak spin-flip scattering leads to a single peak resonance.However, with the spin-flip scattering strength increasing, the AR conductance will develop into a double peak resonannce implying a novel structure in the tunneling spectrum of the AR conductance. Besides, the effect of the spin-dependent tunneling couplings, the matching of Fermi velocity, and the spin polarization of the ferromagnet on the AR conductance is eximined in detail.Comment: 14 pages, 4 figure

Deep Learning-enabled Spatial Phase Unwrapping for 3D Measurement

In terms of 3D imaging speed and system cost, the single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems. This system necessitates a robust spatial phase unwrapping (SPU) algorithm. However, robust SPU remains a challenge in complex scenes. Quality-guided SPU algorithms need more efficient ways to identify the unreliable points in phase maps before unwrapping. End-to-end deep learning SPU methods face generality and interpretability problems. This paper proposes a hybrid method combining deep learning and traditional path-following for robust SPU in FPP. This hybrid SPU scheme demonstrates better robustness than traditional quality-guided SPU methods, better interpretability than end-to-end deep learning scheme, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, the number of fringes, fringe direction, and optics wavelength verify the effectiveness of the proposed method.Comment: 26 page

Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation

In recent years, knowledge distillation methods based on contrastive learning have achieved promising results on image classification and object detection tasks. However, in this line of research, we note that less attention is paid to semantic segmentation. Existing methods heavily rely on data augmentation and memory buffer, which entail high computational resource demands when applying them to handle semantic segmentation that requires to preserve high-resolution feature maps for making dense pixel-wise predictions. In order to address this problem, we present Augmentation-free Dense Contrastive Knowledge Distillation (Af-DCD), a new contrastive distillation learning paradigm to train compact and accurate deep neural networks for semantic segmentation applications. Af-DCD leverages a masked feature mimicking strategy, and formulates a novel contrastive learning loss via taking advantage of tactful feature partitions across both channel and spatial dimensions, allowing to effectively transfer dense and structured local knowledge learnt by the teacher model to a target student model while maintaining training efficiency. Extensive experiments on five mainstream benchmarks with various teacher-student network pairs demonstrate the effectiveness of our approach. For instance, the DeepLabV3-Res18|DeepLabV3-MBV2 model trained by Af-DCD reaches 77.03%|76.38% mIOU on Cityscapes dataset when choosing DeepLabV3-Res101 as the teacher, setting new performance records. Besides that, Af-DCD achieves an absolute mIOU improvement of 3.26%|3.04%|2.75%|2.30%|1.42% compared with individually trained counterpart on Cityscapes|Pascal VOC|Camvid|ADE20K|COCO-Stuff-164K. Code is available at https://github.com/OSVAI/Af-DCDComment: The paper of Af-DCD is accepted to NeurIPS 2023. Code and models are available at https://github.com/OSVAI/Af-DC