527 research outputs found
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
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