126 research outputs found
PM2.5-Related Health Economic Benefits Evaluation Based on Air Improvement Action Plan in Wuhan City, Middle China
On the basis of PM2.5 data of the national air quality monitoring sites, local population data, and baseline all-cause mortality rate, PM2.5-related health economic benefits of the Air Improvement Action Plan implemented in Wuhan in 2013–2017 were investigated using health-impact and valuation functions. Annual avoided premature deaths driven by the average concentration of PM2.5 decrease were evaluated, and the economic benefits were computed by using the value of statistical life (VSL) method. Results showed that the number of avoided premature deaths in Wuhan are 21,384 (95% confidence interval (CI): 15,004 to 27,255) during 2013–2017, due to the implementation of the Air Improvement Action Plan. According to the VSL method, the obtained economic benefits of Huangpi, Wuchang, Hongshan, Xinzhou, Jiang’an, Hanyang, Jiangxia, Qiaokou, Jianghan, Qingshan, Caidian, Dongxihu, and Hannan District were 8.55, 8.19, 8.04, 7.39, 5.78, 4.84, 4.37, 4.04, 3.90, 3.30, 2.87, 2.42, and 0.66 billion RMB (1 RMB = 0.1417 USD On 14 October 2019), respectively. These economic benefits added up to 64.35 billion RMB (95% CI: 45.15 to 82.02 billion RMB), accounting for 4.80% (95% CI: 3.37% to 6.12%) of the total GDP of Wuhan in 2017. Therefore, in the process of formulating a regional air quality improvement scheme, apart from establishing hierarchical emission-reduction standards and policies, policy makers should give integrated consideration to the relationship between regional economic development, environmental protection and residents’ health benefits. Furthermore, for improving air quality, air quality compensation mechanisms can be established on the basis of the status quo and trends of air quality, population distribution, and economic development factors
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Model-based reinforcement learning (RL) often achieves higher sample
efficiency in practice than model-free RL by learning a dynamics model to
generate samples for policy learning. Previous works learn a dynamics model
that fits under the empirical state-action visitation distribution for all
historical policies, i.e., the sample replay buffer. However, in this paper, we
observe that fitting the dynamics model under the distribution for \emph{all
historical policies} does not necessarily benefit model prediction for the
\emph{current policy} since the policy in use is constantly evolving over time.
The evolving policy during training will cause state-action visitation
distribution shifts. We theoretically analyze how this distribution shift over
historical policies affects the model learning and model rollouts. We then
propose a novel dynamics model learning method, named \textit{Policy-adapted
Dynamics Model Learning (PDML)}. PDML dynamically adjusts the historical policy
mixture distribution to ensure the learned model can continually adapt to the
state-action visitation distribution of the evolving policy. Experiments on a
range of continuous control environments in MuJoCo show that PDML achieves
significant improvement in sample efficiency and higher asymptotic performance
combined with the state-of-the-art model-based RL methods.Comment: 16 pages, 5 figure
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
Knowledge Graph Completion (KGC) aims to conduct reasoning on the facts
within knowledge graphs and automatically infer missing links. Existing methods
can mainly be categorized into structure-based or description-based. On the one
hand, structure-based methods effectively represent relational facts in
knowledge graphs using entity embeddings. However, they struggle with
semantically rich real-world entities due to limited structural information and
fail to generalize to unseen entities. On the other hand, description-based
methods leverage pre-trained language models (PLMs) to understand textual
information. They exhibit strong robustness towards unseen entities. However,
they have difficulty with larger negative sampling and often lag behind
structure-based methods. To address these issues, in this paper, we propose
Momentum Contrast for knowledge graph completion with Structure-Augmented
pre-trained language models (MoCoSA), which allows the PLM to perceive the
structural information by the adaptable structure encoder. To improve learning
efficiency, we proposed momentum hard negative and intra-relation negative
sampling. Experimental results demonstrate that our approach achieves
state-of-the-art performance in terms of mean reciprocal rank (MRR), with
improvements of 2.5% on WN18RR and 21% on OpenBG500
Morphological and Physiological Changes in Sedum spectabile during Flower Formation Induced by Photoperiod
Sedum spectabile is an ornamental herbaceous perennial considered as a long-day plant. Varying levels of hormones and sugars possibly affect flower bud formation. This study aimed to determine the changes in endogenous hormones, sugars, and respiration levels in leaves and in apical buds. In addition, the current research was also conducted to observe the morphological changes during the induction, initiation and development of flower buds. Results showed that the periods of floral induction, initiation and development of S. spectabile were the period from 0 d to 1 d, 2 d to 10 d and after 11 d respectively under long day of 20 hours. High zeatin level in apical buds was conducive to floral induction; the increasing levels of gibberrelin and indole acetic acid favor floral initiation; floral development was regulated by mutually synergistic and antagonistic relationships of hormones. The total starch content in leaves remarkably decreased during floral induction. Moreover, soluble sugar content increased and reached the maximum level at 20 d of the treatment period. Afterward, soluble sugar content declined rapidly and was probably transported to the apical buds for rapid floral development. Furthermore, the total respiration of leaves maintained an upward trend; the cytochrome pathway also maintained an increasing trend after the plants were treated for 20 d. Such changes may favour the morphological differentiation of apical buds in floral development
Discriminative and robust zero-watermarking scheme based on completed local binary pattern for authentication and copyright identification of medical images
Authentication and copyright identification are two critical security issues for medical images. Although zerowatermarking schemes can provide durable, reliable and distortion-free protection for medical images, the existing zerowatermarking schemes for medical images still face two problems. On one hand, they rarely considered the distinguishability for medical images, which is critical because different medical images are sometimes similar to each other. On the other hand, their robustness against geometric attacks, such as cropping, rotation and flipping, is insufficient. In this study, a novel discriminative and robust zero-watermarking (DRZW) is proposed to address these two problems. In DRZW, content-based features of medical images are first extracted based on completed local binary pattern (CLBP) operator to ensure the distinguishability and robustness, especially against geometric attacks. Then, master shares and ownership shares are generated from the content-based features and watermark according to (2,2) visual cryptography. Finally, the ownership shares are stored for authentication and copyright identification. For queried medical images, their content-based features are extracted and master shares are generated. Their watermarks for authentication and copyright identification are recovered by stacking the generated master shares and stored ownership shares. 200 different medical images of 5 types are collected as the testing data and our experimental results demonstrate that DRZW ensures both the accuracy and reliability of authentication and copyright identification. When fixing the false positive rate to 1.00%, the average value of false negative rates by using DRZW is only 1.75% under 20 common attacks with different parameters
3D bi-directional transformer U-Net for medical image segmentation
As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics
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