107 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
Imaging conductivity from current density magnitude using neural networks
Conductivity imaging represents one of the most important tasks in medical imaging. In this work we develop a neural network based reconstruction technique for imaging the conductivity from the magnitude of the internal current density. It is achieved by formulating the problem as a relaxed weighted least-gradient problem, and then approximating its minimizer by standard fully connected feedforward neural networks. We derive bounds on two components of the generalization error, i.e., approximation error and statistical error, explicitly in terms of properties of the neural networks (e.g., depth, total number of parameters, and the bound of the network parameters). We illustrate the performance and distinct features of the approach on several numerical experiments. Numerically, it is observed that the approach enjoys remarkable robustness with respect to the presence of data noise
Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks
In this work we develop a novel approach using deep neural networks to
reconstruct the conductivity distribution in elliptic problems from one
internal measurement. The approach is based on a mixed reformulation of the
governing equation and utilizes the standard least-squares objective to
approximate the conductivity and flux simultaneously, with deep neural networks
as ansatz functions. We provide a thorough analysis of the neural network
approximations for both continuous and empirical losses, including rigorous
error estimates that are explicit in terms of the noise level, various penalty
parameters and neural network architectural parameters (depth, width and
parameter bound). We also provide extensive numerical experiments in two- and
multi-dimensions to illustrate distinct features of the approach, e.g.,
excellent stability with respect to data noise and capability of solving
high-dimensional problems.Comment: 28 pages. 12 figure
Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring
Taking an answer and its context as input, sequence-to-sequence models have
made considerable progress on question generation. However, we observe that
these approaches often generate wrong question words or keywords and copy
answer-irrelevant words from the input. We believe that lacking global question
semantics and exploiting answer position-awareness not well are the key root
causes. In this paper, we propose a neural question generation model with two
concrete modules: sentence-level semantic matching and answer position
inferring. Further, we enhance the initial state of the decoder by leveraging
the answer-aware gated fusion mechanism. Experimental results demonstrate that
our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO
datasets. Owing to its generality, our work also improves the existing models
significantly.Comment: Revised version of paper accepted to Thirty-fourth AAAI Conference on
Artificial Intelligenc
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
2.8–1.7 Ga history of the Jiao-Liao-Ji Belt of the North China Craton from the geochronology and geochemistry of mafic Liaohe meta-igneous rocks
Highlights
• Lithospheric mantle stabilization under Jiao-Liao-Ji Belt at 2.8Ga (SmNd isochron)
• Liaohe mafic meta-igneous rocks formed in active continental margin subduction zone
• Emplacement of Liaohe mafic igneous rocks at ~2.2 Ga (LuHf isochron)
• Amphibolite retrograde metamorphism from exhumation at 1824 ± 19 Ma (PbPb isochron)
• Cooling of terrane to ~500 °C at 1671 ± 58 Ma (RbSr isochron)
Abstract
The assembly and long-term evolution of the Eastern Block of the North China Craton are poorly constrained. Here we use bulk rock geochronological and geochemical data from mafic meta-igneous rocks (hornblendites, amphibolites and a metagabbro) of the Liaohe Group to reconstruct the Neoarchean to Paleoproterozoic history of the Jiao-Liao-Ji Belt, located between the Longgang and Nangrim blocks that together form the Eastern Block of the North China Craton. The mafic/ultramafic meta-igneous rocks have intrusive or tectonic contacts with the Liaoji granitic rocks (~2.2–2.0 Ga), which form the basement of the Jiao-Liao-Ji Belt. The major and trace element data indicate that the protoliths had calc-alkaline composition and formed along an active continental margin subduction zone. The mafic rocks form a whole-rock 176Lu/177Hf isochron with an age of 2.25 ± 0.31 Ga, overlapping with UPb zircon ages for mafic and granitic rocks from the Jiao-Liao-Ji Belt and consistent with being the emplacement age of the mafic protoliths along the active continental margin. In contrast, the whole-rock 147Sm/144Nd isochron age of 2.83 ± 0.18 Ga is likely to reflect the average age of the lithospheric mantle source from which the mafic/ultramafic protoliths were extracted. Together with geological evidence, we propose that the southwestern portion of the Longgang Block was an active continental margin since at least the early Paleoproteorozic. Literature age data from metamorphic zircons show that peak granulite metamorphism took place at ~1.96–1.88 Ga, resulting from the collisional event that fused the Longgang and Nangrim blocks into the Eastern Block of the North China Craton. Our bulk-rock 207Pb/206Pb age of 1824 ± 19 Ma and our 87Rb/86Sr age of 1671 ± 58 Ma reflect retrograde (cooling) stages during the exhumation of the Jiao-Liao-Ji Belt after the orogenesis
Validity of the Fitbit Ace and Moki devices for assessing steps during different walking conditions in young adolescents
Maternal Feeding Practices And Toddlers’ Fruit And Vegetable Consumption: Results From The DIT - Coombe Hospital Birth Cohort In Ireland
Health benefits of fruit and vegetable have been well recognized. However, insufficient consumption of fruit and vegetable is prevalent among toddlers, and has become a global public health issue. Maternal feeding practices are potential factors influencing toddlers’ dietary intake, including fruit and vegetable intake. This study was conducted to explore the influence of maternal feeding practices on toddlers’ fruit and vegetable consumption in Ireland
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