159 research outputs found
An analysis of the costs of energy saving and CO 2 mitigation in rural households in China
Households may imperfectly implement energy saving measures. This study identifies two factors resulting in imperfect use of energy-saving technology by households. Households often continue to use old technologies alongside new ones, and the energy-saving technologies have shorter actual lifetimes than their designed lifetimes. These two factors are considered when computing marginal energy conservation cost and marginal CO₂ abatement cost using data collected from a survey of rural households in three provinces in China. The results show that there are cost reduction for most space heating technologies, and their marginal abatement cost under full implementation ranges from −60 to 15 USD/t-CO₂, while the marginal abatement cost of cooking technologies ranges from 12 to 85 USD/t-CO₂. The marginal abatement costs of the majority of technologies increased after accounting for the two implementation factors. The marginal abatement cost in the imperfect implementation scenario is higher, with a range of −1 to 15 USD/t-CO₂ for space heating, and 18 to 165 USD/t-CO₂ for cooking. Assuming implementation factors are constant until 2035, annually achievable CO₂ abatement by 2035 is estimated to be 57, 11, and 10 Mt-CO₂/y in Hebei, Guizhou, and Guangxi Provinces.The authors gratefully acknowledge the financial support of the China Ministry of Science and Technology in the national 973 program: Equity and justice in climate change and regional development (funding code: A.02.12.00301). This research was partly funded by the General Research Fund of the Hong Kong Research Grants Council (14619315
Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown
Background
In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics.
Discussion
This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs.
Conclusions
Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive.This work is supported by an unrestricted grant from the Wellcome Trust (209734/Z/17/Z).Peer Reviewed"Article signat per 26 autors/es: Claudia Di Napoli, Alice McGushin, Marina Romanello, Sonja Ayeb-Karlsson, Wenjia Cai, Jonathan Chambers, Shouro Dasgupta, Luis E. Escobar, Ilan Kelman, Tord Kjellstrom, Dominic Kniveton, Yang Liu, Zhao Liu, Rachel Lowe, Jaime Martinez-Urtaza, Celia McMichael, Maziar Moradi-Lakeh, Kris A. Murray, Mahnaz Rabbaniha, Jan C. Semenza, Liuhua Shi, Meisam Tabatabaei, Joaquin A. Trinanes, Bryan N. Vu, Chloe Brimicombe & Elizabeth J. Robinson "Postprint (published version
Learning Dense UV Completion for Human Mesh Recovery
Human mesh reconstruction from a single image is challenging in the presence
of occlusion, which can be caused by self, objects, or other humans. Existing
methods either fail to separate human features accurately or lack proper
supervision for feature completion. In this paper, we propose Dense Inpainting
Human Mesh Recovery (DIMR), a two-stage method that leverages dense
correspondence maps to handle occlusion. Our method utilizes a dense
correspondence map to separate visible human features and completes human
features on a structured UV map dense human with an attention-based feature
completion module. We also design a feature inpainting training procedure that
guides the network to learn from unoccluded features. We evaluate our method on
several datasets and demonstrate its superior performance under heavily
occluded scenarios compared to other methods. Extensive experiments show that
our method obviously outperforms prior SOTA methods on heavily occluded images
and achieves comparable results on the standard benchmarks (3DPW)
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Robust CRISPR/Cas9 mediated genome editing and its application in manipulating plant height in the first generation of hexaploid Ma bamboo (Dendrocalamus latiflorus Munro).
Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction
As it is hard to calibrate single-view RGB images in the wild, existing 3D
human mesh reconstruction (3DHMR) methods either use a constant large focal
length or estimate one based on the background environment context, which can
not tackle the problem of the torso, limb, hand or face distortion caused by
perspective camera projection when the camera is close to the human body. The
naive focal length assumptions can harm this task with the incorrectly
formulated projection matrices. To solve this, we propose Zolly, the first
3DHMR method focusing on perspective-distorted images. Our approach begins with
analysing the reason for perspective distortion, which we find is mainly caused
by the relative location of the human body to the camera center. We propose a
new camera model and a novel 2D representation, termed distortion image, which
describes the 2D dense distortion scale of the human body. We then estimate the
distance from distortion scale features rather than environment context
features. Afterwards, we integrate the distortion feature with image features
to reconstruct the body mesh. To formulate the correct projection matrix and
locate the human body position, we simultaneously use perspective and
weak-perspective projection loss. Since existing datasets could not handle this
task, we propose the first synthetic dataset PDHuman and extend two real-world
datasets tailored for this task, all containing perspective-distorted human
images. Extensive experiments show that Zolly outperforms existing
state-of-the-art methods on both perspective-distorted datasets and the
standard benchmark (3DPW)
Long-term liver lesion tracking in contrast-enhanced ultrasound videos via a siamese network with temporal motion attention
Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in contrast-enhanced ultrasound videos is the basis for subsequent diagnosis and qualitative treatment, which is a challenging task nowadays.Methods: We propose to upgrade a siamese architecture-based neural network for robust and accurate landmark tracking in contrast-enhanced ultrasound videos. Due to few researches on it, the general inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We use a temporal motion attention based on Lucas Kanade optic flow and Karman filter to model the regular movement and better instruct location prediction. Moreover, we design a pipeline of template update to ensure timely adaptation to feature changes.Results: Eventually, the whole framework was performed on our collected datasets. It has achieved the average mean IoU values of 86.43% on 33 labeled videos with a total of 37,549 frames. In terms of tracking stability, our model has smaller TE of 19.2 pixels and RMSE of 27.6 with the FPS of 8.36 ± 3.23 compared to other classical tracking models.Conclusion: We designed and implemented a pipeline for tracking focal areas in contrast-enhanced ultrasound videos, which takes the siamese network as the backbone and uses optical flow and Kalman filter algorithm to provide position prior information. It turns out that these two additional modules are helpful for the analysis of CEUS videos. We hope that our work can provide an idea for the analysis of CEUS videos
Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown
Background: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. /
Discussion: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. /
Conclusions: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive
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Tracking the impacts of climate change on human health via indicators: lessons from the Lancet Countdown
Background: In the past decades, climate change has been impacting human lives and health via extreme weather and climate events and alterations in labour capacity, food security, and the prevalence and geographical distribution of infectious diseases across the globe. Climate change and health indicators (CCHIs) are workable tools designed to capture the complex set of interdependent interactions through which climate change is affecting human health. Since 2015, a novel sub-set of CCHIs, focusing on climate change impacts, exposures, and vulnerability indicators (CCIEVIs) has been developed, refined, and integrated by Working Group 1 of the “Lancet Countdown: Tracking Progress on Health and Climate Change”, an international collaboration across disciplines that include climate, geography, epidemiology, occupation health, and economics. Discussion: This research in practice article is a reflective narrative documenting how we have developed CCIEVIs as a discrete set of quantifiable indicators that are updated annually to provide the most recent picture of climate change’s impacts on human health. In our experience, the main challenge was to define globally relevant indicators that also have local relevance and as such can support decision making across multiple spatial scales. We found a hazard, exposure, and vulnerability framework to be effective in this regard. We here describe how we used such a framework to define CCIEVIs based on both data availability and the indicators’ relevance to climate change and human health. We also report on how CCIEVIs have been improved and added to, detailing the underlying data and methods, and in doing so provide the defining quality criteria for Lancet Countdown CCIEVIs. Conclusions: Our experience shows that CCIEVIs can effectively contribute to a world-wide monitoring system that aims to track, communicate, and harness evidence on climate-induced health impacts towards effective intervention strategies. An ongoing challenge is how to improve CCIEVIs so that the description of the linkages between climate change and human health can become more and more comprehensive
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The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come
The Lancet Countdown: tracking progress on health and climate change was established to provide an independent, global monitoring system dedicated to tracking the health dimensions of the impacts of, and the response to, climate change. The Lancet Countdown tracks 41 indicators across five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; finance and economics; and public and political engagement. This report is the product of a collaboration of 27 leading academic institutions, the UN, and intergovernmental agencies from every continent. The report draws on world-class expertise from climate scientists, ecologists, mathematicians, geographers, engineers, energy, food, livestock, and transport experts, economists, social and political scientists, public health professionals, and. doctors. The Lancet Countdown’s work builds on decades of research in this field, and was first proposed in the 2015 Lancet Commission on health and climate change,1 which documented the human impacts of climate change and provided ten global recommendations to respond to this public health emergency and secure the public health benefits available (panel 1)
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