基于线性匹配方法的核电站弯管在循环载荷下的安定性分析

Ratcheting effect experiments of 90° elbow pipes under internal pressure and in-plane bending were carried out for nuclear power plants herein, and then the limit load, shakedown load and ratcheting boundary of 90°elbow pipes were studied by numerical method. First, based on twice elastic slope criterion and tangent intersection criterion, limit load of 90° elbow pipes under internal pressure or in-plane bending was determined by ideal elastic-plastic finite element analysis. Meanwhile, limit load and shakedown load of 90°elbow pipes under internal pressure alone or in-plane bending alone and the interaction between them were determined by LMM. Again, ratcheting boundary of 90°elbow pipes was determined by Ohno-Wang model combining with C-TDF and LMM. Finally, ratcheting boundaries of 90°elbow pipes determined by two methods were compared. The results indicate that the errors of limit load determined by twice elastic slope criterion, tangent intersection criterion and LMM are as 10.78%. It is also showed the efficiency and rapidity of LMM. The ratcheting boundary determined by both methods were compared, the results are well consistent when internal pressures are in the range of 20 MPa and 35 MPa, the trends of predicted results of both methods are different when internal pressures are less than 20 MPa

Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation

Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/Comment: This paper has been accepted by ACM Multimedia 202

ASC: Appearance and Structure Consistency for Unsupervised Domain Adaptation in Fetal Brain MRI Segmentation

Automatic tissue segmentation of fetal brain images is essential for the quantitative analysis of prenatal neurodevelopment. However, producing voxel-level annotations of fetal brain imaging is time-consuming and expensive. To reduce labeling costs, we propose a practical unsupervised domain adaptation (UDA) setting that adapts the segmentation labels of high-quality fetal brain atlases to unlabeled fetal brain MRI data from another domain. To address the task, we propose a new UDA framework based on Appearance and Structure Consistency, named ASC. We adapt the segmentation model to the appearances of different domains by constraining the consistency before and after a frequency-based image transformation, which is to swap the appearance between brain MRI data and atlases. Consider that even in the same domain, the fetal brain images of different gestational ages could have significant variations in the anatomical structures. To make the model adapt to the structural variations in the target domain, we further encourage prediction consistency under different structural perturbations. Extensive experiments on FeTA 2021 benchmark demonstrate the effectiveness of our ASC in comparison to registration-based, semi-supervised learning-based, and existing UDA-based methods.Comment: MICCAI 2023, released code: https://github.com/lhaof/AS

A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement.Comment: Accepted by Medical Image Analysis in Auguest, 202

Decomposition of carbon emission driving factors and judgment of peak status in countries along the Belt and Road

Most of the countries along the Belt and Road are still developing, with their carbon emissions yet to peak. There is a lack of comprehensive analysis and research to judge these countries' current carbon peak state and quantify key driving factors contributing to their carbon emissions. This study aims to fill this gap.A new method for judging a country's peak carbon status based on a time series of carbon emissions is developed. We divide the status of all countries along the Belt and Road into four categories: reached the peak, peak plateau period 1 (the downward trend is not significant), peak plateau period 2 (obvious recession), and not reached the peak. LMDI factorization is used to decompose the change in carbon emissions of energy consumption into multiple factors: carbon intensity, energy intensity, economic output, and population size, based on Kaya's identity theory. The carbon emission and socioeconomic databases from 2000 to 2019 are utilized for this analysis. The main positive driving factor of the three countries (Hungary, Romania, Czech Republic) that have reached the peak is GDP PPP per population, while other driving factors make negative contributions to carbon emissions. In some years, these countries briefly experienced a negative contribution of GDP PPP per population to carbon emissions. The driving factors of carbon emissions for countries in the peak plateau period are not stable, with contributions of GDP PPP per population, energy intensity, and carbon intensity fluctuating periodically. In countries that have not reached the peak of carbon emissions, population growth and economic growth are significant positive contributors, while the effect of driving factors that negatively contribute to carbon emissions is less obvious.The study's findings provide valuable insights into the carbon emission peak status and driving factors of countries along the Belt and Road, which can be used to guide policymaking and future research in addressing climate change and promoting sustainable development in these regions

Establishing thresholds of handgrip strength based on mortality using machine learning in a prospective cohort of Chinese population

BackgroundThe relative prognostic importance of handgrip strength (HGS) in comparison with other risk factors for mortality remains to be further clarified, and thresholds used for best identify high-risk individuals in health screening are not yet established. Using machine learning and nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), the study aimed to investigate the prognostic importance of HGS and establish sex-specific thresholds for health screening.MethodsA total of 6,762 participants from CHARLS were enrolled. A random forest model was built using 30 variables with all-cause mortality as outcome. SHapley Additive exPlanation values were applied to explain the model. Cox proportional hazard models and Harrell’s C index change were used to validate the clinical importance of the thresholds.ResultsAmong the participants, 3,102 (45.9%) were men, and 622 (9.1%) case of death were documented follow-up period of 6.78 years. The random forest model identified HGS as the fifth important prognostic variable, with thresholds for identifying high-risk individuals were < 32 kg in men and < 19 kg in women. Low HGS were associated with all-cause mortality [HR (95% CI): 1.77 (1.49–2.11), p < 0.001]. The addition of HGS thresholds improved the predictive ability of an established office-based risk score (C-index change: 0.022, p < 0.001).ConclusionOn the basis of our thresholds, low HGS predicted all-cause mortality better than other risk factors and improved prediction of a traditional office-based risk score. These results reinforced the clinical utility of measurement of HGS in health screening

Essays on FinTech Lending

The three essays in the thesis provide some novel explanations and perspectives regarding the structure of FinTech lending: including the impact of legal enforcement on the FinTech framework, the role of verification in FinTech credit and the effect of crowdsourced reviews on the FinTech lending industry. In “Legal Enforcement and FinTech Innovations: International Evidence”, using 5,540,449 FinTech loan-level observations across 24 countries, we first find loan interest rates are significantly lower when borrowers’ jurisdictions exhibit stronger legal enforcement. Importantly, we find that the call for legal protection on FinTech credit is less pressing when loans are issued by platforms with better risk-sharing innovations, and when borrowers’ jurisdictions have high information-sharing intensity. To establish causality, we apply the difference-in-differences method by using the country-level staggered adoption of good practices that promote court quality and efficiency, as exogenous shocks to enforceability. Our study contributes to the debate on the role of legal protection in FinTech credit market by interacting with FinTech innovations. In “Crowdsourced reviews and FinTech Lending Industry”, this chapter examines the extent to which crowdsourced reviews predict FinTech platform performance and survival probability. We conduct textual analysis of 152,676 reviews published on one of the most popular FinTech information providers between 2015 and 2019. We find that negative sentiment predicts lower trading volume, fewer investors, and fewer loans in a FinTech platform. This result is robust to a series of sensitivity tests and is more prominent that we apply the difference-in-differences approach to establish causality. Moreover, we observe that informative negative reviews are related to worse platform performance while informative positive reviews do not seem to matter. Further analysis uncovers that FinTech platforms experiencing increases in negative reviews are significantly less likely to survive. Our study suggests that crowdsourced review is an important component that could be considered in regulating the FinTech marketplace. In “The Role of Verification in FinTech Lending”, using data from a leading Chinese FinTech lending platform from 2012 to 2015, we investigate the role of verification in the FinTech lending market. We find that borrowers with thorough and complete verification are more likely to obtain funding and also less likely to default on loans. We also find that borrowers that have incomplete verification are more likely to upwardly misrepresent their income. This leads to higher default rates for this group when compared to the default rates of more thoroughly verified borrowers. The further analysis documents that returning borrowers are more likely to maintain a good credit record. We discuss the implications of our findings for the role of verification in the growing FinTech lending sector and the design of a stable financial system

The Effect of Wildfires on Air Quality and Public Health

Spring 2019 CIPA Capstone ReportIn consultation with the U.S. Government Accountability Office (GAO), a CIPA Capstone team conducted research on the air quality and public health effects of wildfires. They found a positive association between wildfire smoke and particulate matter 2.5, respiratory morbidity and mortality. This may entail issues with asthma, chronic obstructive pulmonary disease, bronchitis and pneumonia. The team also explored the efforts of several states including California, Montana, North Carolina and Texas to mitigate air quality pollutants from wildfires and reduce public exposure to air quality pollutants from wildfires. The team sampled several wildfires and analyzed data, which showed the average change in air quality index (AQI) and PM2.5 were significant in California and Montana. All four states are making efforts to monitor air quality, provide access to air quality and wildfire information, and educate the public to protect themselves from wildfire smoke exposure. The final report also provided information about state policies with promise that may be helpful to bolster or replicate in other states

Influence of stress level on uniaxial ratcheting effect and ratcheting strain rate in austenitic stainless steel Z2CND18.12N

Uniaxial ratcheting behavior of Z2CND18.12N austenitic stainless steel used nuclear power plant piping material was studied. The results indicated that ratcheting strain increased with increasing of stress amplitude under the same mean stress and different stress amplitude, ratcheting strain increased with increasing of mean stress under the same stress amplitude and different mean stress. Based on least square method, a suitable method to arrest ratcheting by loading the materials was proposed, namely determined method of zero ratcheting strain rate. Zero ratcheting strain rate occur under specified mean stress and stress amplitudes. Moreover, three dimensional ratcheting boundary surface graph was established with stress amplitude, mean stress and ratcheting strain rate. This represents a graphical surface zone to study the ratcheting strain rates for various mean stress and stress amplitude combinations. The graph showed the ratcheting behavior under various combinations of mean and amplitude stresses. The graph was also expressed with the help of experimental results of certain sets of mean and stress amplitude conditions. Further, experimentation cost and time can be saved