检索结果-内蒙古大学图书馆

The Time-Varying Impact of COVID-19 on the Acute Kidney Disorders:A Historical Matched Cohort Study and Mendelian Randomization Analysis

引用

Health data science 2024年第1期4卷 215-225页

作者： Chunyang Li Chao Zhang Jie Chen Yilong Chen Zhiye Ying Yao Hu Huan Song Ping Fu Xiaoxi Zeng Division of Nephrology West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China Med-X Center for Informatics Sichuan UniversityChengdu 610065China Department of Core Laboratory Sichuan Provincial Peopleʼs HospitalUniversity of Electronic Science and Technology of ChinaChengdu 610054China Centre of Public Health Sciences Faculty of MedicineUniversity of IcelandReykjavíkIceland

Background:This study aimed to explore the time-varying impact of COVID-19 on acute kidney disorders,including acute kidney injury and other acute kidney ***:From the UK Biobank,10,121 participants with COVID-19 were matched with up to 3 historically unexposed controls by age,sex,Townsend deprivation index,and the status of hospitalization or receiving critical *** investigated the association between COVID-19 and incidence of acute kidney disorders,within the ffrst 4 weeks after infection,using conditional and time-varying Cox proportional hazard *** addition,one-sample Mendelian randomization,utilizing the polygenic risk score for COVID-19 as an instrumental variable,was conducted to explore the potential causality of the ***:In the matched cohort study,we observed a signiffcant association between COVID-19 and acute kidney disorders predominantly within the ffrst 3 *** impact of COVID-19 was time dependent,peaking in the second week(hazard ratio,12.77;95%conffdence interval,5.93 to 27.70)and decreasing by the fourth week(hazard ratio,2.28;95%conffdence interval,0.75 to 6.93).In subgroup analyses,only moderate to severe COVID-19 cases were associated with acute worsening of renal function in a time-dependent ***-sample Mendelian randomization analyses further showed that COVID-19 might exert a“short-term”causal effect on the risk of acute kidney disorders,primarily conffned to the ffrst week after ***:The risk of acute kidney disorders following COVID-19 demonstrates a time-varying *** effects were observed only in patients with moderate or severe but not mild COVID-19.

关键词： Kidney matched utilizing

来源：评论

学校读者我要写书评

暂无评论

Environmental Drivers and Spatial Prediction of the Critically Endangered Species Thuja sutchuenensis in Sichuan-Chongqing,China

引用

Phyton-International Journal of Experimental Botany 2022年第9期91卷 2069-2086页

作者： Liang Xie Peihao Peng Haijun Wang Shengbin Chen College of Earth Science Chengdu University of TechnologyChengdu610059China School of Geoscience and Technology Southwest Petroleum UniversityChengdu610500China Engineering and Technical College Chengdu University of TechnologyLeshan614000China College of Ecology and Environment Chengdu University of TechnologyChengdu610041China Spatial Information Technology and Big Data Mining Research Center Southwest Petroleum UniversityChengdu610500China

Identifying the ecological environment suitable for the growth of Thuja sutchuenensis and predicting other potential distribution areas are essential to protect this endangered species. After selecting 24 environmental factors thatcould affect the distribution of T. sutchuenensis, including climate, topography, soil and Normalized DifferenceVegetation Index (NDVI), we adopted the Random Forest-MaxEnt integrated model to analyze our data. Basedon the Random Forest study, the contribution of the mean temperature of the warmest quarter, mean temperatureof the coldest quarter, annual mean temperature and mean temperature of the driest quarter was large. Based onMaxEnt model prediction outputs, the potential distribution map not only identified areas that originallyrecorded T. sutchuenensis, such as Xuanhan County, Kai County and Chengkou County, but also identified highlysuitable distribution areas where T. sutchuenensis may exist, including Wanyuan County, Sichuan Province, andthe junction of Chongqing and Hubei Province. This provides a more explicit geographic range for ex situ conservation and reintroduction of T. sutchuenensis. Our results also indicate that, in addition to climate factors,topography and soil factors are also important environmental factors that affect distribution. This provides a theoretical basis for subsequent laboratory construction to simulate the indoor growth of T. sutchuenensis.

关键词： Thuja sutchuenensis environmental drivers spatial prediction conservation Random Forest-MaxEnt model

来源：评论

学校读者我要写书评

暂无评论

In-beam gamma rays of CSNS Back-n characterized by black resonance filter

引用

Nuclear science and Techniques 2024年第10期35卷 149-160页

作者： Jin-Cheng Wang Jie Ren Wei Jiang Xi-Chao Ruan Ying-Yi Liu Hao-Lan Yang Kuo-Zhi Xu Xin-Yi Pan Qi Sun Jie Bao Han-Xiong Huang Hao-Fan Bai Jiang-Bo Bai Ping Cao Qi-Ping Chen Yong-Hao Chen Wen-Hao Duan An-Chuan Fan Rui-Rui Fan Chang-Qing Feng Min-Hao Gu Chang-Cai Han Zi-Jie Han Guo-Zhu He Yong-Cheng He Yang Hong Yi-Wei Hu Zhi-Jie Jiang Ling Kang Chang-Lin Lan Bo Li Feng Li Qiang Li Xiao Li Yang Li Jie Liu Rong Liu Shu-Bin Liu Yi-Na Liu Guang-Yuan Luan Chang-Jun Ning Yi-Jia Qiu Wen-Kai Ren Zhi-Zhou Ren Zhao-Hui Song Kang Sun Zhi-Xin Tan Jing-Yu Tang Sheng-Da Tang Li-Jiao Wang Peng-Cheng Wang Zhao-Hui Wang Zhong-Wei Wen Xiao-Guang Wu Xuan Wu Ze-Peng Wu Cong Xia Li-Kun Xie Han Yi Tao Yu Yong-Ji Yu Guo-Hui Zhang Hang-Chang Zhang Qi-Wei Zhang Xian-Peng Zhang Yu-Liang Zhang Zhi-Yong Zhang Mao-Yuan Zhao Zhi-Hao Zhou Ke-Jun Zhu Chong Zou Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China Institute of High Energy Physics Chinese Academy of Sciences(CAS)Beijing 100049China Spallation Neutron Source Science Center Dongguan 523803China State Key Laboratory of Nuclear Physics and Technology School of PhysicsPeking UniversityBeijing 100871China Institute of Modern Physics Fudan UniversityShanghai 200433China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Nuclear Physics and Chemistry China Academy of Engineering PhysicsMianyang 621900China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China USTC Archaeometry Lab University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Particle Detection and Electronics Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Northwest Institute of Nuclear Technology Xi’an 710024China University of Chinese Academy of Sciences Beijing 100049China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230027China

The back-streaming white-neutron beamline(Back-n)of the China Spallation Neutron Source is an essential neutronresearch platform built for the study of nuclear data,neutron physics,and neutron *** types of cross-sectional neutron-reaction measurements have been performed at Back-n since early *** measurements have shown that a significant number of gamma rays can be transmitted to the experimental stations of Back-n along with the neutron *** gamma rays,commonly referred to as in-beam gamma rays,can induce a non-negligible experimental background in neutron-reaction *** the characteristics of in-beam gamma rays is important for understanding the experimental ***,measuring in-beam gamma rays is challenging because most gamma-ray detectors are sensitive to neutrons;thus,discriminating between neutron-induced signals and those from in-beam gamma rays is *** this study,we propose the use of the black resonance filter method and a CeBr_(3) scintillation detector to measure the characteristics of the in-beam gamma rays of *** types of black resonance filters,^(181)Ta,^(59)Co,^(nat)Ag,and^(nat)Cd,were used in this *** time-of-flight(TOF)technique was used to select the detector signals remaining in the absorption region of the TOF spectra,which were mainly induced by in-beam gamma *** energy distribution and flux of the in-beam gamma rays of Back-n were determined by analyzing the deposited energy spectra of the CeBr_(3) scintillation detector and using Monte Carlo *** on the results of this study,the background contributions from in-beam gamma rays in neutron-reaction measurements at Back-n can be reasonably evaluated,which is beneficial for enhancing both the experimental methodology and data analysis.

关键词： In-beam gamma rays Back-n CeBr_(3)scintillator Black filter resonance technique

来源：评论

学校读者我要写书评

暂无评论

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network 3

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

引用

3rd International Conference on Computer Applications technology, CCAT 2024

作者： Jiang, Xin Chen, Zhi-Chao Sun, Zheng Liu, Ming-Zhe Chongqing University of Technology School of Artificial Intelligence Chongqing401135 China Chongqing University Cancer Hospital Department of Radiology Chongqing400030 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu610000 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

ISBN: (纸本)9798331539795

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring. © 2024 IEEE.

关键词： Radiotherapy

来源：评论

学校读者我要写书评

暂无评论

Dynamical relaxation of a long-range XY chain

arXiv

引用

arXiv 2023年

作者： Huang, Yu-Huan Zou, Yin-Tao Ding, Chengxiang School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan243002 China

We study the universal real-time relaxation behaviors of a long-range quantum XY chain following a quench. Our research includes both the noncritical and critical quench. In the case of noncritical quench, i.e., neither the initial state nor the postquench Hamiltonian is at a critical point of equilibrium phase transition, a quench to the commensurate phase or incommensurate phase gives a scaling of t−3/2 or t−1/2, respectively, which is the same as the counterpart of the short-range XY model. However, for a quench to the boundary line between the commensurate and incommensurate phases, the scaling law t−µ may be different from the t−3/4 law of the counterpart of the short-range model. More interestingly, the decaying exponent µ may depend on the choice of the parameters of the postquench Hamiltonian because of the different asymptotic behaviors of the energy spectrum. Furthermore, in certain cases, the scaling behavior may be outside the range of predictions made by the stationary phase approximation, because an inflection point emerges in the energy spectrum. For the critical quench, i.e., the initial state or the postquench Hamiltonian is at a critical point of equilibrium phase transition, the aforementioned scaling law t−µ may be changed because of the gap-closing property of the energy spectrum of the critical point. © 2023, CC BY.

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

Smart Healthcare: Harnessing the Power of Machine Learning for Predictive Analysis 6

Smart Healthcare: Harnessing the Power of Machine Learning f...

引用

6th Parul International Conference on Engineering and technology, PICET 2024

作者： Barhate, Aditya Kumar, Praveen Verma, Prateek Jikar, Nayan Tale, Abhay Hikre, Vaibhav Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798350369748

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of Machine Learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry. © 2024 IEEE.

关键词： Disease Detection Machine Learning Medical Imaging Predictive Analytics Smart Healthcare

来源：评论

学校读者我要写书评

暂无评论

Adaptive data-Free Quantization

Adaptive Data-Free Quantization

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Biao Qian Yang Wang Richang Hong Meng Wang Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology China

data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions — how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players — a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaDFQ.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cohetrec: A Recommendation for Collaborative Heterogeneous Information Network

SSRN

引用

SSRN 2024年

作者： Chen, Yao Chen, Yuling Sang, Haiwei Dou, Hui Zhang, Yangwen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Education University Guiyang550018 China

Recommender Systems (RS) aim to predict the latent interests of users in items by leveraging embedding learning from user-item graphs. Graph Neural Networks (GNN) have significantly contributed to RS by enabling the learning of embeddings. However, relying solely on user-item interactions poses limitations, such as the cold-start problem. Social recommendation has gained attention for its potential to improve recommendation outcomes by incorporating social information among users. But, existing social-aware recommendation models have not adequately considered collaborative semantics among items. This paper addresses these limitations by proposing CoHetRec, a recommendation model that leverages GNN and a Collaborative Heterogeneous Information Network (CHIN) with latent collaborative heterogeneous relation factors. CoHetRec preserves factorized representations to capture diverse connections between users and items. It also introduces an attention-enhanced message propagation and aggregation scheme for heterogeneous relations in GNN, enabling the extraction of recursive heterogeneous semantic collaborations. Extensive experiments on three benchmark datasets demonstrate the superiority of CoHetRec over 15 state-of-the-art (SOTA) recommendation techniques. The highest average improvement is 31.88% for HR@5 and 38.39% for NDCG@5. And compared with the best performing method in the baseline on the Ciao dataset, our model improved HR@5 by 6.84%, NDCG@5 by 7.79%, while reducing the training time by 38.64%. © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Low-Complexity Three-Dimensional Parameter Estimation Algorithm for FMCW Radar

A Low-Complexity Three-Dimensional Parameter Estimation Algo...

引用

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Yuan Yuhan Li Yingchun Long Qi Wu Peng Jin Tao Zhou Zhiquan School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Institute of Big Data Science and Industry Shanxi University Taiyuan China

Frequency Modulated Continuous Wave (FMCW) radar has been widely used in the field of vehicle mounted millimeter wave radar to detect the parameters of targets. A low complexity three-dimensional (3D) parameter estimation algorithm for time-division multiplexing multiple-input multiple-output (TDM-MIMO) FMCW radar is proposed to detect the range, velocity, and azimuth angle of targets. The proposed algorithm utilized the data block selection and rank-reduced technique to avoid the high computational burden of the traditional subspace-based algorithm. Moreover, the proposed algorithm solves the problem of velocity ambiguity in conventional TDM-MIMO FMCW signal processing. The simulation results show that the accuracy and resolution of the algorithm are much higher than the conventional algorithms, such as 3D-FFT, and 3D-MUSIC.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

引用

Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：