At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standar...At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.展开更多
Objective:To evaluate how DNA fragmentation index(DFI)and chromatin denaturation index(CDI)relate to semen parameters across different types of male infertility,thereby improving the understanding and assessment of sp...Objective:To evaluate how DNA fragmentation index(DFI)and chromatin denaturation index(CDI)relate to semen parameters across different types of male infertility,thereby improving the understanding and assessment of sperm quality.Methods:A prospective and descriptive cohort study was conducted over two years at the Integrated Physiology Laboratory of the University of Carthage in collaboration with the Alyssa Fertility Group,Tunisia.A total of 163 participants were classified into five groups based on their semen parameters:normozoospermia,oligozoospermia,asthenozoospermia,teratozoospermia,and oligoastheno-teratozoospermia.The normozoospermia group was selected from volunteers who had children.Semen samples were analyzed according to WHO guidelines.DFI was measured using Halosperm®and CDI was tested using aniline blue staining.Results:Both DFI and CDI were significantly higher in all infertility groups,with the oligozoospermia group showing the highest DFI and CDI.Negative correlations were found between DFI/CDI and sperm motility,concentration,and morphology in the affected groups.The normozoospermia group served as a control with the lowest DFI and CDI values.Conclusions:DFI and CDI are increasingly recognized as important biomarkers for evaluating sperm quality in cases of male infertility.Their elevated levels in patients with oligozoospermia,asthenozoospermia,teratozoospermia,and oligo-asthenoteratozoospermia underscore their potential role in not only diagnosing male infertility but also in assessing the overall reproductive outcomes for affected individuals,thus guiding more effective treatment strategies.展开更多
目前人工增雨催化数值模拟研究较少考虑环境场误差对模拟效果的影响,结论往往具有很大的不确定性。有鉴于此,本文将初始场扰动集合预报技术与包含催化模块的柱状云模式进行单向耦合(One Way Coupling),利用中尺度模式所提供包含环境场...目前人工增雨催化数值模拟研究较少考虑环境场误差对模拟效果的影响,结论往往具有很大的不确定性。有鉴于此,本文将初始场扰动集合预报技术与包含催化模块的柱状云模式进行单向耦合(One Way Coupling),利用中尺度模式所提供包含环境场扰动误差的多组热力、微物理量廓线实时驱动柱状云模式,对2022年1月23日浙江省积层混合云降水过程进行多成员、单/多格点AgI催化数值试验,尝试从概率的角度探讨最佳播撒方案以及对应的增雨潜力。从单站(杭州站)的模拟效果来看,23日15:00(协调世界时)在3.6 km高度(−5.2℃)处使用AgI(碘化银催化剂量为1.2×10^(−7)~1.2×10^(−4)g kg^(−1))播撒时所有集合成员均能够取得正增雨效果,其中采用1.2×10^(−5)g kg^(−1)剂量时增雨率最大,所有成员的均值为4.67%,99%分位数为7.77%。在单点模拟中,初始场扰动对于过量播撒是否导致减雨的判断有很大影响,例如,播撒剂量增加至1.2×10^(−2)g kg^(−1)后,超过50%的集合成员表现为减雨效果,但仍然有部分成员表现为增雨。针对这次过程,多格点催化试验表明增雨效果发生概率最优的区域位于浙西北和浙北北部区域,尤其在嘉兴东北部和临安附近,从概率预报的角度来说也往往对应着相对较高的平均过冷水含量和较低的冰晶数浓度均值。展开更多
The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air...The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.展开更多
基于多普勒天气雷达资料、ERA5再分析资料与地面自动站观测资料,利用WRF(Weather Research and Forecasting)模式、雷达径向风质控及三维变分同化系统(GSI)循环同化对2021年4月30日江苏南通的一次雷暴大风过程进行数值模拟研究,对比分...基于多普勒天气雷达资料、ERA5再分析资料与地面自动站观测资料,利用WRF(Weather Research and Forecasting)模式、雷达径向风质控及三维变分同化系统(GSI)循环同化对2021年4月30日江苏南通的一次雷暴大风过程进行数值模拟研究,对比分析不同试验方案模拟的雷达反射率、风场与热动力的时空演变和结构特征。结果表明:逐30 min循环同化的Exp3方案较未同化和非临近循环同化方案模拟效果有较好提升,表明循环同化和增加频次有效改善了初始场;UNRAVEL退模糊算法质控有效消除了速度模糊,退模糊处理雷达资料后循环同化方案(Exp4)对此次雷暴大风的雷达反射率和地面风场模拟结果有显著调整,其相应特征和演变趋势与观测基本一致,表明UNRAVEL退模糊质控后循环同化更好的改善了初始场;从热动力场结果来看,Exp4方案动热力结构改善较明显,上层辐散下层辐合,存在“冷—暖—冷”的热动力结构,伴随着强烈上升运动、北高南低的气压分布和强垂直风切变,有助于下沉气流将中高层的水平动量向近地面底层传递,从而激发此次雷暴大风。展开更多
The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud typ...The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.展开更多
Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the mos...Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.展开更多
Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variat...Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variations over the middle reaches of the Yangtze River basin.For all three types of terrain(i.e.,mountain,foothill,and plain),the amount of TR and SDHR both maximize in June/July,and the contribution of SDHR to TR(CST)peaks in August(amount:23%;frequency:1.74%).Foothill rainfall is characterized by a high TR amount and a high CST(in amount);mountain rainfall is characterized by a high TR frequency but a small CST(in amount);and plain rainfall shows a low TR amount and frequency,but a high CST(in amount).Overall,stations with high TR(amount and frequency)are mainly located over the mountains and in the foothills,while those with high SDHR(amount and frequency)are mainly concentrated in the foothills and plains close to mountainous areas.For all three types of terrain,the diurnal variations of both TR and SDHR exhibit a double peak(weak early morning and strong late afternoon)and a phase shift from the early-morning peak to the late-afternoon peak from May to August.Around the late-afternoon peak,the amount of TR and SDHR in the foothills is larger than over the mountains and plains.The TR intensity in the foothills increases significantly from midnight to afternoon,suggesting that thermal instability may play an important role in this process.展开更多
Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill ...Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.展开更多
Background:Background:Parotid gland neoplasms occur near the facial nerve.Hence,it is crucial to determine whether the malignant neoplasms involve the facial nerve and whether sacrifice of the nerve in surgery is nece...Background:Background:Parotid gland neoplasms occur near the facial nerve.Hence,it is crucial to determine whether the malignant neoplasms involve the facial nerve and whether sacrifice of the nerve in surgery is necessary.Furthermore,while 20%of all neoplasms are malignant,the most common benign neoplasm,pleomorphic adenoma,has a risk for malignant transformation,making early detection and treatment essential.7T magnetic resonance imaging offers increased signal-to-noise ratio(SNR)and sensitivity.Aim:In this work,we address imaging the parotid gland since it remains challenging at 7T because of its spatial location.Materials and Methods:Here,we present a novel three-dimensional surface coil(3D Coil)architecture that offers increased depth penetration and SNR compared to the single channel surface coil.We further developed a deep learning(DL)-based noise reduction method that receives inputs from three elements of the 3D Coil.Results:The 3D coil with DL-based denoising method offers twice the SNR compared to the single channel surface coil for parotid gland imaging at 7T.Discussion and Conclusion:The proposed 3D Coil and DL-based noise reduction method offers a promising way of achieving higher SNR for parotid salivary gland imaging at 7T,paving the road for clinical applications.展开更多
Tuberculosis is a dangerous disease to human life,and we need a lot of attempts to stop and reverse it.Significantly,in theCOVID-19 pandemic,access to medical services for tuberculosis has become very difficult.The la...Tuberculosis is a dangerous disease to human life,and we need a lot of attempts to stop and reverse it.Significantly,in theCOVID-19 pandemic,access to medical services for tuberculosis has become very difficult.The late detection of tuberculosis could lead to danger to patient health,even death.Vietnamis one of the countries heavily affected by the COVID-19 pandemic,andmany residential areas as well as hospitals have to be isolated for a long time.Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessingmedical services,such as an automatic tuberculosis diagnosis system.In our study,aiming to build that system,we were interested in the tuberculosis diagnosis problem from the chest X-ray images of Vietnamese patients.The chest X-ray image is an important data type to diagnose tuberculosis,and it has also received a lot of attention from deep learning researchers.This paper proposed a novel method for tuberculosis diagnosis and visualization using the deeplearning approach with a large Vietnamese X-ray image dataset.In detail,we designed our custom convolutional neural network for the X-ray image classification task and then analyzed the predicted result to provide visualization as a heat-map.To prove the performance of our network model,we conducted several experiments to compare it to another study and also to evaluate it with the dataset of this research.To support the implementation,we built a specific annotation system for tuberculosis under the requirements of radiologists in the Vietnam National Lung Hospital.A large experiment dataset was also from this hospital,and most of this data was for training the convolutional neural network model.The experiment results were evaluated regarding sensitivity,specificity,and accuracy.We achieved high scores with a training accuracy score of 0.99,and the testing specificity and sensitivity scores were over 0.9.Based on the X-ray image classification result,we visualize prediction results as heat-maps and also analyze them in comparison with annotated symptoms of radiologists.展开更多
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22_1136)the National Natural Scientific Foundation of China(No.42275037)+2 种基金the Basic Research Fund of CAMS(No.2023Z016)the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202202)supported by the Jiangsu Collaborative Innovation Center for Climate Change。
文摘At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.
文摘Objective:To evaluate how DNA fragmentation index(DFI)and chromatin denaturation index(CDI)relate to semen parameters across different types of male infertility,thereby improving the understanding and assessment of sperm quality.Methods:A prospective and descriptive cohort study was conducted over two years at the Integrated Physiology Laboratory of the University of Carthage in collaboration with the Alyssa Fertility Group,Tunisia.A total of 163 participants were classified into five groups based on their semen parameters:normozoospermia,oligozoospermia,asthenozoospermia,teratozoospermia,and oligoastheno-teratozoospermia.The normozoospermia group was selected from volunteers who had children.Semen samples were analyzed according to WHO guidelines.DFI was measured using Halosperm®and CDI was tested using aniline blue staining.Results:Both DFI and CDI were significantly higher in all infertility groups,with the oligozoospermia group showing the highest DFI and CDI.Negative correlations were found between DFI/CDI and sperm motility,concentration,and morphology in the affected groups.The normozoospermia group served as a control with the lowest DFI and CDI values.Conclusions:DFI and CDI are increasingly recognized as important biomarkers for evaluating sperm quality in cases of male infertility.Their elevated levels in patients with oligozoospermia,asthenozoospermia,teratozoospermia,and oligo-asthenoteratozoospermia underscore their potential role in not only diagnosing male infertility but also in assessing the overall reproductive outcomes for affected individuals,thus guiding more effective treatment strategies.
基金the National Natural Science Foundation of China (Grant Nos.42175142,42141017 and 41975112) for supporting our study。
文摘The increasing concentration of atmospheric CO_(2) since the Industrial Revolution has affected surface air temperature.However,the impact of the spatial distribution of atmospheric CO_(2) concentration on surface air temperature biases remains highly unclear.By incorporating the spatial distribution of satellite-derived atmospheric CO_(2) concentration in the Beijing Normal University Earth System Model,this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere(NH) under historical conditions from 1976-2005.In comparison with the increase in surface temperature simulated using a uniform distribution of CO_(2),simulation with a nonuniform distribution of CO_(2)produced better agreement with the Climatic Research Unit(CRU) data in the NH under the historical condition relative to the baseline over the period 1901-30.Hemispheric June-July-August(JJA) surface air temperature increased by 1.28℃ ±0.29℃ in simulations with a uniform distribution of CO_(2),by 1.00℃±0.24℃ in simulations with a non-uniform distribution of CO_(2),and by 0.24℃ in the CRU data.The decrease in downward shortwave radiation in the non-uniform CO_(2) simulation was primarily attributable to reduced warming in Eurasia,combined with feedbacks resulting from increased leaf area index(LAI) and latent heat fluxes.These effects were more pronounced in the non-uniform CO_(2)simulation compared to the uniform CO_(2) simulation.Results indicate that consideration of the spatial distribution of CO_(2)concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.
文摘基于多普勒天气雷达资料、ERA5再分析资料与地面自动站观测资料,利用WRF(Weather Research and Forecasting)模式、雷达径向风质控及三维变分同化系统(GSI)循环同化对2021年4月30日江苏南通的一次雷暴大风过程进行数值模拟研究,对比分析不同试验方案模拟的雷达反射率、风场与热动力的时空演变和结构特征。结果表明:逐30 min循环同化的Exp3方案较未同化和非临近循环同化方案模拟效果有较好提升,表明循环同化和增加频次有效改善了初始场;UNRAVEL退模糊算法质控有效消除了速度模糊,退模糊处理雷达资料后循环同化方案(Exp4)对此次雷暴大风的雷达反射率和地面风场模拟结果有显著调整,其相应特征和演变趋势与观测基本一致,表明UNRAVEL退模糊质控后循环同化更好的改善了初始场;从热动力场结果来看,Exp4方案动热力结构改善较明显,上层辐散下层辐合,存在“冷—暖—冷”的热动力结构,伴随着强烈上升运动、北高南低的气压分布和强垂直风切变,有助于下沉气流将中高层的水平动量向近地面底层传递,从而激发此次雷暴大风。
基金supported in part by the National Natural Science Foundation of China (Grant No. 42105127)the Special Research Assistant Project of the Chinese Academy of Sciencesthe National Key Research and Development Plans of China (Grant Nos. 2019YFC1510304 and 2016YFE0201900-02)。
文摘The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types(high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases(ice,mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows:(1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents.(2)The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer;altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer;stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn.(3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly.(4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas.(5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated.(6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds.
基金Key R&D Program of Xizang Autonomous Region(XZ202101ZY0004G)National Natural Science Foundation of China(U2142202)+1 种基金National Key R&D Program of China(2022YFC3004104)Key Innovation Team of China Meteor-ological Administration(CMA2022ZD07)。
文摘Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.
基金supported by the National Natural Science Foundation of China(Grant Nos. U2142202, 41975056, 42230612, and 41975058)Youth Innovation Promotion Association,Chinese Academy of Sciencesthe National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility”(EarthLab)
文摘Based on hourly rain gauge data during May–September of 2016–20,we analyze the spatiotemporal distributions of total rainfall(TR)and short-duration heavy rainfall(SDHR;hourly rainfall≥20 mm)and their diurnal variations over the middle reaches of the Yangtze River basin.For all three types of terrain(i.e.,mountain,foothill,and plain),the amount of TR and SDHR both maximize in June/July,and the contribution of SDHR to TR(CST)peaks in August(amount:23%;frequency:1.74%).Foothill rainfall is characterized by a high TR amount and a high CST(in amount);mountain rainfall is characterized by a high TR frequency but a small CST(in amount);and plain rainfall shows a low TR amount and frequency,but a high CST(in amount).Overall,stations with high TR(amount and frequency)are mainly located over the mountains and in the foothills,while those with high SDHR(amount and frequency)are mainly concentrated in the foothills and plains close to mountainous areas.For all three types of terrain,the diurnal variations of both TR and SDHR exhibit a double peak(weak early morning and strong late afternoon)and a phase shift from the early-morning peak to the late-afternoon peak from May to August.Around the late-afternoon peak,the amount of TR and SDHR in the foothills is larger than over the mountains and plains.The TR intensity in the foothills increases significantly from midnight to afternoon,suggesting that thermal instability may play an important role in this process.
基金sponsored by the Institute of Information Technology(Vietnam Academy of Science and Technology)with Project Code“CS24.01”.
文摘Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.
基金National Institute of Health,Grant/Award Number:R01‐EB032169。
文摘Background:Background:Parotid gland neoplasms occur near the facial nerve.Hence,it is crucial to determine whether the malignant neoplasms involve the facial nerve and whether sacrifice of the nerve in surgery is necessary.Furthermore,while 20%of all neoplasms are malignant,the most common benign neoplasm,pleomorphic adenoma,has a risk for malignant transformation,making early detection and treatment essential.7T magnetic resonance imaging offers increased signal-to-noise ratio(SNR)and sensitivity.Aim:In this work,we address imaging the parotid gland since it remains challenging at 7T because of its spatial location.Materials and Methods:Here,we present a novel three-dimensional surface coil(3D Coil)architecture that offers increased depth penetration and SNR compared to the single channel surface coil.We further developed a deep learning(DL)-based noise reduction method that receives inputs from three elements of the 3D Coil.Results:The 3D coil with DL-based denoising method offers twice the SNR compared to the single channel surface coil for parotid gland imaging at 7T.Discussion and Conclusion:The proposed 3D Coil and DL-based noise reduction method offers a promising way of achieving higher SNR for parotid salivary gland imaging at 7T,paving the road for clinical applications.
基金funded by the Project KC-4.0.14/19-25“Research on Building a Support System for Diagnosis and Prediction Geo-Spatial Epidemiology of Pulmonary Tuberculosis by Chest X-Ray Images in Vietnam”.
文摘Tuberculosis is a dangerous disease to human life,and we need a lot of attempts to stop and reverse it.Significantly,in theCOVID-19 pandemic,access to medical services for tuberculosis has become very difficult.The late detection of tuberculosis could lead to danger to patient health,even death.Vietnamis one of the countries heavily affected by the COVID-19 pandemic,andmany residential areas as well as hospitals have to be isolated for a long time.Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessingmedical services,such as an automatic tuberculosis diagnosis system.In our study,aiming to build that system,we were interested in the tuberculosis diagnosis problem from the chest X-ray images of Vietnamese patients.The chest X-ray image is an important data type to diagnose tuberculosis,and it has also received a lot of attention from deep learning researchers.This paper proposed a novel method for tuberculosis diagnosis and visualization using the deeplearning approach with a large Vietnamese X-ray image dataset.In detail,we designed our custom convolutional neural network for the X-ray image classification task and then analyzed the predicted result to provide visualization as a heat-map.To prove the performance of our network model,we conducted several experiments to compare it to another study and also to evaluate it with the dataset of this research.To support the implementation,we built a specific annotation system for tuberculosis under the requirements of radiologists in the Vietnam National Lung Hospital.A large experiment dataset was also from this hospital,and most of this data was for training the convolutional neural network model.The experiment results were evaluated regarding sensitivity,specificity,and accuracy.We achieved high scores with a training accuracy score of 0.99,and the testing specificity and sensitivity scores were over 0.9.Based on the X-ray image classification result,we visualize prediction results as heat-maps and also analyze them in comparison with annotated symptoms of radiologists.
