The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). I...The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.展开更多
This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart ...This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.展开更多
BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HC...BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HCV is nearly 100%.AIM To analyze the United Network for Organ Sharing(UNOS)database to compare the survival rates between the hepatitis C positive donors and negative recipients and hepatitis C negative donors and recipients.METHODS We analyzed the adult patients in UNOS database who underwent deceased donor liver transplant from January 2014 to December 2017.The primary endpoint was to compare the survival rates among the four groups with different hepatitis C donor and recipient status:(Group 1)Both donor and recipient negative for HCV(Group 2)Negative donor and positive recipient for HCV(Group 3)Positive donor and negative recipient for HCV(Group 4)Both positive donor and recipient for HCV.SAS 9.4 software was used for the data analysis.Kaplan Meier log rank test was used to analyze the estimated survival rates among the four groups.RESULTS A total of 24512 patients were included:Group 1:16436,Group 2:6174,Group 3:253 and Group 4:1649.The 1-year(Group 1:91.8%,Group 2:92.12%,Group 3:87%,Group 4:92.8%),2-year(Group 1:88.4%,Group 2:88.1%,Group 3:84.3%,Group 4:87.5%),3-year(Group 1:84.9%,Group 2:84.3%,Group 3:75.9%,Group 4:83.2%)survival rates showed no statistical significance among the four groups.Kaplan Meier log rank test did not show any statistical significance difference in the estimated survival rates between Group 3 vs all the other groups.CONCLUSION The survival rates in hepatitis C positive donors and negative recipients are similar as compared to both hepatitis C negative donors and recipients.This could be due to the use of DAA therapy with cure rates of nearly 100%.This study supports the use of hepatitis C positive organs in the selected group of recipients with and without HCV infection.Further long-term studies are needed to further validate these findings.展开更多
The interpenetrating network structure provides an interesting avenue to novel materials. Locally resonant phononic crystal (LRPC) exhibits excellent sound attenuation performance based on the periodical arrangement...The interpenetrating network structure provides an interesting avenue to novel materials. Locally resonant phononic crystal (LRPC) exhibits excellent sound attenuation performance based on the periodical arrangement of sound wave scatters. Combining the LRPC concept and interpenetrating network glassy structure, this paper has developed a new material which can achieve a wide band underwater strong acoustic absorption. Underwater absorption coefficients of different samples were measured by the pulse tube. Measurement results show that the new material possesses excellent underwater acoustic effects in a wide frequency range.Moreover, in order to investigate impacts of locally resonant units,some defects are introduced into the sample. The experimental result and the theoretical calculation both show that locally resonant units being connected to a network structure play an important role in achieving a wide band strong acoustic absorption.展开更多
An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated rec...An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.展开更多
With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performa...With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performance, low cost, good connectivity, etc. However the security issue has been complicated because USN responds to block I/O and file I/O requests simultaneously. In this paper, a security system module is developed to prevent many types of attacks against USN based on NAS head. The module not only uses effective authentication to prevent unauthorized access to the system data, but also checks the data integrity. Experimental results show that the security module can not only resist remote attacks and attacks from those who has physical access to the USN, but can also be seamlessly integrated into underlying file systems, with little influence on their performance.展开更多
A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). ...A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.展开更多
BACKGROUND Early in the coronavirus disease 2019(COVID-19)pandemic,there was a significant impact on routine medical care in the United States,including in fields of transplantation and oncology.AIM To analyze the imp...BACKGROUND Early in the coronavirus disease 2019(COVID-19)pandemic,there was a significant impact on routine medical care in the United States,including in fields of transplantation and oncology.AIM To analyze the impact and outcomes of early COVID-19 pandemic on liver transplantation(LT)for hepatocellular carcinoma(HCC)in the United States.METHODS WHO declared COVID-19 as a pandemic on March 11,2020.We retrospectively analyzed data from the United Network for Organ Sharing(UNOS)database regarding adult LT with confirmed HCC on explant in 2019 and 2020.We defined pre-COVID period from March 11 to September 11,2019,and early-COVID period as from March 11 to September 11,2020.RESULTS Overall,23.5%fewer LT for HCC were performed during the COVID period(518 vs 675,P<0.05).This decrease was most pronounced in the months of March-April 2020 with a rebound in numbers seen from May-July 2020.Among LT recipients for HCC,concurrent diagnosis of non-alcoholic steatohepatitis significantly increased(23 vs 16%)and alcoholic liver disease(ALD)significantly decreased(18 vs 22%)during the COVID period.Recipient age,gender,BMI,and MELD score were statistically similar between two groups,while waiting list time decreased during the COVID period(279 days vs 300 days,P=0.041).Among pathological characteristics of HCC,vascular invasion was more prominent during COVID period(P<0.01),while other features were the same.While the donor age and other characteristics remained same,the distance between donor and recipient hospitals was significantly increased(P<0.01)and donor risk index was significantly higher(1.68 vs 1.59,P<0.01)during COVID period.Among outcomes,90-day overall and graft survival were the same,but 180-day overall and graft were significantly inferior during COVID period(94.7 vs 97.0%,P=0.048).On multivariable Coxhazard regression analysis,COVID period emerged as a significant risk factor of post-transplant mortality(Hazard ratio 1.85;95%CI:1.28-2.68,P=0.001).CONCLUSION During COVID period,there was a significant decrease in LTs performed for HCC.While early postoperative outcomes of LT for HCC were same,the overall and graft survival of LTs for HCC after 180 days were significantly inferior.展开更多
Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improv...Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.展开更多
In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simulta...In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.展开更多
Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, p...Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.展开更多
针对实际应用中基于动态工况下电池状态参数的片段数据进行电池健康状态(state of health,SOH)实时估计的问题,提出基于动态工况下锂离子电池状态参数(电压、电流、温度)实测数据二维特征图像和深度学习的锂离子电池容量估计算法。首先...针对实际应用中基于动态工况下电池状态参数的片段数据进行电池健康状态(state of health,SOH)实时估计的问题,提出基于动态工况下锂离子电池状态参数(电压、电流、温度)实测数据二维特征图像和深度学习的锂离子电池容量估计算法。首先,将动态工况下电池状态参数监测量(电压、电流和温度)的片段数据转化为二维特征图像。其次,提出基于残差卷积神经网络(residual convolutional neural network,Res-CNN)和门控循环单元(gate recurrent unit,GRU)网络结合的多通道深度学习模型Res-CNN-GRU,以构建动态工况下电池状态参数特征图像和SOH之间的复杂非线性关系,其中电压、电流和温度的二维特征图像以三通道的方式输入到Res-CNN-GRU模型中,模型输出为对应电池的相邻参考充放电循环实验所获得容量的差值。研究结果表明:此方法在锂电池随机充放电工况下对电池健康状态估计效果更佳,且Res-CNN-GRU模型的泛化性和全局特征提取能力较强。论文研究为现实工况下电池健康状态估计的进一步深入研究提供了参考。展开更多
基金jointly supported by the National Science Foundation of China (Grant Nos. 42275007 and 41865003)Jiangxi Provincial Department of science and technology project (Grant No. 20171BBG70004)。
文摘The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.
基金support from the National Science and Technology Council of Taiwan(Contract Nos.111-2221 E-011081 and 111-2622-E-011019)the support from Intelligent Manufacturing Innovation Center(IMIC),National Taiwan University of Science and Technology(NTUST),Taipei,Taiwan,which is a Featured Areas Research Center in Higher Education Sprout Project of Ministry of Education(MOE),Taiwan(since 2023)was appreciatedWe also thank Wang Jhan Yang Charitable Trust Fund(Contract No.WJY 2020-HR-01)for its financial support.
文摘This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.
文摘BACKGROUND The utility of hepatitis C virus(HCV)organs has increased after the Food and Drug Administration approval of direct acting anti-viral(DAA)medications for the HCV treatment.The efficacy of DAA in treating HCV is nearly 100%.AIM To analyze the United Network for Organ Sharing(UNOS)database to compare the survival rates between the hepatitis C positive donors and negative recipients and hepatitis C negative donors and recipients.METHODS We analyzed the adult patients in UNOS database who underwent deceased donor liver transplant from January 2014 to December 2017.The primary endpoint was to compare the survival rates among the four groups with different hepatitis C donor and recipient status:(Group 1)Both donor and recipient negative for HCV(Group 2)Negative donor and positive recipient for HCV(Group 3)Positive donor and negative recipient for HCV(Group 4)Both positive donor and recipient for HCV.SAS 9.4 software was used for the data analysis.Kaplan Meier log rank test was used to analyze the estimated survival rates among the four groups.RESULTS A total of 24512 patients were included:Group 1:16436,Group 2:6174,Group 3:253 and Group 4:1649.The 1-year(Group 1:91.8%,Group 2:92.12%,Group 3:87%,Group 4:92.8%),2-year(Group 1:88.4%,Group 2:88.1%,Group 3:84.3%,Group 4:87.5%),3-year(Group 1:84.9%,Group 2:84.3%,Group 3:75.9%,Group 4:83.2%)survival rates showed no statistical significance among the four groups.Kaplan Meier log rank test did not show any statistical significance difference in the estimated survival rates between Group 3 vs all the other groups.CONCLUSION The survival rates in hepatitis C positive donors and negative recipients are similar as compared to both hepatitis C negative donors and recipients.This could be due to the use of DAA therapy with cure rates of nearly 100%.This study supports the use of hepatitis C positive organs in the selected group of recipients with and without HCV infection.Further long-term studies are needed to further validate these findings.
基金supported by the National Natural Science Foundation of China (Grant No. 10832011)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KJCX2-YW-L08)
文摘The interpenetrating network structure provides an interesting avenue to novel materials. Locally resonant phononic crystal (LRPC) exhibits excellent sound attenuation performance based on the periodical arrangement of sound wave scatters. Combining the LRPC concept and interpenetrating network glassy structure, this paper has developed a new material which can achieve a wide band underwater strong acoustic absorption. Underwater absorption coefficients of different samples were measured by the pulse tube. Measurement results show that the new material possesses excellent underwater acoustic effects in a wide frequency range.Moreover, in order to investigate impacts of locally resonant units,some defects are introduced into the sample. The experimental result and the theoretical calculation both show that locally resonant units being connected to a network structure play an important role in achieving a wide band strong acoustic absorption.
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.
文摘With development of networked storage and its applications, united storage network (USN) combined with network attached storage (NAS) and storage area network (SAN) has emerged. It has such advantages as high performance, low cost, good connectivity, etc. However the security issue has been complicated because USN responds to block I/O and file I/O requests simultaneously. In this paper, a security system module is developed to prevent many types of attacks against USN based on NAS head. The module not only uses effective authentication to prevent unauthorized access to the system data, but also checks the data integrity. Experimental results show that the security module can not only resist remote attacks and attacks from those who has physical access to the USN, but can also be seamlessly integrated into underlying file systems, with little influence on their performance.
文摘A multi-user view file system (MUVFS) and a security scheme are developed to improve the security of the united storage network (USN) that integrates a network attached storage (NAS) and a storage area network (SAN). The MUVFS offers a storage volume view for each authorized user who can access only the data in his own storage volume, the security scheme enables all users to encrypt and decrypt the data of their own storage view at client-side, and the USN server needs only to check the users’ identities and the data’s integrity. Experiments were performed to compare the sequential read, write and read/write rates of NFS+MUVFS+secure_module with those of NFS. The results indicate that the security of the USN is improved greatly with little influence on the system performance when the MUVFS and the security scheme are integrated into it.
文摘BACKGROUND Early in the coronavirus disease 2019(COVID-19)pandemic,there was a significant impact on routine medical care in the United States,including in fields of transplantation and oncology.AIM To analyze the impact and outcomes of early COVID-19 pandemic on liver transplantation(LT)for hepatocellular carcinoma(HCC)in the United States.METHODS WHO declared COVID-19 as a pandemic on March 11,2020.We retrospectively analyzed data from the United Network for Organ Sharing(UNOS)database regarding adult LT with confirmed HCC on explant in 2019 and 2020.We defined pre-COVID period from March 11 to September 11,2019,and early-COVID period as from March 11 to September 11,2020.RESULTS Overall,23.5%fewer LT for HCC were performed during the COVID period(518 vs 675,P<0.05).This decrease was most pronounced in the months of March-April 2020 with a rebound in numbers seen from May-July 2020.Among LT recipients for HCC,concurrent diagnosis of non-alcoholic steatohepatitis significantly increased(23 vs 16%)and alcoholic liver disease(ALD)significantly decreased(18 vs 22%)during the COVID period.Recipient age,gender,BMI,and MELD score were statistically similar between two groups,while waiting list time decreased during the COVID period(279 days vs 300 days,P=0.041).Among pathological characteristics of HCC,vascular invasion was more prominent during COVID period(P<0.01),while other features were the same.While the donor age and other characteristics remained same,the distance between donor and recipient hospitals was significantly increased(P<0.01)and donor risk index was significantly higher(1.68 vs 1.59,P<0.01)during COVID period.Among outcomes,90-day overall and graft survival were the same,but 180-day overall and graft were significantly inferior during COVID period(94.7 vs 97.0%,P=0.048).On multivariable Coxhazard regression analysis,COVID period emerged as a significant risk factor of post-transplant mortality(Hazard ratio 1.85;95%CI:1.28-2.68,P=0.001).CONCLUSION During COVID period,there was a significant decrease in LTs performed for HCC.While early postoperative outcomes of LT for HCC were same,the overall and graft survival of LTs for HCC after 180 days were significantly inferior.
基金This work is supported by Nanjing Institute of Technology(NIT)fund for Research Startup Projects of Introduced talents under Grant No.YKJ202019Nature Sci-ence Research Project of Higher Education Institutions in Jiangsu Province under Grant No.21KJB510018+1 种基金National Nature Science Foundation of China(NSFC)under Grant No.62001215NIT fund for Doctoral Research Projects under Grant No.ZKJ2020003.
文摘Speech separation is an active research topic that plays an important role in numerous applications,such as speaker recognition,hearing pros-thesis,and autonomous robots.Many algorithms have been put forward to improve separation performance.However,speech separation in reverberant noisy environment is still a challenging task.To address this,a novel speech separation algorithm using gate recurrent unit(GRU)network based on microphone array has been proposed in this paper.The main aim of the proposed algorithm is to improve the separation performance and reduce the computational cost.The proposed algorithm extracts the sub-band steered response power-phase transform(SRP-PHAT)weighted by gammatone filter as the speech separation feature due to its discriminative and robust spatial position in formation.Since the GRU net work has the advantage of processing time series data with faster training speed and fewer training parameters,the GRU model is adopted to process the separation featuresof several sequential frames in the same sub-band to estimate the ideal Ratio Masking(IRM).The proposed algorithm decomposes the mixture signals into time-frequency(TF)units using gammatone filter bank in the frequency domain,and the target speech is reconstructed in the frequency domain by masking the mixture signal according to the estimated IRM.The operations of decomposing the mixture signal and reconstructing the target signal are completed in the frequency domain which can reduce the total computational cost.Experimental results demonstrate that the proposed algorithm realizes omnidirectional speech sep-aration in noisy and reverberant environments,provides good performance in terms of speech quality and intelligibility,and has the generalization capacity to reverberate.
文摘In this paper, a network-based monitoring unit for condition monitoring andfault diagnosis of rotating machinery is designed and implemented. With the technology of DSP(Digital signal processing) , TCP/IP, and simultaneous acquisition, a mechanism of multi-process andinter-process communication, the integrating problem of signal acquisition, the data dynamicmanagement and network-based configuration in the embedded condition monitoring system is solved. Itoffers the input function of monitoring information for network-based condition monitoring and afault diagnosis system.
文摘Being familiar with characteristics of industrial town effluents from various wastewater treatment units, which have high qualitative and quantitative variations and more uncertainties compared to urban wastewaters, plays very effective role in governing them. With regard to environmental issues, proper operation of wastewater treatment plants is of par- ticular importance that in the case of inappropriate utilization, they will cause serious problems. Processes that exist in environmental systems mostly have two major characteristics: they are dependent on many variables;and there are complex relationships between its components which make them very difficult to analyze. In order to achieve a better and efficient control over the operation of an industrial wastewater treatment plant (WWTP), powerful mathematical tool can be used that is based on recorded data from some basic parameters of wastewater during a period of treatment plant operation. In this study, the treatment plant was divided into two main subsystems including: Low TDS (Total Dissolved Solids) treatment unit and Biological unit (extended aeration). The multilayer perceptron feed forward neural network with a hidden layer and stop training method was used to predict quality parameters of the industrial effluent. Data of this study are related to the Fajr Industrial Wastewater Treatment Plant, located in Mahshahr—Iran that qualita- tive and quantitative characteristics of its units were used for training, calibration and validation of the neural model. Also, Principal Component Analysis (PCA) technique was applied to improve performance of generated models of neural networks. The results of L-TDS unit showed good accuracy of the models in estimating qualitative profile of wastewater but results of biological unit did not have sufficient accuracy to being used. This model facilitates evaluating the performance of each treatment plant units through comparing the results of prediction model with the standard amount of outputs.