Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies a...Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations.However,statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering.In contrast,data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data.Herein,a method for identifying adverse geology using machine learning of geochemical anomalies is proposed.The method was identified geochemical anomalies in tunnel that were not identified by statistical methods.We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination(AEC).Using the AEC sample data,we trained an isolation forest model to identify the multi-element anomalies,successfully.We analyzed the adverse geological features based the multi-element anomalies.This study,therefore,extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis.Correspondingly,the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.展开更多
Dear Editor,The coronavirus disease 2019(COVID-19)outbreak,has spread across the world(Wu et al.,2020).The causative agent of COVID-19,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is highly pathogenic a...Dear Editor,The coronavirus disease 2019(COVID-19)outbreak,has spread across the world(Wu et al.,2020).The causative agent of COVID-19,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is highly pathogenic and infectious,which become a major public health hazard that has had a devastating social and economic impact worldwide(Li Q.Q.et al.,2020).Variants of the virus have emerged that behave differently(CDC2021;Gobeil et al.,2020;Leung et al.,2021).Some of them show increased infectivity(Li Q.et al.,2020;Zhang et al.,2020)and may escape from neutralizing antibodies(Weisblum et al.,2020).展开更多
The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels.Geochemical indicators of rocks can be used to identi...The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels.Geochemical indicators of rocks can be used to identify adverse geology and to explain the intrinsic causes of damage to normal rocks.This study proposes a method to identify adverse geology by extracting and imaging the indicator elements.The mapping relationship between rock components and geologic bodies is quickly determined by indicator element extraction based on factor analysis,and then the data are gridded for image output.The location and size of the target adverse geology are visually identified through the distribution images of the indicator elements,thus reducing data dimensions and analysis time.A non-destructive,in-situ and fast element detection technique in tunnels was adopted to speed up the process of geology identification.The accuracy of the detection was validated by comparing field and laboratory test results.This study further confirms and refines the previous research,and the results provide references for geological,mining and underground projects.展开更多
BACKGROUND： Many international studies have shown that plasminogen activator inhibitor-1 （PAl-l） 4G/5G promoter polymorphism does not increase the risk for cerebral infarction. OBJECTIVE： Using PCR methodology and...BACKGROUND： Many international studies have shown that plasminogen activator inhibitor-1 （PAl-l） 4G/5G promoter polymorphism does not increase the risk for cerebral infarction. OBJECTIVE： Using PCR methodology and agarose electrophoresis to detect PAI-1 4G/5G promoter polymorphism in patients with recurrent cerebral infarction in the North Jiangsu Province of China, and to compare results with healthy subjects and patients with first-occurrence cerebral infarction in the same region. DESIGN, TIME AND SETTING： Non-randomized, concurrent, control trial. A total of 122 cerebral infarction patients were admitted to Xuzhou Medical College Hospital＇s Department of Neurology and Xuzhou Central Hospital＇s Department of Neurology between July 2003 and August 2006. PARTICIPANTS： The patients consisted of 63 males and 59 females, aged （62 ± 10） years. They were divided into first-occurrence （n = 58） and recurrence （n = 64） groups. In addition, 50 healthy subjects that underwent physical examination in the outpatient department, including 26 males and 24 females, aged （60 ±12） years, were selected as controls. METHODS AND MAIN OUTCOME MEASURES： PAl-1 4G/5G promoter polymorphism was detected and analyzed using PCR methodology and agarose electrophoresis. RESULTS： Significant differences were determined in terms of genotypic frequency and allele frequency of PAI-1 4G/5G promoter polymorphism, in patients with first-occurrence or recurrent cerebral infarction, when compared with healthy subjects （P 〈 0.05）. There was, however, no significant difference between the first-occurrence and recurrence groups （P 〉 0.05）. CONCLUSION： PAl- 1 4G/5G promoter polymorphism is genetic risk factor for cerebral infarction in China. However, it may be associated with recurrence of cerebral infarction in patients from the North Jiangsu Province of China.展开更多
The interfacial wear between silicon and amorphous silica in water environment is critical in numerous applications.However,the understanding regarding the micro dynamic process is still unclear due to the limitations...The interfacial wear between silicon and amorphous silica in water environment is critical in numerous applications.However,the understanding regarding the micro dynamic process is still unclear due to the limitations of apparatus.Herein,reactive force field simulations are utilized to study the interfacial process between silicon and amorphous silica in water environment,exploring the removal and damage mechanism caused by pressure,velocity,and humidity.Moreover,the reasons for high removal rate under high pressure and high velocity are elucidated from an atomic perspective.Simulation results show that the substrate is highly passivated under high humidity,and the passivation layer could alleviate the contact between the abrasive and the substrate,thus reducing the damage and wear.In addition to more Si-O-Si bridge bonds formed between the abrasive and the substrate,new removal pathways such as multibridge bonds and chain removal appear under high pressure,which cause higher removal rate and severer damage.At a higher velocity,the abrasive can induce extended tribochemical reactions and form more interfacial Si-O-Si bridge bonds,hence increasing removal rate.These results reveal the internal cause of the discrepancy in damage and removal rate under different conditions from an atomic level.展开更多
基金the support from the National Natural Science Foundation of China(Nos.52279103,52379103)the Natural Science Foundation of Shandong Province(No.ZR2023YQ049)。
文摘Geological analysis,despite being a long-term method for identifying adverse geology in tunnels,has significant limitations due to its reliance on empirical analysis.The quantitative aspects of geochemical anomalies associated with adverse geology provide a novel strategy for addressing these limitations.However,statistical methods for identifying geochemical anomalies are insufficient for tunnel engineering.In contrast,data mining techniques such as machine learning have demonstrated greater efficacy when applied to geological data.Herein,a method for identifying adverse geology using machine learning of geochemical anomalies is proposed.The method was identified geochemical anomalies in tunnel that were not identified by statistical methods.We by employing robust factor analysis and self-organizing maps to reduce the dimensionality of geochemical data and extract the anomaly elements combination(AEC).Using the AEC sample data,we trained an isolation forest model to identify the multi-element anomalies,successfully.We analyzed the adverse geological features based the multi-element anomalies.This study,therefore,extends the traditional approach of geological analysis in tunnels and demonstrates that machine learning is an effective tool for intelligent geological analysis.Correspondingly,the research offers new insights regarding the adverse geology and the prevention of hazards during the construction of tunnels and underground engineering projects.
基金supported by the Zhejiang Provincial Key Research and Development Program(#2021C03043 and#2021C03039)。
文摘Dear Editor,The coronavirus disease 2019(COVID-19)outbreak,has spread across the world(Wu et al.,2020).The causative agent of COVID-19,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is highly pathogenic and infectious,which become a major public health hazard that has had a devastating social and economic impact worldwide(Li Q.Q.et al.,2020).Variants of the virus have emerged that behave differently(CDC2021;Gobeil et al.,2020;Leung et al.,2021).Some of them show increased infectivity(Li Q.et al.,2020;Zhang et al.,2020)and may escape from neutralizing antibodies(Weisblum et al.,2020).
基金This research was supported by the National Natural Science Foundation of China(Nos.52022053 and 52279103)the Natural Science Foundation of Shandong Province,China(Nos.ZR201910270116 and ZR2023YQ049).
文摘The lag in quantitative methods and detection techniques for geologic information has resulted in time-consuming and human-experienced geologic analysis in tunnels.Geochemical indicators of rocks can be used to identify adverse geology and to explain the intrinsic causes of damage to normal rocks.This study proposes a method to identify adverse geology by extracting and imaging the indicator elements.The mapping relationship between rock components and geologic bodies is quickly determined by indicator element extraction based on factor analysis,and then the data are gridded for image output.The location and size of the target adverse geology are visually identified through the distribution images of the indicator elements,thus reducing data dimensions and analysis time.A non-destructive,in-situ and fast element detection technique in tunnels was adopted to speed up the process of geology identification.The accuracy of the detection was validated by comparing field and laboratory test results.This study further confirms and refines the previous research,and the results provide references for geological,mining and underground projects.
基金the Xuzhou Social Development Foundation of Jiangsu Province, No. 2006046
文摘BACKGROUND： Many international studies have shown that plasminogen activator inhibitor-1 （PAl-l） 4G/5G promoter polymorphism does not increase the risk for cerebral infarction. OBJECTIVE： Using PCR methodology and agarose electrophoresis to detect PAI-1 4G/5G promoter polymorphism in patients with recurrent cerebral infarction in the North Jiangsu Province of China, and to compare results with healthy subjects and patients with first-occurrence cerebral infarction in the same region. DESIGN, TIME AND SETTING： Non-randomized, concurrent, control trial. A total of 122 cerebral infarction patients were admitted to Xuzhou Medical College Hospital＇s Department of Neurology and Xuzhou Central Hospital＇s Department of Neurology between July 2003 and August 2006. PARTICIPANTS： The patients consisted of 63 males and 59 females, aged （62 ± 10） years. They were divided into first-occurrence （n = 58） and recurrence （n = 64） groups. In addition, 50 healthy subjects that underwent physical examination in the outpatient department, including 26 males and 24 females, aged （60 ±12） years, were selected as controls. METHODS AND MAIN OUTCOME MEASURES： PAl-1 4G/5G promoter polymorphism was detected and analyzed using PCR methodology and agarose electrophoresis. RESULTS： Significant differences were determined in terms of genotypic frequency and allele frequency of PAI-1 4G/5G promoter polymorphism, in patients with first-occurrence or recurrent cerebral infarction, when compared with healthy subjects （P 〈 0.05）. There was, however, no significant difference between the first-occurrence and recurrence groups （P 〉 0.05）. CONCLUSION： PAl- 1 4G/5G promoter polymorphism is genetic risk factor for cerebral infarction in China. However, it may be associated with recurrence of cerebral infarction in patients from the North Jiangsu Province of China.
基金The authors greatly appreciate the National Major Science and Technology Projects of China(Grant No.51991372)the Natural Science Foundation of Liaoning Province,China(Grant No.2020-MS-120).
文摘The interfacial wear between silicon and amorphous silica in water environment is critical in numerous applications.However,the understanding regarding the micro dynamic process is still unclear due to the limitations of apparatus.Herein,reactive force field simulations are utilized to study the interfacial process between silicon and amorphous silica in water environment,exploring the removal and damage mechanism caused by pressure,velocity,and humidity.Moreover,the reasons for high removal rate under high pressure and high velocity are elucidated from an atomic perspective.Simulation results show that the substrate is highly passivated under high humidity,and the passivation layer could alleviate the contact between the abrasive and the substrate,thus reducing the damage and wear.In addition to more Si-O-Si bridge bonds formed between the abrasive and the substrate,new removal pathways such as multibridge bonds and chain removal appear under high pressure,which cause higher removal rate and severer damage.At a higher velocity,the abrasive can induce extended tribochemical reactions and form more interfacial Si-O-Si bridge bonds,hence increasing removal rate.These results reveal the internal cause of the discrepancy in damage and removal rate under different conditions from an atomic level.
