Currently,the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes.This is followed by the process of differencing the average GPS coordina...Currently,the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes.This is followed by the process of differencing the average GPS coordinate time series data,with a time interval of 3 to 5 days before and after the earthquake.In the face of the huge amount of GPS coordinate time series data today,the conventional approach of relying on earthquake catalog data to assist in obtaining coseismic offset signals has become increasingly burdensome.To address this problem,we propose a new method for automatically detecting coseismic offset signals in GPS coordinate time series without an extra earthquake catalog for reference.Firstly,we pre-process the GPS coordinate time series data for filtering out stations with significant observations missing and detecting and removing outliers.Secondly,we eliminate other signals and errors in the GPS coordinate time series,such as trend and seasonal signals,leaving the coseismic offset signals as the primary signal.The resulting coordinate time series is then modeled using the first-order difference and data stacking method.The modeling method enables automatic detection of the coseismic offset signals in the GPS coordinate time series.The aforementioned method is applied to automatically detect coseismic offset signals using simulated data and the Searles Valley GPS data in California,USA.The results demonstrate the efficacy of our proposed method,successfully detecting coseismic offsets from vast amounts of GPS coordinate time series data.展开更多
Objectives:This study aims to not only investigate the prevalence of social alienation among elderly patients undergoing radical prostatectomy for prostate cancer but also identify the contributing factors.Materials a...Objectives:This study aims to not only investigate the prevalence of social alienation among elderly patients undergoing radical prostatectomy for prostate cancer but also identify the contributing factors.Materials and methods:A total of 245 elderly patients diagnosed with prostate cancer and undergoing radical prostatectomy at a tertiary care general hospital in Jinan were included in this study.To assess the patients,several questionnaires were used.These included the General Situation Questionnaire,General Alienation Scale,Social Impact Scale,Modified Memorial Anxiety Scale for Prostate Cancer,and Perceived Social Support Scale.Pearson correlation analysis was conducted to examine the relationships between variables,whereas multiple linear regression was used to identify the factors influencing social alienation among patients who underwent radical prostatectomy.Results:Patients who underwent radical prostatectomy had a mean total score of 44.13±7.24 on the Social Alienation Scale.The results of the Pearson correlation analysis indicated that social alienation showed an inverse association with social support(r=−0.627,p<0.05)and positive associations with age,disease stigma,and anxiety(r=0.325,0.575,0.421,all p’s<0.01)among patients who underwent radical prostatectomy.The findings frommultiple linear regression analysis demonstrated that educational level,age,urinary incontinence,disease stigma,anxiety,and social support significantly influenced social alienation among elderly patients who underwent radical prostatectomy(p<0.05).Conclusions:Elderly patients who undergo radical prostatectomy often experience social alienation.This study found that social alienation was associated with factors such as educational level,age,urinary incontinence,social support,anxiety,and disease stigma.Consequently,healthcare providers should actively monitor the degree of social alienation in elderly patients after radical prostatectomy and provide suitable psychological care to facilitate positive social reintegration and alleviate their feelings of social alienation.展开更多
基金supported by the National Natural Science Foundation of China(No.42104008,42204006,41904031)the Jiangxi Provincial Natural Science Foundation(20232BAB213075)+1 种基金the Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology(DLLJ202016)Open Fund of Hubei Luojia Laboratory(No.230100020,230100019)。
文摘Currently,the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes.This is followed by the process of differencing the average GPS coordinate time series data,with a time interval of 3 to 5 days before and after the earthquake.In the face of the huge amount of GPS coordinate time series data today,the conventional approach of relying on earthquake catalog data to assist in obtaining coseismic offset signals has become increasingly burdensome.To address this problem,we propose a new method for automatically detecting coseismic offset signals in GPS coordinate time series without an extra earthquake catalog for reference.Firstly,we pre-process the GPS coordinate time series data for filtering out stations with significant observations missing and detecting and removing outliers.Secondly,we eliminate other signals and errors in the GPS coordinate time series,such as trend and seasonal signals,leaving the coseismic offset signals as the primary signal.The resulting coordinate time series is then modeled using the first-order difference and data stacking method.The modeling method enables automatic detection of the coseismic offset signals in the GPS coordinate time series.The aforementioned method is applied to automatically detect coseismic offset signals using simulated data and the Searles Valley GPS data in California,USA.The results demonstrate the efficacy of our proposed method,successfully detecting coseismic offsets from vast amounts of GPS coordinate time series data.
基金supported by the Shandong Provincial Nature Science Foundation(ZR2020QH240)the National Nature Science Foundation of China(NSFC82002719)the China Postdoctoral Science Foundation(2022 M711977).
文摘Objectives:This study aims to not only investigate the prevalence of social alienation among elderly patients undergoing radical prostatectomy for prostate cancer but also identify the contributing factors.Materials and methods:A total of 245 elderly patients diagnosed with prostate cancer and undergoing radical prostatectomy at a tertiary care general hospital in Jinan were included in this study.To assess the patients,several questionnaires were used.These included the General Situation Questionnaire,General Alienation Scale,Social Impact Scale,Modified Memorial Anxiety Scale for Prostate Cancer,and Perceived Social Support Scale.Pearson correlation analysis was conducted to examine the relationships between variables,whereas multiple linear regression was used to identify the factors influencing social alienation among patients who underwent radical prostatectomy.Results:Patients who underwent radical prostatectomy had a mean total score of 44.13±7.24 on the Social Alienation Scale.The results of the Pearson correlation analysis indicated that social alienation showed an inverse association with social support(r=−0.627,p<0.05)and positive associations with age,disease stigma,and anxiety(r=0.325,0.575,0.421,all p’s<0.01)among patients who underwent radical prostatectomy.The findings frommultiple linear regression analysis demonstrated that educational level,age,urinary incontinence,disease stigma,anxiety,and social support significantly influenced social alienation among elderly patients who underwent radical prostatectomy(p<0.05).Conclusions:Elderly patients who undergo radical prostatectomy often experience social alienation.This study found that social alienation was associated with factors such as educational level,age,urinary incontinence,social support,anxiety,and disease stigma.Consequently,healthcare providers should actively monitor the degree of social alienation in elderly patients after radical prostatectomy and provide suitable psychological care to facilitate positive social reintegration and alleviate their feelings of social alienation.
