Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall su...Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation conf展开更多
Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected in...Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.展开更多
Background: Anastomotic leak is the most dreaded complication and it accounts for one third of post-operative deaths in colorectal surgery. There is strong need of research in this field in order to reduce anastomotic...Background: Anastomotic leak is the most dreaded complication and it accounts for one third of post-operative deaths in colorectal surgery. There is strong need of research in this field in order to reduce anastomotic leak rate. If there was a test available to signify probability of leak at the time of surgery, it would potentially avoid a leak at a later date. Aim: We propose that intra operative use of narrow band imaging colonoscopy in left sided colonic anastomosis can predict and detect anastomosis related problems, problems i.e. vascularity, anastomotic lumen patency and rigidness of anastomosis which in turn lead to correction at the time of surgery resulting in significant decrease in anastomotic leak rate associated with morbidity and mortality. Methodology: A pilot, single blind, prospective, randomised controlled trial. Aim is to divide patient in 2 groups, containing 15 patients in each group. One group will be assessed by intra-operative NBI colonoscopy and other with standard technique. Analysis: This will be performed using SPSS with the help of statistician. Significance: This area of research is particularly important because it improves survival by reducing morbidity and mortality associated with anastomotic leak. If a leak is avoided then it does not only mean early recovery but it also saves NHS in terms of ICU care, NHS bed, inpatient nursing care and home help, unfortunately all required if leak is to occur. As a result, this research will have better financial implication with significantly improve patient care.展开更多
以国内某核电项目为依托,根据美国核安全管理导则RegulatoryGuide1.78-Evaluatingthe Habitability of a Nuclear Power Plant Control Room During a Postulated Hazardous Chemical Release(RG1.78)评估原则,梳理并筛选核电厂中符合...以国内某核电项目为依托,根据美国核安全管理导则RegulatoryGuide1.78-Evaluatingthe Habitability of a Nuclear Power Plant Control Room During a Postulated Hazardous Chemical Release(RG1.78)评估原则,梳理并筛选核电厂中符合要求的化学品,利用ALOHA软件计算发生泄漏后进入主控室的有毒有害气体浓度,评估泄漏后对主控室可居留性影响。从模拟结果看,由于核电厂核岛厂房为封闭设计,主控室通风口位于核岛厂房内部,当发生有毒有害气体泄漏时,主控室通风口处的有毒气体浓度低于毒性限值,不会对主控室可居留性造成重大影响。展开更多
基金This workwas supported by the Medical and Health Science and Technology Project of Zhejiang Province(No.2021KY180).
文摘Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation conf
基金The National Key Research and Development Program of China:Design and Key Technology Research of Non-metallic Flexible Risers for Deep Sea Mining(2022YFC2803701)The General Program of National Natural Science Foundation of China(52071336,52374022).
文摘Since leaks in high-pressure pipelines transporting crude oil can cause severe economic losses,a reliable leak risk assessment can assist in developing an effective pipeline maintenance plan and avoiding unexpected incidents.The fast and accurate leak detection methods are essential for maintaining pipeline safety in pipeline reliability engineering.Current oil pipeline leakage signals are insufficient for feature extraction,while the training time for traditional leakage prediction models is too long.A new leak detection method is proposed based on time-frequency features and the Genetic Algorithm-Levenberg Marquardt(GA-LM)classification model for predicting the leakage status of oil pipelines.The signal that has been processed is transformed to the time and frequency domain,allowing full expression of the original signal.The traditional Back Propagation(BP)neural network is optimized by the Genetic Algorithm(GA)and Levenberg Marquardt(LM)algorithms.The results show that the recognition effect of a combined feature parameter is superior to that of a single feature parameter.The Accuracy,Precision,Recall,and F1score of the GA-LM model is 95%,93.5%,96.7%,and 95.1%,respectively,which proves that the GA-LM model has a good predictive effect and excellent stability for positive and negative samples.The proposed GA-LM model can obviously reduce training time and improve recognition efficiency.In addition,considering that a large number of samples are required for model training,a wavelet threshold method is proposed to generate sample data with higher reliability.The research results can provide an effective theoretical and technical reference for the leakage risk assessment of the actual oil pipelines.
文摘Background: Anastomotic leak is the most dreaded complication and it accounts for one third of post-operative deaths in colorectal surgery. There is strong need of research in this field in order to reduce anastomotic leak rate. If there was a test available to signify probability of leak at the time of surgery, it would potentially avoid a leak at a later date. Aim: We propose that intra operative use of narrow band imaging colonoscopy in left sided colonic anastomosis can predict and detect anastomosis related problems, problems i.e. vascularity, anastomotic lumen patency and rigidness of anastomosis which in turn lead to correction at the time of surgery resulting in significant decrease in anastomotic leak rate associated with morbidity and mortality. Methodology: A pilot, single blind, prospective, randomised controlled trial. Aim is to divide patient in 2 groups, containing 15 patients in each group. One group will be assessed by intra-operative NBI colonoscopy and other with standard technique. Analysis: This will be performed using SPSS with the help of statistician. Significance: This area of research is particularly important because it improves survival by reducing morbidity and mortality associated with anastomotic leak. If a leak is avoided then it does not only mean early recovery but it also saves NHS in terms of ICU care, NHS bed, inpatient nursing care and home help, unfortunately all required if leak is to occur. As a result, this research will have better financial implication with significantly improve patient care.
文摘以国内某核电项目为依托,根据美国核安全管理导则RegulatoryGuide1.78-Evaluatingthe Habitability of a Nuclear Power Plant Control Room During a Postulated Hazardous Chemical Release(RG1.78)评估原则,梳理并筛选核电厂中符合要求的化学品,利用ALOHA软件计算发生泄漏后进入主控室的有毒有害气体浓度,评估泄漏后对主控室可居留性影响。从模拟结果看,由于核电厂核岛厂房为封闭设计,主控室通风口位于核岛厂房内部,当发生有毒有害气体泄漏时,主控室通风口处的有毒气体浓度低于毒性限值,不会对主控室可居留性造成重大影响。
