Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from C...Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of the metrics of ICP,the predictability of two typical PHR events in June 2010 and June 2011 is estimated.The results show that the"good member"and"poor member"can be identified by ICP and there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research.展开更多
This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictor...This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictors are highly persistent and their innovations are contemporarily correlated with dependent variable, the ordinary least squares estimator has a finite-sample bias, and its limiting distribution relies on some unknown nuisance parameter, which is not consistently estimable. Without correcting these issues, conventional test statistics are subject to a serious size distortion and generate a misleading conclusion in testing pre- dictability of asset returns in real applications. In the past two decades, sequential studies have contributed to this subject and proposed various kinds of solutions, including, but not limit to, the bias-correction procedures, the linear projection approach, the IVX filtering idea, the variable addition approaches, the weighted empirical likelihood method, and the double-weight robust approach. Particularly, to catch up with the fast-growing literature in the recent decade, we offer a selective overview of these methods. Finally, some future research topics, such as the econometric theory for predictive regressions with structural changes, and nonparametric predictive models, and predictive models under a more general data setting, are also discussed.展开更多
Background:Patients with persistent atrial fibrillation(PsAF)have a high risk of recurrence after catheter radiofre-quency ablation.Nevertheless,no effective prognostic tools have been developed to identify these high...Background:Patients with persistent atrial fibrillation(PsAF)have a high risk of recurrence after catheter radiofre-quency ablation.Nevertheless,no effective prognostic tools have been developed to identify these high-risk patients to date.This study sought to develop and validate a simple linear predictive model for predicting postoperative recurrence in patients with PsAF.Methods:From June 2013 to June 2021,patients with PsAF admitted to our hospital were enrolled in this single-center,retrospective,observational study.The characteristics substantially associated with recurrence in patients with PsAF were screened through univariate and multivariate logistic regression analysis.The receiver operating charac-teristic curve was used to assess the predictive significance of the nomogram model after nomogram development.Furthermore,to assess the clinical value of the nomogram,we performed calibration curve and decision curve analyses.Results:A total of 209 patients were included in the study,42(20.10%)of whom were monitored up to 1 year for recurrent AF.The duration of AF episodes,left atrial diameter,BMI,CKMB,and alcohol consumption were found to be independent risk factors(P<0.05)and were integrated into the nomogram model development.The area under the curve was 0.895,the sensitivity was 93.3%,and the specificity was 71.4%,thus indicating the model’s excellent predic-tive ability.The C-index of the predictive nomogram model was 0.906.Calibration curve and decision curve analyses further revealed that the model had robust prediction and strong discrimination ability.Conclusion:This simple,practical,and innovative nomogram can help clinicians in evaluation of the risk of PsAF recurrence after catheter ablation,thus facilitating preoperative evaluation,postoperative monitoring and ultimately the construction of more personalized therapeutic protocols.展开更多
This letter addresses the study titled“Red cell distribution width:A predictor of the severity of hypertriglyceridemia-induced acute pancreatitis”by Lv et al published in the World Journal of Experimental Medicine.T...This letter addresses the study titled“Red cell distribution width:A predictor of the severity of hypertriglyceridemia-induced acute pancreatitis”by Lv et al published in the World Journal of Experimental Medicine.The study offers a valuable analysis of red cell distribution width(RDW)as a predictive marker for persistent organ failure in patients with hypertriglyceridemia-induced acute pancreatitis.The study results suggest that RDW,combined with the Bedside Index for Severity in Acute Pancreatitis score,could enhance the predictive accuracy for severe outcomes.Further investigation into the role of RDW in different severities of acute pancreatitis is recommended.Additionally,the need for large-scale and multicenter prospective studies to validate these findings is emphasized.展开更多
The number of cybersecurity incidents is on the rise despite significant investment in security measures.The existing conventional security approaches have demonstrated limited success against some of the more complex...The number of cybersecurity incidents is on the rise despite significant investment in security measures.The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks.This is primarily due to the sophistication of the attacks and the availability of powerful tools.Interconnected devices such as the Internet of Things(IoT)are also increasing attack exposures due to the increase in vulnerabilities.Over the last few years,we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks.Edge technology brings processing power closer to the network and brings many advantages,including reduced latency,while it can also introduce vulnerabilities that could be exploited.Smart cities are also dependent on technologies where everything is interconnected.This interconnectivity makes them highly vulnerable to cyber-attacks,especially by the Advanced Persistent Threat(APT),as these vulnerabilities are amplified by the need to integrate new technologies with legacy systems.Cybercriminals behind APT attacks have recently been targeting the IoT ecosystems,prevalent in many of these cities.In this paper,we used a publicly available dataset on Advanced Persistent Threats(APT)and developed a data-driven approach for detecting APT stages using the Cyber Kill Chain.APTs are highly sophisticated and targeted forms of attacks that can evade intrusion detection systems,resulting in one of the greatest current challenges facing security professionals.In this experiment,we used multiple machine learning classifiers,such as Naïve Bayes,Bayes Net,KNN,Random Forest and Support Vector Machine(SVM).We used Weka performance metrics to show the numeric results.The best performance result of 91.1%was obtained with the Naïve Bayes classifier.We hope our proposed solution will help security professionals to deal with APTs in a timely and effective manner.展开更多
基金National 973 Program of China(2012CB417204)National Natural Science Foundation of China(41075035,41475044)Special Fund for Meteorological Scientific Research in the Public Interest(GYHY201006015)
文摘Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of the metrics of ICP,the predictability of two typical PHR events in June 2010 and June 2011 is estimated.The results show that the"good member"and"poor member"can be identified by ICP and there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research.
基金supported by the National Natural Science Foundation of China(71631004,71571152)the Fundamental Research Funds for the Central Universities(20720171002,20720170090)the Fok Ying-Tong Education Foundation(151084)
文摘This paper highlights some recent developments in testing predictability of asset returns with focuses on linear mean regressions, quantile regressions and nonlinear regression models. For these models, when predictors are highly persistent and their innovations are contemporarily correlated with dependent variable, the ordinary least squares estimator has a finite-sample bias, and its limiting distribution relies on some unknown nuisance parameter, which is not consistently estimable. Without correcting these issues, conventional test statistics are subject to a serious size distortion and generate a misleading conclusion in testing pre- dictability of asset returns in real applications. In the past two decades, sequential studies have contributed to this subject and proposed various kinds of solutions, including, but not limit to, the bias-correction procedures, the linear projection approach, the IVX filtering idea, the variable addition approaches, the weighted empirical likelihood method, and the double-weight robust approach. Particularly, to catch up with the fast-growing literature in the recent decade, we offer a selective overview of these methods. Finally, some future research topics, such as the econometric theory for predictive regressions with structural changes, and nonparametric predictive models, and predictive models under a more general data setting, are also discussed.
基金supported by grants from the Natural Science Foundation of Jiangsu Province(BK20221229)Changzhou Science and Technology Support Program(Social Development,CE20225051)Changzhou Sci&Tech Program(grant No.CJ20210059).
文摘Background:Patients with persistent atrial fibrillation(PsAF)have a high risk of recurrence after catheter radiofre-quency ablation.Nevertheless,no effective prognostic tools have been developed to identify these high-risk patients to date.This study sought to develop and validate a simple linear predictive model for predicting postoperative recurrence in patients with PsAF.Methods:From June 2013 to June 2021,patients with PsAF admitted to our hospital were enrolled in this single-center,retrospective,observational study.The characteristics substantially associated with recurrence in patients with PsAF were screened through univariate and multivariate logistic regression analysis.The receiver operating charac-teristic curve was used to assess the predictive significance of the nomogram model after nomogram development.Furthermore,to assess the clinical value of the nomogram,we performed calibration curve and decision curve analyses.Results:A total of 209 patients were included in the study,42(20.10%)of whom were monitored up to 1 year for recurrent AF.The duration of AF episodes,left atrial diameter,BMI,CKMB,and alcohol consumption were found to be independent risk factors(P<0.05)and were integrated into the nomogram model development.The area under the curve was 0.895,the sensitivity was 93.3%,and the specificity was 71.4%,thus indicating the model’s excellent predic-tive ability.The C-index of the predictive nomogram model was 0.906.Calibration curve and decision curve analyses further revealed that the model had robust prediction and strong discrimination ability.Conclusion:This simple,practical,and innovative nomogram can help clinicians in evaluation of the risk of PsAF recurrence after catheter ablation,thus facilitating preoperative evaluation,postoperative monitoring and ultimately the construction of more personalized therapeutic protocols.
文摘This letter addresses the study titled“Red cell distribution width:A predictor of the severity of hypertriglyceridemia-induced acute pancreatitis”by Lv et al published in the World Journal of Experimental Medicine.The study offers a valuable analysis of red cell distribution width(RDW)as a predictive marker for persistent organ failure in patients with hypertriglyceridemia-induced acute pancreatitis.The study results suggest that RDW,combined with the Bedside Index for Severity in Acute Pancreatitis score,could enhance the predictive accuracy for severe outcomes.Further investigation into the role of RDW in different severities of acute pancreatitis is recommended.Additionally,the need for large-scale and multicenter prospective studies to validate these findings is emphasized.
基金supported in part by the School of Computing and Digital Technology at Birmingham City UniversityThe work of M.A.Rahman was supported in part by the Flagship Grant RDU190374.
文摘The number of cybersecurity incidents is on the rise despite significant investment in security measures.The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks.This is primarily due to the sophistication of the attacks and the availability of powerful tools.Interconnected devices such as the Internet of Things(IoT)are also increasing attack exposures due to the increase in vulnerabilities.Over the last few years,we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks.Edge technology brings processing power closer to the network and brings many advantages,including reduced latency,while it can also introduce vulnerabilities that could be exploited.Smart cities are also dependent on technologies where everything is interconnected.This interconnectivity makes them highly vulnerable to cyber-attacks,especially by the Advanced Persistent Threat(APT),as these vulnerabilities are amplified by the need to integrate new technologies with legacy systems.Cybercriminals behind APT attacks have recently been targeting the IoT ecosystems,prevalent in many of these cities.In this paper,we used a publicly available dataset on Advanced Persistent Threats(APT)and developed a data-driven approach for detecting APT stages using the Cyber Kill Chain.APTs are highly sophisticated and targeted forms of attacks that can evade intrusion detection systems,resulting in one of the greatest current challenges facing security professionals.In this experiment,we used multiple machine learning classifiers,such as Naïve Bayes,Bayes Net,KNN,Random Forest and Support Vector Machine(SVM).We used Weka performance metrics to show the numeric results.The best performance result of 91.1%was obtained with the Naïve Bayes classifier.We hope our proposed solution will help security professionals to deal with APTs in a timely and effective manner.
