This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves f...This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves for the characteristics of newborn associated with newborn death after using Kaplan-Meier methods to estimate and graph survival curves for the variable of interest as (sex of newborn, weight of newborn, gestational age, mode of delivery and resident type), at the hospital of River Nile state—Sudan, with a sample size 700 of newborn in which the admission to the Neonatal Intensive Care Unit (NICU) of those hospitals during the period 2018-2020. In term of risk of death for newborn we found that 25% of sample study for newborns who were born in River Nile State-Sudan died. In addition, we conclude that after the log-rank statistics and Kaplan-Meier methods were applied, gender does not affect the newborn’s risk of survival, while the risk of survival increases when the birth weight is greater than 4.35 kg and the gestational age is greater than 42 weeks. There is no difference in the probability of survival for newborns whether the delivery is normal or cesarean. However, newborns are significantly more likely to survive in urban areas than in rural areas.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α whi...A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α while guaranteeing the maximum power of the two constituent tests. Critical values, obtained via Monte Carlo methods, are uniformly smaller than the Bonferroni-Dunn adjustment, giving it power superiority when testing for treatment alternatives of shift in location parameter when data are sampled from non-normal distributions.展开更多
Let X<sub>1</sub>,…,X<sub>m</sub> and Y<sub>1</sub>,…,Y<sub>n</sub> be two independent random simple samples drawn from FandG respectively, which are unknown continuou...Let X<sub>1</sub>,…,X<sub>m</sub> and Y<sub>1</sub>,…,Y<sub>n</sub> be two independent random simple samples drawn from FandG respectively, which are unknown continuous distributions on R. Considering hypothesistesting problem:展开更多
Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences of an event of interest. In this paper, we evaluate two popular nonparametric tests fo...Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences of an event of interest. In this paper, we evaluate two popular nonparametric tests for recurrent event time data in terms of their relative effciency. One is the log-rank test for classical survival data and the other a more recently developed nonparametric test based on comparing mean recurrent rates. We show analytically that, somewhat surprisingly, the log-rank test that only makes use of time to the first occurrence could be more effcient than the test for mean occurrence rates that makes use of all available recurrence times, provided that subject-to-subject variation of recurrence times is large. Explicit formula are derived for asymptotic relative effciencies under the frailty model. The findings are demonstrated via extensive simulations.展开更多
We propose a new nonparametric test based on the rank difference between the paired sample for testing the equality of the marginal distributions from a bivariate distribution. We also consider a modification of the n...We propose a new nonparametric test based on the rank difference between the paired sample for testing the equality of the marginal distributions from a bivariate distribution. We also consider a modification of the novel nonparametric test based on the test proposed by Baumgartern, Weiβ, and Schindler (1998). An extensive numerical power comparison for various parametric and nonparametric tests was conducted under a wide range of bivariate distributions for small sample sizes. The two new nonparametric tests have comparable power to the paired t test for the data simulated from bivariate normal distributions, and are generally more powerful than the paired t test and other commonly used nonparametric tests in several important bivariate distributions.展开更多
目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计...目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计算其观测功效,然后作对比分析。结果:所得观测功效在多数试验集均低于期望功效。与上半部相比,寿命表下半部期望和观测功效均较低。所得观测功效在不同终检水平或不同生存分布各不相同。结论:L ach in-Fou lkes法产生的样本量偏小,不能满足L og rank检验的预定功效。L og rank检验所需样本量因终检水平、生存时间或生存分布而异,L ach in-Fou lkes法无视这些事实,无法作出切合实际的测定,因此必须寻求与这种检验匹配的样本量测定方法。展开更多
Objective This paper propses a family of summary chi square tests for comparing survival rates at all points of time between two groups. Methods They are respectively derived from the Peto et al. expression for the lo...Objective This paper propses a family of summary chi square tests for comparing survival rates at all points of time between two groups. Methods They are respectively derived from the Peto et al. expression for the log rank test, the Mantel Haenszel expression for the log rank test, and the generalized Wilcoxon test by means of using the homogenetic effective sample size in place of the number at risk and using the corresponding numerator of the conditional probability surviving in place of the death number. Results After such derivations they become clearer in clinical significance, more powerful, and free from the assumption of proportional hazard. Conclusion These tests can be employed in analyzing the clinical data of cancer. A worked example illustrates the methodology.展开更多
Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we...Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud environments.SCF integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel exploits.The framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities proactively.Experimental evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational overhead.SCF provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.展开更多
文摘This paper concerns the Log-rank test for comparing survival curves of neonatal mortality characteristic groups in River Nile State, Sudan. In this paper, log-rank test is used to compare two or more survival curves for the characteristics of newborn associated with newborn death after using Kaplan-Meier methods to estimate and graph survival curves for the variable of interest as (sex of newborn, weight of newborn, gestational age, mode of delivery and resident type), at the hospital of River Nile state—Sudan, with a sample size 700 of newborn in which the admission to the Neonatal Intensive Care Unit (NICU) of those hospitals during the period 2018-2020. In term of risk of death for newborn we found that 25% of sample study for newborns who were born in River Nile State-Sudan died. In addition, we conclude that after the log-rank statistics and Kaplan-Meier methods were applied, gender does not affect the newborn’s risk of survival, while the risk of survival increases when the birth weight is greater than 4.35 kg and the gestational age is greater than 42 weeks. There is no difference in the probability of survival for newborns whether the delivery is normal or cesarean. However, newborns are significantly more likely to survive in urban areas than in rural areas.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
文摘A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α while guaranteeing the maximum power of the two constituent tests. Critical values, obtained via Monte Carlo methods, are uniformly smaller than the Bonferroni-Dunn adjustment, giving it power superiority when testing for treatment alternatives of shift in location parameter when data are sampled from non-normal distributions.
基金Project supported by the National Natural Science Foundation of China.
文摘Let X<sub>1</sub>,…,X<sub>m</sub> and Y<sub>1</sub>,…,Y<sub>n</sub> be two independent random simple samples drawn from FandG respectively, which are unknown continuous distributions on R. Considering hypothesistesting problem:
基金supported by US National Science Foundation (Grant No. DMS-0504269)
文摘Recurrent event time data are common in biomedical follow-up studies, in which a study subject may experience repeated occurrences of an event of interest. In this paper, we evaluate two popular nonparametric tests for recurrent event time data in terms of their relative effciency. One is the log-rank test for classical survival data and the other a more recently developed nonparametric test based on comparing mean recurrent rates. We show analytically that, somewhat surprisingly, the log-rank test that only makes use of time to the first occurrence could be more effcient than the test for mean occurrence rates that makes use of all available recurrence times, provided that subject-to-subject variation of recurrence times is large. Explicit formula are derived for asymptotic relative effciencies under the frailty model. The findings are demonstrated via extensive simulations.
文摘We propose a new nonparametric test based on the rank difference between the paired sample for testing the equality of the marginal distributions from a bivariate distribution. We also consider a modification of the novel nonparametric test based on the test proposed by Baumgartern, Weiβ, and Schindler (1998). An extensive numerical power comparison for various parametric and nonparametric tests was conducted under a wide range of bivariate distributions for small sample sizes. The two new nonparametric tests have comparable power to the paired t test for the data simulated from bivariate normal distributions, and are generally more powerful than the paired t test and other commonly used nonparametric tests in several important bivariate distributions.
文摘目的:L og rank检验是生存资料比较的标准方法,但无与之匹配的样本量测定方法。论述了这种检验的功效,为样本量研究提供依据。方法:由L ach in-Fou lkes法计算期望功效作为参照,回顾L og rank检验的3种形式,按M on te C arlo方法分别计算其观测功效,然后作对比分析。结果:所得观测功效在多数试验集均低于期望功效。与上半部相比,寿命表下半部期望和观测功效均较低。所得观测功效在不同终检水平或不同生存分布各不相同。结论:L ach in-Fou lkes法产生的样本量偏小,不能满足L og rank检验的预定功效。L og rank检验所需样本量因终检水平、生存时间或生存分布而异,L ach in-Fou lkes法无视这些事实,无法作出切合实际的测定,因此必须寻求与这种检验匹配的样本量测定方法。
文摘Objective This paper propses a family of summary chi square tests for comparing survival rates at all points of time between two groups. Methods They are respectively derived from the Peto et al. expression for the log rank test, the Mantel Haenszel expression for the log rank test, and the generalized Wilcoxon test by means of using the homogenetic effective sample size in place of the number at risk and using the corresponding numerator of the conditional probability surviving in place of the death number. Results After such derivations they become clearer in clinical significance, more powerful, and free from the assumption of proportional hazard. Conclusion These tests can be employed in analyzing the clinical data of cancer. A worked example illustrates the methodology.
文摘Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud environments.SCF integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel exploits.The framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities proactively.Experimental evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational overhead.SCF provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.
