This study tackles the issue of chloramphenicol(CAP)in wastewater by exploring its removal using rattan waste-based metal functionalized carbon(RMFC).The study provides new insights into the adsorption mechanism by in...This study tackles the issue of chloramphenicol(CAP)in wastewater by exploring its removal using rattan waste-based metal functionalized carbon(RMFC).The study provides new insights into the adsorption mechanism by investigating the role of Cu^(2+)functionalization in enhancing CAP uptake through ion-dipole andπ-πinteractions.The RMFC surface was enriched with Cu^(2+)ions through modification with CuN_(2)O_(6),resulting in the production of copper-enriched RMFC(Cu^(2+)-RMFC).The conditions for preparing Cu^(2+)-RMFC were optimized through response surface methodology(RSM).Following this,an F-test was conducted to evaluate the differences in variance distinguishing linear from non-linear ap-proaches pertaining to isotherm together with kinetic models,with the null hypothesis proposing that these variances are the same.The adsorption capacities of CAP by pristine RMFC and Cu^(2+)-RMFC were 53.69 mg/g and 77.14 mg/g,respectively,indicating a 30.40%increase.Besides hydrogen bonds,dipole-dipole bonds,andπ-πinteractions,the enhanced CAP removal by Cu^(2+)-RMFC was attributed to the ion-dipole interaction between Cu^(2+)ions and more electronegative oxygen(O)atoms in CAP molecules.The RSM identified the optimal conditions as 660 W,8.07 min,and a metal loading ratio(MLR)of 0.47 g/g in relation to radiation power,duration of radiation,and MLR,correspondingly.These circumstances brought about predicted CAP uptake values of 76.15 mg/g(actual:77.14 mg/g;error:1.28%)and a Cu^(2+)-RMFC yield of 31.54%(actual:32.36%;error:2.53%).The adsorption process was well represented by the non-linear Freundlich and non-linear pseudo-first-order(PFO)models.The adsorption capacity of the Langmuir monolayer(Q_(m))was 101.01 mg/g for the linear model and 108.00 mg/g for the non-linear model.The F-test results indicated that for all isotherm models studied,the F value was smaller than the F-critical value,leading to the acceptance of the null hypothesis.In contrast,the F values for all ki-netic models exceeded the F-critical value,resulting in the refusal of the null hypothesis.展开更多
In linear regression model, the influence on the regression coefficients has beed paid great attention and other aspects such as the influence on confidence regions have also been studied. However, influence on F-test...In linear regression model, the influence on the regression coefficients has beed paid great attention and other aspects such as the influence on confidence regions have also been studied. However, influence on F-test in linear regression model received few consideration. This paper examines the local influence of small perturbations on Fstatistic. The diagnostic results permit one to check the sensitivity of F-statistic to the exact perturbations of error variance, explanatory variables and response variables. This method is applied to testing problem of transformation parameter in transformation model.Diagnostics are illustrated with two examples and compared with standard method.展开更多
This paper proposes a test procedure for testing the regression coefficients in high dimensional partially linear models based on the F-statistic. In the partially linear model, the authors first estimate the unknown ...This paper proposes a test procedure for testing the regression coefficients in high dimensional partially linear models based on the F-statistic. In the partially linear model, the authors first estimate the unknown nonlinear component by some nonparametric methods and then generalize the F-statistic to test the regression coefficients under some regular conditions. During this procedure, the estimation of the nonlinear component brings much challenge to explore the properties of generalized F-test. The authors obtain some asymptotic properties of the generalized F-test in more general cases,including the asymptotic normality and the power of this test with p/n ∈(0, 1) without normality assumption. The asymptotic result is general and by adding some constraint conditions we can obtain the similar conclusions in high dimensional linear models. Through simulation studies, the authors demonstrate good finite-sample performance of the proposed test in comparison with the theoretical results. The practical utility of our method is illustrated by a real data example.展开更多
F-test is the most popular test in the general linear model. However, there is few discussions on the robustness of F-test under the singular linear model. In this paper, the necessary and sufficient conditions of rob...F-test is the most popular test in the general linear model. However, there is few discussions on the robustness of F-test under the singular linear model. In this paper, the necessary and sufficient conditions of robust F-test statistic are given under the general linear models or their partition models, which allows that the design matrix has deficient rank and the covariance matrix of error is a nonnegative definite matrix with parameters. The main results obtained in this paper include the existing findings of the general linear model under the definite covariance matrix. The usage of the theorems is illustrated by an example.展开更多
The usual F--test has been used to test a general linear hypothesis for a two--stage least squaresmethod in a system of economic equations. However, we find that this F--test is actuallyasymptotically invalid. Some su...The usual F--test has been used to test a general linear hypothesis for a two--stage least squaresmethod in a system of economic equations. However, we find that this F--test is actuallyasymptotically invalid. Some suggestions are given for testing a general linear hypothesis in thissituation.展开更多
Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)ecosystem.Numerous phishing attack detection solutions have been developed for IoT;however,many of these are eithe...Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)ecosystem.Numerous phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application.This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection.Our model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning model.Additionally,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)models.The proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’class.Moreover,the model’s recall and F1-score are 91%for both classes.We also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more accurate.This study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.展开更多
Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There i...Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.展开更多
基金Deanship of Research and Graduate Studies at King Khalid University for generously supporting this study through the Large Research Projects grant, awarded under grant number RGP2/563/45.
文摘This study tackles the issue of chloramphenicol(CAP)in wastewater by exploring its removal using rattan waste-based metal functionalized carbon(RMFC).The study provides new insights into the adsorption mechanism by investigating the role of Cu^(2+)functionalization in enhancing CAP uptake through ion-dipole andπ-πinteractions.The RMFC surface was enriched with Cu^(2+)ions through modification with CuN_(2)O_(6),resulting in the production of copper-enriched RMFC(Cu^(2+)-RMFC).The conditions for preparing Cu^(2+)-RMFC were optimized through response surface methodology(RSM).Following this,an F-test was conducted to evaluate the differences in variance distinguishing linear from non-linear ap-proaches pertaining to isotherm together with kinetic models,with the null hypothesis proposing that these variances are the same.The adsorption capacities of CAP by pristine RMFC and Cu^(2+)-RMFC were 53.69 mg/g and 77.14 mg/g,respectively,indicating a 30.40%increase.Besides hydrogen bonds,dipole-dipole bonds,andπ-πinteractions,the enhanced CAP removal by Cu^(2+)-RMFC was attributed to the ion-dipole interaction between Cu^(2+)ions and more electronegative oxygen(O)atoms in CAP molecules.The RSM identified the optimal conditions as 660 W,8.07 min,and a metal loading ratio(MLR)of 0.47 g/g in relation to radiation power,duration of radiation,and MLR,correspondingly.These circumstances brought about predicted CAP uptake values of 76.15 mg/g(actual:77.14 mg/g;error:1.28%)and a Cu^(2+)-RMFC yield of 31.54%(actual:32.36%;error:2.53%).The adsorption process was well represented by the non-linear Freundlich and non-linear pseudo-first-order(PFO)models.The adsorption capacity of the Langmuir monolayer(Q_(m))was 101.01 mg/g for the linear model and 108.00 mg/g for the non-linear model.The F-test results indicated that for all isotherm models studied,the F value was smaller than the F-critical value,leading to the acceptance of the null hypothesis.In contrast,the F values for all ki-netic models exceeded the F-critical value,resulting in the refusal of the null hypothesis.
文摘In linear regression model, the influence on the regression coefficients has beed paid great attention and other aspects such as the influence on confidence regions have also been studied. However, influence on F-test in linear regression model received few consideration. This paper examines the local influence of small perturbations on Fstatistic. The diagnostic results permit one to check the sensitivity of F-statistic to the exact perturbations of error variance, explanatory variables and response variables. This method is applied to testing problem of transformation parameter in transformation model.Diagnostics are illustrated with two examples and compared with standard method.
基金supported by the Natural Science Foundation of China under Grant Nos.11231010,11471223,11501586BCMIIS and Key Project of Beijing Municipal Educational Commission under Grant No.KZ201410028030
文摘This paper proposes a test procedure for testing the regression coefficients in high dimensional partially linear models based on the F-statistic. In the partially linear model, the authors first estimate the unknown nonlinear component by some nonparametric methods and then generalize the F-statistic to test the regression coefficients under some regular conditions. During this procedure, the estimation of the nonlinear component brings much challenge to explore the properties of generalized F-test. The authors obtain some asymptotic properties of the generalized F-test in more general cases,including the asymptotic normality and the power of this test with p/n ∈(0, 1) without normality assumption. The asymptotic result is general and by adding some constraint conditions we can obtain the similar conclusions in high dimensional linear models. Through simulation studies, the authors demonstrate good finite-sample performance of the proposed test in comparison with the theoretical results. The practical utility of our method is illustrated by a real data example.
基金Supported by National Social Science Foundation of China(Grant No.13CTJ012)National Natural Science Foundation of China(Grant No.11171058)+2 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ13A010002)Guangdong Provincial Natural Science Foundation of China(Grant No.S2012040007622)he National Statistical Science Research Project(Grant No.2012LY129)
文摘F-test is the most popular test in the general linear model. However, there is few discussions on the robustness of F-test under the singular linear model. In this paper, the necessary and sufficient conditions of robust F-test statistic are given under the general linear models or their partition models, which allows that the design matrix has deficient rank and the covariance matrix of error is a nonnegative definite matrix with parameters. The main results obtained in this paper include the existing findings of the general linear model under the definite covariance matrix. The usage of the theorems is illustrated by an example.
文摘The usual F--test has been used to test a general linear hypothesis for a two--stage least squaresmethod in a system of economic equations. However, we find that this F--test is actuallyasymptotically invalid. Some suggestions are given for testing a general linear hypothesis in thissituation.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343),Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia for funding this research work through the Project number“NBU-FFR-2024-1092-09”.
文摘Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)ecosystem.Numerous phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical application.This paper proposes and optimizes a lightweight deep-learning model for phishing attack detection.Our model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning model.Additionally,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)models.The proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’class.Moreover,the model’s recall and F1-score are 91%for both classes.We also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more accurate.This study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.
文摘Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.
