Statistical comparison of two remediation methods: Remedial nutrient solution and enhanced natural attenuation were analyzed in terms of TPH of different soil samples collected from Khana Local Government Area of Rive...Statistical comparison of two remediation methods: Remedial nutrient solution and enhanced natural attenuation were analyzed in terms of TPH of different soil samples collected from Khana Local Government Area of Rivers State, Nigeria at different locations and placed inside sample bottles labelled A to D and replicated into two, one for each of the above treatment technique. The TPH of the soil was determined using GC analyzer after solvent extraction was carried out using hexane/dichloromethane mixture. Three batches of treatment were performed on the samples at every interval of eight weeks for a duration of six months. The result obtained was analyzed using a two-way ANOVA factorial experimental design to test the significance of the various sources of variation. From the result obtained, source of variation for sample and interactions were non-significantly different from each other which means that irrespective of the number of samples analyzed or the combination of both samples and batches of treatment, they will still not be significantly different from each other. The source of variation for batch and replications were significantly different from each other and this means that irrespective of the batches of treatment applied or the number of replications (methods of treatment used), they will always be significantly different from each other. The individual comparison of each sample showed that the efficiency of the Remedial Nutrient Solution method was better than Enhanced Natural Attenuation method.展开更多
In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps)...In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.展开更多
文摘Statistical comparison of two remediation methods: Remedial nutrient solution and enhanced natural attenuation were analyzed in terms of TPH of different soil samples collected from Khana Local Government Area of Rivers State, Nigeria at different locations and placed inside sample bottles labelled A to D and replicated into two, one for each of the above treatment technique. The TPH of the soil was determined using GC analyzer after solvent extraction was carried out using hexane/dichloromethane mixture. Three batches of treatment were performed on the samples at every interval of eight weeks for a duration of six months. The result obtained was analyzed using a two-way ANOVA factorial experimental design to test the significance of the various sources of variation. From the result obtained, source of variation for sample and interactions were non-significantly different from each other which means that irrespective of the number of samples analyzed or the combination of both samples and batches of treatment, they will still not be significantly different from each other. The source of variation for batch and replications were significantly different from each other and this means that irrespective of the batches of treatment applied or the number of replications (methods of treatment used), they will always be significantly different from each other. The individual comparison of each sample showed that the efficiency of the Remedial Nutrient Solution method was better than Enhanced Natural Attenuation method.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R 308)。
文摘In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic processes.Prospects of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital solutions.An essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G lines.Integrating Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine learningmodels.Thus,the model performs and yields more accurate results.This work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees algorithms.The ensemble model shows better prediction performance with the coefficient of determination R squared value equal to.99348.The proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank test.Some of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.
