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
Owing to the significant increase in energy consumption,contemporary power systems are transitioning to a new standard characterized by enhanced access to renewable energy sources(RESs).RESs require interfaces to regu...Owing to the significant increase in energy consumption,contemporary power systems are transitioning to a new standard characterized by enhanced access to renewable energy sources(RESs).RESs require interfaces to regulate the power generation.Maximum power point tracking(MPPT)is a technique employed in solar photovoltaic(PV)systems to modify operational parameters to ensureoptimal extraction of power from solar panels.MPPT operates under fluctuating conditions such as sunlight intensity and temperature.An inverter is a device that transforms a direct current into a sinusoidal alternating current.A multilevel inverter(MLI)can be utilized for RESs in two distinct modes:power-generating mode(stand-alone mode)and compensator mode(STATCOM).Limited research has been conducted on the optimization of controller gains in response to variations in a single phase load,particularly reactive load variations,across several scenarios.This load may exhibit an imbalance;hence,a more robust optimization approach must be used to address this problem.This study presents a control system that incorporates an optimized auxiliary MPPT controller for a seven-level inverter.The system uses a sophisticated greylag goose optimization(GGO)random search algorithm combined with the MPPT technique.The main objective is to create a system that enhances performance under diverse and imbalanced loading scenarios by utilizing sophisticated optimization techniques that determine the optimal switching angles for a seven-level inverter.This approach aims to eliminate specific harmonics and achieve a low total harmonic distortion(THD).The inverter THD output voltage was used as the objective function,and the proposed method is particularly beneficial in agricultural settings.The proposed MPPT-based seven-level invertersystem was simulated using MATLAB.The proposed GGO algorithm achieved a minimal THD of 1.95%,surpassing methods such as salp swarm optimization(6.14%),artificial neural networks with fuzzy logic(5.9%),hybrid global selective algorithm(GSA)selective harmonic elimination(7.7%),and genetic algorithms with particle swarm optimization(10.84%),demonstrating its exceptional efficacy in improving power quality.展开更多
Twenty Far East Greylag Geese,Anser anser rubrirostris,were captured and fitted with Global Positioning System/Global System for Mobile Communications(GPS/GSM)loggers to identify breeding and wintering areas,migration...Twenty Far East Greylag Geese,Anser anser rubrirostris,were captured and fitted with Global Positioning System/Global System for Mobile Communications(GPS/GSM)loggers to identify breeding and wintering areas,migration routes and stopover sites.Telemetry data for the first time showed linkages between their Yangtze River wintering areas,stopover sites in northeastern China,and breeding/molting grounds in eastern Mongolia and northeast China.10 of the 20 tagged individuals provided sufficient data.They stopped on migration at the Yellow River Estuary,Beidagang Reservoir and Xar Moron River,confirming these areas as being important stopover sites for this population.The median spring migration duration was 33.7 days(individuals started migrating between 25 February and 16 March and completed migrating from 1 to 9 April)compared to 52.7 days in autumn(26 September-13 October until 4 November-11 December).The median stopover duration was 31.1 and 51.3 days and the median speed of travel was 62.6 and 47.9 km/day for spring and autumn migration,respectively.The significant differences between spring and autumn migration on the migration duration,the stopover duration and the migration speed confirmed that tagged adult Greylag Geese traveled faster in spring than autumn,supporting the hypothesis that they should be more time-limited during spring migration.展开更多
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.
文摘Owing to the significant increase in energy consumption,contemporary power systems are transitioning to a new standard characterized by enhanced access to renewable energy sources(RESs).RESs require interfaces to regulate the power generation.Maximum power point tracking(MPPT)is a technique employed in solar photovoltaic(PV)systems to modify operational parameters to ensureoptimal extraction of power from solar panels.MPPT operates under fluctuating conditions such as sunlight intensity and temperature.An inverter is a device that transforms a direct current into a sinusoidal alternating current.A multilevel inverter(MLI)can be utilized for RESs in two distinct modes:power-generating mode(stand-alone mode)and compensator mode(STATCOM).Limited research has been conducted on the optimization of controller gains in response to variations in a single phase load,particularly reactive load variations,across several scenarios.This load may exhibit an imbalance;hence,a more robust optimization approach must be used to address this problem.This study presents a control system that incorporates an optimized auxiliary MPPT controller for a seven-level inverter.The system uses a sophisticated greylag goose optimization(GGO)random search algorithm combined with the MPPT technique.The main objective is to create a system that enhances performance under diverse and imbalanced loading scenarios by utilizing sophisticated optimization techniques that determine the optimal switching angles for a seven-level inverter.This approach aims to eliminate specific harmonics and achieve a low total harmonic distortion(THD).The inverter THD output voltage was used as the objective function,and the proposed method is particularly beneficial in agricultural settings.The proposed MPPT-based seven-level invertersystem was simulated using MATLAB.The proposed GGO algorithm achieved a minimal THD of 1.95%,surpassing methods such as salp swarm optimization(6.14%),artificial neural networks with fuzzy logic(5.9%),hybrid global selective algorithm(GSA)selective harmonic elimination(7.7%),and genetic algorithms with particle swarm optimization(10.84%),demonstrating its exceptional efficacy in improving power quality.
基金Our study was supported by the National Key Research and Development Program of China(Grant No.2017YFC0505800)the National Natural Science Foundation of China(Grant No.31870369)+3 种基金the Chinese Academy of Sciences Key Strategic Program,Water Ecological Security Assessment,the Major Research Strategy for Middle and Lower Yangtze River(Grant No.ZDRW-ZS-2017-3-3)International Cooperation and Exchange project NSFC(Grant No.31661143027)the National Natural Science Foundation of China(Grant No.31670424)China Biodiversity Observation Networks(Sino BON).
文摘Twenty Far East Greylag Geese,Anser anser rubrirostris,were captured and fitted with Global Positioning System/Global System for Mobile Communications(GPS/GSM)loggers to identify breeding and wintering areas,migration routes and stopover sites.Telemetry data for the first time showed linkages between their Yangtze River wintering areas,stopover sites in northeastern China,and breeding/molting grounds in eastern Mongolia and northeast China.10 of the 20 tagged individuals provided sufficient data.They stopped on migration at the Yellow River Estuary,Beidagang Reservoir and Xar Moron River,confirming these areas as being important stopover sites for this population.The median spring migration duration was 33.7 days(individuals started migrating between 25 February and 16 March and completed migrating from 1 to 9 April)compared to 52.7 days in autumn(26 September-13 October until 4 November-11 December).The median stopover duration was 31.1 and 51.3 days and the median speed of travel was 62.6 and 47.9 km/day for spring and autumn migration,respectively.The significant differences between spring and autumn migration on the migration duration,the stopover duration and the migration speed confirmed that tagged adult Greylag Geese traveled faster in spring than autumn,supporting the hypothesis that they should be more time-limited during spring migration.
