The reported study examines the diurnal and seasonal variations of tropospheric ozone and its precursors in Bangalore,India,from January to December 2020,and explores the impact of meteorological parameters and the ve...The reported study examines the diurnal and seasonal variations of tropospheric ozone and its precursors in Bangalore,India,from January to December 2020,and explores the impact of meteorological parameters and the ventilation coefficient(VC)on ozone levels.Tropospheric ozone,a significant secondary pollutant,poses a major environmental and health challenge in urban areas.The study focuses on ozone,nitrogen oxides(NO and NO2),Sulphur dioxide(SO2),and carbon monoxide(CO),revealing that ozone peaks in the early afternoon due to solar radiation,while precursor pollutants show morning and evening peaks linked to traffic emissions.Higher ozone levels occur in winter(due to reduced boundary layer height)and summer(due to increased photochemical activity),while the monsoon period shows the lowest levels due to the washout effect.The VC values are generally higher during the day(587 m^(2)/s)compared to night(246 m^(2)/s),with the highest recorded in summer(1,935 m^(2)/s)and the lowest in the post-monsoon season(209 m^(2)/s).Higher VC enhances pollutant dispersion,while lower VC leads to accumulation.However,surface ozone concentrations increase with higher VC due to photochemical processes.The findings highlight the complex interplay of meteorology,emissions,and boundary layer dynamics,informing strategies for urban air quality management.展开更多
Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled mul...Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning(ML)training and aggregation,often leading to increased energy consumption and reduced model generalization.To solve this problem,this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with Res Net50 and Scaled Exponential Linear Unit activation function(WPPFL-RS).The proposed method optimizes resource allocation such as CPU and memory,through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds.The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption,solving complex challenges in 6G-enabled edge computing.This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks.The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks,a significant improvement over the baseline Deep Reinforcement Learning(DRL),which recorded 11.39 s.This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.展开更多
文摘The reported study examines the diurnal and seasonal variations of tropospheric ozone and its precursors in Bangalore,India,from January to December 2020,and explores the impact of meteorological parameters and the ventilation coefficient(VC)on ozone levels.Tropospheric ozone,a significant secondary pollutant,poses a major environmental and health challenge in urban areas.The study focuses on ozone,nitrogen oxides(NO and NO2),Sulphur dioxide(SO2),and carbon monoxide(CO),revealing that ozone peaks in the early afternoon due to solar radiation,while precursor pollutants show morning and evening peaks linked to traffic emissions.Higher ozone levels occur in winter(due to reduced boundary layer height)and summer(due to increased photochemical activity),while the monsoon period shows the lowest levels due to the washout effect.The VC values are generally higher during the day(587 m^(2)/s)compared to night(246 m^(2)/s),with the highest recorded in summer(1,935 m^(2)/s)and the lowest in the post-monsoon season(209 m^(2)/s).Higher VC enhances pollutant dispersion,while lower VC leads to accumulation.However,surface ozone concentrations increase with higher VC due to photochemical processes.The findings highlight the complex interplay of meteorology,emissions,and boundary layer dynamics,informing strategies for urban air quality management.
文摘Sixth-generation(6G)communication system promises unprecedented data density and transformative applications over different industries.However,managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning(ML)training and aggregation,often leading to increased energy consumption and reduced model generalization.To solve this problem,this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with Res Net50 and Scaled Exponential Linear Unit activation function(WPPFL-RS).The proposed method optimizes resource allocation such as CPU and memory,through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds.The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption,solving complex challenges in 6G-enabled edge computing.This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks.The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks,a significant improvement over the baseline Deep Reinforcement Learning(DRL),which recorded 11.39 s.This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.
