Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologie...Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologies of the 20th century.Localization of sensors in needed locations is a very serious problem.The environment is home to every living being in the world.The growth of industries after the industrial revolution increased pollution across the environment.Owing to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are needed.An interesting and challenging task is choosing the place to fit the sensors.Many meta-heuristic techniques have been introduced in node localization.Swarm intelligent algorithms have proven their efficiency in many studies on localization problems.In this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor network.First,our work aims at selecting industrial areas in the sensed location.We use random forest regression methodology to select the polluted area.Then,the elephant herding algorithm is used in sensor node localization.These two algorithms are combined to produce the best standard result in localizing the sensor nodes.To check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with algorithms.The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer perceptron.Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology.Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.展开更多
The present study was aimed to green synthesize of α-Fe_(2)O_(3)nanoparticles(NPs)using flower extract of Musa acuminata and examination of their antibacterial and photocatalytic activities.The synthesized NPs were i...The present study was aimed to green synthesize of α-Fe_(2)O_(3)nanoparticles(NPs)using flower extract of Musa acuminata and examination of their antibacterial and photocatalytic activities.The synthesized NPs were investigated using UV-visible spectroscopy,which exhibited a colour change pattern,and the maximum absorption peak at 265 nm confirmed the formation of α-Fe_(2)O_(3)NPs.The FTIR analysis showed the presence of various functional groups coated over the synthesizedα-Fe_(2)O_(3)NPs.The XRD pattern showed that the formation of rhombohedral structure with an average crystallite size was 21.86 nm.FESEM micrographs revealed that α-Fe_(2)O_(3)NPs were roughly spherical in shape.EDX spectrum confirmed the presence of Fe and O elements.By TEM analysis,the average particle size was calculated to be 32 nm.Using the well diffusion method,the antibacterial activity of α-Fe_(2)O_(3)NPs was tested against both gram positive and negative bacterial strains of Staphylococcus aureus(S.aureus)and Escherichia coli(E.coli).The NPs exhibited good antibacterial activity against the tested bacteria.Finally,the synthesized α-Fe_(2)O_(3)NPs demonstrated the photocatalytic degradation of Crystal Violet(CV)dye under sunlight.The efficiency of degradation within 150 min was determined to be 90.27%for CV.This effective removal method under sunlight may support a cost-effective method for degradation of CV dyes from wastewater.展开更多
文摘Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologies of the 20th century.Localization of sensors in needed locations is a very serious problem.The environment is home to every living being in the world.The growth of industries after the industrial revolution increased pollution across the environment.Owing to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are needed.An interesting and challenging task is choosing the place to fit the sensors.Many meta-heuristic techniques have been introduced in node localization.Swarm intelligent algorithms have proven their efficiency in many studies on localization problems.In this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor network.First,our work aims at selecting industrial areas in the sensed location.We use random forest regression methodology to select the polluted area.Then,the elephant herding algorithm is used in sensor node localization.These two algorithms are combined to produce the best standard result in localizing the sensor nodes.To check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with algorithms.The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer perceptron.Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology.Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.
文摘The present study was aimed to green synthesize of α-Fe_(2)O_(3)nanoparticles(NPs)using flower extract of Musa acuminata and examination of their antibacterial and photocatalytic activities.The synthesized NPs were investigated using UV-visible spectroscopy,which exhibited a colour change pattern,and the maximum absorption peak at 265 nm confirmed the formation of α-Fe_(2)O_(3)NPs.The FTIR analysis showed the presence of various functional groups coated over the synthesizedα-Fe_(2)O_(3)NPs.The XRD pattern showed that the formation of rhombohedral structure with an average crystallite size was 21.86 nm.FESEM micrographs revealed that α-Fe_(2)O_(3)NPs were roughly spherical in shape.EDX spectrum confirmed the presence of Fe and O elements.By TEM analysis,the average particle size was calculated to be 32 nm.Using the well diffusion method,the antibacterial activity of α-Fe_(2)O_(3)NPs was tested against both gram positive and negative bacterial strains of Staphylococcus aureus(S.aureus)and Escherichia coli(E.coli).The NPs exhibited good antibacterial activity against the tested bacteria.Finally,the synthesized α-Fe_(2)O_(3)NPs demonstrated the photocatalytic degradation of Crystal Violet(CV)dye under sunlight.The efficiency of degradation within 150 min was determined to be 90.27%for CV.This effective removal method under sunlight may support a cost-effective method for degradation of CV dyes from wastewater.
