This study aims to evaluate the solar and wind energy potential across Razavi Khorasan Province,Iran,with a specific focus on the Khaf region.A preliminary assessment of mean solar radiation,mean wind speeds,and Weibu...This study aims to evaluate the solar and wind energy potential across Razavi Khorasan Province,Iran,with a specific focus on the Khaf region.A preliminary assessment of mean solar radiation,mean wind speeds,and Weibull distribution parameters was conducted for different towns and zones within the province.The findings showed that Khaf has favorable characteristics for further analysis.The solar and wind energy metrics examined include global horizontal irradiance,clearness index,wind rose patterns,and turbulence intensity.At a height of 40 m,Khaf’s wind power density reached 1650 W/m^(2),indicating exceptional wind energy generation potential.Additionally,Khaf received an average annual solar radiation of 2046 kW·h/m^(2),representing significant solar energy potential.Harnessing these substantial renewable resources in Khaf could allow Razavi Khorasan Province to reduce reliance on fossil fuels,improve energy sustainability,and mitigate climate change impacts.This research contributes an in-depth assessment of Razavi Khorasan's solar and wind energy potential,particularly for the promising Khaf region.Further work may examine optimal sites for renewable energy projects and grid integration strategies to leverage these resources.展开更多
The seismic safety of offshore wind turbines is an important issue that needs to be solved urgently.Based on a unified computing framework,this paper develops a set of seawater-seabed-wind turbine zoning coupling anal...The seismic safety of offshore wind turbines is an important issue that needs to be solved urgently.Based on a unified computing framework,this paper develops a set of seawater-seabed-wind turbine zoning coupling analysis methods.A 5 MW wind turbine and a site analysis model are established,and a seismic wave is selected to analyze the changes in the seismic response of offshore monopile wind turbines under the change of seawater depth,seabed wave velocity and seismic wave incidence angle.The analysis results show that when the seawater increases to a certain depth,the seismic response of the wind turbine increases.The shear wave velocity of the seabed affects the bending moment and displacement at the bottom of the tower.When the angle of incidence increases,the vertical displacement and the acceleration of the top of the tower increase in varying degrees.展开更多
In the present study, we explored the therapeutic potential of Cang Zhu-Huang Bai (CZ-HB) against rheumatoid arthritis (RA) and elucidated the associated mechanisms. The approach involved a systematic examination of t...In the present study, we explored the therapeutic potential of Cang Zhu-Huang Bai (CZ-HB) against rheumatoid arthritis (RA) and elucidated the associated mechanisms. The approach involved a systematic examination of the chemical ingredients of CZ-HB using TCMSP database. Subsequently, we predicted the targets corresponding to the active ingredients through the SwissTargetPrediction database. We constructed a comprehensive drug-ingredient-target network using Cytoscape (v 3.8.0), with the main ingredients of the drugs identified based on their degree values. We conducted a meticulous search across GEO, GeneCards, Therapeutic Target Database (TTD), and PharmGkb databases to identify target proteins associated with RA. The intersection of targets corresponding to the drugs' active ingredients and those associated with RA provided crucial insights. Functional analysis, including GO and KEGG pathway enrichment analyses, was performed on the intersecting targets using R (v 4.2.2). Additionally, a protein-protein interaction (PPI) network of the intersecting targets was constructed using the String platform. The resulting drug-ingredient-target-disease topology network was visualized using Cytoscape (v 3.8.0), and the Cytohubba plugin facilitated the identification of hub genes. The study revealed 35 active ingredients of CZ-HB and their corresponding 673 targets. We identified 14 major active ingredients crucial to the drug’s effects by focusing on the degree values. Furthermore, our investigation uncovered 784 targets associated with RA. Through the intersection of drug and disease targets, we pinpointed 34 active ingredients of CZ-HB capable of acting on 126 targets implicated in RA. The topological network analysis of the intersected genes identified five hub genes. The binding affinity of these hub genes to the 14 primary active ingredients of the drug was confirmed through molecular docking. The enrichment results of the intersecting genes suggested that CZ-HB exerted its anti-RA effects through a multi-component, multi-target, and multi-pathway approach.展开更多
Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the predi...Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.展开更多
文摘This study aims to evaluate the solar and wind energy potential across Razavi Khorasan Province,Iran,with a specific focus on the Khaf region.A preliminary assessment of mean solar radiation,mean wind speeds,and Weibull distribution parameters was conducted for different towns and zones within the province.The findings showed that Khaf has favorable characteristics for further analysis.The solar and wind energy metrics examined include global horizontal irradiance,clearness index,wind rose patterns,and turbulence intensity.At a height of 40 m,Khaf’s wind power density reached 1650 W/m^(2),indicating exceptional wind energy generation potential.Additionally,Khaf received an average annual solar radiation of 2046 kW·h/m^(2),representing significant solar energy potential.Harnessing these substantial renewable resources in Khaf could allow Razavi Khorasan Province to reduce reliance on fossil fuels,improve energy sustainability,and mitigate climate change impacts.This research contributes an in-depth assessment of Razavi Khorasan's solar and wind energy potential,particularly for the promising Khaf region.Further work may examine optimal sites for renewable energy projects and grid integration strategies to leverage these resources.
基金supported in part by the National Natural Science Foundation of China(Nos.51978337,U2039209).
文摘The seismic safety of offshore wind turbines is an important issue that needs to be solved urgently.Based on a unified computing framework,this paper develops a set of seawater-seabed-wind turbine zoning coupling analysis methods.A 5 MW wind turbine and a site analysis model are established,and a seismic wave is selected to analyze the changes in the seismic response of offshore monopile wind turbines under the change of seawater depth,seabed wave velocity and seismic wave incidence angle.The analysis results show that when the seawater increases to a certain depth,the seismic response of the wind turbine increases.The shear wave velocity of the seabed affects the bending moment and displacement at the bottom of the tower.When the angle of incidence increases,the vertical displacement and the acceleration of the top of the tower increase in varying degrees.
基金National Natural Science Foundations of China (Grant No. 81960863)the Education Department of Yunnan Province (Grant No. 2023Y0463)。
文摘In the present study, we explored the therapeutic potential of Cang Zhu-Huang Bai (CZ-HB) against rheumatoid arthritis (RA) and elucidated the associated mechanisms. The approach involved a systematic examination of the chemical ingredients of CZ-HB using TCMSP database. Subsequently, we predicted the targets corresponding to the active ingredients through the SwissTargetPrediction database. We constructed a comprehensive drug-ingredient-target network using Cytoscape (v 3.8.0), with the main ingredients of the drugs identified based on their degree values. We conducted a meticulous search across GEO, GeneCards, Therapeutic Target Database (TTD), and PharmGkb databases to identify target proteins associated with RA. The intersection of targets corresponding to the drugs' active ingredients and those associated with RA provided crucial insights. Functional analysis, including GO and KEGG pathway enrichment analyses, was performed on the intersecting targets using R (v 4.2.2). Additionally, a protein-protein interaction (PPI) network of the intersecting targets was constructed using the String platform. The resulting drug-ingredient-target-disease topology network was visualized using Cytoscape (v 3.8.0), and the Cytohubba plugin facilitated the identification of hub genes. The study revealed 35 active ingredients of CZ-HB and their corresponding 673 targets. We identified 14 major active ingredients crucial to the drug’s effects by focusing on the degree values. Furthermore, our investigation uncovered 784 targets associated with RA. Through the intersection of drug and disease targets, we pinpointed 34 active ingredients of CZ-HB capable of acting on 126 targets implicated in RA. The topological network analysis of the intersected genes identified five hub genes. The binding affinity of these hub genes to the 14 primary active ingredients of the drug was confirmed through molecular docking. The enrichment results of the intersecting genes suggested that CZ-HB exerted its anti-RA effects through a multi-component, multi-target, and multi-pathway approach.
基金supported by National Natural Science Foundation of China(Nos.61662042,62062049)Science and Technology Plan of Gansu Province(Nos.21JR7RA288,21JR7RE174).
文摘Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.
