The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(L...The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).展开更多
【目的】为了揭示山东省韭菜迟眼蕈蚊Bradysia odoriphaga Yang et Zhang种群共生菌Wolbachia的感染率及其分类地位,探讨该共生菌对韭菜迟眼蕈蚊的潜在影响。【方法】利用线粒体细胞色素氧化酶I（mt COI）基因引物（LCO1490/HCO2198）,...【目的】为了揭示山东省韭菜迟眼蕈蚊Bradysia odoriphaga Yang et Zhang种群共生菌Wolbachia的感染率及其分类地位,探讨该共生菌对韭菜迟眼蕈蚊的潜在影响。【方法】利用线粒体细胞色素氧化酶I（mt COI）基因引物（LCO1490/HCO2198）,通过扩增测序和序列比对对采自山东省12个地区的根蛆种群进行了分类鉴定。在上述基础上,利用Wolbachia的16S r DNA和wsp基因特异引物（分别为16S-F/16S-R和81F/691R）对鉴别出的11个韭菜迟眼蕈蚊种群体内Wolbachia感染情况进行了PCR检测;对感染个体体内Wolbachia依据16S r DNA基因片段序列进行分类鉴定。【结果】山东省12个根蛆种群中,11个种群为韭菜迟眼蕈蚊种群。基于Wolbachia的16S r DNA基因特异引物检测结果发现,这些韭菜迟眼蕈蚊种群广泛感染Wolbachia（感染率为6.67%~93.33%）,而利用wsp基因特异引物检测的感染率（0.00%~40.00%）相对较低些。基于Wolbachia的16S r DNA基因构建系统发育树表明,这些韭菜迟眼蕈蚊种群感染的Wolbachia全部属于A组。【结论】确定了Wolbachia在山东省韭菜迟眼蕈蚊体内的感染率及其分类地位,为研究Wolbachia对韭菜迟眼蕈蚊生物学及生态学的影响奠定了基础。展开更多
文摘The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).
文摘【目的】为了揭示山东省韭菜迟眼蕈蚊Bradysia odoriphaga Yang et Zhang种群共生菌Wolbachia的感染率及其分类地位,探讨该共生菌对韭菜迟眼蕈蚊的潜在影响。【方法】利用线粒体细胞色素氧化酶I（mt COI）基因引物（LCO1490/HCO2198）,通过扩增测序和序列比对对采自山东省12个地区的根蛆种群进行了分类鉴定。在上述基础上,利用Wolbachia的16S r DNA和wsp基因特异引物（分别为16S-F/16S-R和81F/691R）对鉴别出的11个韭菜迟眼蕈蚊种群体内Wolbachia感染情况进行了PCR检测;对感染个体体内Wolbachia依据16S r DNA基因片段序列进行分类鉴定。【结果】山东省12个根蛆种群中,11个种群为韭菜迟眼蕈蚊种群。基于Wolbachia的16S r DNA基因特异引物检测结果发现,这些韭菜迟眼蕈蚊种群广泛感染Wolbachia（感染率为6.67%~93.33%）,而利用wsp基因特异引物检测的感染率（0.00%~40.00%）相对较低些。基于Wolbachia的16S r DNA基因构建系统发育树表明,这些韭菜迟眼蕈蚊种群感染的Wolbachia全部属于A组。【结论】确定了Wolbachia在山东省韭菜迟眼蕈蚊体内的感染率及其分类地位,为研究Wolbachia对韭菜迟眼蕈蚊生物学及生态学的影响奠定了基础。
