In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under...In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current （AC） stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.展开更多
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hami...Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.展开更多
The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established...The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.展开更多
Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-set annotations.As an interpretable neural network,BA...Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-set annotations.As an interpretable neural network,BANNs model SNP and SNP-set effects in their input and hidden layers,respectively.Furthermore,the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales.However,its application in genomic prediction has yet to be explored.Results This study extended the BANNs framework to the area of genomic selection and explored the optimal SN P-set partitioning strategies by using dairy cattle datasets.The SN P-sets were partitioned based on two strategiesgene annotations and 100 kb windows,denoted as BANN_gene and BANN_100kb,respectively.The BANNs model was compared with GBLU P,random forest(RF),BayesB and BayesCπthrough five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits,type traits,and one health trait of 6,558,6,210and 5,962 Chinese Holsteins,respectively.Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLU P,RF and Bayesian methods.Specifically,the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP,RF,BayesB and BayesCrr across all traits.The average accuracy improvements of BANN_100kb over GBLU P,RF,BayesB and BayesCrr were 4.86%,3.95%,3.84%and 1.92%,and the accuracy of BANN_gene was improved by3.75%,2.86%,2.73%and 0.85%compared to GBLUP,RF,BayesB and BayesCπ,respectively across all seven traits.Meanwhile,both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP,RF and Bayesian methods.Conclusion Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios,and might serve as a promising alternative approach for genomic prediction in dairy cattle.展开更多
One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques t...One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.展开更多
基金The project supported by National Natural Science Foundation of China under Grant Nos. 70571017 and 10547004 and the Key Projects of National Natural Science Foundation of China under Grant No. 70431002
文摘In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current （AC） stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.
文摘Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
基金supported by the National Natural Science Foundation of China(grant No.52375247)Natural Science Foundation of Jiangsu Province(grant No.BK20201421)+3 种基金Graduate Research and Innovation Projects of Jiangsu Province(grant No.KYCX21-3380)Jiangsu Agricultural Science and Technology Independent Innovation Fund(grant No.CX(22)3090)Taizhou Science and Technology Project(grant No.TN202101)a Project Funded by the Priority Academic Program Development of Jiangsu Higher。
文摘The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.
基金supported by the National Key Research and Development Program of China(2022YFD1302204)the earmarked fund CARS36+1 种基金Ningxia Key Research and Development Program of China(2023BCF010042019NYYZ09)。
文摘Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-set annotations.As an interpretable neural network,BANNs model SNP and SNP-set effects in their input and hidden layers,respectively.Furthermore,the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales.However,its application in genomic prediction has yet to be explored.Results This study extended the BANNs framework to the area of genomic selection and explored the optimal SN P-set partitioning strategies by using dairy cattle datasets.The SN P-sets were partitioned based on two strategiesgene annotations and 100 kb windows,denoted as BANN_gene and BANN_100kb,respectively.The BANNs model was compared with GBLU P,random forest(RF),BayesB and BayesCπthrough five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits,type traits,and one health trait of 6,558,6,210and 5,962 Chinese Holsteins,respectively.Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLU P,RF and Bayesian methods.Specifically,the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP,RF,BayesB and BayesCrr across all traits.The average accuracy improvements of BANN_100kb over GBLU P,RF,BayesB and BayesCrr were 4.86%,3.95%,3.84%and 1.92%,and the accuracy of BANN_gene was improved by3.75%,2.86%,2.73%and 0.85%compared to GBLUP,RF,BayesB and BayesCπ,respectively across all seven traits.Meanwhile,both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP,RF and Bayesian methods.Conclusion Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios,and might serve as a promising alternative approach for genomic prediction in dairy cattle.
基金support from Taif University Researchers supporting Project Number(TURSP-2020/215),Taif University,Taif,Saudi Arabia.
文摘One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.
