Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored ...Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.展开更多
Objective To explore the medication rules of traditional Chinese medicine(TCM)and mechanism of action of hub herb pairs for treating insomnia.Methods Totally 104 prescriptions were statistically analyzed.The associati...Objective To explore the medication rules of traditional Chinese medicine(TCM)and mechanism of action of hub herb pairs for treating insomnia.Methods Totally 104 prescriptions were statistically analyzed.The association rule algorithm was applied to mine the hub herb pairs.Network pharmacology was utilized to analyze the mechanism of the hub herb pairs,while molecular docking was applied to simulate the interaction between receptors and herb molecules,thereby predicting their binding affinities.Results The most frequently used herbs in TCM prescriptions for treating insomnia included Semen Ziziphi Spinosae,Radix Glycyrrhizae,Radix et Rhizoma Ginseng,and Poria cum Radix Pini.Among them,the most commonly used were the supplementing herbs,followed by heat-clearing,mind-calming,and exterior-releasing ones,with their properties of warm and cold,flavors of sweet,Pungent,and bitter,and meridian tropisms of liver,lungs,spleen,kidneys,heart,and stomach.The hub herb pairs based on the association rules included Radix Astragali-Radix et Rhizoma Ginseng,Rhizoma Chuanxiong-Radix Glycyrrhizae,Seman Platycladi-Semen Ziziphi Spinosae,Pericarpium Citri Reticulatae-Radix Glycyrrhizae,Radix Polygalae-Semen Ziziphi Spinosae,and Radix Astragali-Semen Ziziphi Spinosae.Network pharmacology revealed that the cAMP signaling pathway might play a key role in treating insomnia synergistically with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway.Molecular docking indicated that there was good binding between the active ingredients of the hub herb pairs and the hub targets.Conclusions This study identified six hub herb pairs for treating insomnia in TCM.These hub herb pairs may synergistically treat insomnia with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway through the cAMP signaling pathway.展开更多
Objective:To explore the acupoint selection rules of acupuncture for trigeminal neuralgia(TN) based on complex network.Methods:The articles on clinical research of acupuncture for TN published up to March 2019 were se...Objective:To explore the acupoint selection rules of acupuncture for trigeminal neuralgia(TN) based on complex network.Methods:The articles on clinical research of acupuncture for TN published up to March 2019 were searched from the databases,i.e.CNKI,Wanfang,VIP,PubMed,Web of Science and Science Direct.A prescription database of acupuncture for TN was established.Using complex network,the core acupoints and acupoint selection rules were analyzed for TN treated with acupuncture.Results:A total of 304 articles,including 272 acupoint prescriptions were obtained.The complex network constructed for TN treated with acupuncture was in compliance with the small world effect.Using k-core analytic hierarchy process,36 acupoints were screened,and the total frequency of acupoints is1175.Regarding the meridian distribution,the points of yangming meridians of hand and foot were predominated,accounting for 50.21% of the overall(590/1175).In terms of acupoint location,the acupoints on the head and face were predominated,accounting for 52.51%(617/1175).For the types of acupoint,the specific acupoints were predominated,accounting for 71.32%(838/1175) and the majority was the intersecting points,accounting for 33.87%(398/1175).Based on community structure partition,the treatment of TN with acupuncture was divided into the treatment for symptoms,etiological treatment,and mind regulation.Besides,the supplementary acupoints based on the involved nerve branches of TN and those based on syndrome differentiation were recommended.Conclusion:The core acupoints of acupuncture for TN are Hégu(合谷LI4),Xiaguan(下关ST7),Tàichong(太冲LR3),Fēngchí(风池GB20) and Sìbái(四白ST2).In clinical treatment,the main therapeutics include the local analgesia for the symptoms and etiological treatment,associated with mind regulation.展开更多
Objective:To analyze the basis and medication rules of Chinese herbs in the regulation of necroptosis.Methods:With the help of GeneCards,DrugBank,TTD,DisGeNET,OMIM database to collect the action targets of necroptosis...Objective:To analyze the basis and medication rules of Chinese herbs in the regulation of necroptosis.Methods:With the help of GeneCards,DrugBank,TTD,DisGeNET,OMIM database to collect the action targets of necroptosis,the TCMSP database to obtain the target‑related compounds and Chinese herbs,and the ADME criteria and Lipinski rule as the conditions for screening,to build the target‑compound,target‑compound‑Chinese herbs network.The information of Chinese herbal medicine's sexual taste and meridian was collected,and the drug use pattern was analyzed.The information on the property,flavor and channel tropism of Chinese herbs was collected to analyze the medication laws.Molecular docking of core targets and compounds in the network with AutoDockTools software,and PyMOL software was used to display the combinations with good docking results.Results:A total of 12 potential targets acting on necroptosis were obtained,matching to 191 candidate compounds and 366 herbal medicines.Quercetin,wogonin,triptolide,licochalcone a,ellipticine are more important and may be the main small molecule substances underlying the regulation of necroptosis.The more important Chinese herbs are Licorice,Forsythia,Salivae Miltiorrhizae,Ginkgo Leaf,Eucommia ulmoides Oliv,etc.The herbal medicines are mainly bitter and pungent,with cold and warm taste,which were attributed to the liver and lung meridians.BCL2‑beta‑sitosterol、MAPK14‑luteolin、MAPK14‑formononetin、TP53‑formononetin are better molecular docking results,which have strong docking activity.Conclusion:The study systematically analyzes the material basis of regulating necroptosis and summarizes the general rule of regulating necroptosis in Chinese medicine,which provides ideas for clinical development of agents to interfere with necroptosis.展开更多
Background:On December 8,2019,Wuhan City,Hubei Province,a new type of coronavirus disease 2019(COVID-2019)was firstly discovered,and COVID-2019 spread rapidly in China.The number of confirmed cases in various province...Background:On December 8,2019,Wuhan City,Hubei Province,a new type of coronavirus disease 2019(COVID-2019)was firstly discovered,and COVID-2019 spread rapidly in China.The number of confirmed cases in various provinces and cities rose sharply in China.In clinical treatment,Chinese medicine treatment showed significant efficacy.Since the outbreak,the National Health Commission(NHS)of China has issued seven editions of the“Pneumonitis Diagnosis and Treatment Program for COVID-2019”,at the same time,most provincial health boards and the Chinese Medicine Administration had also released information on the prevention and control scheme of COVID-2019 by Chinese medicine.The purpose of this study is to explore the compatibility rules of the main drugs in the prescription and the potential mechanism on COVID-2019 pneumonia,in order to provide reference for clinical research and new drug development of COVID-2019.Methods:This article uses the TCM inheritance assistance system and network pharmacology BATMAN-TCM online analysis system to collect and summarize the national“Pneumonitis Diagnosis and Treatment Program for COVID-2019(trial version sixth)”and formulae for adult treatment from the TCM prevention program of 23 provinces and cities.Results:We found that the most formulae for the treatment of COVID-2019 were modified on the basis of Maxing Shigan decoction and the top 5 high-frequencyn drugs are Xingren(Armeniacae semen amarum),Mahuang(Ephedrae herba),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),and Haungqin(Radix scutellariae).High frequency traditional Chinese medicines are mainly used for relieving the symptoms,clearing away heat,eliminating dampness,resolving phlegm,relieving cough and asthma,promoting water and dampness,and tonifying deficiency.Warm medicine and bitter medicine are the most frequently used drugs in four Qi attribute and five flavor attribute,respectively.Most of drugs are belong to lung,stomach and spleen channel.Mahuang(Ephedrae herba),Xingren(Armeniacae semen amarum),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),Cangzhu(Atractylodis rhizama)and Huoxiang(Pogostemonis herba)are the core drugs for treating COVID-2019.The TTD disease enrichment,target and signal transduction pathways of the six drugs showed that pneumonia and asthma were most closely related to COVID-2019.And the inflammatory reaction-related pathways may be the main pathways through which these drugs function.Conclusions:The modified Maxing Shigan decoction is the main prescription for the treatment of COVID-2019.The Xingren(Armeniacae semen amarum),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),Cangzhu(Atractylodis rhizama)and Huoxiang(Pogostemonis herba)have certain theoretical and experimental basis for the treatment of COVID-2019 through network pharmacology analysis,but further experiments are needed to verify the effects.展开更多
In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate（MRR）. After the advent ...In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate（MRR）. After the advent of high-speed milling（HSM） pro cess, lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters. It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism. In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network（FNN） so as to have the most effective knowledge-base for given set of data. Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters. A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed. After training process, raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules. The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.展开更多
Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose...Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.展开更多
The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic ...The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.展开更多
Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper descri...Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.展开更多
This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfac...This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.展开更多
Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.Th...Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.展开更多
There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from u...There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from underground to aboveground. The second is an underground medium such as tunnel, cave etc. and the data is transmitted from underground to the aboveground through partially solid medium. The quality of communication is greatly influenced by the humidity of the soil in both environments. The placement of wireless underground sensor nodes at hard-to-reach locations makes energy efficient work compulsory. In this paper, rule based collector station selection scheme is proposed for lossless data transmission in underground sensor networks. In order for sensor nodes to transmit energy-efficient lossless data, rulebased selection operations are carried out with the help of fuzzy logic. The proposed wireless underground sensor network is simulated using Riverbed software, and fuzzy logic-based selection scheme is implemented utilizing Matlab software. In order to evaluate the performance of the sensor network;the parameters of delay, throughput and energy consumption are investigated. Examining performance evaluation results, it is seen that average delay and maximum throughput are accomplished in the proposed underground sensor network. Under these conditions, it has been shown that the most appropriate collector station selection decision is made with the aim of minimizing energy consumption.展开更多
A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the...A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart pole balancing system and simulation result shows significant improvements on the rule generation.展开更多
Fetal ECG extraction has the vital significance for fetal monitoring.This paper introduces a method of extracting fetal ECG based on adaptive linear neural network.The method can be realized by training a small quanti...Fetal ECG extraction has the vital significance for fetal monitoring.This paper introduces a method of extracting fetal ECG based on adaptive linear neural network.The method can be realized by training a small quantity of data.In addition,a better result can be achieved by improving neural network structure.Thus,more easily identified fetal ECG can be extracted.Experimental results show that the adaptive linear neural network can be used to extract fetal ECG from maternal abdominal signal effectively.What's more,a clearer fetal ECG can be extracted by improving neural network structure.展开更多
Based on the problem that the service entity only has the partial field of vision in the network environment,a trust evolvement method of the macro self-organization for Web service combination was proposed.In the met...Based on the problem that the service entity only has the partial field of vision in the network environment,a trust evolvement method of the macro self-organization for Web service combination was proposed.In the method,the control rule of the trust degree in the Dempster-Shafer(D-S)rule was utilized based on the entity network interactive behavior,and a proportion trust control rule was put up.The control rule could make the Web service self-adaptively study so as to gradually form a proper trust connection with its cooperative entities and to improve the security performance of the whole system.The experimental results show that the historical successful experience is saved during the service combination alliance,and the method can greatly improve the reliability and success rate of Web service combination.展开更多
We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-...We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.展开更多
基金supported by the National Key Research and Development Program of China (Nos.2022YFC3702000 and 2022YFC3703500)the Key R&D Project of Zhejiang Province (No.2022C03146).
文摘Severe ground-level ozone(O_(3))pollution over major Chinese cities has become one of the most challenging problems,which have deleterious effects on human health and the sustainability of society.This study explored the spatiotemporal distribution characteristics of ground-level O_(3) and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021.Then,a high-performance convolutional neural network(CNN)model was established by expanding the moment and the concentration variations to general factors.Finally,the response mechanism of O_(3) to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables.The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern.When the wind direction(WD)ranges from east to southwest and the wind speed(WS)ranges between 2 and 3 m/sec,higher O_(3) concentration prone to occur.At different temperatures(T),the O_(3) concentration showed a trend of first increasing and subsequently decreasing with increasing NO_(2) concentration,peaks at the NO_(2) concentration around 0.02mg/m^(3).The sensitivity of NO_(2) to O_(3) formation is not easily affected by temperature,barometric pressure and dew point temperature.Additionally,there is a minimum IRNO_(2) at each temperature when the NO_(2) concentration is 0.03 mg/m^(3),and this minimum IRNO_(2) decreases with increasing temperature.The study explores the response mechanism of O_(3) with the change of driving variables,which can provide a scientific foundation and methodological support for the targeted management of O_(3) pollution.
基金National Natural Science Foundation of China(82360905)Gansu Provincial University Teachers'Innovation Fund Projects(2023A-092 and 2024B-109).
文摘Objective To explore the medication rules of traditional Chinese medicine(TCM)and mechanism of action of hub herb pairs for treating insomnia.Methods Totally 104 prescriptions were statistically analyzed.The association rule algorithm was applied to mine the hub herb pairs.Network pharmacology was utilized to analyze the mechanism of the hub herb pairs,while molecular docking was applied to simulate the interaction between receptors and herb molecules,thereby predicting their binding affinities.Results The most frequently used herbs in TCM prescriptions for treating insomnia included Semen Ziziphi Spinosae,Radix Glycyrrhizae,Radix et Rhizoma Ginseng,and Poria cum Radix Pini.Among them,the most commonly used were the supplementing herbs,followed by heat-clearing,mind-calming,and exterior-releasing ones,with their properties of warm and cold,flavors of sweet,Pungent,and bitter,and meridian tropisms of liver,lungs,spleen,kidneys,heart,and stomach.The hub herb pairs based on the association rules included Radix Astragali-Radix et Rhizoma Ginseng,Rhizoma Chuanxiong-Radix Glycyrrhizae,Seman Platycladi-Semen Ziziphi Spinosae,Pericarpium Citri Reticulatae-Radix Glycyrrhizae,Radix Polygalae-Semen Ziziphi Spinosae,and Radix Astragali-Semen Ziziphi Spinosae.Network pharmacology revealed that the cAMP signaling pathway might play a key role in treating insomnia synergistically with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway.Molecular docking indicated that there was good binding between the active ingredients of the hub herb pairs and the hub targets.Conclusions This study identified six hub herb pairs for treating insomnia in TCM.These hub herb pairs may synergistically treat insomnia with HIF-1 signaling pathway,prolactin signaling pathway,chemical carcinogenesis receptor activation,and PI3K-Akt signaling pathway through the cAMP signaling pathway.
基金Supported by National Program on Key Basic Research Project:No.2014CB543201Tianjin College Students’Innovative and Entrepreneurial Training Program,grant number:No.201910063032Tianjin University of Traditional Chinese Medicine College Students’Innovative and Entrepreneurial Training Program,No.CXJJ2019YG03。
文摘Objective:To explore the acupoint selection rules of acupuncture for trigeminal neuralgia(TN) based on complex network.Methods:The articles on clinical research of acupuncture for TN published up to March 2019 were searched from the databases,i.e.CNKI,Wanfang,VIP,PubMed,Web of Science and Science Direct.A prescription database of acupuncture for TN was established.Using complex network,the core acupoints and acupoint selection rules were analyzed for TN treated with acupuncture.Results:A total of 304 articles,including 272 acupoint prescriptions were obtained.The complex network constructed for TN treated with acupuncture was in compliance with the small world effect.Using k-core analytic hierarchy process,36 acupoints were screened,and the total frequency of acupoints is1175.Regarding the meridian distribution,the points of yangming meridians of hand and foot were predominated,accounting for 50.21% of the overall(590/1175).In terms of acupoint location,the acupoints on the head and face were predominated,accounting for 52.51%(617/1175).For the types of acupoint,the specific acupoints were predominated,accounting for 71.32%(838/1175) and the majority was the intersecting points,accounting for 33.87%(398/1175).Based on community structure partition,the treatment of TN with acupuncture was divided into the treatment for symptoms,etiological treatment,and mind regulation.Besides,the supplementary acupoints based on the involved nerve branches of TN and those based on syndrome differentiation were recommended.Conclusion:The core acupoints of acupuncture for TN are Hégu(合谷LI4),Xiaguan(下关ST7),Tàichong(太冲LR3),Fēngchí(风池GB20) and Sìbái(四白ST2).In clinical treatment,the main therapeutics include the local analgesia for the symptoms and etiological treatment,associated with mind regulation.
基金National Natural Science Foundation Project(No.82174415)Science and Technology Innovation Project of the China Academy of Chinese Medical Sciences(No.CI2021A05054)Science and Technology Innovation Project of the China Academy of Chinese Medical Sciences(No.CI2021A01818)。
文摘Objective:To analyze the basis and medication rules of Chinese herbs in the regulation of necroptosis.Methods:With the help of GeneCards,DrugBank,TTD,DisGeNET,OMIM database to collect the action targets of necroptosis,the TCMSP database to obtain the target‑related compounds and Chinese herbs,and the ADME criteria and Lipinski rule as the conditions for screening,to build the target‑compound,target‑compound‑Chinese herbs network.The information of Chinese herbal medicine's sexual taste and meridian was collected,and the drug use pattern was analyzed.The information on the property,flavor and channel tropism of Chinese herbs was collected to analyze the medication laws.Molecular docking of core targets and compounds in the network with AutoDockTools software,and PyMOL software was used to display the combinations with good docking results.Results:A total of 12 potential targets acting on necroptosis were obtained,matching to 191 candidate compounds and 366 herbal medicines.Quercetin,wogonin,triptolide,licochalcone a,ellipticine are more important and may be the main small molecule substances underlying the regulation of necroptosis.The more important Chinese herbs are Licorice,Forsythia,Salivae Miltiorrhizae,Ginkgo Leaf,Eucommia ulmoides Oliv,etc.The herbal medicines are mainly bitter and pungent,with cold and warm taste,which were attributed to the liver and lung meridians.BCL2‑beta‑sitosterol、MAPK14‑luteolin、MAPK14‑formononetin、TP53‑formononetin are better molecular docking results,which have strong docking activity.Conclusion:The study systematically analyzes the material basis of regulating necroptosis and summarizes the general rule of regulating necroptosis in Chinese medicine,which provides ideas for clinical development of agents to interfere with necroptosis.
文摘Background:On December 8,2019,Wuhan City,Hubei Province,a new type of coronavirus disease 2019(COVID-2019)was firstly discovered,and COVID-2019 spread rapidly in China.The number of confirmed cases in various provinces and cities rose sharply in China.In clinical treatment,Chinese medicine treatment showed significant efficacy.Since the outbreak,the National Health Commission(NHS)of China has issued seven editions of the“Pneumonitis Diagnosis and Treatment Program for COVID-2019”,at the same time,most provincial health boards and the Chinese Medicine Administration had also released information on the prevention and control scheme of COVID-2019 by Chinese medicine.The purpose of this study is to explore the compatibility rules of the main drugs in the prescription and the potential mechanism on COVID-2019 pneumonia,in order to provide reference for clinical research and new drug development of COVID-2019.Methods:This article uses the TCM inheritance assistance system and network pharmacology BATMAN-TCM online analysis system to collect and summarize the national“Pneumonitis Diagnosis and Treatment Program for COVID-2019(trial version sixth)”and formulae for adult treatment from the TCM prevention program of 23 provinces and cities.Results:We found that the most formulae for the treatment of COVID-2019 were modified on the basis of Maxing Shigan decoction and the top 5 high-frequencyn drugs are Xingren(Armeniacae semen amarum),Mahuang(Ephedrae herba),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),and Haungqin(Radix scutellariae).High frequency traditional Chinese medicines are mainly used for relieving the symptoms,clearing away heat,eliminating dampness,resolving phlegm,relieving cough and asthma,promoting water and dampness,and tonifying deficiency.Warm medicine and bitter medicine are the most frequently used drugs in four Qi attribute and five flavor attribute,respectively.Most of drugs are belong to lung,stomach and spleen channel.Mahuang(Ephedrae herba),Xingren(Armeniacae semen amarum),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),Cangzhu(Atractylodis rhizama)and Huoxiang(Pogostemonis herba)are the core drugs for treating COVID-2019.The TTD disease enrichment,target and signal transduction pathways of the six drugs showed that pneumonia and asthma were most closely related to COVID-2019.And the inflammatory reaction-related pathways may be the main pathways through which these drugs function.Conclusions:The modified Maxing Shigan decoction is the main prescription for the treatment of COVID-2019.The Xingren(Armeniacae semen amarum),Gancao(Glycyrrhizae radix et rhizoma),Shigao(Gypsum fibrosum),Cangzhu(Atractylodis rhizama)and Huoxiang(Pogostemonis herba)have certain theoretical and experimental basis for the treatment of COVID-2019 through network pharmacology analysis,but further experiments are needed to verify the effects.
基金supported by International Science and Technology Cooperation project (Grant No. 2008DFA71750)
文摘In metal cutting industry it is a common practice to search for optimal combination of cutting parameters in order to maximize the tool life for a fixed minimum value of material removal rate（MRR）. After the advent of high-speed milling（HSM） pro cess, lots of experimental and theoretical researches have been done for this purpose which mainly emphasized on the optimization of the cutting parameters. It is highly beneficial to convert raw data into a comprehensive knowledge-based expert system using fuzzy logic as the reasoning mechanism. In this paper an attempt has been presented for the extraction of the rules from fuzzy neural network（FNN） so as to have the most effective knowledge-base for given set of data. Experiments were conducted to determine the best values of cutting speeds that can maximize tool life for different combinations of input parameters. A fuzzy neural network was constructed based on the fuzzification of input parameters and the cutting speed. After training process, raw rule sets were extracted and a rule pruning approach was proposed to obtain concise linguistic rules. The estimation process with fuzzy inference showed that the optimized combination of fuzzy rules provided the estimation error of only 6.34 m/min as compared to 314 m/min of that of randomized combination of rule s.
基金supported in part by the National Natural Science Foundation of China under Grant 72264036in part by the West Light Foundation of The Chinese Academy of Sciences under Grant 2020-XBQNXZ-020+1 种基金Social Science Foundation of Xinjiang under Grant 2023BGL077the Research Program for High-level Talent Program of Xinjiang University of Finance and Economics 2022XGC041,2022XGC042.
文摘Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.
基金the Research of New Intelligent Integrated Transport Information System,Technical Plan Project of Binhai New District,Tianjin(No.2015XJR21017)
文摘The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.
文摘Simultaneous perturbation stochastic approximation （SPSA） belongs to the class of gradient-free optimization methods that extract gradient information from successive objective function evaluation. This paper describes an improved SPSA algorithm, which entails fuzzy adaptive gain sequences, gradient smoothing, and a step rejection procedure to enhance convergence and stability. The proposed fuzzy adaptive simultaneous perturbation approximation （FASPA） algorithm is particularly well suited to problems involving a large number of parameters such as those encountered in nonlinear system identification using neural networks （NNs）. Accordingly, a multilayer perceptron （MLP） network with popular training algorithms was used to predicate the system response. We found that an MLP trained by FASPSA had the desired accuracy that was comparable to results obtained by traditional system identification algorithms. Simulation results for typical nonlinear systems demonstrate that the proposed NN architecture trained with FASPSA yields improved system identification as measured by reduced time of convergence and a smaller identification error.
文摘This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.
基金supported by the National Natural Science Foundation of China(Grant No.82104701)Science Fund Program for Outstanding Young Scholars in Universities of Anhui Province(Grant No.2022AH030064)+3 种基金Key Project at Central Government Level:the Ability Establishment of Sustainable Use for Valuable Chinese Medicine Resources(Grant No.2060302)Foundation of Anhui Province Key Laboratory of Pharmaceutical Preparation Technology and Application(Grant No.2021KFKT10)China Agriculture Research System of MOF and MARA(Grant No.CARS-21)Talent Support Program of Anhui University of Chinese Medicine(Grant No.2020rcyb007).
文摘Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.
文摘There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from underground to aboveground. The second is an underground medium such as tunnel, cave etc. and the data is transmitted from underground to the aboveground through partially solid medium. The quality of communication is greatly influenced by the humidity of the soil in both environments. The placement of wireless underground sensor nodes at hard-to-reach locations makes energy efficient work compulsory. In this paper, rule based collector station selection scheme is proposed for lossless data transmission in underground sensor networks. In order for sensor nodes to transmit energy-efficient lossless data, rulebased selection operations are carried out with the help of fuzzy logic. The proposed wireless underground sensor network is simulated using Riverbed software, and fuzzy logic-based selection scheme is implemented utilizing Matlab software. In order to evaluate the performance of the sensor network;the parameters of delay, throughput and energy consumption are investigated. Examining performance evaluation results, it is seen that average delay and maximum throughput are accomplished in the proposed underground sensor network. Under these conditions, it has been shown that the most appropriate collector station selection decision is made with the aim of minimizing energy consumption.
文摘A reinforcemen based fuzzy neural network control with automatic rule generation (RBFNNC) is proposed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart pole balancing system and simulation result shows significant improvements on the rule generation.
基金Foundation of Young Backbone Teacher of Beijing Citygrant number:102KB000845
文摘Fetal ECG extraction has the vital significance for fetal monitoring.This paper introduces a method of extracting fetal ECG based on adaptive linear neural network.The method can be realized by training a small quantity of data.In addition,a better result can be achieved by improving neural network structure.Thus,more easily identified fetal ECG can be extracted.Experimental results show that the adaptive linear neural network can be used to extract fetal ECG from maternal abdominal signal effectively.What's more,a clearer fetal ECG can be extracted by improving neural network structure.
基金Project(60673169)supported by the National Natural Science Foundation of China
文摘Based on the problem that the service entity only has the partial field of vision in the network environment,a trust evolvement method of the macro self-organization for Web service combination was proposed.In the method,the control rule of the trust degree in the Dempster-Shafer(D-S)rule was utilized based on the entity network interactive behavior,and a proportion trust control rule was put up.The control rule could make the Web service self-adaptively study so as to gradually form a proper trust connection with its cooperative entities and to improve the security performance of the whole system.The experimental results show that the historical successful experience is saved during the service combination alliance,and the method can greatly improve the reliability and success rate of Web service combination.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61873002, 61703004, 61973199, 61573008, and 61973200)。
文摘We investigate the problem of H_(∞) state estimation for discrete-time Markov jump neural networks. The transition probabilities of the Markov chain are assumed to be piecewise time-varying, and the persistent dwell-time switching rule,as a more general switching rule, is adopted to describe this variation characteristic. Afterwards, based on the classical Lyapunov stability theory, a Lyapunov function is established, in which the information about the Markov jump feature of the system mode and the persistent dwell-time switching of the transition probabilities is considered simultaneously.Furthermore, via using the stochastic analysis method and some advanced matrix transformation techniques, some sufficient conditions are obtained such that the estimation error system is mean-square exponentially stable with an H_(∞) performance level, from which the specific form of the estimator can be obtained. Finally, the rationality and effectiveness of the obtained results are verified by a numerical example.
