Background Rumen bacterial groups can affect growth performance,such as average daily gain(ADG),feed intake,and efficiency.The study aimed to investigate the inter-relationship of rumen bacterial composition,rumen fer...Background Rumen bacterial groups can affect growth performance,such as average daily gain(ADG),feed intake,and efficiency.The study aimed to investigate the inter-relationship of rumen bacterial composition,rumen fermentation indicators,serum indicators,and growth performance of Holstein heifer calves with different ADG.Twelve calves were chosen from a trail with 60 calves and divided into higher ADG(HADG,high pre-and post-weaning ADG,n=6)and lower ADG(LADG,low pre-and post-weaning ADG,n=6)groups to investigate differences in bacterial composition and functions and host phenotype.Results During the preweaning period,the relative abundances of propionate producers,including g_norank_f_Butyricicoccaceae,g_Pyramidobacter,and g_norank_f_norank_o_Clostridia_vadin BB60_group,were higher in HADG calves(LDA>2,P<0.05).Enrichment of these bacteria resulted in increased levels of propionate,a gluconeogenic precursor,in preweaning HADG calves(adjusted P<0.05),which consequently raised serum glucose concentrations(adjusted P<0.05).In contrast,the relative abundances of rumen bacteria in post-weaning HADG calves did not exert this effect.Moreover,no significant differences were observed in rumen fermentation parameters and serum indices between the two groups.Conclusions The findings of this study revealed that the preweaning period is the window of opportunity for rumen bacteria to regulate the ADG of calves.展开更多
In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily re...In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.展开更多
The study of average convection in a rotating cavity subjected to modulated rotation is an interesting area for the development of both fundamental and applied science.This phenomenon finds application in the field of...The study of average convection in a rotating cavity subjected to modulated rotation is an interesting area for the development of both fundamental and applied science.This phenomenon finds application in the field of mass transfer and fluid flow control,relevant examples being crystal growth under reduced gravity and fluid mixing in microfluidic devices for cell cultures.In this study,the averaged flow generated by the oscillating motion of a fluid in a planar layer rotating about a horizontal axis is experimentally investigated.The boundaries of the layer are maintained at constant temperatures,while the lateral cylindrical wall is thermally insulated.It is demonstrated that libration results in intense oscillatory fluid motion,which in turn produces a time-averaged flow.For the first time,quantitative measures for the instantaneous velocity field are obtained using the Particle Image Velocimetry technique.It is revealed that the flow has the form of counter-rotating vortices.The vortex circulations sense changes during a libration cycle.An increase in the rotation rate and amplitude of the cavity libration results in an increase in the flow intensity.The heat transfer and time-averaged velocity are examined accordingly as a function of the dimensionless oscillation frequency,and resonant excitation of heat transfer and average oscillation velocity are revealed.The threshold curve for the onset of the averaged convection is identified in the plane of control parameters(dimensionless rotational velocity and pulsation Reynolds number).It is found that an increase in the dimensionless rotational velocity has a stabilizing effect on the onset of convection.展开更多
By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning...By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.展开更多
In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries fa...In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.展开更多
Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-e...Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.展开更多
The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector ...The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.展开更多
Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world ove...Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the average degree and the average path length of edge vertices of China aviation netwo...In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the average degree and the average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the average degree and average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Through regression analysis,it was found that the average degree had a logarithmic relationship with the average path length of edge vertices and the two parameters of the logarithmic relationship had linear evolutionary trace.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average...In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network, the probability distribution and evolution trace of average degree of edge vertices of China aviation network w...In order to reveal the complex network characteristics and evolution principle of China aviation network, the probability distribution and evolution trace of average degree of edge vertices of China aviation network were studied based on the statistics data of China civil aviation network in 1988, 1994, 2001, 2008 and 2015. According to the theory and method of complex network, the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network. Based on the statistical data, the average degrees of edge vertices in China aviation network in 1988, 1994, 2001, 2008 and 2015 were calculated. Using the probability statistical analysis method and regression analysis approach, it was found that the average degree of edge vertices had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001...In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.展开更多
基金funded by National Key R&D Program of China(2022YFA1304204)Agricultural Science and Technology Innovation Program(CAAS-ASTIP-2017-FRI-04)Beijing Innovation Consortium of livestock Research System(BAIC05-2023)。
文摘Background Rumen bacterial groups can affect growth performance,such as average daily gain(ADG),feed intake,and efficiency.The study aimed to investigate the inter-relationship of rumen bacterial composition,rumen fermentation indicators,serum indicators,and growth performance of Holstein heifer calves with different ADG.Twelve calves were chosen from a trail with 60 calves and divided into higher ADG(HADG,high pre-and post-weaning ADG,n=6)and lower ADG(LADG,low pre-and post-weaning ADG,n=6)groups to investigate differences in bacterial composition and functions and host phenotype.Results During the preweaning period,the relative abundances of propionate producers,including g_norank_f_Butyricicoccaceae,g_Pyramidobacter,and g_norank_f_norank_o_Clostridia_vadin BB60_group,were higher in HADG calves(LDA>2,P<0.05).Enrichment of these bacteria resulted in increased levels of propionate,a gluconeogenic precursor,in preweaning HADG calves(adjusted P<0.05),which consequently raised serum glucose concentrations(adjusted P<0.05).In contrast,the relative abundances of rumen bacteria in post-weaning HADG calves did not exert this effect.Moreover,no significant differences were observed in rumen fermentation parameters and serum indices between the two groups.Conclusions The findings of this study revealed that the preweaning period is the window of opportunity for rumen bacteria to regulate the ADG of calves.
基金This research was funded by the Basic Research Funds for Universities in Inner Mongolia Autonomous Region(No.JY20220272)the Scientific Research Program of Higher Education in InnerMongolia Autonomous Region(No.NJZZ23080)+3 种基金the Natural Science Foundation of InnerMongolia(No.2023LHMS05054)the NationalNatural Science Foundation of China(No.52176212)We are also very grateful to the Program for Innovative Research Team in Universities of InnerMongolia Autonomous Region(No.NMGIRT2213)The Central Guidance for Local Scientific and Technological Development Funding Projects(No.2022ZY0113).
文摘In winter,wind turbines are susceptible to blade icing,which results in a series of energy losses and safe operation problems.Therefore,blade icing detection has become a top priority.Conventional methods primarily rely on sensor monitoring,which is expensive and has limited applications.Data-driven blade icing detection methods have become feasible with the development of artificial intelligence.However,the data-driven method is plagued by limited training samples and icing samples;therefore,this paper proposes an icing warning strategy based on the combination of feature selection(FS),eXtreme Gradient Boosting(XGBoost)algorithm,and exponentially weighted moving average(EWMA)analysis.In the training phase,FS is performed using correlation analysis to eliminate redundant features,and the XGBoost algorithm is applied to learn the hidden effective information in supervisory control and data acquisition analysis(SCADA)data to build a normal behavior model.In the online monitoring phase,an EWMA analysis is introduced to monitor the abnormal changes in features.A blade icing warning is issued when themonitored features continuously exceed the control limit,and the ambient temperature is below 0℃.This study uses data fromthree icing-affected wind turbines and one normally operating wind turbine for validation.The experimental results reveal that the strategy can promptly predict the icing trend among wind turbines and stably monitor the normally operating wind turbines.
基金supported by the Russian Science Foundation(Grant No.22-71-00086).
文摘The study of average convection in a rotating cavity subjected to modulated rotation is an interesting area for the development of both fundamental and applied science.This phenomenon finds application in the field of mass transfer and fluid flow control,relevant examples being crystal growth under reduced gravity and fluid mixing in microfluidic devices for cell cultures.In this study,the averaged flow generated by the oscillating motion of a fluid in a planar layer rotating about a horizontal axis is experimentally investigated.The boundaries of the layer are maintained at constant temperatures,while the lateral cylindrical wall is thermally insulated.It is demonstrated that libration results in intense oscillatory fluid motion,which in turn produces a time-averaged flow.For the first time,quantitative measures for the instantaneous velocity field are obtained using the Particle Image Velocimetry technique.It is revealed that the flow has the form of counter-rotating vortices.The vortex circulations sense changes during a libration cycle.An increase in the rotation rate and amplitude of the cavity libration results in an increase in the flow intensity.The heat transfer and time-averaged velocity are examined accordingly as a function of the dimensionless oscillation frequency,and resonant excitation of heat transfer and average oscillation velocity are revealed.The threshold curve for the onset of the averaged convection is identified in the plane of control parameters(dimensionless rotational velocity and pulsation Reynolds number).It is found that an increase in the dimensionless rotational velocity has a stabilizing effect on the onset of convection.
基金funded by National Natural Science Foundation of China(No.62063006)Guangxi Science and Technology Major Program(No.2022AA05002)+1 种基金Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region(No.2022GXZDSY003)Central Leading Local Science and Technology Development Fund Project of Wuzhou(No.202201001).
文摘By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile robots.However,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality data.Targeting those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was proposed.First,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q network.Next,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory information.Additionally,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality data.Finally,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random environments.The experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average steps.The performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random environments.It not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.
文摘In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.
基金the National Natural Science Foundation of China under Grants 62001517 and 61971474the Beijing Nova Program under Grant Z201100006820121.
文摘Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.
文摘The energy sector is the second largest emitter of greenhouse (GHG) gases in Kenya, emitting about 31.2% of GHG emissions in the country. The aim of this study was to model Kenya’s GHG emissions by the energy sector using ARIMA models for forecasting future values. The data used for the study was that of Kenya’s GHG emissions by the energy sector for the period starting from 1970 to 2022 obtained for the International Monetary Fund (IMF) database that was split into training and testing sets using the 80/20 rule for modelling purposes. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE). ARIMA (1, 1, 1) was identified as the best model for modelling Kenya’s GHG emissions and forecasting future values. Using this model, Kenya’s GHG emissions by the energy sector were forecasted to increase to a value of about 43.13 million metric tons of carbon dioxide equivalents by 2030. The study, therefore, recommends that Kenya should accelerate the adjustment of industry structure and improve the efficient use of energy, optimize the energy structure and accelerate development and promotion of energy-efficient products to reduce the emission of GHGs by the country’s energy sector.
文摘Food and non-alcoholic beverages are highly important for individuals to continue staying alive and living healthy lives. The increase in the prices of food and non-alcoholic beverages experienced across the world over years has continued to make food and non-alcoholic beverages not to be accessible and affordable to individuals and families having a low income. The aim of this particular research study was to identify how Kenya’s CPI of food and non-alcoholic beverages could be modelled using Autoregressive Integrated Moving Average (ARIMA) models for forecasting future values for the next two years. The data used for the study was that of Kenya’s CPI of food and non-alcoholic beverages for the period starting from February 2009 to April 2024 obtained from the International Monetary Fund (IMF) database. The best specification for the ARIMA model was identified using Akaike Information Criterion (AIC), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and mean absolute scaled error (MASE) and assessing whether residuals of the model were independent and normally distributed with a variance that is constant an whether the model has most of its coefficients being significant statistically. ARIMA (3, 1, 0) (1, 0, 0) model was identified as the best ARIMA model for modeling Kenya’s CPI of food and non-beverages for forecasting future values among the ARIMA models considered. Using this particular model, Kenya’s CPI of food and non-alcoholic beverages was forecasted to increase only slightly with time to reach a value of about 165.70 by March 2026.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the average degree and the average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the average degree and average path length of edge vertices of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Through regression analysis,it was found that the average degree had a logarithmic relationship with the average path length of edge vertices and the two parameters of the logarithmic relationship had linear evolutionary trace.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the probability distribution and evolution trace of arithmetic average of edge vertices nearest neighbor average degree values of China aviation network were studied based on the statistics data of China civil aviation network in 1988,1994,2001,2008 and 2015.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network.Based on the statistical data,the arithmetic averages of edge vertices nearest neighbor average degree values of China aviation network in 1988,1994,2001,2008 and 2015 were calculated.Using the probability statistical analysis method,it was found that the arithmetic average of edge vertices nearest neighbor average degree values had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network, the probability distribution and evolution trace of average degree of edge vertices of China aviation network were studied based on the statistics data of China civil aviation network in 1988, 1994, 2001, 2008 and 2015. According to the theory and method of complex network, the network system was constructed with the city where the airport was located as the network node and the route between cities as the edge of the network. Based on the statistical data, the average degrees of edge vertices in China aviation network in 1988, 1994, 2001, 2008 and 2015 were calculated. Using the probability statistical analysis method and regression analysis approach, it was found that the average degree of edge vertices had the probability distribution of normal function and the position parameters and scale parameters of the probability distribution had linear evolution trace.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.
