[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theo...[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.展开更多
In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results sh...In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,a...In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.展开更多
Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problem...Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.展开更多
By using the data concerning China's urban-rural residents'income gap from 1978 to 2010,this paper mainly researches the application of several kinds of models in predicting China's urban-rural residents&#...By using the data concerning China's urban-rural residents'income gap from 1978 to 2010,this paper mainly researches the application of several kinds of models in predicting China's urban-rural residents'income gap.By conducting empirical analysis,we establish ARIMA prediction model,grey prediction model and quadratic-polynomial prediction model and conduct accuracy comparison.The results show that quadratic-polynomial prediction model has excellent fitting effect.By using quadratic-polynomial prediction model,this paper conducts prediction on trend of China's urban-rural residents'income gap from 2011 to 2013,and the prediction value of income gap of urban-rural residents in China from 2011 to 2013 is 14173.20,15212.92 and 16289.67 yuan respectively.Finally,on the basis of analysis,corresponding countermeasures are put forward,in order to provide scientific basis for energy planning and policy formulation:first,strengthen government's function of public service,coordinate resources,and strive to provide an equal opportunity of development for social members,so as to promote people's welfare and promote social equality;second,breach industrial monopoly and bridge income gap between employees in monopoly industry and general industry;last but not the least,support,encourage and call for government to establish social relief fund,adjust residents'income distribution from the non-governmental perspective,and endeavor to promote the income level of low-income class.展开更多
This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,da...This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,dairy,and medicine.Through selecting the consumption data of urban residents on transported products via cold chain in Jiangsu Province from 2005 to 2000 as sample,this paper establishes grey prediction model GM(1,1)of cold chain logistics demand and uses DPS7.05 software for test,to predict the cold chain logistics demand of urban residents in Jiangsu Province during the Twelfth Five-Year Plan period.The results show that in the period 2010-2015,the cold chain logistics demand of urban residents in Jiangsu Province is 1151.5891,1185.1366,1219.6613,1255.1918,1291.7573,1329.3881 t respectively;in the period 2005-2010,the cold chain logistics demand of urban residents in Jiangsu Province increases at annual growth rate of 3.9%;in the period 2011-2015,the growth rate declines to some extent,increasing slowly at rate of 2.9%.展开更多
Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller ...Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller with PID parameters self-tuning. According to the characteristics of target tracking system in a robot weapon, grey prediction theory and fuzzy PID control ideas are combined. A grey prediction mathematical model is constructed and a fuzzy PID controller with grey prediction was developed. Simulation result shows fuzzy PID control algorithm with grey prediction is an efficient method that can improve the control equality and robustness of traditional PID control and fuzzy PID control, and has much better performance for target tracking.展开更多
Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）....Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）. After establishing the grey predict system of the catastrophe regularity of 10 month-average volume of water inflowing, the grey forewarning for mine water inflowing catastrophe periods was established which was used to analyze water disaster in 400 meter level of Wennan Colliery. Based on residual analysis, it shows that the result of grey predict system is almost close to the actual value. And the scene actual result also shows the reliability of prediction. Both the theoretical analysis and the scene actual result indicate feasibility and reliability of the method of grey catastrophe predict system.展开更多
Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competiti...Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.展开更多
The average relative simulation and prediction percentage errors of the new model are only 0.092%and 3.023%,respectively.The simulation and prediction errors obtained from the classical GM(1,1)and the DGM(1,1)models a...The average relative simulation and prediction percentage errors of the new model are only 0.092%and 3.023%,respectively.The simulation and prediction errors obtained from the classical GM(1,1)and the DGM(1,1)models are,respectively,2.064%and 6.980%in the first case,and 1.942%and 7.360%in the second.The findings show that the GM(1,1,4)model has the best performance,which confirms the effectiveness of the structure improvement.The new model can enhance the smoothness of the background value and weaken the effects of extreme values in the raw sequence in the model’s performance.Therefore,the simulation and prediction performances of the GM(1,1,4)model are better than those of the traditional grey prediction models.The prediction show that the ownership for automobiles in China will grow rapidly in future.Findings could help the government in formulating adjustments to the industrial structures,and facilitate making rational yield plans for automobile firms.展开更多
The new energy vehicle(NEV)subsidy policy introduced in China in 2013 has significantly boosted the adoption and sales of NEVs,with sales increasing more than 40-fold.However,the mechanisms by which subsidy policies i...The new energy vehicle(NEV)subsidy policy introduced in China in 2013 has significantly boosted the adoption and sales of NEVs,with sales increasing more than 40-fold.However,the mechanisms by which subsidy policies influence the diffusion of NEVs in China remain unclear,posing challenges for governments to design future strategies.Thus,the primary objective of this paper is to empirically examine the impact of subsidy policy on the diffusion of new energy vehicles and to forecast future development trends using the grey Bass model,a predictive model suited for new product adoption forecasting.Our findings suggest that while the sales of NEVs in China will continue to rise,the growth rate will slow.Key milestones include the first inflection points for new energy vehicles and battery electric vehicles,anticipated in 2025 and 2024 respectively,with peak sales expected in 2028 and 2027.These insights are crucial for manufacturers,enabling them to adjust their production strategies timely and enhance their resilience in the market.展开更多
This paper designs a set of VD furnace temperature control decision-making system.In the refining process,the harsh environment and harsh conditions can be overcome,and the control and prediction of VD furnace outlet ...This paper designs a set of VD furnace temperature control decision-making system.In the refining process,the harsh environment and harsh conditions can be overcome,and the control and prediction of VD furnace outlet temperature can be realised.According to the known parameters of the current smelting information,a matching model is established,and a GM(1,N)model based on grey theory is designed to predict the temperature.At the same time,the control parameter optimisation model is designed,and the local optimisation method is used to optimise the control parameters to calculate the prediction accuracy.The experimental results show that the system can accurately control the refining temperature of the VD furnace and effectively improve the quality of steel.This is of great significance to improve steelmaking production efficiency and reduce costs.展开更多
Background Monkeypox(mpox)is an emerging zoonotic disease that has persistently impacted public health in endemic regions of West and Central Africa for over half a century.The Democratic Republic of the Congo(DRC)rem...Background Monkeypox(mpox)is an emerging zoonotic disease that has persistently impacted public health in endemic regions of West and Central Africa for over half a century.The Democratic Republic of the Congo(DRC)remains one of the countries most affected.Understanding the risk factors for disease transmission from a One Health perspective is of great importance in the risk assessment,prevention,and control of zoonotic diseases.Therefore,this study aimed to investigate the risk factors for human mpox transmission at the human-animal-environment interface in the DRC.Methods Epidemiological,environmental,socioeconomic,and sociocultural data from the DRC from 2000 to 2015 were obtained from publicly available dataset.Using these data,we applied negative binomial regression model,least absolute shrinkage and selection operator regression model,and principal component analysis(PCA)to identify key environmental,socioeconomic,and sociocultural factors contributing to mpox transmission.Moreover,a grey prediction model GM(1,n)was constructed to predict the epidemic trend of mpox post-2015 and validated using suspected mpox case data in the DRC from 2016 to 2021,sourced from the United States Centers for Disease Control and Prevention.Results Between 2000 and 2021,a total of 43,628 suspected mpox cases were reported in the DRC,with a peak of 6216 cases in 2020.From 2016 to 2021,suspected cases accounted for over half(24,379/43,628,55.9%)of the total reported during the 2000-2021 period.The proportion of primary forest[incidence rate ratio(IRR):1.023,95%confidence interval(CI):1.018-1.027],index of economic well-being(IRR:1.046,95%CI:1.039-1.052),and mean annual precipitation(IRR 1.040,95%CI:1.031-1.049)were positively associated with mpox incidence.PCA identified five principal components,explaining 69%of the variance in the environmental,socioeconomic,and sociocultural variables.The first component was characterized by socioeconomic factors.The GM(1,n)model,based on the proportion of primary forest,index of economic well-being,and mean annual precipitation,predicted the epidemic trend(revealed relative error:2.69).Conclusions Both socioeconomic and environmental factors play important roles in mpox transmission.Our study further highlighted the importance of considering the interconnectedness among humans,animals,and the environment,and treating these factors as a whole to explain the transmission and emergence of mpox outbreaks in the DRC according to the One Health concept.展开更多
基金Supported by National Natural Science Fund Item(61064005)~~
文摘[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.
基金supported by the National Natural Science Foundation of China(1147105951375517+5 种基金71271226)the China Postdoctoral Science Foundation Funded Project(2014M560712)Chongqing Frontier and Applied Basic Research Project(cstc2014jcyj A00024)the Ministry of Education of Humanities and Social Sciences Youth Foundation(14YJAZH033)the Chongqing Municipal Education Scientific Planning Project(2012-GX-142)the Higher School Teaching Reform Research Project in Chongqing(1202010)
文摘In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
文摘In order to realize the accurate prediction of the total output value of construction industry in the future,the grey prediction model is used to compare the measured value with the predicted value from 2012 to 2021,and based on the existing data,the total output value of construction industry in Jiangxi Province in the next five years is predicted.The results show that the grey prediction model has a good prediction effect,and the error between the predicted value and the measured value is within 14%,which provides a basis for policy adjustment and resource optimization.
基金supported by the National Natural Science Foundation of China (51479151,61403288)。
文摘Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.
文摘By using the data concerning China's urban-rural residents'income gap from 1978 to 2010,this paper mainly researches the application of several kinds of models in predicting China's urban-rural residents'income gap.By conducting empirical analysis,we establish ARIMA prediction model,grey prediction model and quadratic-polynomial prediction model and conduct accuracy comparison.The results show that quadratic-polynomial prediction model has excellent fitting effect.By using quadratic-polynomial prediction model,this paper conducts prediction on trend of China's urban-rural residents'income gap from 2011 to 2013,and the prediction value of income gap of urban-rural residents in China from 2011 to 2013 is 14173.20,15212.92 and 16289.67 yuan respectively.Finally,on the basis of analysis,corresponding countermeasures are put forward,in order to provide scientific basis for energy planning and policy formulation:first,strengthen government's function of public service,coordinate resources,and strive to provide an equal opportunity of development for social members,so as to promote people's welfare and promote social equality;second,breach industrial monopoly and bridge income gap between employees in monopoly industry and general industry;last but not the least,support,encourage and call for government to establish social relief fund,adjust residents'income distribution from the non-governmental perspective,and endeavor to promote the income level of low-income class.
基金Supporte by College Philosophical Social Science Foundation of Jiangsu Provincial Department of Education in 2009(09SJB790008)Science and Technology Support Project of Huaian City in 2009(HAS2009045-1)Funds from Huaian Municipal Bureau of Communications
文摘This paper takes the total yield of products that need refrigerated transport as the impact factors of transport aggregate of cold chain logistics,such as meat,aquatic products,quick-frozen noodle,fruits,vegetables,dairy,and medicine.Through selecting the consumption data of urban residents on transported products via cold chain in Jiangsu Province from 2005 to 2000 as sample,this paper establishes grey prediction model GM(1,1)of cold chain logistics demand and uses DPS7.05 software for test,to predict the cold chain logistics demand of urban residents in Jiangsu Province during the Twelfth Five-Year Plan period.The results show that in the period 2010-2015,the cold chain logistics demand of urban residents in Jiangsu Province is 1151.5891,1185.1366,1219.6613,1255.1918,1291.7573,1329.3881 t respectively;in the period 2005-2010,the cold chain logistics demand of urban residents in Jiangsu Province increases at annual growth rate of 3.9%;in the period 2011-2015,the growth rate declines to some extent,increasing slowly at rate of 2.9%.
基金the Ministerial Level Advanced Research Foundation (061103)
文摘Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller with PID parameters self-tuning. According to the characteristics of target tracking system in a robot weapon, grey prediction theory and fuzzy PID control ideas are combined. A grey prediction mathematical model is constructed and a fuzzy PID controller with grey prediction was developed. Simulation result shows fuzzy PID control algorithm with grey prediction is an efficient method that can improve the control equality and robustness of traditional PID control and fuzzy PID control, and has much better performance for target tracking.
文摘Based on the theory of grey system, established GM （1, 1） grey catastrophe predict model for the first time in order to forecast the catastrophe periods of mine water inflowing （not the volume of water inflowing）. After establishing the grey predict system of the catastrophe regularity of 10 month-average volume of water inflowing, the grey forewarning for mine water inflowing catastrophe periods was established which was used to analyze water disaster in 400 meter level of Wennan Colliery. Based on residual analysis, it shows that the result of grey predict system is almost close to the actual value. And the scene actual result also shows the reliability of prediction. Both the theoretical analysis and the scene actual result indicate feasibility and reliability of the method of grey catastrophe predict system.
基金National Social Science Fund of China,No.24BTJ037Significant Project of the National Social Science Foundation of China,No.23&ZD102+1 种基金The Key Research Base for Philosophy and Social Sciences in Hangzhou:ESG and Sustainable Development Research Center,No.25JD053Zhejiang Provincial Statistical Scientific Research Project,No.25TJZZ12。
文摘Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.
基金supported by National Natural Science Foundation of China(71771033)Foundation Research and Frontier Exploration in Chongqing of China(cstc2019jcyjmsxm1385)+2 种基金the Ministry of Education Humanities and Social Sciences Planning Project of China(18XJC630003)Chongqing Municipal Educational Science for the 13th-Five Year Planning Project of China(2017-GX-304)Science and technology research project of Chongqing Education Commission(KJQN201800805).
文摘The average relative simulation and prediction percentage errors of the new model are only 0.092%and 3.023%,respectively.The simulation and prediction errors obtained from the classical GM(1,1)and the DGM(1,1)models are,respectively,2.064%and 6.980%in the first case,and 1.942%and 7.360%in the second.The findings show that the GM(1,1,4)model has the best performance,which confirms the effectiveness of the structure improvement.The new model can enhance the smoothness of the background value and weaken the effects of extreme values in the raw sequence in the model’s performance.Therefore,the simulation and prediction performances of the GM(1,1,4)model are better than those of the traditional grey prediction models.The prediction show that the ownership for automobiles in China will grow rapidly in future.Findings could help the government in formulating adjustments to the industrial structures,and facilitate making rational yield plans for automobile firms.
基金Supported by the National Social Science Foundation of China(23BTJ021)the National Natural Science Foundation of China(71971194)。
文摘The new energy vehicle(NEV)subsidy policy introduced in China in 2013 has significantly boosted the adoption and sales of NEVs,with sales increasing more than 40-fold.However,the mechanisms by which subsidy policies influence the diffusion of NEVs in China remain unclear,posing challenges for governments to design future strategies.Thus,the primary objective of this paper is to empirically examine the impact of subsidy policy on the diffusion of new energy vehicles and to forecast future development trends using the grey Bass model,a predictive model suited for new product adoption forecasting.Our findings suggest that while the sales of NEVs in China will continue to rise,the growth rate will slow.Key milestones include the first inflection points for new energy vehicles and battery electric vehicles,anticipated in 2025 and 2024 respectively,with peak sales expected in 2028 and 2027.These insights are crucial for manufacturers,enabling them to adjust their production strategies timely and enhance their resilience in the market.
文摘This paper designs a set of VD furnace temperature control decision-making system.In the refining process,the harsh environment and harsh conditions can be overcome,and the control and prediction of VD furnace outlet temperature can be realised.According to the known parameters of the current smelting information,a matching model is established,and a GM(1,N)model based on grey theory is designed to predict the temperature.At the same time,the control parameter optimisation model is designed,and the local optimisation method is used to optimise the control parameters to calculate the prediction accuracy.The experimental results show that the system can accurately control the refining temperature of the VD furnace and effectively improve the quality of steel.This is of great significance to improve steelmaking production efficiency and reduce costs.
基金supported by the National Natural Science Foundation of China(72374178)International Cooperation and Exchange of the National Natural Science Foundation of China(72481220010)+3 种基金National Natural Science Foundation of China(71904165)the Open Project Program of Jiangsu Key Laboratory of Zoonosis(R2208)the Open Project Program of International Research Laboratory of Prevention and Control of Important Animal Infectious Diseases and Zoonotic Diseases of Jiangsu Higher Education Institutions(01)the Open Project Program of National Health Commission Key Laboratory of Parasitic Disease Control and Prevention and Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology(wk023-007).
文摘Background Monkeypox(mpox)is an emerging zoonotic disease that has persistently impacted public health in endemic regions of West and Central Africa for over half a century.The Democratic Republic of the Congo(DRC)remains one of the countries most affected.Understanding the risk factors for disease transmission from a One Health perspective is of great importance in the risk assessment,prevention,and control of zoonotic diseases.Therefore,this study aimed to investigate the risk factors for human mpox transmission at the human-animal-environment interface in the DRC.Methods Epidemiological,environmental,socioeconomic,and sociocultural data from the DRC from 2000 to 2015 were obtained from publicly available dataset.Using these data,we applied negative binomial regression model,least absolute shrinkage and selection operator regression model,and principal component analysis(PCA)to identify key environmental,socioeconomic,and sociocultural factors contributing to mpox transmission.Moreover,a grey prediction model GM(1,n)was constructed to predict the epidemic trend of mpox post-2015 and validated using suspected mpox case data in the DRC from 2016 to 2021,sourced from the United States Centers for Disease Control and Prevention.Results Between 2000 and 2021,a total of 43,628 suspected mpox cases were reported in the DRC,with a peak of 6216 cases in 2020.From 2016 to 2021,suspected cases accounted for over half(24,379/43,628,55.9%)of the total reported during the 2000-2021 period.The proportion of primary forest[incidence rate ratio(IRR):1.023,95%confidence interval(CI):1.018-1.027],index of economic well-being(IRR:1.046,95%CI:1.039-1.052),and mean annual precipitation(IRR 1.040,95%CI:1.031-1.049)were positively associated with mpox incidence.PCA identified five principal components,explaining 69%of the variance in the environmental,socioeconomic,and sociocultural variables.The first component was characterized by socioeconomic factors.The GM(1,n)model,based on the proportion of primary forest,index of economic well-being,and mean annual precipitation,predicted the epidemic trend(revealed relative error:2.69).Conclusions Both socioeconomic and environmental factors play important roles in mpox transmission.Our study further highlighted the importance of considering the interconnectedness among humans,animals,and the environment,and treating these factors as a whole to explain the transmission and emergence of mpox outbreaks in the DRC according to the One Health concept.
