The study was carried out to assess the effect of management practices on agronomic parameters of cocoa agroecosystems in the peripheral zone of Ebo Forest Reserve. Purposive random sampling was conducted to establish...The study was carried out to assess the effect of management practices on agronomic parameters of cocoa agroecosystems in the peripheral zone of Ebo Forest Reserve. Purposive random sampling was conducted to establish experimental plots on the farms of willing farmers. Demonstration plots were established and agronomic parameters were monitored for “farmers’ practice (FP) and integrated crop pest and disease management (ICPM) practice” using indicators of Cocoa agro-ecosystem analysis (AESA). The FP and ICPM treatments were replicated in ten sites. From AESA records of agronomic parameters, the “observe, learn, decide and act” (OLDA) model was implemented in the ICPM treatments only. The effects of management practices were analyzed using a two-way analysis of variance (ANOVA), and treatment means compared using Turkey’s T-test at 5% probability. Results of ANOVA between the two Management practices showed that over 50% of the response variables were statistically significant. Means separated through GLM ANOVA with Tukey pairwise comparisons at α = 0.05 showed that 14 (53.8%) out of 26 response variables monitored were statistically significant between the two management practices. Pruning, shade management, phytosanitary harvest, rational use of pesticides, farm sanitation, pod harvesting, breaking, fermentation of beans and drying were regular in the ICPM treatment and time-bound in the FP treatment. The average total production varied from 385.83 kg/ha in FP treatment to 572.8 kg/ha in the ICPM treatment, still below the average standard of 1000 kg/ha. The OLDA model applied in ICPM treatment following AESA is a relevant tool to enhance sustainability in the management of cocoa agroecosystems. Farmers should be sensitized and trained on appropriate farm management techniques and enhance access to extension services as well as make available improved and grafted planting materials to ensure appropriate productivity levels.展开更多
Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve t...Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.展开更多
The data-driven methods extract the feature information from data to build system models, which enable estimation and identification of the systems and can be utilized for prognosis and health management(PHM). However...The data-driven methods extract the feature information from data to build system models, which enable estimation and identification of the systems and can be utilized for prognosis and health management(PHM). However, most data-driven models are still black-box models that cannot be interpreted. In this study, we use the neural ordinary differential equations(ODEs), especially the inherent computational relationships of a system added to the loss function calculation, to approximate the governing equations. In addition, a new strategy for identifying the local parameters of the system is investigated, which can be utilized for system parameter identification and damage detection. The numerical and experimental examples presented in the paper demonstrate that the strategy has high accuracy and good local parameter identification. Moreover, the proposed method has the advantage of being interpretable. It can directly approximate the underlying governing dynamics and be a worthwhile strategy for system identification and PHM.展开更多
From the mathematical point of view,the flexible inventory control model is proved in the practical problem application and the rationality of the capacity parameter selection and calculation.The purpose is to activel...From the mathematical point of view,the flexible inventory control model is proved in the practical problem application and the rationality of the capacity parameter selection and calculation.The purpose is to actively respond to demand fluctuations when there is a demand forecast error or a missing part of the demand information,and to avoid the risk of passive variable demand forecasting to set the immutable inventory capacity.At the same time,the game is controlled by the flexible and variable inventory control strategy and the customer’s willingness to demand.The paper mainly studies the influence of the setting of capacity parameters on the booking-limit decision and its benefits under the control of flexible space with variable total capacity.Through the two trends of capacity increase flexibility and capacity reduction flexibility in the flexible inventory control model,the mathematical performance and marginal utility methods are introduced to change the performance of the booking-limit control decision model under different scenarios.The correlation analysis between the capacity limit level and the return under the optimal Bookinglimit decision,and the above two flexibility parameters are obtained.展开更多
The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of inter...The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support.展开更多
随着新能源汽车技术的快速迭代,燃料电池混合动力汽车因功率密度高和零排放等优点被广泛关注。参考某品牌汽车的并联式混合驱动动力传动系统参数,基于MATLAB平台搭建了燃料电池电动汽车基于模糊控制策略的能量管理系统,利用CRUISE M搭...随着新能源汽车技术的快速迭代,燃料电池混合动力汽车因功率密度高和零排放等优点被广泛关注。参考某品牌汽车的并联式混合驱动动力传动系统参数,基于MATLAB平台搭建了燃料电池电动汽车基于模糊控制策略的能量管理系统,利用CRUISE M搭建整车模型,实施联合仿真,实现了功率分配、锂离子蓄电池的荷电状态(State of Charge,SOC)等参数的优化匹配。和实车整车性能参数对比显示:测试模型的最高车速和百公里加速时间均提高1.3%以上,验证了控制策略的正确性。展开更多
Soil capacity to support life and to produce economic goods and services is strongly linked to the maintenance of good soil physical quality(SPQ). In this study, the SPQ of citrus orchards was assessed under three dif...Soil capacity to support life and to produce economic goods and services is strongly linked to the maintenance of good soil physical quality(SPQ). In this study, the SPQ of citrus orchards was assessed under three different soil managements, namely no-tillage using herbicides, tillage under chemical farming, and no-tillage under organic farming. Commonly used indicators, such as soil bulk density,organic carbon content, and structural stability index, were considered in conjunction with capacitive indicators estimated by the Beerkan estimation of soil transfer parameter(BEST) method. The measurements taken at the L'Alcoleja Experimental Station in Spain yielded optimal values for soil bulk density and organic carbon content in 100% and 70% of cases for organic farming. The values of structural stability index indicated that the soil was stable in 90% of cases. Differences between the soil management practices were particularly clear in terms of plant-available water capacity and saturated hydraulic conductivity. Under organic farming, the soil had the greatest ability to store and provide water to plant roots, and to quickly drain excess water and facilitate root proliferation.Management practices adopted under organic farming(such as vegetation cover between the trees, chipping after pruning, and spreading the chips on the soil surface) improved the SPQ. Conversely, the conventional management strategies unequivocally led to soil degradation owing to the loss of organic matter, soil compaction, and reduced structural stability. The results in this study show that organic farming has a clear positive impact on the SPQ, suggesting that tillage and herbicide treatments should be avoided.展开更多
Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities nec...Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.展开更多
Groundwater contamination has been on the rise in Afghanistan.It has become a major concern among the policy makers.This paper aims to propose practical options for the management of nitrate contamination in one of Af...Groundwater contamination has been on the rise in Afghanistan.It has become a major concern among the policy makers.This paper aims to propose practical options for the management of nitrate contamination in one of Afghanistan’s groundwater polluted provinces,Kabul.The management framework utilized Mann-Kendall and Sen Slope tests to detect nitrate trend and geostatistical analysis option in Arc GIS 10.5 to assess the nitrate change.To explore the impact of various management options,a number of legislative documents were reviewed.The results indicate a decline in the nitrate storage of Kabul aquifers from 108 mg/L in 2005 to 0.044 mg/L in 2010.Considering the whole period of the study,the results show that the nitrate volumes remain lower than the nitrate concentration range proposed by World Health Organization(50 mg/L).Groundwater dynamics in Kabul aquifers were influenced by nitrate derived from precipitation and nitrate input from root zones in agricultural areas.Finally,different management options for groundwater pollution from nitrate and corresponding authorities,incorporated urban,rural and agriculture,were proposed.It is expected that this study will help policy makers to better manage the nitrate storage of Kabul aquifers by implementing the proposed management options.展开更多
文摘The study was carried out to assess the effect of management practices on agronomic parameters of cocoa agroecosystems in the peripheral zone of Ebo Forest Reserve. Purposive random sampling was conducted to establish experimental plots on the farms of willing farmers. Demonstration plots were established and agronomic parameters were monitored for “farmers’ practice (FP) and integrated crop pest and disease management (ICPM) practice” using indicators of Cocoa agro-ecosystem analysis (AESA). The FP and ICPM treatments were replicated in ten sites. From AESA records of agronomic parameters, the “observe, learn, decide and act” (OLDA) model was implemented in the ICPM treatments only. The effects of management practices were analyzed using a two-way analysis of variance (ANOVA), and treatment means compared using Turkey’s T-test at 5% probability. Results of ANOVA between the two Management practices showed that over 50% of the response variables were statistically significant. Means separated through GLM ANOVA with Tukey pairwise comparisons at α = 0.05 showed that 14 (53.8%) out of 26 response variables monitored were statistically significant between the two management practices. Pruning, shade management, phytosanitary harvest, rational use of pesticides, farm sanitation, pod harvesting, breaking, fermentation of beans and drying were regular in the ICPM treatment and time-bound in the FP treatment. The average total production varied from 385.83 kg/ha in FP treatment to 572.8 kg/ha in the ICPM treatment, still below the average standard of 1000 kg/ha. The OLDA model applied in ICPM treatment following AESA is a relevant tool to enhance sustainability in the management of cocoa agroecosystems. Farmers should be sensitized and trained on appropriate farm management techniques and enhance access to extension services as well as make available improved and grafted planting materials to ensure appropriate productivity levels.
基金Aeronautical Science Foundation of China (20100751010, 2010ZD11007)
文摘Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.
基金Project supported by the National Natural Science Foundation of China (Nos. 12132010 and12021002)the Natural Science Foundation of Tianjin of China (No. 19JCZDJC38800)。
文摘The data-driven methods extract the feature information from data to build system models, which enable estimation and identification of the systems and can be utilized for prognosis and health management(PHM). However, most data-driven models are still black-box models that cannot be interpreted. In this study, we use the neural ordinary differential equations(ODEs), especially the inherent computational relationships of a system added to the loss function calculation, to approximate the governing equations. In addition, a new strategy for identifying the local parameters of the system is investigated, which can be utilized for system parameter identification and damage detection. The numerical and experimental examples presented in the paper demonstrate that the strategy has high accuracy and good local parameter identification. Moreover, the proposed method has the advantage of being interpretable. It can directly approximate the underlying governing dynamics and be a worthwhile strategy for system identification and PHM.
文摘From the mathematical point of view,the flexible inventory control model is proved in the practical problem application and the rationality of the capacity parameter selection and calculation.The purpose is to actively respond to demand fluctuations when there is a demand forecast error or a missing part of the demand information,and to avoid the risk of passive variable demand forecasting to set the immutable inventory capacity.At the same time,the game is controlled by the flexible and variable inventory control strategy and the customer’s willingness to demand.The paper mainly studies the influence of the setting of capacity parameters on the booking-limit decision and its benefits under the control of flexible space with variable total capacity.Through the two trends of capacity increase flexibility and capacity reduction flexibility in the flexible inventory control model,the mathematical performance and marginal utility methods are introduced to change the performance of the booking-limit control decision model under different scenarios.The correlation analysis between the capacity limit level and the return under the optimal Bookinglimit decision,and the above two flexibility parameters are obtained.
文摘The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support.
文摘随着新能源汽车技术的快速迭代,燃料电池混合动力汽车因功率密度高和零排放等优点被广泛关注。参考某品牌汽车的并联式混合驱动动力传动系统参数,基于MATLAB平台搭建了燃料电池电动汽车基于模糊控制策略的能量管理系统,利用CRUISE M搭建整车模型,实施联合仿真,实现了功率分配、锂离子蓄电池的荷电状态(State of Charge,SOC)等参数的优化匹配。和实车整车性能参数对比显示:测试模型的最高车速和百公里加速时间均提高1.3%以上,验证了控制策略的正确性。
基金supported by the RECARE Project from the European Union Seventh Framework Programme (FP7/2007-2013) (No. 603498)COST actions 1306
文摘Soil capacity to support life and to produce economic goods and services is strongly linked to the maintenance of good soil physical quality(SPQ). In this study, the SPQ of citrus orchards was assessed under three different soil managements, namely no-tillage using herbicides, tillage under chemical farming, and no-tillage under organic farming. Commonly used indicators, such as soil bulk density,organic carbon content, and structural stability index, were considered in conjunction with capacitive indicators estimated by the Beerkan estimation of soil transfer parameter(BEST) method. The measurements taken at the L'Alcoleja Experimental Station in Spain yielded optimal values for soil bulk density and organic carbon content in 100% and 70% of cases for organic farming. The values of structural stability index indicated that the soil was stable in 90% of cases. Differences between the soil management practices were particularly clear in terms of plant-available water capacity and saturated hydraulic conductivity. Under organic farming, the soil had the greatest ability to store and provide water to plant roots, and to quickly drain excess water and facilitate root proliferation.Management practices adopted under organic farming(such as vegetation cover between the trees, chipping after pruning, and spreading the chips on the soil surface) improved the SPQ. Conversely, the conventional management strategies unequivocally led to soil degradation owing to the loss of organic matter, soil compaction, and reduced structural stability. The results in this study show that organic farming has a clear positive impact on the SPQ, suggesting that tillage and herbicide treatments should be avoided.
文摘Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.
文摘Groundwater contamination has been on the rise in Afghanistan.It has become a major concern among the policy makers.This paper aims to propose practical options for the management of nitrate contamination in one of Afghanistan’s groundwater polluted provinces,Kabul.The management framework utilized Mann-Kendall and Sen Slope tests to detect nitrate trend and geostatistical analysis option in Arc GIS 10.5 to assess the nitrate change.To explore the impact of various management options,a number of legislative documents were reviewed.The results indicate a decline in the nitrate storage of Kabul aquifers from 108 mg/L in 2005 to 0.044 mg/L in 2010.Considering the whole period of the study,the results show that the nitrate volumes remain lower than the nitrate concentration range proposed by World Health Organization(50 mg/L).Groundwater dynamics in Kabul aquifers were influenced by nitrate derived from precipitation and nitrate input from root zones in agricultural areas.Finally,different management options for groundwater pollution from nitrate and corresponding authorities,incorporated urban,rural and agriculture,were proposed.It is expected that this study will help policy makers to better manage the nitrate storage of Kabul aquifers by implementing the proposed management options.
