Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the ...Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.展开更多
Fractional order controllers have been used intensively over the last decades in controlling different types of processes. The main methods for tuning such controllers are based on a frequency domain approach followed...Fractional order controllers have been used intensively over the last decades in controlling different types of processes. The main methods for tuning such controllers are based on a frequency domain approach followed by optimization routine, generally in the form of the Matlab fminsearch, but also evolving to more complex routines, such as the genetic algorithms. An alternative to these time consuming optimization routines, a simple graphical method has been proposed. However, these graphical methods are not suitable for all combinations of the imposed performance specifications. To preserve their simplicity, but also to make these graphical methods generally applicable, a modified graphical method using a very straightforward and simple optimization routine is proposed within the paper. Two case studies are presented, for tuning fractional order PI and PD controllers.展开更多
Sampling frequency is an important factor to be considered during the design of a water monitoring network,and the cost-effective selection of possible ways and means for the optimization of sampling frequency is stil...Sampling frequency is an important factor to be considered during the design of a water monitoring network,and the cost-effective selection of possible ways and means for the optimization of sampling frequency is still needed.This paper introduces water pollution index deviation ratio comparison(WPI DRC),a procedure for the optimization of sampling frequency for a routine river water quality monitoring system.Sampling frequency optimized using WPI DRC at monitoring station X5 in the mainstream of Xiangjiang River is compared with that established using the traditional Statistical Algorithm method.The result of comparison indicates that WPI DRC is more feasible than the traditional one.And then,the sampling frequencies for other 16 monitoring stations also have been optimized,and the results show the sampling frequencies of all the stations except that X4 are reduced,and there is no unacceptable difference between water quality evaluation results at 17 stations before and after the optimization.Therefore,it is concluded that WPI DRC is an effective optimization process with operable results,which can be used to fulfill the requirement of practical monitoring work.展开更多
基金This study was funded by GCRF UK and was carried out as part of project CoNTINuE-Capacity building in technology-driven innovation in healthcare.
文摘Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
文摘Fractional order controllers have been used intensively over the last decades in controlling different types of processes. The main methods for tuning such controllers are based on a frequency domain approach followed by optimization routine, generally in the form of the Matlab fminsearch, but also evolving to more complex routines, such as the genetic algorithms. An alternative to these time consuming optimization routines, a simple graphical method has been proposed. However, these graphical methods are not suitable for all combinations of the imposed performance specifications. To preserve their simplicity, but also to make these graphical methods generally applicable, a modified graphical method using a very straightforward and simple optimization routine is proposed within the paper. Two case studies are presented, for tuning fractional order PI and PD controllers.
基金the funding from the National Water Pollution Control and Management Technology Major Projects of China(2012ZX07503-002)the Special Research Funding for the Public Benefits sponsored by Ministry of Environmental Protection of PRC(201309067)
文摘Sampling frequency is an important factor to be considered during the design of a water monitoring network,and the cost-effective selection of possible ways and means for the optimization of sampling frequency is still needed.This paper introduces water pollution index deviation ratio comparison(WPI DRC),a procedure for the optimization of sampling frequency for a routine river water quality monitoring system.Sampling frequency optimized using WPI DRC at monitoring station X5 in the mainstream of Xiangjiang River is compared with that established using the traditional Statistical Algorithm method.The result of comparison indicates that WPI DRC is more feasible than the traditional one.And then,the sampling frequencies for other 16 monitoring stations also have been optimized,and the results show the sampling frequencies of all the stations except that X4 are reduced,and there is no unacceptable difference between water quality evaluation results at 17 stations before and after the optimization.Therefore,it is concluded that WPI DRC is an effective optimization process with operable results,which can be used to fulfill the requirement of practical monitoring work.
