Purpose:A new point of view in the study of impact is introduced.Design/methodology/approach:Using fundamental theorems in real analysis we study the convergence of well-known impact measures.Findings:We show that poi...Purpose:A new point of view in the study of impact is introduced.Design/methodology/approach:Using fundamental theorems in real analysis we study the convergence of well-known impact measures.Findings:We show that pointwise convergence is maintained by all well-known impact bundles(such as the h-,g-,and R-bundle)and that theμ-bundle even maintains uniform convergence.Based on these results,a classification of impact bundles is given.Research limitations:As for all impact studies,it is just impossible to study all measures in depth.Practical implications:It is proposed to include convergence properties in the study of impact measures.Originality/value:This article is the first to present a bundle classification based on convergence properties of impact bundles.展开更多
The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,wh...The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,whilst usually labor-intensive and time-consuming.This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method.The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method.The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists(AOAC).When the moisture content of meat samples(beef and pork)was varied from 69.46%to 74.21%,the relative error of the meat moisture content(MMC)calculated by the proposed algorithm was 0.0017-0.0117,the absolute errors were less than 1%.The testing time was about 40.18%-56.87%less than the standard detection procedure.展开更多
基金The author thanks Li Li(National Science Library,CAS)for drawing Figure 1.
文摘Purpose:A new point of view in the study of impact is introduced.Design/methodology/approach:Using fundamental theorems in real analysis we study the convergence of well-known impact measures.Findings:We show that pointwise convergence is maintained by all well-known impact bundles(such as the h-,g-,and R-bundle)and that theμ-bundle even maintains uniform convergence.Based on these results,a classification of impact bundles is given.Research limitations:As for all impact studies,it is just impossible to study all measures in depth.Practical implications:It is proposed to include convergence properties in the study of impact measures.Originality/value:This article is the first to present a bundle classification based on convergence properties of impact bundles.
基金This work was supported in part by the National Natural Science Foundation of China(Grant 61663039)the National Natural Science Foundation of China(Grant 51775185)Equipment and materials for the research were provided by the Natural Science Foundation of Ningxia Hui Autonomous Region(Grant 2020AAC03008).
文摘The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,whilst usually labor-intensive and time-consuming.This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method.The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method.The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists(AOAC).When the moisture content of meat samples(beef and pork)was varied from 69.46%to 74.21%,the relative error of the meat moisture content(MMC)calculated by the proposed algorithm was 0.0017-0.0117,the absolute errors were less than 1%.The testing time was about 40.18%-56.87%less than the standard detection procedure.
