This paper analyzes the effect of subgroup size on the x-bar chart characteristics using sample influx (SIF) into forensic science laboratory (FSL). The characteristics studied include changes in out-or-control points...This paper analyzes the effect of subgroup size on the x-bar chart characteristics using sample influx (SIF) into forensic science laboratory (FSL). The characteristics studied include changes in out-or-control points (OCP), upper control limit UCLx, and zonal demarcations. Multi-rules were used to identify the number of out-of-control-points, Nocp as violations using five control chart rules applied separately. A sensitivity analysis on the Nocp was applied for subgroup size, k, and number of sigma above the mean value to determine the upper control limit, UCLx. A computer code was implemented using a FORTRAN code to create x-bar control-charts and capture OCP and other control-chart characteristics with increasing k from 2 to 25. For each value of k, a complete series of average values, Q(p), of specific length, Nsg, was created from which statistical analysis was conducted and compared to the original SIF data, S(t). The variation of number of out-of-control points or violations, Nocp, for different control-charts rules with increasing k was determined to follow a decaying exponential function, Nocp = Ae–α, for which, the goodness of fit was established, and the R2 value approached unity for Rule #4 and #5 only. The goodness of fit was established to be the new criteria for rational subgroup-size range, for Rules #5 and #4 only, which involve a count of 6 consecutive points decreasing and 8 consecutive points above the selected control limit (σ/3 above the grand mean), respectively. Using this criterion, the rational subgroup range was established to be 4 ≤ k ≤ 20 for the two x-bar control chart rules.展开更多
Brine shrimp are vital inhabitants of saltwater lakes,contributing significantly to economic and ecological systems.With increasing threats from environmental degradation and overharvesting,effec-tive monitoring is ur...Brine shrimp are vital inhabitants of saltwater lakes,contributing significantly to economic and ecological systems.With increasing threats from environmental degradation and overharvesting,effec-tive monitoring is urgenttly needed.Traditional field sampling meth-ods are limited in scope and efficiency,necessitating a reliable remote sensing-based approach.However,Ebinur Lake's complex spectral environment,characterized by poor water quality and diverse suspended particulates,poses challenges for satellite remote sensing accuracy.To overcome these issues,we developed a novel multi-rule extraction model based on Landsat data,lever-aging the distinct short-wave infrared signatures of brine shrimp to enhance detection accuracy.We evaluated and validated this method using Landsat 8 and Sentinel-2 datasets,achieving a classification accuracy of 94.5%and a kappa coefficient of 0.88,surpassing existing methods.Additionally,our analysis of a decade of Landsat data in Ebinur Lake via Google Earth Engine revealed a correlation between brine shrimp distribution and lake surface area.Our model demonstrates high accuracy and scalability in mapping brine shrimp,making it a valuable tool for long-term,large-scale assessments in saline lakes.This capability holds signifi-cant potential for advancing fisheries research and informing con-servation strategies.展开更多
文摘This paper analyzes the effect of subgroup size on the x-bar chart characteristics using sample influx (SIF) into forensic science laboratory (FSL). The characteristics studied include changes in out-or-control points (OCP), upper control limit UCLx, and zonal demarcations. Multi-rules were used to identify the number of out-of-control-points, Nocp as violations using five control chart rules applied separately. A sensitivity analysis on the Nocp was applied for subgroup size, k, and number of sigma above the mean value to determine the upper control limit, UCLx. A computer code was implemented using a FORTRAN code to create x-bar control-charts and capture OCP and other control-chart characteristics with increasing k from 2 to 25. For each value of k, a complete series of average values, Q(p), of specific length, Nsg, was created from which statistical analysis was conducted and compared to the original SIF data, S(t). The variation of number of out-of-control points or violations, Nocp, for different control-charts rules with increasing k was determined to follow a decaying exponential function, Nocp = Ae–α, for which, the goodness of fit was established, and the R2 value approached unity for Rule #4 and #5 only. The goodness of fit was established to be the new criteria for rational subgroup-size range, for Rules #5 and #4 only, which involve a count of 6 consecutive points decreasing and 8 consecutive points above the selected control limit (σ/3 above the grand mean), respectively. Using this criterion, the rational subgroup range was established to be 4 ≤ k ≤ 20 for the two x-bar control chart rules.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 42471390 and Grant 42425104the Third Comprehensive Scientific Expedition to Xinjiang under Grant 2021xjkk1403.
文摘Brine shrimp are vital inhabitants of saltwater lakes,contributing significantly to economic and ecological systems.With increasing threats from environmental degradation and overharvesting,effec-tive monitoring is urgenttly needed.Traditional field sampling meth-ods are limited in scope and efficiency,necessitating a reliable remote sensing-based approach.However,Ebinur Lake's complex spectral environment,characterized by poor water quality and diverse suspended particulates,poses challenges for satellite remote sensing accuracy.To overcome these issues,we developed a novel multi-rule extraction model based on Landsat data,lever-aging the distinct short-wave infrared signatures of brine shrimp to enhance detection accuracy.We evaluated and validated this method using Landsat 8 and Sentinel-2 datasets,achieving a classification accuracy of 94.5%and a kappa coefficient of 0.88,surpassing existing methods.Additionally,our analysis of a decade of Landsat data in Ebinur Lake via Google Earth Engine revealed a correlation between brine shrimp distribution and lake surface area.Our model demonstrates high accuracy and scalability in mapping brine shrimp,making it a valuable tool for long-term,large-scale assessments in saline lakes.This capability holds signifi-cant potential for advancing fisheries research and informing con-servation strategies.
