BACKGROUND Advanced chronic kidney disease(CKD) is a common complication for people with type 1 and 2 diabetes and can often lead to glucose instability. Continuous glucose monitoring(CGM) helps users monitor and stab...BACKGROUND Advanced chronic kidney disease(CKD) is a common complication for people with type 1 and 2 diabetes and can often lead to glucose instability. Continuous glucose monitoring(CGM) helps users monitor and stabilize their glucose levels. To date, CGM and intermittent scanning CGM are only approved for people with diabetes but not for those with advanced CKD.AIM To compare the performance of Dexcom G5 and FreeStyle Libre sensors in adults with type 1 or 2 diabetes and advanced CKD.METHODS This was a non-randomized clinical trial that took place in two outpatient clinics in western Sweden. All patients with type 1 or 2 diabetes and an estimated glomerular filtration rate(eGFR) of < 30 mL/min per 1.73 m^(2) were invited to participate. Forty patients(full analysis set = 33) carried the Dexcom G5 sensor for 7 d and FreeStyle Libre sensor for 14 d simultaneously. For referencing capillary blood glucose(SMBG) was measured with a high accuracy glucose meter(HemoCue®) during the study period. At the end of the study, all patients were asked to answer a questionnaire on their experience using the sensors.RESULTS The mean age was 64.1(range 41-77) years, hemoglobin A1 c was 7.0% [standard deviation(SD) 3.2], and diabetes duration was 28.5(SD 14.7) years. A total of 27.5% of the study population was on hemodialysis and 22.5% on peritoneal dialysis. The mean absolute relative difference for Dexcom G5 vs SMBG was significantly lower than that for FreeStyle Libre vs SMBG [15.2%(SD 12.2) vs 20.9%(SD 8.6)], with a mean difference of 5.72 [95% confidence interval(CI): 2.11-9.32;P = 0.0036]. The mean absolute difference was also significantly lower for Dexcom G5 than for FreeStyle Libre, 1.21 mmol/L(SD 0.78) and 1.76 mmol/L(SD 0.78), with a mean diffrenec of 0.55(95%CI: 0.27-0.83;P = 0.0004).The mean difference(MD) was-0.107 mmol/L and-1.10 mmol/L(P = 0.0002), respectively. In all, 66% of FreeStyle Libre values were in the no risk zone on the surveillance error grid compared to 82% of Dexcom G5 values.CONCLUSION Dexcom G5 produces more accurate sensor values than FreeStyle Libre in people with diabetes and advanced CKD and is likely safe to be used by those with advanced CKD.展开更多
It was suggested by Pantanen that the mean squared error may be used to measure the inefficiency of the least squares estimator. Styan[2] and Rao[3] et al. discussed this inefficiency and it's bound later. In this...It was suggested by Pantanen that the mean squared error may be used to measure the inefficiency of the least squares estimator. Styan[2] and Rao[3] et al. discussed this inefficiency and it's bound later. In this paper we propose a new inefficiency of the least squares estimator with the measure of generalized variance and obtain its bound.展开更多
This work analyzes the quality of crustal tilt and strain observations during 2014, which were acquired from 269 sets of ground tiltmeters and 212 sets of strainmeters. In terms of data quality, the water tube tiltmet...This work analyzes the quality of crustal tilt and strain observations during 2014, which were acquired from 269 sets of ground tiltmeters and 212 sets of strainmeters. In terms of data quality, the water tube tiltmeters presented the highest rate of excellent quality,approximately 91%, and the pendulum tiltmeters and ground strainmeters yielded rates of81% and 78%, respectively. This means that a total of 380 sets of instruments produced high-quality observational data suitable for scientific investigations and analyses.展开更多
This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often lim...This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.展开更多
文摘BACKGROUND Advanced chronic kidney disease(CKD) is a common complication for people with type 1 and 2 diabetes and can often lead to glucose instability. Continuous glucose monitoring(CGM) helps users monitor and stabilize their glucose levels. To date, CGM and intermittent scanning CGM are only approved for people with diabetes but not for those with advanced CKD.AIM To compare the performance of Dexcom G5 and FreeStyle Libre sensors in adults with type 1 or 2 diabetes and advanced CKD.METHODS This was a non-randomized clinical trial that took place in two outpatient clinics in western Sweden. All patients with type 1 or 2 diabetes and an estimated glomerular filtration rate(eGFR) of < 30 mL/min per 1.73 m^(2) were invited to participate. Forty patients(full analysis set = 33) carried the Dexcom G5 sensor for 7 d and FreeStyle Libre sensor for 14 d simultaneously. For referencing capillary blood glucose(SMBG) was measured with a high accuracy glucose meter(HemoCue®) during the study period. At the end of the study, all patients were asked to answer a questionnaire on their experience using the sensors.RESULTS The mean age was 64.1(range 41-77) years, hemoglobin A1 c was 7.0% [standard deviation(SD) 3.2], and diabetes duration was 28.5(SD 14.7) years. A total of 27.5% of the study population was on hemodialysis and 22.5% on peritoneal dialysis. The mean absolute relative difference for Dexcom G5 vs SMBG was significantly lower than that for FreeStyle Libre vs SMBG [15.2%(SD 12.2) vs 20.9%(SD 8.6)], with a mean difference of 5.72 [95% confidence interval(CI): 2.11-9.32;P = 0.0036]. The mean absolute difference was also significantly lower for Dexcom G5 than for FreeStyle Libre, 1.21 mmol/L(SD 0.78) and 1.76 mmol/L(SD 0.78), with a mean diffrenec of 0.55(95%CI: 0.27-0.83;P = 0.0004).The mean difference(MD) was-0.107 mmol/L and-1.10 mmol/L(P = 0.0002), respectively. In all, 66% of FreeStyle Libre values were in the no risk zone on the surveillance error grid compared to 82% of Dexcom G5 values.CONCLUSION Dexcom G5 produces more accurate sensor values than FreeStyle Libre in people with diabetes and advanced CKD and is likely safe to be used by those with advanced CKD.
文摘It was suggested by Pantanen that the mean squared error may be used to measure the inefficiency of the least squares estimator. Styan[2] and Rao[3] et al. discussed this inefficiency and it's bound later. In this paper we propose a new inefficiency of the least squares estimator with the measure of generalized variance and obtain its bound.
基金supported by Special Foundation of Earthquake Science(201408006)Director Foundation of Institute of Seismology,China Earthquake Administration(201516214)
文摘This work analyzes the quality of crustal tilt and strain observations during 2014, which were acquired from 269 sets of ground tiltmeters and 212 sets of strainmeters. In terms of data quality, the water tube tiltmeters presented the highest rate of excellent quality,approximately 91%, and the pendulum tiltmeters and ground strainmeters yielded rates of81% and 78%, respectively. This means that a total of 380 sets of instruments produced high-quality observational data suitable for scientific investigations and analyses.
文摘This paper introduces the integration of the Social Group Optimization(SGO)algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model(COCOMO).COCOMO’s fixed coefficients often limit its adaptability,as they don’t account for variations across organizations.By fine-tuning these parameters with SGO,we aim to improve estimation accuracy.We train and validate our SGO-enhanced model using historical project data,evaluating its performance with metrics like the mean magnitude of relative error(MMRE)and Manhattan distance(MD).Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.
