Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef heifers resulting from an experimental combined viral-bacterial challenge

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with remote continuous data collection could accurately differentiate between animals experimentally inoculated with a viral-bacterial (VB) challenge or phosphate buffer solution (PBS). Crossbred heifers (N = 38; BW = 230 ± 16.4 kg) were randomly assigned to treatments by initial weight, ADG, BHV-1 and MH serum titers. Feeding behavior, DMI, animal activity and rumen temperature were continuously monitored remotely prior to and following VB challenge. VB-challenged heifers exhibited decreased (P < 0.01) ADG and DMI, as well as increased (P < 0.01) neutrophils and rumen temperature consistent with a bovine respiratory disease (BRD) infection. However, none of the heifers displayed overt clinical signs of disease. Shewhart and cumulative summation (CUSUM) charts were evaluated, with sensitivity and specificity computed on the VB-challenged heifers (n = 19), and PBS-challenged heifers (n = 19) respectively, accuracy was determined as the average of sensitivity and specificity. To address the diurnal nature of rumen temperature responses, summary statistics (Mean, minimum, maximum) were computed for daily quartiles (6-h intervals), and these quartile temperature models were evaluated separately. In the Shewhart analysis, DMI was the most accurate (95%) at deciphering between PBS- or VB-challenged heifers, followed by rumen temperature (94%) collected in the 2 nd and 3 rd quartiles. Rest was most the accurate accelerometer-based traits (89%), and meal duration (87%) and bunk visit (BV) frequency (82%) were the most accurate feeding behavior traits. Rumen temperature collected in the 3 rd quartile signaled the earliest (2.5 d) of all the variables monitored with the Shewhart, followed by BV frequency (2.8 d), meal duration (2.8 d), DMI (3.0 d) and rest (4.0 d). Rumen temperature and DMI and remained the most accurate variables in the CUSUM at 80 and 79%, respectively. Meal duration (58%), BV frequency (71%) and rest (74%) were less accurate when monitored with the CUSUM analysis. Furthermore, signal day was greater for DMI, rumen temperature and meal duration (4.4, 5.0 and 3.7 d, respectively) in the CUSUM compared to Shewhart analysis. These results indicate that Shewhart and CUSUM charts can effectively identify deviations in feeding behavior, activity and rumen temperature patterns for the purpose of detecting sub-clinical BRD in beef cattle.

GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)

Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

The Design of GLR Control Chart for Monitoring the Geometric Observations Using Sequential Sampling Scheme

The control charts’ design is focused on system forecasting which is important in mathematics and statistics; these techniques are commonly employed in manufacturing industries. The need for a control chart that can conceptualize and identify the symmetric or asymmetric structure of the monitoring phase with more than one aspect of the standard attribute is a necessity of industries. The generalized likelihood ratio (GLR) chart is a well-known method to track both the decrease and increase in the mechanism effectively. A control chart, termed as a GLR control chart, is established in this article, focusing on a sequential sampling scheme (the SS GLR chart) to evaluate the geometrically distributed process parameter. The SS GLR chart statistic is examined on a window of past samples. In contexts of the steady-state average time to signal, the output of the SS GLR control chart is analyzed and compared with the non-sequential geometric GLR chart and the cumulative sum (CUSUM) charts. In this article, the optimum parameter options are presented, and regression equations are established to calculate the SS GLR chart limits.

Climatic variability at Gangtok and Tadong weather observatories in Sikkim, India, during 1961–2017

The present study documents the long-term trends in the temperature and precipitation of a poorly represented region, the Sikkim, eastern Himalaya using the Mann–Kendall non-parametric test and the Sen’s slope estimator. Additionally, the normal distribution curves and Cusum charts have been used to identify the shifts in extreme events and to detect the points of change in the climatic data series for robust analysis. The minimum temperatures recorded a positive trend in Gangtok (0.036 ˚C year−1 from 1961 to 2017) as well as in Tadong (0.065 ˚C year−1 from 1981 to 2010) stations, while the maximum temperatures showed no trend in Tadong station from 1981 to 2010 which is consistent with the trend in Gangtok station for the overlapped period. However, it was negative for the overall assessed period (− 0.027 ˚C year−1 from 1961 to 2017) in Gangtok. The average temperatures in Gangtok recorded no trend whereas a positive trend (0.035 ˚C year−1 from 1981 to 2010) was observed at Tadong station. A similar positive trend in the average temperatures has been detected at Gangtok also for the overlapped period. Accelerated warming was noticed during the last two decades with an increase in the probability of extreme events of temperatures (minimum, maximum, average) at the higher end. Precipitation was found to be more variable across the observed period and suggested no trend in the study area.