Variation In Performance Measures Research Articles

How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the greenhouse gas emission from anthropogenic sources. Recent studies have shown that the selection of lower CH4 emitting cows is possible, but this would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emission from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A data set with 3 experiments, a total of 149 lactating dairy cows with repeated measures, was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed. The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53, when only performance measures were used, and to 0.66, when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure, since the latter avoid over fitting and simplify the model. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

Inter- and intra-operator variations in manual segmentation of hippocampus from MRI

PurposeWe evaluate inter- and intra-operator variations in manual segmentation of hippocampus and present their potential sources. Hippocampal atrophy is common in mesial temporal lobe epilepsy (mTLE). Effective diagnosis and treatment of mTLE depends on accurate and efficient segmentation of hippocampus from magnetic resonance imaging (MRI) data. Manual segmentation by expert radiologists remains the gold standard, although automated segmentation methods exist. MethodsHippocampus was segmented in MRI of 118 unilateral mTLE patients and 25 non-epileptic subjects (65 males, 78 females; mean age 39 years) by three operators (M1, M2, M3) manually and by three software tools (FreeSurfer, LocalInfo, ABSS) automatically. Segmentation results were evaluated using 7 volume-, voxel-, and distance-based performance measures. Inter-operator variation was evaluated by comparing the segmentation results of the three operators. Intra-operator variation was evaluated by comparing manual and automatic segmentation results. Segmentation results were used to lateralize epileptogenicity in mTLE patients. ResultsRanking of performance measures differed when using M3 segmentations as ground truth rather than M1 or M2 segmentations. The standard deviation tended to be higher when using M3 as ground truth and M1 or M2 as test segmentation. Variation in performance measures and increased standard deviation when using M3 as ground truth are indicative of inter-operator variability. Standard deviations were larger when using M2 segmentations as ground truth and automated segmentations as test segmentation, rather than M1 or M3. Large standard deviations between M2 segmentations and automated segmentations are indicative of intra-operator variation. Among automated segmentation methods, ABSS produced segmentations most similar to manual segmentations, but FreeSurfer and LocalInfo lateralized epileptogenicity more accurately. ConclusionsPart of inter-operator variation might be due to temporal separation of M3 segmentations from M1 and M2 segmentations (M3 performed segmentations a few years before operators M1 and M2). Inter-operator variation will likely reduce if all operators segment within the same time frame, reducing discrepancies in training of operators. Intra-operator variation can likely be mitigated with additional oversight by a neuroradiologist. Inter- and intra-operator variability may generate inconsistencies in outcomes. Future automated segmentation techniques may integrate neural-network-based (ABSS) and atlas-based (FreeSurfer and LocalInfo) segmentation techniques for optimal performance.

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers’ static behavior. This paper’s objective is to calculate daily churn based on dynamic variations in client behavior. Training excellent models to further identify potential churning customers helps insurance companies make decisions to retain customers while also identifying areas for improvement. Thus, it is possible to identify and analyse clients who are likely to churn, allowing for a reduction in the cost of support and maintenance. Binary Golden Eagle Optimizer (BGEO) is used to select optimal features from the datasets in a preprocessing step. As a result, this research characterized the customer's daily behavior using various models such as RFM (Recency, Frequency, Monetary), Multivariate Time Series (MTS), Statistics-based Model (SM), Survival analysis (SA), Deep learning (DL) based methodologies such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Customized Extreme Learning Machine (CELM) are framed the problem of daily forecasting using this description. It can be concluded that all models produced better overall outcomes with only slight variations in performance measures. The proposed CELM outperforms all other models in terms of accuracy (96.4).

Analysis of single server finite buffer queue under discouraged arrival and retention of reneging

Queuing models where arrival rates go down consequent to increase in the number of customers are called systems with discouraged arrivals. Discouraged arrivals are distinct from balking in the sense that balking implies that arriving customers do not join. In this paper, we model a single server finite buffer Markovian queuing model with discouraged arrival, balking, reneging and retention of reneged customers. The steady state probabilities are obtained using Markov process method. Closed form expression of traditional as well as some freshly designed performance measures are presented. We also perform sensitivity analysis to examine the variations in performance measures with the variations in system parameters. Our results are numerically illustrated through a field level problem with design connotations.

Abstract P03: Identifying sources of variation in manual segmentation of hippocampus on from magnetic resonance images (MRI)

Abstract The purpose of the present study is to evaluate inter-operator and intra-operator variations in the manual segmentation of the hippocampus from high resolution T1-weighted magnetic resonance (MR) images. The hippocampus was segmented manually in MR images of 118 epileptic and 25 non-epileptic patients (65 males, 78 females; median age of 36 years, mean age of 39 years) by three operators (M1, M2, M3) and three automated methods (FreeSurfer, LocalInfo, ABSS). To determine how much the manual segmentations performed by one operator differ from those of another operator, inter-operator variability was evaluated. To determine how much the manual segmentations done by an operator vary over time, i.e., to assess intra-operator variability, manual segmentations from each operator were compared to the automated segmentations. To this end, rational absolute value degree (RAVD), volume asymmetry, Dice coefficient, precision, similarity, specificity, accuracy, negative predictive value (NPV), Hausdorff distance, root mean square (RMS), average symmetric surface distance (ASSD), and mean distance were calculated. The segmentation results of one of the operators were considered as the ground truth for the evaluation of the segmentation results of the other operators and the automatic segmentation methods. Hausdorff distance and precision were different when using automated techniques as the test segmentation and the M3 segmentation as the ground truth, rather than M1 and M2 segmentations. The standard deviation of performance measures tended to be higher when using operator M3 as the ground truth and either operator M1 or M2 as the test segmentation. Variation in performance measures when using M3 as the ground truth is indicative of inter-operator variation. When comparing performance measures generated by automated versus manual techniques, standard deviations were larger when using operator M2 as the ground truth than when using operator M1. This suggests that operator M2 exhibited a larger intra-operator variation than operator M1. Among the automatic segmentation methods, ABSS was the most effective method in many regards (RAVD Dice coefficient, similarity, specificity, accuracy, NPV, Hausdorff distance, RMS, ASSD) while FreeSurfer and LocalInfo were more effective for the precision, mean distance, and lateralization of epileptogenicity. Inter-operator error was likely due to the temporal separation of the segmentations and thus, it may be reduced by having all operators working in the laboratory simultaneously and undergoing the same training, although some inter-operator variability may be unavoidable. Intra-operator variation can likely be reduced with further training and supervision of the operators by a neuroradiologist with expertise in hippocampus anatomy. Future automated segmentation techniques may incorporate elements of both atlas-based (FreeSurfer and LocalInfo) and neural-network-based (ABSS) segmentation techniques for optimal performance. Citation Format: Benjamin Huber, Esmaeil Davoodi-Bojd, Hamid Soltanian-Zadeh. Identifying sources of variation in manual segmentation of hippocampus on from magnetic resonance images (MRI) [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr P03.

Extended analysis and computationally efficient results for the queueing system

We consider an infinite waiting space queueing system in which customers arrive according to a renewal process. The server serves the customers in batches under general bulk-service rule. The successive service times of batches are mutually independent and have a common exponential distribution. To obtain the queue-length distributions at prearrival and random epochs, we have used the displacement operator which assists to solve simultaneous non-homogeneous difference equations. An analytically simple and computationally efficient procedures have developed to compute the waiting-time distribution of an arrival customer. Our formula to determine the waiting-time distribution is also useful even when multiple poles occur in the Laplace-Stieltjes transform of the interarrival time distribution. We have determined closed-form analytical expression for the distribution of size of a service batch of an arrived customer. We also have derived the results of some particular well-known queueing models from our model. Some numerical results are provided in the form of tables for a variety of interarrival-time distributions to demonstrate the variation of performance measures of the system.

1261-P: Assessing Data Availability and Benchmarking Performance of Quality Measures for the T1D Exchange Quality Improvement Collaborative

The T1D Exchange QI Collaborative was formed in 2016 to accelerate quality improvement (QI) at clinics serving people with type 1 diabetes (T1D). Through an evidence-based review and idealized design process, the collaborative chose seven process and outcome measures to be the focus of improvement activities directed at high-risk T1D patients (2 or more A1c values >9% in the preceding year) aged 12-26 years. We report monthly aggregate statistics from seven Collaborative clinics for the measures and show the statistics for May and August 2018. A substantial percent of patients meet high risk criteria across all centers, and there is wide variation in performance measures. No center has the capacity to report on all measures, but there are incremental improvements in data availability. Outcomes remain suboptimal, particularly in this high-risk population. These measures will be continuously tracked to define network progress and to design QI interventions to improve glycemic outcomes. The work of the Collaborative and the creation of a technology platform will improve reporting capacity and drive QI activities. Disclosure A.B. Cabrera: None. G.T. Alonso: None. S. Corathers: None. J.M. Lee: Consultant; Self; Taiho Oncology, Inc. P. Prahalad: None. N. Rioles: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

Drilling of micro-holes on titanium alloy using pulsed Nd:YAG laser: Parametric appraisal and prediction of performance characteristics

Laser drilling is a preferable method in drilling micro and slender holes because of its non-physical contact between the tool and the workpiece leading to elimination of the problems like chatter and vibration. In this article, an experimental investigation of the laser drilling process is carried out on thin sheet (0.5 mm) of titanium alloy (Ti6Al4V) using pulsed Nd:YAG laser. The study investigates the effect of laser parameters such as laser energy, pulse repetition rate, pulse width and gas pressure on hole quality characteristics, namely, circularity at entry and exit, taper and spatter area. Taguchi’s L27 orthogonal array is adopted for conducting the experiments to establish significance of control parameters on the performance measures with less number of experimental run. Analysis of variance shows that laser energy and pulse width significantly influence on circularity, taper and spatter area. Optimal parameter setting for all the performance measures is suggested. Statistically valid empirical models relating hole quality characteristics with laser parameters have been developed based on least square regression analysis. Backpropagation artificial neural network is used predict the output responses considering laser process parameters as inputs. A mean square error within 5% for both the training and testing data suggests the adequacy and robustness of the proposed artificial neural network model. Variation of performance measures with respect to process parameters is predicted through the well-trained artificial neural network model. The pattern of variation exhibits good agreement with the main effect plots obtained through Taguchi method. Artificial neural network model produces mean relative error of 0.001, 0.001, 0.0437 and 0.0234 for circularity at entry, circularity at exit, taper and spatter area, respectively, when compared with experimental values. Since small values of mean relative error are obtained, the proposed artificial neural network model can be satisfactorily used for prediction of quality characteristics of laser micro-drilled holes.

Multi response optimization of wire-EDM process parameters of ballistic grade aluminium alloy

In the current investigation, a multi response optimization technique based on Taguchi method coupled with Grey relational analysis is planned for wire-EDM operations on ballistic grade aluminium alloy for armour applications. Experiments have been performed with four machining variables: pulse-on time, pulse-off time, peak current and spark voltage. Experimentation has been planned as per Taguchi technique. Three performance characteristics namely material removal rate (MRR), surface roughness (SR) and gap current (GC) have been chosen for this study. Results showed that pulse-on time, peak current and spark voltage were significant variables to Grey relational grade. Variation of performance measures with process variables was modelled by using response surface method. The confirmation tests have also been performed to validate the results obtained by Grey relational analysis and found that great improvement with 6% error is achieved.

Driver Behavior in Road Tunnels: Association with Driver Stress and Performance

Complex traffic environments can affect driver stress and driving performance negatively. Driving through tunnels can be particularly stressful, and these segments have been associated with higher crash rates. However, the constraints of the natural environment (e.g., mountains, waterways) often restrict the flexibility to make major modifications in tunnels. The purpose of this study was to evaluate stress as drivers traversed along an interstate route that included tunnel and nontunnel segments, as well as a 75-m transition period before the tunnels. Data from 50 drivers, including information from electrocardiogram recordings such as heart rate and standard deviation of interbeat intervals (SDNN), were collected. Driving performance measures included vehicle speed and braking. Multivariate and univariate analyses of variance were used to identify increases in drivers' stress during several road segments: the transition to a tunnel entrance, within a tunnel, and open road segments. The largest variations in performance measures were observed in tunnels, followed by the periods in transition segments. Evaluation of continuous speed profiles along the route showed that drivers tended to decrease their speed before entering a tunnel and increase speed just before exiting a tunnel. The highest level of stress (denoted by the largest positive change in heart rate and lowest variability in SDNN) was observed along the transition segments, followed by the tunnel segments. Identifying situations in which drivers may experience higher levels of stress and the corresponding impact on driving performance is important for future road and tunnel designs.

A Study on Machining Capability of a 2-DoF PKM-based Milling Machine

In the recent past, Parallel Kinematic Machines (PKM) with fixed-length legs have been claimed for their potential use as machine tools due to smaller moving masses leading to the high dynamic characteristics and accuracy. In this paper, the machining capability of a 2-Degrees-of-Freedom (DoF) PKM-based milling machine is explored. Stiffness of the PKM was estimated using finite element analysis approach. Extensive experimentation was carried out by varying machining conditions, i.e., cutting speed, feed rate and depth of cut to study the variation in performance measures, namely, surface roughness and material removal rate. Regression models were developed to predict the surface roughness. An attempt was made to find the optimum machining conditions for PKM using Genetic Algorithm. The experimental results confirm the capability of fixed- length leg PKM-based machine tool to perform machining operations upon metals like hard Aluminium alloys.

Impacts of spatial uncertainty on performance of age structure-based harvest strategies for blue eye trevalla ( Hyperoglyphe antarctica)

Harvest control rules (HCRs), key components of fisheries management strategies, are used to calculate recommended catch levels given estimates of present stock biomass or levels of fishing mortality. Spatial variability, either in the population dynamics, fishery operations, or in data collection, has the potential to impact HCR performance as this can drive variability in indicators used for stock assessment. A management strategy evaluation (MSE) approach was used to evaluate the performance of HCRs for blue eye trevalla ( Hyperoglyphe antarctica), a species that exhibits spatial variability in monitoring data in southeast Australia. Employing catch curve analysis, the HCRs rely solely on information from the age structure of the catch. Several versions of the HCRs were tested, varying the reference points used to determine management actions, and the way spatial variability was accounted for when setting catch limits. The results suggest that effective implementation of the HCRs is challenging, requiring appropriate choice of reference points and estimators. Spatial disaggregation of data leads to imprecise estimates of mortality rates. However, appropriate weighting of spatial estimates of stock status leads to higher relative stock size than when data are aggregated spatially. Variation in performance measures is dominated by uncertainty associated with mis-specification of the rate of natural mortality and the steepness of the stock–recruitment relationship. Such uncertainties can be expected for an information-poor, spatially heterogeneous resource, therefore additional considerations besides the HCR should be taken to achieve a desired precautionary result in contrast to the situation for more data-rich scenarios.

Robust Design for Acetabular Cup Stability Accounting for Patient and Surgical Variability

The stability of cementless acetabular cups depends on a close fit between the components and reamed acetabular cavities to promote bone ingrowth. Cup performance and stability are affected by both design and environmental (patient-dependent and surgical) factors. This study used a statistically based metamodel to determine the relative influences of design and environmental factors on acetabular cup stability by incorporating a comprehensive set of patient-dependent and surgical variables. Cup designs with 2 mm or 3 mm intended equatorial bone-implant interferences appeared to perform the best, improving implant stability with smaller mean and variability in cup relative motions and greater mean and smaller variability in ingrowth areas. Cup eccentricity was found to have no effect on implant performance. Design variables did not contribute as much to the variation in performance measures compared to the environmental variables, except for potential ingrowth areas.

Performance assessment of irrigation water management of heterogeneous irrigation schemes: 2. A case study

The non-uniformity of soils, weather, fields, cropping pattern and canal systems in most surface irrigation schemes makes irrigation water management complex, but optimum performance is important particularly in irrigation schemes with limited water supply. This paper focuses on the performance of irrigation water management during the area and water allocation with a case study of an irrigation scheme in the semi-arid region of India. Often the irrigation managers or authorities of these heterogeneous irrigation schemes also need to deal with different allocation rules. The allocation plans and the corresponding water delivery schedules during the allocation process were estimated with the help of a simulation–optimisation model for different allocation rules based on cropping distributions (free and fixed), water distributions (free and fixed-area proportionate), irrigation depth (full, fixed depth and variable depth irrigation) and irrigation interval (from 14 to 35 days). The performance measures of productivity (in terms of net benefits and area irrigated), equity (in water distribution), adequacy and excess were assessed for these different allocation plans and schedules. These were further compared with the performance measures of the existing rule (fixed depth irrigation at a fixed interval). The analysis revealed that these performance measures are in some cases complimentary and in other cases conflicting with each other. Therefore, it would be appropriate for the irrigation managers to understand fully the nature of the variation in performance measures for different allocation rules prior to deciding the allocation plans for the irrigation scheme.

Ownership, performance, and innovation in China's large- and medium-size industrial enterprise sector

China's 22,000 large- and medium-size enterprises (LMEs) stand at the pinnacle of Chinese industry. Although they account for less than a fraction of a percent of China's nearly 8 million industrial enterprises, they collectively account for one third of the nation's total industrial output. Using a panel of these enterprise data for 1994–1999, we find a rapidly diversifying ownership structure in which the role of the state is steadily retreating. At the same time, we find considerable variation in measures of performance across ownership types and see emerging within Chinese industry, evidence of high-intensity R&D performers that exhibit substantial innovation capabilities.

Variations in Capacity and Delay Estimates from Microscopic Traffic Simulation Models

One of the issues involved in using microscopic simulation models is the variation in the simulation results. This study examined some of the more popular microscopic traffic simulation models, CORSIM, SimTraffic, and VISSIM, and investigated the variations in the performance measures generated by these models. The study focused on the capacity and delay estimates at a signalized intersection. The effects of link length, speed, and vehicle headway generation distribution were also investigated. With regard to variations in performance measures, the study found that CORSIM yields the lowest variations, whereas SimTraffic yields the highest. The highest variation in each simulation model normally occurs when the traffic demand approaches capacity. It was also found that delays are affected by the link length and speed in simulation models. Such an impact on delays is closely related to the range of speed variations. In general, shorter links and higher link speeds result in lower delays. There is no strong evidence that the headway distribution used to generate vehicles in the simulated network has any effect on capacity and delay estimates. Multiple simulation runs are necessary to achieve an accurate estimate on the true system performance measures. With a 10% error range in estimated delay, two to five runs may be enough for under-capacity conditions, but more than 40 multiple runs may be necessary to accurately estimate delay at, near, or over capacity.