Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase.

Round-robin testing on Sound power level measurement for Reference Sound Source and Office printer

Round-robin testing for two samples, Reference Sound Source in accordance with ISO 6926 and Office printer with electrophotographic engine, were executed by seven testing laboratories in Japan on 2020. All tests were executed with parallelepiped measurement surface in hemi anechoic chamber in accordance with testing standard for engineering-grade sound power determination, ISO 3744:2010. The results show that sample standard deviation for RSS is better than printer. Standard deviations for overall A-weighted sound power level for two samples are better than combined standard uncertainties calculated with reference example of standard deviations in ISO 3744 (1.5 dB for Reference Sound Source as standard deviation of operating/mounting condition is negligibly small, 1.6 dB for printer as stable operating/mounting condition 0.5 dB). This paper also indicates tips for those who would conduct round-robin testing to obtain valid results by obviating incorrect operations and malfunctions of printers or similar equipment from the experience of some round-robin tests.

Securing the quality of mesh weld joints in bolt support through robotized operation

Stabile strength properties of the mesh weld joints is one of the key factors in ensuring the required load-bearing capacity of the bolt support as a whole. In accordance with the basic principles of the statistical process control concept, the best quality is ensured by the production process that has the minimum variability of the results. To identify the initial parameters of the control charts used to monitor the stability of the process, the strength properties of the mesh weld joints were studied for manual, contact and robotic welding in the conditions of OKS LLC. Robotic welding is made using the ABB robotic complex consisting of three manipulators, two of which are designed to execute the movement of the welding arc and one to displace the mesh into and out of the welding zone. It is shown that the distribution of welding strength values in robotic welding is characterized with the lowest value of sample standard deviation. It was found that robotic welding was the only method among those investigated that ensured the values of additional samples to fall within the initially set range in the control charts.

Improving the methodology for calculation of energy losses in axial turbine cascades

Представлена оценка неопределенности прогнозирования потерь энергии в решетках профилей осевых турбин. В сравнении с экспериментальными данными рассмотрены эмпирическая модель ЦИАМ и метод CFD анализа в рамках RANS модели. Геометрические и режимные параметры решеток профилей варьируются в широком диапазоне. Результаты CFD расчета отличаются существенно в зависимости от модели турбулентности. Наименьшая неопределенность получена для модели рейнольдсовых напряжений RSM. Определено выборочное стандартное относительное отклонение для анализируемой базы данных. Применительно к CFD расчету данное отклонение составило 18,6%, применительно к эмпирической модели ЦИАМ 46,4%. Разработана эмпирическая модель коррекции потерь полученных по результатам CFD анализа с моделью турбулентности RSM. Корректирующая функция включает в себя геометрические и режимные параметры решеток и особенности течения в межлопаточном канале (всего 14 параметров). Использование разработанного подхода позволило снизить неопределённость прогнозирования потерь в 2 раза. В результате работы выборочное стандартное относительное отклонение предсказания потерь для рассматриваемой базы решеток профилей составило 9,3%. Estimation of the uncertainty in predicting profile losses using various models was performed. In comparison with the experimental data, empirical model of CIAM and method of CFD analysis are considered. RANS models are used. The geometric and operating parameters of the analyzed turbine cascades vary over a wide range. Turbulence models strongly influence loss prediction uncertainty. The smallest uncertainty was obtained using the RSM turbulence model. The sample standard deviation for the considered turbine cascades base was determined. The deviation for CFD analysis is 18.6%. For the empirical model of CIAM the deviation is 46.4%. The new empirical model has been created to correct the results of calculating losses according to the RANS model using the RSM turbulence model. The corrective function takes into account the influence of the geometric and operating parameters of the turbine cascades and the features of the airfoil flow (14 parameters in total). The developed approach allows reducing the uncertainty in the estimation of losses according to the RANS model by 2 times. As a result, the sample standard deviation in the prediction of losses is 9.3% for the considered turbine cascades base.

Managerial styles and their relationship with job satisfaction in information technology companies

The purpose of this paper is to identify and evaluate which managerial style prevails in the operational area of five IT companies and their relationship with the job satisfaction of their employees. For data collection, a questionnaire was applied to 120 participants, validating 110 responses. Questions regarding the three factors of EAEG were integrated in order to identify the focus in which the leadership predominantly operates in these companies, and the five dimensions of EST were used to measure the level of employees satisfaction regarding each dimension evaluated. Descriptive statistical processing allowed to observe that the managerial style focused on the Task prevails, with an overall average score of vt = 4.11. As dispersion measures, the sample standard deviation (St = 0.39) and sample amplitude (At = 0.91) corresponding to this factor (Task) were calculated, respectively, showing that the values are very close to the average score. It indicates that there is uniformity between the scores for each item of the factor Task. It was found that 65 % of participants confirm the role of the leadership focused on the Task. The Satisfaction with the nature of work dimension reached an average score of vd = 5.09, showing employee satisfaction. The dimension Satisfaction with colleagues reached an average score of vd = 4.34 indicating indifference on the level of satisfaction, whereas the dimensions Satisfaction with salary, promotions and leadership, reached averages of vd = 3.87, vd = 3.70 and vd = 3.46, respectively, indicating levels of dissatisfaction. Finally, were highlighted some criteria as motivators for avoid turnover in organizations that contribute to the job satisfaction of these employees.

Analysis of measurements of defects in multiaxial warp knitted fabrics for CFRP composites

Multiaxial Warp Knitted (MWK) Fabrics are used to create Carbon Fibre Reinforced Plastic (CFRP) laminates. In contrast to Prepregs, CFRP laminates made with MWK fabrics are of interest because they could lower costs and processing time by being already constructed with multiple layers and through the use of a hot air oven instead of an autoclave. Defect in the form of fibre angle orientation plays an important role in the compression strength for laminates made with MWK fabrics. The in-plane and out-of-plane waviness of the fibres were characterised by the standard deviation of the angular waviness: sample Standard deviation of Fibre In-plane (SFI) and the sample Standard deviation of Fibre Out-of-plane (SFO). The SFI value was found in two ways: analysis (Multiple Field Image Analysis (MFIA) technique) software and Fibre Image Analysis software. Measurements of the holes in the carbon fibre textile, colloquially known as “fisheyes,” caused by sewing the textile together were also gathered. The SFI, SFO, and “fisheye” dimensions were together used in the FMB-PMB model and the Unit Cell Model to calculate the compression strength. These predicted compression strengths were compared to the laboratory results. Also, a reliability model was developed to find R, the reliability of each textile, to be used as a textile classification tool. It has been found that the compression strength predictions found using analysis and Fibre Image Analysis yielded similar results, with predictions from analysis closer to the laboratory results. The R value yielded a positive correlation with the results from analysis. A large percentage of difference between the predicted and the actual compression strength was observed for some textiles. This could be attributed to the inherent lack of regularity for some of the examined textiles and variability in determining the SFI and “fisheye” parameters. Improvements would involve devising rules and methods to determine the SFI and “fisheye” parameters, modifying the FMB-PMB and Unit Cell Models, and making the analysis process faster and more applicable for on-line quality process control.

Analysis of measurements of defects in multiaxial warp knitted fabrics for CFRP composites

Multiaxial Warp Knitted (MWK) Fabrics are used to create Carbon Fibre Reinforced Plastic (CFRP) laminates. In contrast to Prepregs, CFRP laminates made with MWK fabrics are of interest because they could lower costs and processing time by being already constructed with multiple layers and through the use of a hot air oven instead of an autoclave. Defect in the form of fibre angle orientation plays an important role in the compression strength for laminates made with MWK fabrics. The in-plane and out-of-plane waviness of the fibres were characterised by the standard deviation of the angular waviness: sample Standard deviation of Fibre In-plane (SFI) and the sample Standard deviation of Fibre Out-of-plane (SFO). The SFI value was found in two ways: analysis (Multiple Field Image Analysis (MFIA) technique) software and Fibre Image Analysis software. Measurements of the holes in the carbon fibre textile, colloquially known as “fisheyes,” caused by sewing the textile together were also gathered. The SFI, SFO, and “fisheye” dimensions were together used in the FMB-PMB model and the Unit Cell Model to calculate the compression strength. These predicted compression strengths were compared to the laboratory results. Also, a reliability model was developed to find R, the reliability of each textile, to be used as a textile classification tool. It has been found that the compression strength predictions found using analysis and Fibre Image Analysis yielded similar results, with predictions from analysis closer to the laboratory results. The R value yielded a positive correlation with the results from analysis. A large percentage of difference between the predicted and the actual compression strength was observed for some textiles. This could be attributed to the inherent lack of regularity for some of the examined textiles and variability in determining the SFI and “fisheye” parameters. Improvements would involve devising rules and methods to determine the SFI and “fisheye” parameters, modifying the FMB-PMB and Unit Cell Models, and making the analysis process faster and more applicable for on-line quality process control.

A New Mathematical Metric for Inclusive Excellence in Teaching Applied Before and During the COVID-19 Era

In this paper a novel metric for evaluating inclusive excellence in teaching is introduced and applied to students' performance in classes before and during the COVID-19 era. The novel metric, named the Inclusive Excellence Ratio (IER), is designed to simultaneously reflect the two desirable characteristics embraced by inclusive excellence teaching: strong student performance and low variation in performance across all students. The computation of the IER given student test score data is simple and straightforward: it is the statistical sample mean divided by the sample standard deviation. Consequently, the IER is high when the students' test scores are high and variance is low, suggesting it may provide a useful quantitative measure for those educational innovators seeking to experiment with new, effective teaching methodologies that boost inclusive excellence. The IER is applied to evaluate a posteriori student performance taken from cumulative aggregate data from the University of California, Irvine (UCI) undergraduate math finance classes involving 378 students over two academic years (2018 to 2020), spanning five quarters before and one quarter during the COVID-19 era of remote teaching at UCI. Conclusions are drawn and discussed comparing the quality of in-person teaching environments to remote teaching environments.

A Residual Analysis for the Removal of Biological Oxygen Demand through Rotating Biological Contactor

Regression is a statistical method that is generally used for forecasting and prediction. It helps us to estimate the relationship between a dependent variable and one or more independent variables. This is the most widely used technique that best approximates the individual data points. It has found numerous successful applications in Engineering, Science, business and other fields. Getting average removal % of Biological Oxygen Demand (BOD5) from greywater through Rotating Biological Contactor (RBC), following experiment was conducted in Sindh University hostels using different parameters such as Hydraulic Retention Time (HRT) i.e. 2 hours (0.42 liter per min), 2.5 hours (0.33 l/min) and 3 hours (0.28 l/min) and multiple number of discs i.e. 40, 42, 44, 46, 48, 50 and 52. Consequences reveal that linear estimate of HRTs and numbers of disc are considerable whereas linear and quadratic estimates of number of discs are highly significant, which evidence the significance of time and discs. However, as p-value is greater than 0.05, hence quadratic estimate of HRT is not significant. By using coefficients of the table the regression equation is Removal = - 79.995 + 6.88 time + 2.90 disc, where the sample standard deviation is 7.151, coefficient of correlation is 0.86 and coefficient of determination is 0.742. Distributions of errors are approximately normal as probability plot of the residuals is approximately linear. Residual analysis shows that against each predicted variable, residuals plot falls approximately in a horizontal band symmetric and centered about the horizontal axis and against predicted y-values. Moreover, Residual plot shows the constant standard deviations and linearity assumptions appear to be met.

Sensitivity analysis of stochastic frontier analysis models

The efficiency measures of the Stochastic Frontier Analysis (SFA) models are dependent on distributional assumptions of the one-sided error or inefficiency term. Given the intent of earlier researchers in the evaluation of a single inefficiency distribution using Monte Carlo (MC) simulation, much attention has not been paid to the comparative analysis of SFA models. Our paper aims to evaluate the effects of the assumption of the inefficiency distribution and thus compares different SFA model assumptions by conducting a MC simulation. In this paper, we derive the population statistical parameters of truncated normal, half-normal, and exponential inefficiency distributions of SFA models with the objective of having comparable sample mean and sample standard deviation during MC simulation. Thus, MC simulation is conducted to evaluate the statistical properties and robustness of the inefficiency distributions of SFA models and across three different misspecification scenarios, sample sizes, production functions, and input distributions. MC simulation results show that the misspecified truncated normal SFA model provides the smallest mean absolute deviation and mean square error when the true data generating process is a half-normal inefficiency distribution.