A Comparative Study of Energy Big Data Analysis for Product Management in a Smart Factory

Energy has been obtained as one of the key inputs for a country's economic growth and social development. Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in a smart factory. This study aims to present and analyse the predictive models of the data-driven system to be used by appliances and find out the most significant product item. With repeated cross-validation, three statistical models were trained and tested in a test set: 1) General Linear Regression Model (GLM), 2) Support Vector Machine (SVM), and 3) boosting Tree (BT). The performance of prediction models measured by R2 error, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Variation (CV). The best model from the study is the Support Vector Machine (SVM) that has been able to provide R2 of 0.86 for the training data set and 0.85 for the testing data set with a low coefficient of variation, and the most significant product of this smart factory is Skelp.

Evaluation of Different Internal Designs of Hydraulic Nozzles under an Accelerated Wear Test

The use of worn-out agricultural nozzles in pesticide application has a negative effect on the efficiency and cost of the application process. It also has an effect on environmental pollution due to an excessive amount of pesticide being applied when spraying with worn-out nozzles. In this paper, the resistance to wear of three different internal design hydraulic nozzles was ascertained. Changes in the flow rate and spray distribution as a result of this wear were also investigated. The wear test was done inside a closed system, and it was accelerated using an abrasive material to generate 100 h of wear. The tested nozzles were the Turbo TeeJet (TT)-twin chambered, Turbo Twinjet (TTj60)-dual outlet, and Drift Guard (DG)-pre-orifice. Wear rate, flow rate, and the virtual coefficient of variation (CVv) were measured at different wear intervals. The results showed that the TTj60 type was the most resistant to wear, followed by the TT type and DG. The latter two types showed an increase in the flow rate only in the first 45 h of wear. Virtual coefficient of variation (CVv) values were less than 10% after finishing the test (after 100 h of wear) for the three types of nozzles, which are acceptable values according to International Organization for Standardization (ISO) 16122-2, 2015.

Long-term glycemic variability and risk of stroke in patients with diabetes: a meta-analysis

Objectives Long-term glycemic variability has been related to increased risk of vascular complication in patients with diabetes. However, the association between parameters of long-term glycemic variability and risk of stroke remains not fully determined. We performed a meta-analysis to systematically evaluate the above association. Methods Medline, Embase, and Web of Science databases were searched for longitudinal follow-up studies comparing the incidence of stroke in diabetic patients with higher or lower long-term glycemic variability. A random-effect model incorporating the potential heterogeneity among the included studies were used to pool the results. Results Seven follow-up studies with 725,784 diabetic patients were included, and 98% of them were with type 2 diabetes mellitus (T2DM). The mean follow-up duration was 7.7 years. Pooled results showed that compared to those with lowest category of glycemic variability, diabetic patients with the highest patients had significantly increased risk of stroke, as evidenced by glycemic variability analyzed by fasting plasma glucose coefficient of variation (FPG-CV: risk ratio [RR] = 1.24, 95% confidence interval [CI] 1.11 to 1.39, P < 0.001; I2 = 53%), standard deviation of FPG (FPG-SD: RR = 1.16, 95% CI 1.02 to 1.31, P = 0.02; I2 = 74%), HbA1c coefficient of variation (HbA1c-CV: RR = 1.88, 95% CI 1.61 to 2.19 P < 0.001; I2 = 0%), and standard deviation of HbA1c (HbA1c-SD: RR = 1.73, 95% CI 1.49 to 2.00, P < 0.001; I2 = 0%). Conclusions Long-term glycemic variability is associated with higher risk of stroke in T2DM patients.

ECOLOGICAL PLASTICITY AND STABILITY OF PROSPECTIVE PEA LINES

The studies were carried out in order to assess the parameters of the adaptability of promising pea samples in terms of yield to identify the best genotypes for the conditions of middle Volga region. The work was carried out in 2018-2020 in the central zone of Ulyanovsk region. The object of the research was 10 pea samples, the standard was Ukaz variety. According to the methods of S.A. Eberhart, W.A. Russell, V.V. Khangildina and S.P. Martynova determined the adaptability of breeding samples using the following indicators: coefficient of variation (V%), homeostaticity (Hom), breeding value (Sc), stability index (Sj2), linear coefficient regression (bi), point stability estimate (Hi). On average, over three years of research, the greatest increase in yield, compared to the standard, was noted for Ulyanovskiy yubileiny variety - 0.43 t/ha. The genotypes of Ulyanovskiy yubileiny, Viridis and line 657/14 with the smallest values of the coefficients of variation - 14.6, 22.4, 23.4%, respectively, are attributed to the most stable in terms of the coefficient of variation V. The most valuable in terms of plasticity and stability were the Ukaz variety (bi=1.15 and Sj2=0.02) and line 559/11 (1.14 and 0.00 respectively). Line 621/14 (bi=1.42 and Sj2=0.15) was recognized as an intensive variety with very low phenotypic stability and line 752/14 (1.29 and 0.11 respectively), with a reduced variety. Lines 215/11, 533/14, 657/14 were distinguished by very high phenotypic stability (bi=0,91…1,07, Sj2=0,00…0,03). The highest level of homeostaticity in combination with breeding value was observed in the promising pea cultivar Ulyanovskiy Yubileiny (Hom=15.33 and Sc=1.67) and line 215/11 (Hom=7.74 and Sc=1.22). According to the point assessment of Hi stability, significant advantages were revealed in Ulyanovskiy yubileiny variety (Hi =4.22) and line 215/11 (1.33). According to the sum of the ranks of the six parameters of adaptability, the leading positions were occupied by lines 533/14 (27), 215/11 (32) and promising varieties Ulyanovskiy yubileiny (32), Viridis (32). According to the test results, two samples in 2020 were submitted for state variety testing

Elucidation of Genetic Parameters Controlling Yield and Quality Traits in Rice (Oryza sativa L.)

Background: Forty two crosses involving seven lines and six testers were studied for economically important yield contributing and quality traits to test the magnitude of genetic components and diversity. Formulation of efficient breeding methodology is possible by targeting the genetic architecture of genotypes. Methods: The systematic breeding programme involves generating genetic variability besides sorting off the diverse genotypes and utilizing the extreme phenotypes for producing stable varieties. Genetic diversity helps to achieve the greater continuum of genetic variability in segregating populations to reach for ideal selection of progenies. Heritability and genetic advance are other important selection parameters for retrieving better genotype through selection. Result: Significant differences in analysis of variance were recorded for all the traits. The results signified the greater value of phenotypic coefficient of variation (PCV) than genotypic coefficient of variation (GCV) and environment coefficient of variation (ECV) pertaining to the test traits studied. Among agronomical characters, the GCV and PCV were reported to be in higher estimate for number of productive tillers per plant, number of grains per panicle, single plant yield and among quality characters for gelatinization temperature (GT), length breadth (LB) ratio, gel consistency and amylose content. The present study adverted that among the yield and grain quality characters viz., number of productive tillers, number of grains per panicle, single plant yield, plant height, 1000 grain weight, milling percentage and grain length could be easily inherited to next generation due to high heritability. Whereas breadth elongation ratio and linear elongation ratio are influenced by environmental factors due to their low heritability. Further, the number of productive tillers, number of grains per panicle, single plant yield, plant height, Gel consistency and amylose content exhibited higher PCV, GCV, heritability and genetic advance and hence direct selection can be made for target traits.

Evaluation Algorithm for Team Strength Based on the Collected Healthcare Data through IoT and Smart Devices

In the past, the fans used to evaluate the strength of the team according to the victory and defeat ranking or according to their own intuition and preferences, however, the strength of the team is difficult to measure in analytical figures. The team’s winning rate is not the only factor to be considered to determine the strength of the team. There are many factors to be considered for determining the strength of the team. According to the variation coefficient of basketball scoring frequency, the paper designs the principal model of basketball players’ pitching target system. The data is captured by IoT devices and smart devices. The algorithm sets the number of the frequency of Gabor filter transformation features, controls the error accumulation, extracts the cascade features of basketball score video, constructs the video conversion discrimination rules, detects the basketball target, and obtains the tracking target contour to frame information. Finally, it realizes the target tracking detection of the team based on the team strength using an evaluation algorithm. The aim of this research work is to determine the strength of the team based on the healthcare data, team cohesiveness, and variance coefficient of basketball score frequency. The study on the coefficient of variation for basketball score frequency in teams can provide a theoretical research direction for team strength evaluation and meet the real-time needs of the coefficient of variation of basketball score frequency in teams. The empirical results show that the designed algorithm has the optimal execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team.

Marginal modeling in community randomized trials with rare events: Utilization of the negative binomial regression model

Background/aims This work is motivated by the HEALing Communities Study, which is a post-test only cluster randomized trial in which communities are randomized to two different trial arms. The primary interest is in reducing opioid overdose fatalities, which will be collected as a count outcome at the community level. Communities range in size from thousands to over one million residents, and fatalities are expected to be rare. Traditional marginal modeling approaches in the cluster randomized trial literature include the use of generalized estimating equations with an exchangeable correlation structure when utilizing subject-level data, or analogously quasi-likelihood based on an over-dispersed binomial variance when utilizing community-level data. These approaches account for and estimate the intra-cluster correlation coefficient, which should be provided in the results from a cluster randomized trial. Alternatively, the coefficient of variation or R coefficient could be reported. In this article, we show that negative binomial regression can also be utilized when communities are large and events are rare. The objectives of this article are (1) to show that the negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model and to explain why the estimates may differ; (2) to derive formulas relating the negative binomial overdispersion parameter k with the intra-cluster correlation coefficient, coefficient of variation, and R coefficient; and (3) analyze pre-intervention data from the HEALing Communities Study to demonstrate and contrast models and to show how to report the intra-cluster correlation coefficient, coefficient of variation, and R coefficient when utilizing negative binomial regression. Methods Negative binomial and over-dispersed binomial regression modeling are contrasted in terms of model setup, regression parameter estimation, and formulation of the overdispersion parameter. Three specific models are used to illustrate concepts and address the third objective. Results The negative binomial regression approach targets the same marginal regression parameter(s) as an over-dispersed binomial model, although estimates may differ. Practical differences arise in regard to how overdispersion, and hence the intra-cluster correlation coefficient is modeled. The negative binomial overdispersion parameter is approximately equal to the ratio of the intra-cluster correlation coefficient and marginal probability, the square of the coefficient of variation, and the R coefficient minus 1. As a result, estimates corresponding to all four of these different types of overdispersion parameterizations can be reported when utilizing negative binomial regression. Conclusion Negative binomial regression provides a valid, practical, alternative approach to the analysis of count data, and corresponding reporting of overdispersion parameters, from community randomized trials in which communities are large and events are rare.

Genetic Variability in Growth Characteristics among Different Clones of Eucalyptus tereticornis Sm.

The present investigation was carried out at the College of Forestry, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India during August, 2018 to June, 2019 to study the genetic variability in growth characteristics among different clones of Eucalyptus tereticornis. Different clones of Eucalyptus tereticornis Sm. were planted in RCBD, with 4 replications revealed significant variations among all eight treatments (clones) with respect to 9 different characters. Based on the mean performance, treatment-1 (clone-526) showed maximum value for characters like biomass (1124.17), plant height (247.9 cm), collar diameter (23.25 mm), and a number of leaves plant-1 (463.25 number). Similarly, the maximum value was observed in treatment-8 (clone-136) for traits like leaf area (42.70 cm2), leaf length (15 cm), and leaf width (5.75 cm). The highest leaf length to leaf width ratio (3.57) and lowest number of branches plant-1 (18.75 number) were found in treatment-2 (clone-288). All characters had exhibited higher genotypic variance than an environmental variance. Similarly, the genetic coefficient of variation in the case of all variables was also found greater than an environmental coefficient of variation. Heritability was found maximum in plant height (87.35%) and all other characters also showed high heritability. Genetic advance as % of mean was found maximum in biomass (71.15%). Based on the overall mean performance of growth characters, Treatment-1 (Clone-526) was found as a superior clone with respect to the most important character biomass for the test locality. High GCV, heritability, and GAM value for biomass indicated that the character would respond to selection for the improvement program.

ESTIMATING VARIABILITY, HERITABILITY, AND GENETIC ADVANCE OF RICE GENOTYPES IN MID-HILLS OF NEPAL

<p>Rice is an essential staple food in Nepal but researches and varietal improvement programs are rarely carried out due to inadequate variability study. The field study was carried to diagnose the influence of genetic and environmental factors on yield traits to aid future rice breeding programs. Twelve genotypes were arranged in randomized complete block design with three replications from July to November 2019 at the research field of the Institute of Agriculture and Animal Science, Tribhuvan University, in the hilly area of Nepal. Analysis of variance showed significant difference for days to 50% booting, days to 50% flowering, plant height, panicle length, flag leaf area, filled grains per panicle, unfilled grains per panicle, fertility percentage, effective tillers m^-2, straw yield, grain yield, 1000-grain weight, and harvesting index indicating the presence of variation in genotypes. LPN BR-1615 was the most promising genotype in grain yield. The values of Phenotypic Coefficient of Variation (PCV) were higher than Genotypic Coefficient of Variation (GCV) for each trait and low difference between them was found for days to 50% booting, days to 50% flowering, plant height, panicle length, grain yield, thousand-grain weight, fertility percentage, and harvesting index. Plant height, effective tillers m^-2, and grain yield showed high heritability (i.e. 93.2%, 60.5% and 92.6%, respectively) and higher genetic advance as percentage of mean (i.e. 46.5, 34.6 and 50.1, respectively) . Thus, the experiment revealed that selections favoring plant height, effective tillers m^-2, and grain yield would help in effective breeding programs of rice in future.</p>