Standard Error Values Research Articles

Patterns of Hatchery‐Produced Returns of American Shad in the James River, Virginia

AbstractAmerican Shad Alosa sapidissima is an anadromous clupeid that once supported a robust fishery but has declined drastically throughout its native range due to overfishing, dam proliferation, and poor water quality. A hatchery program on the James River in Virginia was introduced in 1992 to support the recovery of stocks. Following a moratorium of the fishery enacted in 1994, a fisheries‐independent survey was initiated in 1998 to monitor the population recovery efforts and status of American Shad stocks in Virginia. This paper examined 22 years of monitoring data for the James River and determined the effect of hatchery inputs on the James River stock of American Shad. The spawning stock index increased from 2.57 in 1998 to a peak of 9.33 in 2003 but has generally been declining since and has been at very low levels in most recent years. The hatchery prevalence for female American Shad (i.e., the percentage of fish derived from the hatchery) ranged between 3.6% and 60.5%. Years with higher spawning stock index values were significantly correlated to higher percentages of hatchery fish returning to spawn. The stock–recruitment relationship was best explained by the Ricker model, which had the lowest residual standard error and Akaike information criterion value. A threshold level of hatchery‐released individuals (approximately 4 million larvae) was necessary to achieve the highest numbers of returning spawners, but stocking above 7 million larvae correlated with declining returns. Long‐term monitoring of the James River American Shad spawning population allowed for the critical examination of the contribution of hatchery individuals to the yearly spawning run and the relative success rate of each hatchery year‐class. From these data, we consider that the James River spawning stock of American Shad was dependent upon hatchery inputs, with ideal hatchery returns occurring during years of moderate levels of hatchery stocking.

Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur

Negative Binomial Regression (NBR) is an alternative regression model to model the relationship between the dependent variable in overdispersion count data and one or more independent variables. Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson regression model has a standard error value that is not under-estimated. The NBR model was applied to modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in 2019 indicated overdispersion. Infant mortality is an indicator that can measure the progress of development outcomes in the health sector in a region. In the last three years, from 2017 to 2019, infant mortality data in East Kalimantan has increased. Therefore, it is necessary to do modeling to get the factors that cause it. The modeling results with NBR show that the percentage of the complete neonatal visit of KN3, the percentage of infant health services, and the percentage of visits by pregnant women K4 significantly affect infant mortality in East Kalimantan in 2019.

The Effect of Teacher Communication Through Storytelling Method on the Creativity Level of Kindergarten Students in Bekasi City

Background - Children's creativity can be increased through learning methods. Creativity in kindergarten student learning aims to develop students' curiosity and develop their imagination. It is suspected that teacher communication in telling a story using the storytelling method will help increase student creativity in Kindergarten in the city of Bekasi.
 Purpose – This study aims to: 1) analyze the effect of teacher communication variables on the creativity level of kindergarten students in Bekasi City. 2) analyze the storytelling method variable as a mediator variable between teacher communication and students' creativity level.
 Design/methodology/approach – The research method uses the quantitative-correlation method, namely calculating the relationship between variables using the SPSS application and the Sobel calculator to calculate the mediator variable, whether it can be used as a mediator variable after being calculated by using a Sobel calculator. The population of this study was kindergarten students in Bekasi City who were taken randomly. The research sample was determined in kindergarten schools using the storytelling method as a learning method. Randomly selected, namely private kindergarten "Islam Mentari" and Islamic kindergarten "Gema Nurani". Of the two kindergartens, the respondents were parents of students from the two kindergartens. as many as 60 parents of students, namely 30 parents of Mentari Islam students, and 30 parents of "Gema Nurani" students..
 Findings – The results showed 1) there was an influence of teacher communication variables on the creativity level of kindergarten students in Bekasi City. The value of R = 0.381, and R² = 0.145, so it can be said that teacher communication contributes to the storytelling method by 14.5%. This means that there is an influence of teacher communication of 14.5%, and the remaining 85.5% is influenced by other variables not examined in this study 2) there is an effect of teacher communication on the level of creativity of students, and the storytelling method as a mediator is significant at the level of p = 0.05. Results of the Sobel Calculator calculation, can be explained that the Sobel test value is test statistic value = 2.080, standard error value = 0.090, P-value = 0.038. it can be seen that the value is 2,080 > p-value = 0.038, meaning that the Sobel Test calculation shows a significant level of = 0.05, indicating that the Storytelling method can be a mediator variable. This means that the test results show that the storytelling method can be a mediator variable in research between teacher communication variables and students' creativity levels.
 Limitations– there are limitations of researchers when taking data into the field, then it is done using google form. In addition, the limitations of this study were that it was conducted in kindergarten schools in 2 private kindergartens..
 Originality/value – This research can be trusted as the researcher's original research and case selection in kindergarten schools using the storytelling method

Novel metal based nanocomposite for rapid and efficient removal of lead from contaminated wastewater sorption kinetics, thermodynamics and mechanisms

A sol–gel method was utilized to prepare a novel nanocomposite adsorbent (nMgO/bentonite) and was tested for Pb(II) removal from aqueous solutions. The produced nanocomposite was investigated using, SEM–EDX, XRD, and FTIR analyses before and after Pb adsorption. Adsorption equilibrium and kinetic experiments were run in batch system under different conditions of pH, adsorbent dose, competitive cations, contact time and temperature. The results exhibited rapid Pb(II) adsorption by the nanocomposite in the first five min. Experimental lead adsorption equilibrium and kinetics data fitted well to Langmuir and power function models, respectively as indicated from the lowest standard error (SE) values. The calculated Langmuir maximum adsorption capacity (qmax) value of nanocomposite (75 mg g−1) was 4.5 times higher than that of bentonite (16.66 mg g−1). Moreover, the highest quantity of Pb(II) uptake was achieved at temperature of 307 K and pH 9. The Langmuir sorption capacity of the nanocomposite for Pb(II) increased from 75 to 145 mg g−1 with increasing temperature from 287 to 307 K. The thermodynamic parameters of Pb(II) adsorption by the nanocomposite affirm the spontaneous and endothermic nature of the adsorption process. Lead adsorption mechanisms by the nanocomposite were proposed and discussed.

The Prevalence and Comorbidity of Tic Disorders and Obsessive-Compulsive Disorder in Chinese School Students Aged 6-16: A National Survey.

Background: Obsessive-compulsive disorder (OCD) and tic disorders (TDs) are closely related and considered to etiologically overlap. Both disorders are characterized by repetitive behaviors. TD and OCD often co-occur. The high comorbidity between OCD and TD individuals suggests that we also need to pay more attention to the homogeneity and heterogeneity between TS and OCD. To date, there has been no systematic nationwide epidemiological survey of the mental health (including tic disorders and obsessive-compulsive disorder) of children and adolescents in China. Methods: A two-stage epidemiological study of psychiatric point prevalence was conducted. We used the multistage cluster stratified random sampling strategy to assess five provinces of China. The Child Behavior Checklist was used to identify behavioral problems among the enrolled students in the first stage. The results from the Mini-International Neuropsychiatric Interview for Children and Adolescents and evaluations from two psychiatrists based on the Diagnostic and Statistical Manual-IV were used to make a diagnosis. Point weighted prevalence for TD and OCD was estimated. We adjusted prevalence estimates with the product of sampling weights and poststratification weights. Standard error values and 95% confidential intervals were generated with Taylor series linearization. Rao–Scott adjusted chi-square (χ2) tests were employed to compare the prevalence estimates of different age and sex groups. Results: In the first stage, 73,992 participants aged 6–16 years old were selected. The prevalence rates of OCD and TDs were 1.37% (95% CI: 1.28–1.45) and 2.46% (95% CI: 2.35–2.57), respectively. The prevalence of OCD was found to be higher in girls (p < 0.001) and higher in boys with transient tic disorder (TTD) (p < 0.001) and Tourette’s syndrome (TS) (p < 0.001). The most common comorbidity of TS was OCD (40.73%), and for OCD, it was TS (11.36%). Conclusions: Our study is the first nationwide survey on the prevalence of TD (2.46%) and OCD (1.37%) in school students aged 6–16 years old in China. The high comorbidity between OCD and TD individuals suggested overlap based on the prevalence dimensions, which might be influenced by age and sex. This result suggested that we also need to pay more attention to the homogeneity and heterogeneity between TS and OCD.

Robust PC with wild bootstrap estimation of linear model in the presence of outliers, multicollinearity and heteroscedasticity error variance

The regression model estimator is considered efficient if it is robust and resistant to the presence of heteroscedasticity variance, multicollinearity or unusual observations called outliers. However, in regard to these problems, the wild bootstrap and robust wild bootstrap are no longer efficient since they could not produce the smallest variance. Hence this research investigates the use of robust PC with wild bootstrap techniques on regression model as an estimator for real and simulation data in a situation where multicollinearity, heteroscedasticity and multiple outliers are present. This paper proposed a robust procedure based on the weighted residuals which combined the Tukey bisquare weighted function, principal component analysis (PCA) to remedy the multicollinearity problems, least trimmed squares (LTS) estimator, robust location and scale, and the wild bootstrap sampling procedure of Wu and Liu that remedy the heteroscedasticity error variance. RPCW Boot Wu and RPCW Boot Liu were obtained through a modified version of R Boot Wu and R Boot Liu. Finally, based on the real data and simulation study, the performance of the RPCW Boot Wu and RPCW Boot Liu is compared with the existing R Boot Wu, R Boot Liu and also with Boot Wu, Boot Liu using the biased, RMSE and standard error. The numerical example and simulation study shows that the RPCW Boot Wu and RPCW Boot Liu techniques have proven to be a good alternative estimator for regression model with lower standard error values.

Semen characteristics and second successful artificial insemination of Asian elephant (Elephas maximus) in Thailand.

Background and Aim:As the number of wild Asian elephants (Elephas maximus) continues to decline, maintaining healthy populations under human care is vital. Male fertility assessment is essential for understanding the reproductive status, which can help to uncover underlying problems and improve the rate of pregnancy success. The objectives of this study in Asian elephants were as follows: (1) To investigate the semen characteristics; (2) to compare the relative seminal vesicle size and semen characteristics; (3) to compare the semen characteristics between good-motile (>60% progressive motility) and poor-motile (<60% progressive motility) ejaculates; and (4) to investigate the pregnancy success rate after artificial insemination (AI) with combined chilled and frozen semen.Materials and Methods:In total, 153 ejaculates were collected by manual rectal stimulation from 25 bulls. The volume, pH, sperm concentration, progressive motility, viability, morphology, and membrane integrity were investigated in each ejaculate. Assessment of accessory sex glands was conducted using transrectal ultrasonography to compare the relative seminal vesicle size and semen characteristics, and the bulls were divided into two groups according to the size of the ampulla (<7 or ≥7 cm2). For the comparison of good and poor-motile ejaculates and semen characteristics, the samples were divided into two groups: Good-motile (>60% progressive motility) and poor-motile (<60% progressive motility) ejaculates. Semen ejaculates for AI were collected from three bulls. The estrous cycles of four females were monitored using an enzyme immunoassay. Seven AI attempts were conducted using frozen and/or chilled semen by endoscopic visualization. AI was repeated 1 day before the luteinizing hormone surge, on the day of the surge, and 1 day after the surge. Pregnancy was confirmed by monitoring the serum progesterone profile and the abdomen and mammary glands changes.Results:From 153 ejaculates, the mean±standard error values of progressive motility, semen volume, sperm concentration, pH, and viability were 40.18%±2.28%, 40.94±3.86 mL, 1,205.58±62.26×106 sperm/mL, 7.50±0.10, and 56.17%±1.96%, respectively. Comparing ampulla size and semen characteristics revealed that the bulls with ampullae of ≥7 cm2 yielded significantly larger volume ejaculates. However, there were no significant differences in sperm motility and concentration. The comparison of semen characteristics between good- and poor-motile ejaculates revealed that the former had significantly higher pH, viability, normal acrosomes, intact membranes, and normal head and tail morphology but often had a significantly lower volume and sperm concentration. From seven AI attempts in four females, one female had a confirmed pregnancy (14.3% pregnancy rate), and delivered a healthy live female baby weighing 128 kg at 21 months and 12 days of gestation. The baby is now 3 years old and in a healthy condition, with normally developing growth and behavior.Conclusion:The semen characteristics of Asian elephants can be used as the baseline reference for further applications. The ampullae size indicates semen quantity but not quality. Our success in producing an elephant calf from AI using frozen and chilled semen demonstrated that AI can be used as an alternative approach for the breeding management of Asian elephants. However, the semen of Asian elephants is of poor quality, especially in terms of membrane integrity; thus, the improvement in semen quality through intensive and careful management of elephant health and fertility remains a challenge for the future. Furthermore, a sperm bank should be established to develop sperm cryopreservation, which will be invaluable for improving the genetic diversity of the Asian elephant.

Optimization of extraction conditions for anthocyanin from Hibiscus rosasinensis and its characterisation by Chromatography techniques

The present study was conducted to identify the better extraction conditions for anthocyanin from fresh and dry petals of hibiscus with potent antioxidant activity and polyphenol content. The variables used in this study are solvents (aqueous, ethanol, methanol and each added with acidified agents (1% citric acid, 0.1% HCl and 1% acetic acid), temperatures (40 ºC, 50 ºC, 60 ºC, 70 ºC and 80 ºC), steeping time (interval viz., 60min, 90min, 120 min, 150min, 180min and 210min and pH (1-9). The study revealed that the anthocyanin content was higher for fresh petals of hibiscus extracted with test solvent methanol acidified with 0.1% HCl with the mean and standard error values of 167.69±0.41 mg CAG/100g. From the results, the optimized condition for anthocyanin extraction chosen was at the temperature of 60ºC for 120min at pH of 3-3.5. However, for the food uses, fresh petals extracted with ethanol (acidified with 1% citric acid) was recommended. Their yield at optimized extraction condition was found to be 156.25 ± 0.13 mg CAG/100g with the total phenolic content of 4965.83±1.19 (mg./100g) and 75.33±0.33% antioxidant activity. The F-values for each response implied high significance of the fittest models. Thin Layer Chromatography (TLC) and Paper Chromatography (PC) chromatograms predicted that the major types of anthocyanin present in hibiscus petals were cyanidin and delphinidin.

Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy

The present study was performed to develop Near-infrared spectroscopy based prediction method for the quantification of the maize flour adulteration in chickpea flour. Adulterated samples of Chickpea flour (besan) were prepared by spiking different concentrations of maize flour with pure Chickpea flour in the range of 1–90% (w/w). The spectra of pure Chickpea flour, pure maize flour, and adulterated samples of Chickpea flour with maize flour were acquired as the logarithm of reciprocal of reflectance (log 1/R) in the entire Visible-NIR wavelength range of 400–2498 nm. The acquired spectra were pre-processed by Ist derivative, standard normal variate, and detrending. The calibration models were developed using modified partial least square regression (MPLSR), partial least square regression and principal component regression. The optimal model was selected on the basis of highest values of the coefficient of determination (RSQ), one minus variance ratio (1-VR) and lowest values of standard errors of calibration (SEC), and standard error of cross-validation (SECV). MPLSR model having RSQ and 1-VR value of 0.999 and 0.996 having SEC and SECV value of 1.092 and 2.042 was developed for quantification of maize flour adulteration in chickpea flour. Cross validation and external validation of the developed models resulted in RSQ of 0.999, 0.997 and standard error of prediction of 1.117, and 2.075, respectively.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13197-022-05456-7.

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R2, and root mean standard error values of 0.42 and 0.83 [log(cells mL−1)], respectively. All simulated plankton results closely matched the measured concentrations. Particularly, the simulated cyanobacteria showed good agreement with the measured cell concentration, with an R2 value of 0.72. In addition, our simulated result showed good agreement in peak concentration compared to observations. However, a limitation remained in following the temporal variation of Tintinnopsis sp. and Bosmia sp. Using an importance map from the TF model, water temperature, total phosphorus, and total nitrogen were identified as significant variables influencing phytoplankton and zooplankton blooms. Overall, our study demonstrated that DL can be used for modeling HABs at the phylum/class and genus levels.

Semiparametric Regression Models and Applicability in Agriculture

Parametric regression models assume that the dependent variable is a linear relationship with the independent variables and the form of the relationship is known. Nonparametric regression methods are applied in cases where the relationship type is not known or assumptions cannot be provided. However, when there is more than one independent variable, some of the independent variables may be in a linear relationship with the dependent variable, while some may be in a nonlinear relationship. In order to model these variables, semiparametric regression models, which are a combination of parametric and nonparametric regression methods, are used. In this study parametric, nonparametric and semiparametric regression models, parametric estimates, fit statistical values of the models, confidence intervals and standard error values were calculated. As a result of the analysis, the parameters of the milking unit and the quarantine area among the parametric variables, the operation area, the ventilation area, the number of ventilation, the quarantine area, the infirmary area, the manure pit and the distance to the center among the non-parametric variables were found to be statistically very important (P

Physiotherapeutic approach in seamstresses with neck pain: A single-blind, randomized clinical trial

PurposeTo investigate and compare the effects of therapeutic exercise, therapeutic ultrasound and photobiomodulation on pain, functionality and recruitment pattern of motor units, after a rehabilitation protocol for seamstresses with neck pain. Materials and methodsAll 36 female, randomly divided into three groups; i)exercise control group, ii)exercise and photobiomodulation group, iii)exercise and ultrasound group. The groups were composed of sewing machine operators with complaints neck pain. Clinical evaluations: Visual analog pain scale, questionnaires Nordic Musculoskeletal Questionnaire and the International Physical Activity Questionnaire, and electromyographic evaluations of the sternocleidomastoid and upper trapezius muscles. All variables were compared before and after the protocol. For statistical analysis, the values of mean, standard deviation and standard error of the mean were used. The values obtained were compared using the One-Way ANOVA with post-hoc Tukey, and Cohen's-d, with a significance coefficient of p < 0.05. ResultsIn the Ultrasound and laser groups there was a greater tendency to improve pain (p < 0.0001) and size of the effect on pain reduction (ultrasound = d:1.99; photobiomodulation = d:1.81). Between groups, there was a significant difference in post-treatment for the onset of right trapezius (p = 0.024) in the exercise and photobiomodulation groups (p = 0.0347). The photobiomodulation group showed pre and post-intervention differences in the left trapezius maximum onset (p = 0.010). ConclusionInterventions with photobiomodulation, ultrasound, and exercise assist to pain, function, and muscular activation in seamstresses with neck pain.

Evaluation of the Influence of Processing Parameters in Structure-from-Motion Software on the Quality of Digital Elevation Models and Orthomosaics in the Context of Studies on Earth Surface Dynamics

The fully automated Structure-from-Motion approach for developing digital elevation models and orthomosaics has been known and used in photogrammetry for at least 15 years. Years of practice and experience have allowed researchers to provide a solid description of the applicability and limitations of this method. That being said, the impact of input processing parameters in software on the quality of photogrammetric products has yet to be fully ascertained empirically. This study is aimed at identifying the most advantageous processing workflow to fill this research gap by testing 375 different setup variations in the Agisoft Metashape software for the same set of images acquired using an unmanned aerial vehicle in a proglacial area. The purpose of the experiment was to determine three workflows: (1) the fastest, which has the shortest calculation time; (2) the best quality, which is as accurate as possible, regardless of the time taken for the calculations; and (3) the optimal, which is a compromise between accuracy and calculation time. Each of the 375 processing setup variations was assessed based on final product accuracy, i.e., orthomosaics and digital elevation models. The three workflows were selected based on calculating the height differences between the digital elevation models and the control points that did not participate in their georeferencing. The analyses of the root mean square errors (RMSE) and standard deviations indicate that excluding some of the optimization parameters during the camera optimization stage results in high RMSE and an increase in the values of standard deviation errors. Furthermore, it was shown that increasing the detail of individual processing steps in software does not always positively affect the accuracy of the resulting models. The experiment resulted in the development of three different workflows in the form of Python scripts for Agisoft Metashape software, which will help users to process image sets efficiently in the context of earth surface dynamics studies.

Capability assessment of conventional and data-driven models for prediction of suspended sediment load.

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6-3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6-3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312g/l, 65.15 and - 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.

Rheological behavior of oil sludge from Algerian refinery storage tanks

The consumption and demand for petroleum are increasing dramatically with the rapid development of industry and energy sector. As a result, petroleum refineries produce the greatest amount of oily sludge formed at the bottom of storage tanks during oil storage operations, which has a severely negative impact on the storage capacity and the operational safety of the storage tank. The present study focuses on the rheology of this complex fluid from Algerian crude oil storage tanks. Rheological measurements were performed at different temperatures under steady shear and dynamic oscillometry using AR-2000 Rheometer. The results obtained show that the sludge exhibits yield-pseudoplastic flow behavior at low shear rates, which is adequately described by the Herschel Bulkley model based on the standard error and correlation coefficient values. However, quasi-Newtonian flow behavior occurs at very high shear rates. The increase in temperature had positive effects on the rheological properties of the sludge, including dynamic viscosity, shear stress, yield stress, complex modulus, elastic modulus and viscous modulus. The dynamic rheology studies have shown that the sludge material behaves more like a solid than a liquid under all experimental conditions studied.

KURVA PERTAMBAHAN BOBOT BADAN DOMBA GARUT JANTAN 13-16 BULAN DIBERI 80% HIJAUAN DAN 20 % KONSENTRAT

The research was carried out at the UPTD of the Margawati Sheep and Goat Breeding Center, the Department of Food Security and Livestock of West Java Province with the aim of finding a body weight gain curve model for Garut sheep. Eight male Garut sheep aged 13-16 months were reared for 12 weeks and given a ration with a composition of 80% forage and 20% concentrate. The sheep were measured and weighed every week during the study to obtain data on body weight gain. The collected data were analyzed using Curve Expert. The results showed that body weight gain increased until week 10 and then decreased until week 12 forming a curve. The Polynomial model curve gives the best curve with the equation = 2.61+9.53X-3.50X2+0.676X3-0.03X4 and has a standard error value (SE) of 7.27 and a coefficient of determination (R2) of 0.94.

Assessing Performance of the Generalized Exponential Model in the Presence of the Interval Censored Data with Covariate

This study aims to extend the generalized exponential model (GEM) to include covariates in the presence of interval-censored data. The maximum likelihood estimator (MLE) was obtained for the parameter of the model formulated. Afterward, a thorough simulation study was carried out to evaluate the estimator's performance based on the values of bias, standard error (SE), and root mean square error (RMSE). The result indicated that the (SE) and (RMSE) decrease with the increase in sample sizes and decrease in censoring proportions. Finally, the performance of the Wald confidence interval estimation technique for the GE model with interval-censored data covariate was assessed by a coverage probability study at several censoring proportions and different sample sizes.

Estimation of Total Fertility Rate (TFR) Using Small Area Estimation (SAE) in Nusa Tenggara Timur (NTT) Province

The large population in Indonesia has an impact on providing basic services for population which is not optimal so the condition and distribution of the population in a country must be addressed through fertility control methods. Total Fertility Rate (TFR) is one of fertility measures used in Indonesia. The estimation of TFR at the district level is very important, especially for the Nusa Tenggara Timur (NTT) Province as the province with the highest TFR in Indonesia. The availability of TFR data up to the district level is difficult to obtain every year due to data limitations. This study uses the National Socio-Economic Survey to address these problems. TFR estimation through survey data (direct estimation) generally results in a large Relative Standard Error (RSE) value, so it is necessary to estimate using an indirect estimate in the form of Small Area Estimation (SAE). By using SAERestricted Maximum Likelihood (REML) procedure, TFR with an RSE that is lower than the direct estimate is obtained. There are 5 district that have a medium-high TFR, namely: Sumba Barat Daya, Sumba Tengah, Sabu Raijua, Sumba Barat, and Manggarai Barat. The government is recommended to focus more on that 5 districts to suppress the high TFR in NTT.

Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98

Effect of Prana vidya practices on intelligence quotient and performance quotient of adolescents: A randomized control trial

Background: Intelligence is one of the most important aspects of one's consciousness. Various practices have been given in yogic texts to develop human consciousness in its entirety. The present study assesses whether Prana vidya practices (PVPs) are effective for the enhancement of intelligence in adolescents. Various studies have established that prana-related practices such as pranayama and pranic healing have played an important role in the prevention and treatment of various aspects of human health. Aim: To study the effect of PVPs on the intelligence quotient (IQ) and performance quotient (PQ) of adolescents. Materials and Methods: The present study was a randomized control trial, conducted to find the effect of 5-week Prana vidya intervention on IQ and PQ of adolescents where five different PVPs were given every day for 35 min. Thirty six adolescents aged between 11 and 16 years were selected for this study and assessed by Bhatia's Battery of Performance Tests of Intelligence before and after the intervention. Data Analysis: The data were analyzed for the variables IQ and PQ under the descriptive statistics, correlation, and the paired t-test. Results: The results show a significant increase (P < 0.01) in the IQ and PQ scores of the experimental group after the given intervention. The mean ± standard error values of IQ and PQ were found as be IQ (pre = 109.44 ± 3.46, post = 123.40 ± 2.43) and PQ (pre = 111.66 ± 3.48, post = 124.27 ± 2.53). The correlation coefficient was found to be significantly positive between pre- and post-tests of both IQ and PQ. Conclusions: The study findings suggest that PVPs increase IQ and PQ in adolescents.