مقایسه برخی توابع توصیف کننده شکل منحنی شیردهی در گاو نژاد هلشتاین

Penelitian ini bertujuan mengkaji karakteristik emisi CO2 dari tanah gambut di bawah tegakan kelapa sawit yang dihubungkan dengan faktor lingkungan spesifik lokasi penelitian di daerah Kabupaten Labuhan Batu, Sumatera Utara. Gas CO2 diambil dengan menggunakan close chamber technique, sedangkan konsentrasi gas tersebut diukur menggunakan portable micro Gas Chromatograph CP 4900. Faktor lingkungan sebagai variabel bebas yang diamati meliputi suhu tanah (TS), suhu atmosfer (TA), kelembaban tanah lapisan 0 – 5 cm (SM5), kelembaban tanah lapisan 0 – 30 cm (SM30), dan pH gambut (A). Model hubungan CO2 dengan faktor dianalisis menggunakan analisis korelasi Spearman dan regresi multivariant. Analisis regresi multivariant dilakukan menggunakan regresi stepwise untuk mengetahui faktor lingkungan yang mempunyai hubungan dengan CO2 pada taraf beda nyata 5%. Analisis variance inflation factor (vif) dilakukan untuk melihat multicollinearity dari model regresi multivariant. Berdasarkan analisis korelasi Spearman, faktor lingkungan A (pH gambut) mempunyai koefisiensi korelasi yang tegolong sedang (R = 0.637). Hasil regresi stepwise menunjukkan bahwa emisi CO2 nyata dipengaruhi oleh kelembaban tanah SM30 dan A (n = 216; p < 0.05) dengan nilai vif <3. Regresi multivariant, menghasilkan persamaan terbaik (R2 = 0.420) dimana emisi CO2 = 7.394*A – 0.008*SM30 – 16.659. Secara parsial, penelitian ini juga memperoleh model hubungan emisi CO2 dengan kelembaban tanah gambut, dimana emisi CO2 tertinggi dicapai pada kondisi kelembaban tanah sekitar kapasitas lapang (354 – 376% w w-1) dan menurun dengan meningkatnya kelembaban tanah di atas kadar air kapasitas lapangan. Emisi CO2 tanah gambut mengalami penurunan pada kondisi tanah gambut mengering di bawah zone kadar air kritis, hal ini karena hidrofobisitas tanah gambut meningkat.

The first lactation weekly test day milk yield (WTDMY) records (57233) and monthly test day milk yield (MTDMY) records (11970) spread over a period of 23 years (1984– 2006) sired by 82 bulls of Karan Fries cattle were used to develop the best lactation curve model. The lactation curve parameters of Quadratic cum log model (QCLM), Gamma function (GF), Cobby Le Du model (CLDM), Polynomial regression function (PRF) and Multiphasic logistic function (MLF) were estimated. The average weekly test day milk yield was predicted with high degree of accuracy (R2 > 85%) by all the models with the maximum accuracy (R2 = 99.50%) obtained by polynomial regression function (PRF) and the least fit (R2 = 87.90%) was obtained by Gamma function (GF). However Quadratic cum log model (R2 = 99.20%) was almost equal to polynomial regression function and was better than Cobby Le Du model (R2 = 92.80%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0121 Kg) followed by MLF (Triphasic). Thus the best fit model was polynomial regression function, which was better than other functions for prediction of first lactation WTDMY. The average monthly test day milk yield was predicted with high degree of accuracy (R2 > 75%) by all the models with the maximum accuracy (R2 > 99.41%) obtained by polynomial regression function (PRF) and the least fit was obtained with gamma function (R2 = 79.05%). However, quadratic-cum-log model (R2 = 99.28%) was almost equal to polynomial regression function and was better than CLDM (R2 = 89.50%). The average root mean square error (RMSE) was found to be minimum with PRF (0.0061 kg) followed by QCLM (0.0620). Thus the best fit model was polynomial regression function for prediction of both weekly and monthly test day milk yield of first lactation in Karan Fries cows.

The purpose of the authors conducted research to determine the effectiveness of E-Marketing Interests Against Tourist Visits in Gorontalo Saronde Island and can be beneficial for the institutions, the object of research, author and upcoming research. Location of the researched, is Saronde Island in Gorontalo. E-Marketing is one of the factors that play an important role in running a marketing business. E marketing or Electronic Marketing is one of the breakthroughs that are quite reliable in marketing a tourism product. Saronde Island as one of the tourist attraction managed by GAB (Natural Gorontalo Maritime) uses the concept of E Marketing as one way powerful enough to market their products. This research uses quantitative, the measurement data in the form of numbers. Variables examined include facebook marketing, email marketing, web marketing and blackberry messenger marketing. The number of samples from total sample is 60 respondents to the normality test, autocorrelation test results and test multikolinear data formula that relies on the analysis of the results of Durbin Watson and VIF (Variance Inflation Factor). With the technique of multiple regression analysis. The analysis result showed Blackberry Massenger is most effective changer variable, which conducted to influent visiting of tourist, than others variable. The results showed a significant effect on interest visiting of tourists at Saronde island of Gorontalo. The implications of this study, is to be helpful to the reader, or for anyone who wants to continue the same thing in future research. Hypothesis Testing depend variable to independent variable, the probability value is < 0.01. Keywords: E-Marketing, Interests For Visit, Facebook, Email, Weblogs, Blackberry Massenger, Tourist

The test day milk yield data on 511 crossbred cattle were obtained and utilized according to monthly interval upto 305 th day maintained at Instructional Dairy Farm, of G. B. Pant University of Agriculture and Technology, Pantnagar (Uttarakhand). The different statistical models considered in this study for predicting the shape of lactation curve were Exponential Function, Parabolic Exponential Function, Inverse Polynomial Function and Gamma Type Function. Maximum coefficient of determination (R 2 ) value was observed for Inverse Polynomial function (R 2 = 99.97%) followed by Gamma function (R 2 = 88.28%), Parabolic function (R 2 = 80.95 %), and Exponential function (R 2 = 39.10%). The root mean square error (RMSE) was least for Inverse Polynomial function (0.07 kg) followed by Gamma Type function (0.10kg ), Parabolic Exponential function (0.12 kg ) and Exponential function (0.21 kg ). The absolute mean deviation (AMD) was least for Inverse Polynomial (0.16 Kg) followed by Gamma Type function (0.24 Kg), Parabolic Exponential (0.31Kg) and Exponential function (0.48) confirming the same order of superiority.

در این مطالعه به منظور برآورد پارامترهای ژنتیکی و فنوتیپی و نیز روند فنوتیپی و ژنتیکی صفات تولیدی گاوهای هلشتاین منطقه ی مدیترانه ای ایران(استانهای اردبیل، همدان، زنجان، آدربایجان شرقی و غربی) از اطلاعات مربوط به8808 گله ی گاو هلشتاین، شامل 105118 رکورد روزآزمون استفاده شد. ویرایش لازم روی تمامی رکورد ها با استفاده از نرم افزارهای Access2010 و Fox Pro 8.0 انجام گرفت. برازش اثرات ثابت موثر برروی رکوردهای روزآزمون با استفاده از رویه GLM نرم افزار SAS انجام گرفت که تاثیر سال- فصل، گله- تاریخ روزآزمون و استان روی داده های روزآزمون و برای صفات مقدار شیر، درصد چربی و پروتین شیر معنی دار بود. همچنین اثر خطی و درجه دوم سن زایش به عنوان عامل کمکی بر روی هر سه صفت معنی دار بود. برای برآورد روند ژنتیکی و فنوتیپی، از نرم افزار SAS و رویه ی GLM5 استفاده شد. میزان وراثت پذیری برای صفات مقدار شیر، در صد چربی و درصد پروتئین با استفاده از رکودهای روزآزمون به ترتیب، 126/0، 02/0 و059/0 برآورد شد. نتایج نشان می دهند که دقت حاصل از رکورد های روزآزمون با استفاده از روش رگرسیون تصادفی بالاتر از رکودهای تصحیح شده برای 305 روز است و لذا در این مطالعه بر نتایج حاصل از رکوردهای روزآزمون و روش رگرسیون تصادفی تاکید می شود. نتایج حاصل از مدل رگرسیون تصادفی و رکوردهای روزآزمون نشان می دهند که وراثت پذیری تولید شیر، بیشترین و وراثت پذیری درصد چربی کمترین مقدار را به خود اختصاص داده و وراثت پذیری درصد پروتئین در حد وسط قرار گرفته است.

Drying is one of the major unit operation in food industry and it kinetics data is required for optimization. The aim of this study was to evaluate the effect of air temperature (50, 60, and 70°C), air velocity (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm) on the moisture ratio of yam slice during drying. Ten (10) different empirical models were used in fitting the experimental moisture ratio data, the prediction performance was evaluated with sum of square error (SSE), coefficient of determination (R2) and root mean square error (RMSE). The model fitting shows that the Two term model was most performed based on R2, SSE, and RMSE value. This result can be use to control the drying systems for yam slice.

Chapter 4 - Special functions

Estimation of genetic parameters using a random regression monthly test-day model in an Ethiopian dairy cattle population

Evaluation of artificial neurocomputing algorithms and their metacognitive robustness in predictive modeling of fuel consumption rates during tillage

The aim of the present study was to evaluate the performance of the Wood model to describe the characteristics of lactation curves of dairy ewes under organic management in Mexico. In total, 4861 weekly test-day milk yield records from 194 lactations of crossbred dairy ewes were analysed to assess the performance of an empirical model to fit their lactation curve. We used the mathematical model proposed by Wood. The evaluation criteria were the correlation coefficient (r) between the values of total milk yield observed and estimated, the coefficient of determination (R2), and the mean square prediction error (MSPE). In addition, the peak yield (PYest) and time at peak yield (PTest) were calculated. The Wood model showed adequate goodness of fit (r = 0.95, R2 = 0.92 and MSPE = 0.024). The Wood model detected that 52.06% of lactation curves had a continuously decreasing shape (atypical curve), probably as a consequence of the characteristic management of the organic system, mainly due to the genotype used and the nutritional management. Residuals were greater for atypical curves than for typical ones, indicating differences in the ability of the Wood model to fit the two types of shapes. In typical curves, the Wood model showed adequate estimates of total milk yield and time at peak yield. The peak yield was underestimated both in typical and atypical curves. The Wood model in atypical curves underestimated the time at peak yield and milk yields in late lactation. The Wood model showed a reasonable fit of lactation curve in dairy sheep in organic systems but presented deficiencies of fit in atypical curves; therefore, estimates should be interpreted carefully.

Effect of dipping treatment on air drying of plums

A simple approach to enable water-management agencies employing free data to create a single set of water quality predictive equations with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS) and Total Nitrogen (TN) were derived using a hybrid forward-selection method that considers both p-value and Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. Through splitting data into calibration and validation groups, the coefficients of determination are 0.73 for TSS calibration and 0.70 for TSS validation, respectively. The coefficients of determination for TN calibration and validation are 0.64 and 0.37, respectively. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, and time difference (in days) between the image acquisition and water sampling were found to be important in water-quality quantity estimation. The hybrid forward-selection method consistently yielded higher validation accuracy than that of the conventional forward-selection approach.

In order to conveniently and efficiently determine the Permissible Exposure Limits (PELs) of organic chemicals in the workplace, this study employed Quantitative Structure–Activity Relationship (QSAR) modeling to predict properties related to occupational health and safety. The predictive study was conducted by correlating the PELs of 75 hydrocarbons and their oxygen-containing derivatives with the molecular structures of the organic compounds. Meanwhile, this study conducted a comprehensive and in-depth comparative analysis of the four developed predictive models. The sample set was partitioned using the Affinity Propagation (AP) clustering algorithm. Four characteristic molecular descriptors were selected by integrating the Genetic Algorithm (GA) with the variance inflation factor (VIF) value. Subsequently, the Multiple Linear Regression (MLR) model and two nonlinear models, namely the Support Vector Machine (SVM) and the Extreme Gradient Boosting (XGBoost), were developed and used for predictive comparison. Furthermore, the performance of the models was evaluated through both internal and external validation methods, and the Williams plots were constructed to define the model’s applicability domain. The results indicated that the XGBoost model achieved high performance, with a coefficient of determination (R2) of 0.9962 on the training set and 0.8892 on the testing set. The corresponding root mean square errors (RMSE) were 0.1012 and 0.6623 for the training and testing sets, respectively. The internal validation coefficient (Q2loo) was 0.8975, while the external validation coefficient (Q2ext) was 0.832. Moreover, the majority of the sample data (approximately 96%) fell within the application domain defined by ±3 times the standard residue-to-critical arm ratio, where h* = 0.2. This demonstrates that the XGBoost model exhibits excellent fitting capability, stability, and predictive power, thereby uncovering a significant nonlinear relationship between the molecular structure of compounds and the PELs. As outlined above, the utilization of the QSAR method for predicting the PELs of hydrocarbons and their oxygen-containing derivatives constitutes a highly effective approach.

The unmanned aerial vehicle (UAV) image spectral information and texture feature (TF) information were fused to develop an improved model for winter wheat leaf nitrogen content (LNC) monitoring model to provide a reference for wheat nitrogen status monitoring and accurate management. The data of wheat LNC and UAV-hyperspectral imaging were simultaneously obtained at the main growth stages (jointing, booting, and filling stages) of various winter wheat varieties under various nitrogen fertilizer treatments. The correlation between the vegetation indexes (VIs) in combination of any two bands, the TFs, and LNC were systematically analyzed. Then, the optimal VIs and TFs without multicollinearity problems were screened using a variance inflation factor (VIF) to form a ‘graph–spectrum’ fusion index. Four machine learning methods, namely ridge regression (RR), partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), were used to construct respective quantitative winter wheat LNC estimation models. The results revealed that the model for estimating LNC constructed using the ‘graph–spectrum’ information formed by eight parameters, including the normalized vegetation index NDVI (R 578, R 490), the difference vegetation index DVI (R 830, R 778), MEA490, MEA778, VAR490, VAR578, VAR778, and HOM578 as input and the RR algorithm, performed the best. It outperformed the models developed by the implementation of VIs and TFs as input. The coefficient of determination (R 2 ), root mean square error (RMSE), and relative percent deviation (RPD) of the calibration set were 0.84, 0.25, and 2.50, correspondingly, and those of the validation set were 0.87, 0.27, and 2.33, respectively. The model of winter wheat LNC constructed by fusing spectral and TF information considerably improved the prediction accuracy. The present research results provide a basis and reference for the application of UAV hyperspectral technology in wheat nitrogen nutrient monitoring.

Statistical literature has several methods for coping with multicollinearity problem.Ridge regression “RR” is compared with multiple linear regression for studying “Suction Characteristics of Subgrade Soils from North Iraq- SCSSNI”. Multiple linear regression “MLR” of “SCSSNI” data usually encounters a collinearly problem, which adversely affects long term prediction performance. The collinearly problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method with potentially smaller mean square error “MSE” as an alternative to ordinary least square “OLS”. In this study, ridge regression (a biased estimation method has been evaluated with a constant bias ) and the prediction performance was compared with that of ordinary least square“OLS” based multiple linear regression “MLR”. The bias constant of was selected bu examining the ridge trace. At this point, the estimated coefficients are stable and their variance inflation factors “VIFs” become smaller. To evaluate the robustness of each model the standard error of prediction” SEPs” has been compared, the prediction of original values using MLR model shows slightly better results comparing to that of ridge regression model, which is due to an intentional bias is associated in the ridge model. To compare RR and MLR, the coefficient of determination, “VIFs”, and standard error “SE” of parameters has been studied. If the variance of the ridge estimator could be tremendously reduced, the mean square error tends to be smaller than the OLS. The prediction results of a ridge model showed more stable prediction performance as compared to that of MLR, by removing or decreasing the collinearly problem.