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Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine the models: the weighted regression model (WRM), Gaussian process regression model (GPRM), and Gaussian process panel model (GPPM). A posterior distribution was derived using these parameters, and a weight prediction system was implemented. An experiment was conducted using image data to evaluate model performance. The GPRM with the squared exponential kernel had the best predictive power. Next, GPRMs with polynomial and rational quadratic kernels, the linear model, and WRM had the next-best predictive power. Finally, the GPRM with the linear kernel, the linear model, and the latent growth curve model, and types of GPPM had the next-best predictive power. GPRM and WRM are statistical probability models that apply predictions to the entire cattle population. These models are expected to be useful for predicting cattle growth on farms at a population level. However, GPPM is a statistical probability model designed for measuring the weight of individual cattle. This model is anticipated to be more efficient when predicting the weight of individual cattle on farms. [ABSTRACT FROM AUTHOR]

Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine the models: the weighted regression model (WRM), Gaussian process regression model (GPRM), and Gaussian process panel model (GPPM). A posterior distribution was derived using these parameters, and a weight prediction system was implemented. An experiment was conducted using image data to evaluate model performance. The GPRM with the squared exponential kernel had the best predictive power. Next, GPRMs with polynomial and rational quadratic kernels, the linear model, and WRM had the next-best predictive power. Finally, the GPRM with the linear kernel, the linear model, and the latent growth curve model, and types of GPPM had the next-best predictive power. GPRM and WRM are statistical probability models that apply predictions to the entire cattle population. These models are expected to be useful for predicting cattle growth on farms at a population level. However, GPPM is a statistical probability model designed for measuring the weight of individual cattle. This model is anticipated to be more efficient when predicting the weight of individual cattle on farms.

The article tried to study the public transport role as one of the means that contribute to sustainable urban development by investigating the effect of the coverage rate for public transport on the number of those who use public transport in the neighbourhoods of Kut city. This article investigates the link between public transportation and supportive urban design trends. The research spatial and temporal limits are represented by the municipal boundaries Al-Kut city, which are shown in the master plan of the city for the period 2008–2012. The results showed a positive but weak relationship between public transport (variable factor) and the proportion of public transport coverage and population density (independent factors). These factors do not explain the apparent, which is confirmed by the high Bo value and its great reliability, and this result is inconsistent with the hypothesis that states a positive effect between public transport and the coverage rate. However, this relationship is abnormal and indicates an imbalance in the distribution of land uses and the provision of services in the structure of Kut city. Furthermore, it is a negative indicator of the city’s structure, where a good and efficient structure requires a strong relationship. As for public transport passengers, they seem to be restricted by this for poor services.

The article tried to study the public transport role as one of the means that contribute to sustainable urban development by investigating the effect of the coverage rate for public transport on the number of those who use public transport in the neighbourhoods of Kut city. This article investigates the link between public transportation and supportive urban design trends. The research spatial and tem- poral limits are represented by the municipal boundaries Al-Kut city, which are shown in the master plan of the city for the period 2008–2012. The results showed a positive but weak relationship between public transport (variable factor) and the proportion of public transport coverage and population density (independent fac- tors). These factors do not explain the apparent, which is confirmed by the high Bo value and its great reliability, and this result is inconsistent with the hypothesis that states a positive effect between public transport and the coverage rate. However, this relationship is abnormal and indicates an imbalance in the distribution of land uses and the provision of services in the structure of Kut city. Furthermore, it is a negative indicator of the city’s structure, where a good and efficient structure requires a strong relationship. As for public transport passengers, they seem to be restricted by this for poor services. [ABSTRACT FROM AUTHOR]

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Sousa and Michailidis (2004) developed the sum plot based on the Hill (1975) estimator as a diagnostic tool for selecting the optimal k when the distribution is heavy tailed. We generalize their method to any consistent estimator with any tail type (heavy, normal and light tail). We illustrate the method associated to the generalized Hill estimator and the moment estimator. As an attempt to reduce the bias of the generalized Hill estimator, we propose new estimators based on the regression model which are based on the estimates of the generalized Hill estimator. Here weighted least squares and weighted trimmed least squares is proposed. The bias and the mean squared error (MSE) of the estimators is studied using a simulation study. A few practical examples are proposed.