Insurance is a policy that eliminates or decreases loss costs occurred by various risks. Various factors influence the cost of insurance. These considerations contribute to the insurance policy formulation. Machine learning (ML) for the insurance industry sector can make the wording of insurance policies more efficient. This study demonstrates how different models of regression can forecast insurance costs. And we will compare the results of models, for example, Multiple Linear Regression, Generalized Additive Model, Support Vector Machine, Random Forest Regressor, CART, XGBoost, k-Nearest Neighbors, Stochastic Gradient Boosting, and Deep Neural Network. This paper offers the best approach to the Stochastic Gradient Boosting model with an MAE value of 0.17448, RMSE value of 0.38018and R -squared value of 85.8295.

تهدف هذه الدراسة إلى التعرف على المسئولية الاجتماعية للشركات المصرية فى ظل جائحة كورونا (COVID-19) ، ولتحقيق هذا الهدف تم اتباع المنهج الوصفى التحليلى لملائمته لطبيعة الدراسه، حيث خلصت الدراسة إلى مجموعه من النتائج كان من اهمها: أن الشركات المصرية تلعب دوراً فعالاً تجاه المجتمع فى ظل جائحة كورونا مما يؤدى إلى زيادة ثقة اصحاب المصالح من حملة الاسهم والمستهلكين والعملاء والموردين تجاه الشركات. كما كشفت الدراسة عن بعض نقاط الضعف والقصور فى المسئولية الاجتماعية للشركات المصرية فى ظل جائحة كورونا.

In this paper, a robust nonparametric derivative estimator is proposed to estimate the derivative function of nonparametric regression when the data contain noise and have curves. A robust estimation of the derivative function is important for understanding trend analysis and conducting statistical inferences. The methods for simultaneously assessing the functional relationship between response and covariates as well as estimating its derivative function without trimming noisy data are quite limited. Our robust nonparametric derivative functions were developed by constructing three weights and then incorporating them into kernel-smoothing. Various simulation studies were conducted to evaluate the performance of our approach and to compare our proposed approach with other existing approaches. The advantage of our robust nonparametric approach is demonstrated using epidemiology data on mortality and temperature in Seoul, South Korea.

Graphical models have played an important role in inferring dependence structures, discovering multivariate interactions among high‐dimensional data associated with classes of interest such as disease status, and visualizing their association. When data are modeled with Gaussian Markov random fields, the graphical model is called a Gaussian graphical model. It has been used to investigate the conditional dependency structure between random variables by estimating sparse precision matrices. Although the Gaussian model has been widely applied, the normality assumption is rather restrictive. Hence, several methods have been proposed under assumptions weaker than the Gaussian assumptions to handle continuous, discrete, and mixed data. However, modeling data of heterogeneous classes and multilevel networks still poses challenges. Addressing these challenges stresses open problems and points

The present study aimed to investigate the clinical blood gas indices and histopathological consequences after intrathecal injection of tolfenamic acid and lidocaine Hcl and moreover, to elucidate the spinal safety of tolfenamic acid as a cyclooxygenase inhibitor in donkeys. Ten clinically healthy donkeys were divided into two groups, 5 animals each. The first group received lidocaine Hcl 2% and the second one received tolfenamic acid 4% intrathecally. The physical parameters and ataxia, analgesia, and motor blockade scores were recorded. Blood gases and acid base balance indices and histopathological examination were done. Blood pH level was significantly decreased (P  0.05) and the blood pCO2 level was significantly increased (P  0.05) 15 min after intrathecal injection of tolfenamic acid. Additionally, there was a significant difference in the motor block scores between the two groups at 2 and 4 h post-injection. Histopathological findings of the spinal cord of tolfenamic acid–injected group revealed neurodegeneration and necrosis which were manifested clinically by paraplegia. In conclusion, the present study uncovered the analgesic and motor effects of commercially prepared tolfenamic acid following intrathecal injection in donkeys. Nevertheless, it is unsafe because of its neurotoxic effect on the spinal cord which was manifested clinically by paraplegia of donkeys. On the other hand, intrathecal administration of lidocaine Hcl was safe and causes nonserious cardiopulmonary changes.

The present study aimed to investigate the clinical blood gas indices and histopathological consequences after intrathecal injection of tolfenamic acid and lidocaine Hcl and moreover, to elucidate the spinal safety of tolfenamic acid as a cyclooxygenase inhibitor in donkeys. Ten clinically healthy donkeys were divided into two groups, 5 animals each. The first group received lidocaine Hcl 2% and the second one received tolfenamic acid 4% intrathecally. The physical parameters and ataxia, analgesia, and motor blockade scores were recorded. Blood gases and acid base balance indices and histopathological examination were done. Blood pH level was significantly decreased (P  0.05) and the blood pCO2 level was significantly increased (P  0.05) 15 min after intrathecal injection of tolfenamic acid. Additionally, there was a significant difference in the motor block scores between the two groups at 2 and 4 h post-injection. Histopathological findings of the spinal cord of tolfenamic acid–injected group revealed neurodegeneration and necrosis which were manifested clinically by paraplegia. In conclusion, the present study uncovered the analgesic and motor effects of commercially prepared tolfenamic acid following intrathecal injection in donkeys. Nevertheless, it is unsafe because of its neurotoxic effect on the spinal cord which was manifested clinically by paraplegia of donkeys. On the other hand, intrathecal administration of lidocaine Hcl was safe and causes nonserious cardiopulmonary changes.

The present study aimed to investigate the clinical blood gas indices and histopathological consequences after intrathecal injection of tolfenamic acid and lidocaine Hcl and moreover, to elucidate the spinal safety of tolfenamic acid as a cyclooxygenase inhibitor in donkeys. Ten clinically healthy donkeys were divided into two groups, 5 animals each. The first group received lidocaine Hcl 2% and the second one received tolfenamic acid 4% intrathecally. The physical parameters and ataxia, analgesia, and motor blockade scores were recorded. Blood gases and acid base balance indices and histopathological examination were done. Blood pH level was significantly decreased (P  0.05) and the blood pCO2 level was significantly increased (P  0.05) 15 min after intrathecal injection of tolfenamic acid. Additionally, there was a significant difference in the motor block scores between the two groups at 2 and 4 h post-injection. Histopathological findings of the spinal cord of tolfenamic acid–injected group revealed neurodegeneration and necrosis which were manifested clinically by paraplegia. In conclusion, the present study uncovered the analgesic and motor effects of commercially prepared tolfenamic acid following intrathecal injection in donkeys. Nevertheless, it is unsafe because of its neurotoxic effect on the spinal cord which was manifested clinically by paraplegia of donkeys. On the other hand, intrathecal administration of lidocaine Hcl was safe and causes nonserious cardiopulmonary changes.

Environmental health studies are of often interest in human research to evaluate the relationship between mortality and temperature by incorporating spatial correlation and other weather variables. Since this relationship cannot be expressed by a parametric model, a nonparametric model is often used to estimate this relationship. A semiparametric integrated-spatial mixed effects single index model is proposed. It can detect subtle changes among spatial effects, covariates, and nonparametric function. This model is not only to estimate this nonparametric relationship but also to incorporate spatial effects and other weather variables. It is useful when the spatial areas are located close to each other because the nonparametric function may not be separated from spatially correlated random effects. Based on the simulation study, the semiparametric integrated-spatial mixed effects single index model provides more accurate estimates of spatial correlation and prediction. The advantage of the semiparametric integrated-spatial mixed effects single index model is further demonstrated using mortality data of six cities in South Korea from January 2000 to December 2007.

Donkeys are a cornerstone in human existence, having played an important role throughout history in different economic activities, such as working in brick kilns in Egypt. This study was conducted from January 2017 to the end of April 2017 in the El-Saf brick kilns, which are located to the south of the Giza Governorate and 57 Km away from Cairo. Physical clinical health and behavior data were collected from 179 donkeys spanning over a random sample of 20 brick kilns selected from the El-Saf brick kilns. Behavioral, physical health, harness, and environmental parameters were assessed and recorded. The study found that 80 ± 3% (n = 179) of kiln donkeys have some type of wound, and the most serious wound is a beating wound (49 ± 3.7%), which is caused by drivers hitting the donkeys. The drivers are mostly children, who have insufficient knowledge, skills, and attitudes to effectively communicate with their donkeys and no motivation to enhance the welfare of these equids. Other wounds are related to the harness, such as the breeching (10 ± 2.2%), saddle (43 ± 3.7%), neck collar (40 ± 3.6%), and shaft of the cart (12 ± 2.4%). A poor body condition was seen in 56 ± 3.7% of kiln donkeys. A correlation in terms of the prevalence of wounds was found between the body condition (p-value 0.01) and/or cleanliness of the harness. There was a negative association between the body condition and wound prevalence in brick kilns (Pearson coefficient of correlation −0.71). The physical enviromental factors that affect the body condition of working donkeys are the working hours of donkeys/day, the number of donkeys in a kiln, the distance from loading to the oven, and the concentrated food/donkey (p-value 0.01). These three variables can explain 78.85% of the variability in body conditions based on a 1–5 scale. In addition to health parameters, behavior parameters, such as the donkeys’ general attitude, reaction to observers, and chin contact are associated with the body condition (p-value 0.01). As a consequence, it is important for the owners of working donkeys to pay attention to their body condition in order to avoid compromising their body condition and welfare.

Variance components and functions thereof are important in many fields such as industry, agriculture, quantitative genetics and animal breeding. This paper contributes to evaluating the variance components estimation approaches and assessing their robustness to outliers. Using an intensive simulation study and under different settings, it was found that researchers can decide which method of estimation is appropriate to their study to estimate the variance components based on the source of outliers (error term, εi j or random effects, αi), the variance components ratio, and the sample size.