Estimation of the organic carbon content by the pattern recognition method

Studying the status of agricultural soils is one of the most important concerns in the agricultural sector. The soil organic carbon (SOC) is one of the main parameters and it plays an important role in improving soil properties. Hence, knowing this parameter is important in soil science. This study applied the pattern recognition (PR) method in predicting the SOC. Also, the ability of this method was compared with different methods such as the Radial Basis Function Network (RBF), Multilayer Perceptron Neural Network (MLP), Multiple Linear Regression (MLR) and Support Vector Regression (SVR). To compare the results, four performance criteria, namely, root mean square errors (RMSE), the Nash–Sutcliffe efficiency (NS), Willmott’s Index of agreement (WI), mean absolute error (MAE) and Taylor diagrams were used. Results indicated that the PR model performed significantly better than the MLP, MLR, SVR, and RBF models for the estimation of the SOC.     

Morphological and Biomechanical Properties of Equine Laminar Junction

The interlocking of dermis and epidermis is called the laminar junction or interface. Laminae provide a sturdy junction in between the distal phalanx and the keratinized hoof wall transferring the forces between those structures. Although the horse's weight is imposed on the distal phalanx and the hoof capsule, the laminar junction plays a crucial role in absorbing and dissipating the applied forces both from the weight of the animal and the ground reaction forces. Understanding the morphologic and mechanical properties of the laminar junction is a key to recognizing influencing factors and is paramount to understanding performance and health in horses. This article will present a review of the significance of the morphological and biomechanical properties of the equine laminar junction.