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THIS STUDY DEALS WITH THE VIBRATION RESPONSE of porous functionally graded (FG) nanobeams under hygro-thermal loadings. The FG nanobeam model is developed based on the Euler-Bernoulli beam theory, in which the doublet mechanics is implemented to account for the size effect. The material properties of the FG nanobeam are assumed to vary along the thickness direction of the beam according to the power-law form with the temperature dependent and porosity phases. The approximate Ritz method is employed to obtain the natural frequencies of porous FG nanobeam models for various boundary conditions. The influences of several parameters such as temperature rise, moisture concentration, porosity volume fraction, material gradient index, material length scale parameter and mode number on the free vibration response of the porous FG nanobeams under hygro-thermal environments are examined in detail. It is explicitly shown that the proposed approach can provide accurate frequency results of FG nanobeams as compared to existing studies in open literature. These study's results may be useful for the optimal and safety design of nano-electro-mechanical systems. [ABSTRACT FROM AUTHOR]

Traditionally, diagnosis and monitoring of agricultural diseases are carried out through on-site observation and inspection. These methods are time-consuming and may represent limited samples. Therefore, remote sensing technology has become an important tool in disease detection and monitoring in agriculture. In the research, Cercospora leaf spot (Cercospora beticola sacc.) disease, which cause significant economic losses in sugar beet production, were detected in the early stages using machine learning algorithms using non-invasive multispectral images taken with UAV under field conditions is intended to be determined. The research was fulfilled using images from the grower fields in two regions where the disease was observed intensively. Index value data from digital surface model maps created by processing the images taken were used as training and test data. Numerical data was tested using five different supervised machine learning methods. The success of the analyzed models in predicting disease formation from the index values obtained from the images taken and the physiological changes that occur before the disease agents appear on sugar beet leaves was over 70%. Among the models compared in the study, the k-nearest neighbor classifier (KNN) model gave the highest success in both diseases, with 83% accuracy and 76% and 86% f1-score values. The support vector machines model followed the KNN model with 77% accuracy, 75%, and 86% f1-score values. According to the results of the research, it has been revealed that plant diseases have the potential for pre-symptomatic detection, and by processing the images obtained with UAV-based MS images, it is possible to detect diseases in the early period.