Allan deviation is a statistical parameter normally used to measure the frequency stability of a signal source in time domain. The paper describes two important methods namely, the three-corner hat method and the standard reference source method for measuring the Allan deviations of signal sources. The short-term frequency stability (Allan deviations) of three numbers of highly stable 10 MHz ground Rubidium sources were measured by both the three-cornered hat method and the standard reference source method (where, a highly stable 10 MHz Passive Hydrogen Maser is used as reference source). The results obtained from both these methods are then compared and verified. A close match between the two indicates that both the methods are equally accurate and hence both can be used for measuring the short-term frequency stability of frequency sources.

Better yields can only be achieved by the prompt diagnosis and treatment of crop diseases. Due to its remarkable success in machine vision, Deep Neural-Network (DNN) model with vision transformer has been used in this research to detect and diagnose plant illnesses via their leaves. The approach used in this implementation was trained on plant Village dataset (includes data for 14 plant species, 38 disease classifications, and healthy plant leaves). Parameters including batch size, dropout, and the total number of epochs were used to measure the accuracy of the models.  Results from the disease identification accuracy has shown the DNN model with vision transformer obtained an accuracy of 99.67% in plant leaf disease classification, which was significantly higher than the accuracies achieved by the other models in the comparative analysis study. This suggests that the DNN model has a promise for use in the real-time detection of illnesses in agricultural systems.

The goal of this study is to create a Secured Flow of Data (SFD) mechanism for Electronic Patient Records (EPR) systems. Through user authentication at data exchange endpoints, the system emphasizes the confidentiality, accessibility, and integrity of patient records. The SFD method, which was created in Python, is being tested against current alternatives to preserve the privacy of patient records. It uses asymmetric encryption with a "Public and Private key pair" approach and the Diffie-Hellman Algorithm for key exchange. The SFD mechanism is compared to other alternatives, such as EMR with modified Blowfish algorithm and Extended Role Based Model, in the study. The results show that the SFD mechanism beats the alternatives in terms of data preservation and retrieval, indicating its usefulness in preventing illegal access to EPRs.