It is difficult to perform real time object detection in Uncrewed Aerial Vehicle(UAV) based video surveillance because of the dynamic movement of camera, scale, occlusion, variation of illumination and limited power availability on-board of the computer. Purpose: In this paper of researches, the author suggests the proposed UAV Spatial-Temporal Network(UAV-STNet), which is a hybrid model of spatio-temporal deep learning model that is expected to improve the accuracy of the detection, balance of time, and real-time performance. Approaches: techniques: The proposed framework incorporates U-Net representing the system of total encoder involving extraction of multiscale space features mode, Transformer attention module which will involve global

In this search, a new pyrophosphate technique was proved. The technique was employed to single- nucleotide polymorphisms (SNPs), which diagnosis using a one-base extension reaction. Three Mycobacterium tuberculosis genes were chosen (Rpob, InhA, KatG) genes. Fifty-four specimens were used in this study fifty-three proved as drug-resistant specimens by The Iraqi Institute of Chest and Respiratory Diseases in Baghdad.; also one specimen was used as a negative control. The steps of this technique were by used a specific primer within each aliquot that has a short 3-OH end of the base of the target gene that was hybridized to the single-stranded DNA template. Then, the Taq polymerase enzyme and one of either α-thio-dATP, dTTP, dGTP, or dCTP

This study aimed to detect of contamination of milk and local soft cheese with Staphylococcus aureus and their enterotoxins with attempt to detect the enterotoxin genes in some isolates of this bacteria. A total of 120 samples, 76 of raw milk and 44 of soft cheese were collected from different markets of Baghdad city. Enterotoxins in these samples were detected by VIDAS Set 2 system and it was found that enterotoxin A is present in a rate of 44.74% in milk samples and in a rate 54.50% in cheese samples. While other enterotoxins B, C, D, E were not found in any rate in any samples.
Through the study 60 isolates obtained from milk and cheeses were identified as Staphylococcus aureus by cultural, morphological and biochemical test by u

Metal-organic frameworks (MOFs) have emerged as revolutionary materials for developing advanced biosensors, especially for detecting reactive oxygen species (ROS) and hydrogen peroxide (H₂O₂) in biomedical applications. This comprehensive review explores the current state-of-the-art in MOF-based biosensors, covering fundamental principles, design strategies, performance features, and clinical uses. MOFs offer unique benefits, including exceptional porosity (up to 10,400 m²/g), tunable structures, biocompatibility, and natural enzyme-mimicking properties, making them ideal platforms for sensitive and selective detection of ROS and H₂O₂. Recent advances have shown significant improvements in detection capabilities, with limit

In this study, concentrations of radon and uranium were measured for twenty six samples of soil. The radon concentrations in soil samples measured by registrant alpha-emitting radon (²²²Rn) by using CR-39 track detector. The uranium concentrations in soil samples measured by using registrar fission fragments tracks in CR-39 track detector that caused by the bombardment of U with thermal neutrons from ²⁴¹ Am-Be neutron source that has flux of 5 ×10³n cm^-2 s^-1.
The concentrations values were calculated by a comparison with standard samples The results show that the radon concentrations are between (91.931-30.645Bq/m³).
The results show that also the uranium concentrat

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E