The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

Background: Propolis has received great interest because of its wide range antimicrobial activity. Propolis also called (bee glue) due to its collection by (Apismellifera) honeybees from various plants resinous substance. The aim of this study was to determine the antibacterial effect of propolis extracts (aqueous and alcoholic) on anaerobic periodontal pathogen namely Aggregatibacteractinomycetemcomitans. Materials and Methods: Strains of Aggregatibacter actinomycetemcomitans wasisolated from pockets of systemically healthy patients aged between 35-55 years old suffering from chronic periodontitis with pocket depths of 5-6 mm, the bacteria cultured on special blood Agar plates solid media. Propolis was extracted by using water and alcohol.

This study was aimed to extract the effective material from the dry nests of termites and detect its antibacterial activity against some pathogenic bacterial isolates and inhibit synthesis of its biofilm. Termites dry nests were collected and the effective material was extracted then the antibacterial activity was detected using the disc diffusion assay. Results were showed that the extract have antibacterial material from the Termites dry nests, this extract showed antibacterial activity against Gram positive bacteria (Staphylococcus aureus) at (21.5mm) and Gram negative bacteria ( Enterobacter sp. and Pseudomonas aeruginosa) at (26 mm and 20 mm) respectively by inhibiting their growth, as well as its effect on biofilm production o

Objective: To determine the ability of uVDBP to discern SRNS from steroid-sensitive nephrotic syndrome (SSNS) in Iraqi children. Materials and Methods: This cross-sectional study enrolled children with SRNS (n=31) and SSNS (n=32) from the pediatric nephrology clinic of Babylon Hospital for Maternity and Pediatrics over three months. Patients' characteristics in terms of demographics, clinical data, and urinary investigations were collected. Quantitative analysis of uVDBP levels was undertaken via a commercially available ELISA kit. Results: The median uVDBP values were significantly higher (p-value<0.001) in the SRNS group (median=10.26, IQR=5.91 μg/mL) than in the SSNS group (median=0.953, IQR=4.12 μg/mL). A negative correlati

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

The current study was conducted to determine the sensitivity of some pathogenic bacterial isolates isolated from wounds and burns water toward the disposer of the Yas Rue tested five crude bacterial isolates isolated from wounds and burns which these isolates sensitive to aqueous extract crude