in this paper, we study and investigate a simple donor-acceptor model for charge transfer formation using a quantum transition theory. The transfer parameters which enhanced the charge transfer and the rate of the charge transfer have been calculated. Then, we study the net charge transfer through interface of Cu/F8 contact devices and evaluate all transfer coefficients. The charge transfer rate of transfer processes is found to be dominated in the low orientation free energy and increased a little in decreased potential at interface comparison to the high potential at interface. The increased transition energy results in increasing the orientation of Cu to F8. The transfer in the system was more active when the system has large driving for

In this work, optical system with different aperture shapes (circular, square, elliptical and triangle aperture) has been used for efficiency evaluation when the system involved moving factor in ideal case (aberration free). The optical system evaluate far moving object, therefore the image forming at image plane due to point spread function (image formula of incoherently illuminated point object). A mathematical treatment has been used to getting results by Gaussian numerical calculations method. The results show priority of circular aperture when optical system that submits of moving factor.

Aeration system in the cultivation of Chlorella Sp. Microalgae using dairy wastewater as culture media was addressed in the current study. This research aimed to study the effect of aeration in the bubble column bioreactor on the biological synergy between microalgae and bacteria if they are present in the same place. The results show that the sterilization stage is not the dominant step in the success of microalgae cultivation in water-rich organic waste. There is a clear convergence between the growth rate of Chlorella microalgae in the sterilized and non-sterilized culture media, which gives realism if the proposal is applied industrially. Through the information obtained the aerobic bacteria in the non-sterilized me

This study includes using green or biosynthesis-friendly technology, which is effective in terms of low cost and low time and energy to prepare V2O5NPs nanoparticles from vanadium sulfate VSO4.H2O using aqueous extract of Punica Granatum at a concentration of 0.1M and with a basic medium PH= 8-12. The V2O5NPs nanoparticles were diagnosed using several techniques, such as FT-IR, UV-visible with energy gap Eg = 3.734eV, and the X-Ray diffraction XRD was calculated using the Debye Scherrer equation. It was discovered to be 34.39nm, Scanning Electron Microscope (SEM), Transmission Electron Microscopy TEM. The size, structure, and composition of synthetic V2O5NPs were determined using the (EDX) pattern, Atomic force microscopy AFM. The a

The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

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