In the current work, Punica granatum L. peel, Artemisia herba-alba Asso., Matricaria chamomilla L., and Camellia sinensis extracts were used to prepare manganese dioxide (MnO₂) nanoparticles utilizing a green method. Energy-dispersive X-ray (EDX) analysis, Fourier Transform Infrared Spectroscopy (FTIR) analysis, and Filed emission-scanning electron microscopy (FE-SEM) analysis were used to evaluate the produced MnO₂ NPs. FE-SEM pictures demonstrated how agglomerated nanoparticles formed. According to FE-SEM calculations, the particle size ranged from 18.7-91.5 nm. FTIR spectra show that pure Mn-O is formed, while EDX results show that Mn and O are present. The ability to suppress biofilm growth in the produced MnO

In this work, the plasma parameters (electron temperature and
electron density) were determined by optical emission spectroscopy
(OES) produced by the RF magnetron Zn plasma produced by
oxygen and argon at different working pressure. The spectrum was
recorded by spectrometer supplied with CCD camera, computer and
NIST standard of neutral and ionic lines of Zn, argon and oxygen.
The effects of pressure on plasma parameters were studied and a
comparison between the two gasses was made.

          In this work, the optical emission characteristics of the ZnO plasma were presented. The plasma parameters: electron temperature (T_e), electron density( n_e), plasma frequency (f_p) and Debye length (λ_D) were  studied with  a spectrometer that collects the spectrum ZnO plasma in air produced by Nd:YAG laser,(λ=1064 nm) at ratio X=0.5 in the range of energy of (700-1000 mJ), duration (10 ns). The Boltzmann plot methodwas employed to calculate the electron temperature (T_e), while the Stark broadening  was used to determine the electron density (n_e), Debye duration (λ_D), and plasma frequency (f_p).  T_e, ne, and f_p

Some microorganisms, including fungi, are characterized by their removal efficiency and reducing the concentrations of heavy metals such as Pb and Cr from industrial water. The present study aims to estimate the efficiency of Penicillium digitatum (Pers.) Sacc. as a low-cost biosorbent in reducing Pb and Cr from industrial water with optimum biosorption conditions (acidity of 1.5 , 4, and 5; temperature of 30 °C). The Fourier transform infrared spectroscopy (FTIR) analysis was also used for determining the roles of the functional groups in this biosorbent. The results indicated that the highest P. digitatum efficiency values for reducing the levels of Pb and Cr were 84% and 70% , respectively, at pH of 5 after 24 h.

Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of

Biogas is one of the most important sources of renewable energy and is considered as an environment friendly energy source. The major goal of this research is to see if rice husk (Rh) waste and pomegranate peels (PP) waste are suitable for anaerobic digestion and what effect NaOH pre-treatment has on biogas generation. Rice husk and pomegranate peels were tested in anaerobic digestion under patch anaerobic conditions as separate wastes as well as blended together in equal proportions. The cumulative biogas output for the blank test (no pretreatment) was 1923 and 2526 ml, respectively using a single rice husk (Rh) and pomegranate peel (PP) substrates. The 50% rice husk digestion and 50% of pomegranate peels for blank test gave the result 224