In this paper, we have extracted Silica from rice husk ash (RHA) by sodium hydroxide to produce sodium silicate. 3-(chloropropyl)triethoxysilane (CPTES) functionalized with sodium silicate via a sol-gel method in one pot synthesis to prepare RHACCl. Chloro group in compound RHACCl replacement in iodo group to prepere RHACI. The FT-IR clearly showed absorption band of C-I at 580 cm^-1. Functionalized silica RHACI has high surface area (410 m²/g) and average pore diameter (3.8 nm) within mesoporous range. X-ray diffraction pattern showed that functionalized silica RHACI has amorphous phase .Thermogravemitric analysis (TGA) showed two decomposition stages and SEM morphology of RHACI showed that the particles have irregu

Phthalimide formation of Phthalic anhydride with various amines using microwave or without a method with the difference of the catalyst used in a prepared Phthalimide, either structure general are C6H4CONRCO and used as starting materials in synthesis several compounds derivative phthalimides are an important compounds because spectrum wide biological activities including Antimicrobial activity, anticonvulsant activity, Anti-inflammatory activity,Analgesic activity, Anti- influenza activity and Thromboxane inhibitory activity

In this work Nano crystalline (Cu2S) thin films pure and doped 3% Al with a thickness of 400±20 nm was precipitated by thermic steaming technicality on glass substrate beneath a vacuum of ~ 2 × 10− 6 mbar at R.T to survey the influence of doping and annealing after doping at 573 K for one hour on its structural, electrical and visual properties. Structural properties of these movies are attainment using X-ray variation (XRD) which showed Cu2S phase with polycrystalline in nature and forming hexagonal temple ,with the distinguish trend along the (220) grade, varying crystallites size from (42.1-62.06) nm after doping and annealing. AFM investigations of these films show that increase average grain size from 105.05 nm to 146.54 nm

In this present work, [4,4`-(biphenyl-4,4`-diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1-ylidene)bis(2-methoxyphenl)(A1),4,4`-(biphenyl-4,4`-diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1-ylidene)diphenol(A2),1,1`-(biphenyl-4,4`-diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1-ylidene) dinaphthalen-2-ol (A3)]C.S was prepared in 3.5% NaCl. Corrosion prevention at (293-323) K has been studied by using electrochemical measurements. It shows that the utilized inhibitors are of mixed type based on the polarization curves. The results indicated that the inhibition efficiency changes were used with a change according to the functional groups on the benzene ring and through the electrochemical technique. Temperature increases with corrosion current

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

In this paper, we characterize normal composition operators induced by holomorphic self-map , when and .Moreover, we study other related classes of operators, and then we generalize these results to polynomials of degree n.