Two local fish Himri Carasobarbus luteus (Heckel, 1843)  and Hishni Liza abu (Heckel, 1843) were stained with Alizarin Red and featured some anatomical qualities which cleared the difference of the muscular and skeletal fabric for each fish. Since clear Histologic differences appeared in these two species, it was intended from this study the possibility of adopting a diagnosis between local fish species by staining bones and tissues.

This work represents the preparation of the starting material, 3-chloro-2-oxo-1,4-dithiacyclohexane (S) using a new method. This material was reacted with, 4-phenylthiosemicarbazide to give (H3NS3) as a tetradentate ligand H3L. New complex of rhenium (V) with this ligand of the formula [ReO(L)] was prepared. New complexes of the general formula [M(HL)] of this ligand when reacted with some metal ions where: M = Ni(II), Cu(II), Cd(II), Zn(II), Hg(II) have been reported. The ligand and the complexes were characterized by infrared, ultraviolet–visible, mass, 1H nuclear magnetic resonance and atomic absorption spectroscopic techniques and by (HPLC), elemental analysis, and electrical conductivity. The proposed structure for H3L with Re (V) i

The complexes of the 2-hydroxy-4-Nitro phenyl piperonalidene with metal ions Cr(III), Ni(II), Pt(IV) and Zn(II) were prepared in ethanolic solution. These complexes were characterized by spectroscopic methods, conductivity, metal analyses and magnetic moment measurements. The nature of the complexes formed in ethanolic solution was study following the molar ratio method. From the spectral studies, monomer structures proposed for the nickel (II) and Zinc (II) complexes while dimeric structures for the chromium (III) and platinum (IV) were proposed. Octahedral geometry was suggested for all prepared complexes except zinc (II) has tetrahedral geometry, Structural geometries of these compounds were also suggested in gas phase by using

The compound [L] was produced in the current study through the reaction of 4-aminoacetophenon with 4-methoxyaniline in the cold, concentrated HCl with 10% NaNO2. Curcumin, several transition metal complexes (Ni (II), La (III), and Hg (II)), and compound [L] were combined in EtOH to create new complexes. UV-vis spectroscopy, FTIR, AA, TGA-DSC, conductivity, chloride content, and elemental analysis (CHNS) were used to describe the structure of produced complexes. Biological activities against fungi, S. aureus (G+), Pseudomonas (G-), E. coli (G-), and Proteus (G-) were demonstrated using complexes. Depending on the outcomes of the aforementioned methods, octahedral formulas were given as the geometrical structures for each created comp

Limitations of the conventional diagnostic techniques urged researchers to seek novel methods to predict, diagnose, and monitor periodontal disease. Use of the biomarkers available in oral fluids could be a revolutionary surrogate for the manual probing/diagnostic radiograph. Several salivary biomarkers have the potential to accurately discriminate periodontal health and disease. This study aimed to determine the diagnostic sensitivity and specificity of salivary interleukin (IL)‐17, receptor activator of nuclear factor‐κB ligand (RANKL), osteoprotegerin (OPG), RANKL/OPG for differentiating (1) periodontal health from disease and (2) stable a

5-((2,4-dibromo-6-((cyclohexyl(methyl)amino)methyl)phenyl)diazenyl)quinolin-8-ol azo ligand (L) has been synthesized through the reaction of diazonium salt for 2,4-dibromo-6-((cyclohexyl(methyl)amino)methyl)aniline with 8-hydroxyquinoline. The azo ligand (L) was characterized utilizing spectroscopic techniques, including FTIR, UV-Vis, 1H and 13C NMR, as well as mass spectrometry and micro-elemental analysis (C.H.N). Metal complexes containing Co(II), Ni(II), Cu(II), and Zn(II) were synthesized and analyzed through mass spectrometry, flame atomic absorption, elemental analysis (C.H.N), infrared and UV-Vis spectroscopy, along with measurements of conductivity and magnetic properties. The experimental findings suggested that all met

Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed.  A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing

The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a co

Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed. A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec