Two well-known fluorescent molecules: fluorescein sodium salt (FSS) and 2,7-dichloro fluorescein (DCF) were tried to prove the efficiency, trustability and repeatability of ISNAG fluorimeter by using discrete and continuous flow injection analysis modes.A linear range of 0.002-1 mmol/L for FSS and 0.003-0.7 mmol/L was for DCF, with LOD 0.0018 mmol/L and 0.002 mmol/L for FSS and DCF respectively, were obtained for discrete mode of analysis. While the continuous mode gave a linear range of 0.002-0.7 mmol/L and 0.003-0.5 mmol/L for FSS and DCF respectively, the LOD were 0.0016mmol/L and 0.0018 mmol/L for FSS and DCF respectively. The results were compared with classical method at variable λ_ex for both fluorescent molecules at 95

New Schiff base ligand (E)-6-(2-(4-(dimethylamino)benzylideneamino)-2-(4-hydroxyphenyl)acetamido)-3,3- dimethyl-7-oxo-4-thia-1- azabicyclo[3.2.0]heptane-2-carboxylic acid = (HL) was synthesized via condensation of Amoxicillin and 4(dimethylamino)benzaldehyde in methanol. Figure -1 Polydentate mixed ligand complexes were obtained from 1:1:2 molar ratio reactions with metal ions and HL, 2NA on reaction with MCl2 .nH2O salt yields complexes corresponding to the formulas [M(L)(NA)2Cl],where M=Fe(II),Co(II),Ni(II),Cu(II),and Zn(II), A=nicotinamide .

The effect of thickness variation on some physical properties of hematite α-Fe2O3 thin films was investigated. An Fe2O3 bulk in the form of pellet was prepared by cold pressing of Fe2O3 powder with subsequent sintering at 800 . Thin films with various thicknesses were obtained on glass substrates by pulsed laser deposition technique. The films properties were characterized by XRD, and FT-IR. The deposited iron oxide thin films showed a single hematite phase with polycrystalline rhombohedral crystal structure .The thickness of films were estimated by using spectrometer to be (185-232) nm. Using Debye Scherrerś formula, the average grain size for the samples was found to be (18-32) nm. Atomic force microscopy indicated that the films had

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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

     This article reviews a decade of research in transforming smartphones into smart measurement tools for science and engineering laboratories. High-precision sensors have been effectively utilized with specific mobile applications to measure physical parameters. Linear, rotational, and vibrational motions can be tracked and studied using built-in accelerometers, magnetometers, gyroscopes, proximity sensors, or ambient light sensors, depending on each experiment design. Water and sound waves were respectively captured for analysis by smartphone cameras and microphones. Various optics experiments were successfully demonstrated by replacing traditional lux meters with built-in ambient light sensors. These smartphone-based measurement

Heart disease identification is one of the most challenging task that requires highly experienced cardiologists. However, in developing nations such as Ethiopia, there are a few cardiologists and heart disease detection is more challenging. As an alternative solution to cardiologist, this study proposed a more effective model for heart disease detection by employing random forest and sequential feature selection (SFS). SFS is an effective approach to improve the performance of random forest model on heart disease detection. SFS removes unrelated features in heart disease dataset that tends to mislead random forest model on heart disease detection. Thus, removing inappropriate and duplicate features from the training set with sequential f

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings