Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In this study, an easy, low-cost, green, and environmentally
friendlier reagents have been used to prepare CdS QDs, in chemical
reaction method by mixed different ratio of CdO and sulfur in
paraffin liquid as solvent and oleic acid as the reacting media in
different concentration to get the optimum condition of the reaction
to formation CdS QDs. The results give an indication that the
behavior is at small concentration of 4ml of the oleic acid is best
concentration which give CdS QDs of small about to 9.23 nm with
nano fiber configuration.

Synthesis and study liquid crystalline properties of new compounds with terminal groups of amides ([III]a-c,[IV]a-c and [VI]n), alkoxy series[V]n or ester with azo linkage ([IX]a-c and[X]a-c) containing thaizole ring. These series were synthesized by many steps starting from 4- hydroxyacetophenone or 4-aminoacetophenone. The synthesized compounds were characterized using melting points, FTIR, C.H.N.S analysis and for some of them 1H NMR spectroscopy. The liquid crystalline properties were studied by hot stage polarizing microscopy and differential scanning calorimetry DSC. All compounds of series [III]a-c,[IV]a-c and compounds [V]n showed enantiotropic liquid crystal. While the series [VI]n showed nematic mesomorphism except [VI]8 did not s

Twelve compounds containing a sulphur- or oxygen-based heterocyclic core, 1,3- oxazole or 1,3-thiazole ring with hydroxy, methoxy and methyl terminal substituent, were synthesized and characterized. The molecular structures of these compounds were performed by elemental analysis and different spectroscopic tequniques. The liquid crystalline behaviors were studied by using hot-stage optical polarizing microscopy and differential scanning calorimetry. All compounds of 1,4- disubstituted benzene core with oxazole ring display liquid crystalline smectic A (SmA) mesophase. The compounds of 1,3- and 1,4-disubstituted benzene core with thiazole ring exhibit exclusively enantiotropic nematic liquid crystal phases.

Bromocriptine mesylate is a semisynthetic ergot alkaloid derivative with potent dopaminergic activity, used in the treatment of pituitary tumors, Parkinson's disease (PD), hyperprolactinaemia, neuroleptic malignant syndrome, and type 2 diabetes ,the oral bioavailability is  approximately 6%, therefore aim  its  prepare and evaluate bromocriptine mesylate  as liquid self nano emulsifying drug delivery system to enhance its solubility , dissolution and stability . Solubility study was made in different vehicles to select the best excipients for dissolving bromocriptine mesylate. Pseudo-ternary phase diagrams were constructed at 1:1, 2:1, 3:1 and 4:1 ratios of surfactant and co-surfactant, four formulations were pre

A simple, environmental friendly and selective sample preparation technique employing porous membrane protected micro-solid phase extraction (μ-SPE) loaded with molecularly imprinted polymer (MIP) for the determination of ochratoxin A (OTA) is described. After the extraction, the analyte was desorbed using ultrasonication and was analyzed using high performance liquid chromatography. Under the optimized conditions, the detection limits of OTA for coffee, grape juice and urine were 0.06 ng g−1, 0.02 and 0.02 ng mL−1, respectively while the quantification limits were 0.19 ng g−1, 0.06 and 0.08 ng mL−1, respectively. The recoveries of OTA from coffee spiked at 1, 25 and 50 ng g−1, grape juice and urine samples at 1, 25 and 50 ng mL