This work reports the development of an analytical method for the simultaneous analysis of three fluoroquinolones; ciprofloxacin (CIP), norfloxacin (NOR) and ofloxacin (OFL) in soil matrix. The proposed method was performed by using microwave-assisted extraction (MAE), solid-phase extraction (SPE) for samples purification, and finally the pre-concentrated samples were analyzed by HPLC detector. In this study, various organic solvents were tested to extract the test compounds, and the extraction performance was evaluated by testing various parameters including extraction solvent, solvent volume, extraction time, temperature and number of the extraction cycles. The current method showed a good linearity over the concentration ranging from

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

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

The current trend worldwide is searching plant extracts towards prevention of neurodegenerative disorders. This study aimed to investigate the neuroprotective effect of Alpinia galanga leaves (ALE), Alpinia galanga rhizomes (ARE), Vitis vinifera seeds (VSE), Moringa oleifera leaves (MLE), Panax ginseng leaves (PLE) and Panax ginseng rhizomes (PRE) ethanolic extracts on human neuroblastoma (SHSY5Y) cells. The 1‐diphenyl‐1‐picrylhydrazyl (DPPH) radical scavenging of VSE and MLE were 81% and 58%, respectively. Ferric‐reducing antioxidant power (FRAP) of ALE and MLE (33.57 ± 0.20 and 26.76 ± 0.30 μmol Fe(ΙΙ)/g dry wt., respectively) were higher than for the other extracts. Liquid chromatography coupled to quadrupole time‐of‐fli

One of the most important problems in the oil production process and when its continuous flow, is emulsified oil (w/o emulsion), which in turn causes many problems, from the production line to the extended pipelines that are then transported to the oil refining process. It was observed that the nanomaterial (SiO₂) supported the separation process by adding it to the emulsion sample and showed a high separation rate with the demulsifiers (RB6000) and (sebamax) where the percentage of separation was greater than (90 and 80 )%  respectively, and less than that when dealing with (Sodium dodecyl sulfate and Diethylene glycol), the percentage of separation was (60% and 50%) respectively.

   The high proportion

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

The presence of the most important steroidal sapogenin “Tigogenin” in the leaves of Agave americana  cultivated in Iraq was detected. The absence of any study concerning the Tigogenin content of this medicinal plant in Iraq, and the industrial importance of Tigogenin depending on its role as a precursor in the synthesis of some steroidal drugs, acquired this study its value. This search include extraction,  isolation,  purification and dentification of Tigogenin from the leaves of Agave americana. Extraction of this compound was carried out using two methods. Identification of this compound is carried out by using thin layer chromatography (TLC) where three different mobile phase have been use