Co-crystals are new solid forms of drugs that could resolve more than one problem associated with drugs formulations like solubility, stability, bioavailability, mechanical and tableting properties. A preliminary theoretical study for estimating the possible bonding between the co-crystal components (paracetamol and naproxen) was performed using the ChemOffice program. The results revealed a high possibility for bonding between paracetamol and naproxen and indicated the ability of molecular mechanics study to predict the co-crystal design.

       In this work, four different methods were used for the preparation of three different ratios 1:1, 2:1, and 1:2 of paracetamol:naproxen co-crystals. The four

The preparation of low cost activated carbon from date stones and microwave method by using K₂CO₃ as chemical activator were investigated.

   The prepared activated carbon was used to remove fluoroquinolones antibiotics from aqueous solution. The characterizations of the activated carbon is represented by surface area, pore volume, ash content, moisture content, bulk density, and iodine number. The adsorbed fluoroquinolones antibiotics are Ciprofloxcin (CIP), Norfloxcin (NOR) and Levofloxcin (LEVO). Different variables as pH, initial concentrations and contact time were studied to show the efficieny of prepared activated carbon. The experimental adsorption data were analyzed by Lungmuir, Freundlich

To accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens

A reversed-phase HPLC method with fluorescence detection for the determination of the aflatoxins B1, B2, G1 and G2 in 42 animal feeds, comprising corn (16), soya bean meal (8), mixed meal (13), sunflower, wheat, canola, palm kernel, copra meals (1 each) was carried out. The samples were first extracted using acetonitrile:water (9:1), and was further cleaned-up using a multifunctional column. Optimum conditions for the extraction and chromatographic separation were investigated. By adopting an isocratic chromatographic system using a mobile phase comprising acetonitrile:methanol:water (8:27:65, v/v/v), the separation of the four aflatoxins was possible within 30 min. Recoveries for aflatoxins B1, B2, G1 and G2 were 98 ± 0.7%, 95 ± 1.0%, 94

In this research, the effect of reinforcing epoxy resin composites with a filler derived from chopped agriculture waste from oil palm (OP). Epoxy/OP composites were formed by dispersing (1, 3, 5, and 10 wt%) OP filler using a high-speed mechanical stirrer utilizing a hand lay-up method. The effect of adding zinc oxide (ZnO) nanoparticles, with an average size of 10-30 nm, with different wt% (1,2,3, and 5wt%) to the epoxy/oil palm composite,  on the behavior of an epoxy/oil palm composite was studied with different ratios (1,2,3, and 5wt%) and an average size of  10-30 nm.  Fourier Transform Infrared (FTIR) spectrometry and mechanical properties (tensile, impact, hardness, and wear rate) were used to examine the composites. The FTIR

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