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

  This work intends to illustrate the methods of using the authentic literary text in the process of spreading Italian, especially in Baghdad where there is a strong propensity to learn the Italian language. The concept of the language that arises from literature is an idea closely linked to the mentality of the Arab learner towards Italian culture: an idea also created by the first Arabisations of literary texts in the early years of the previous century. The research was carried out in Baghdad by two researchers, an Italianist from Baghdad and an Italian mother language linguist, with the aim of bringing together the two sectors in favor of the diffusion of the Italian language. The study also aims to clarify the models from Italian l

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

Endophytic fungi live inside plants or any part of them without creating any visible pathogenic signs. Endophytic fungi are found within medicinal plants and have shown strong biologic activity, such as anticancer and antioxidant activities, as well as producing extracellular enzymes. In this study, different fungal strains were isolated from the leaves of the medicinal plant Ziziphus spina, including Aspergillus flavus, Aspergillus fumigatus, Aspergillus niger, Cladosporium sp., Rhizopus sp., and Mucor sp. Extracellular enzymes have been quantified using agar plate-based methods in which fungi were grown in specified growth media to detect the enzymes produced. The results showed that A. niger has the highest ability to produce amy

Experimental tests were conducted to study the behavior of skirted foundations rested on dry medium sandy soil subjected to vertical and inclined loads. To achieve this goal, a small-scale physical model was designed and performed which contained an aluminum circular footing (100 mm) in diameter and (10 mm) in thickness and skirts with different heights, local medium poorly graded dry sand is placed in a steel soil container (2 mm) thick with internal dimensions (1000 mm x 1000 mm in cross section and 800 mm in height). The main objective of this study was to evaluate the response of skirt attached to the foundation at different skirt (L/D) ratios (0.0, 0.5, 1.0 and 1.5) and is subjected to point load at different angles of inclinat

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

Reinforced concrete (RC) beams containing a longitudinal cavity have become an innovative development and advantage for economic purposes of light-weight members without largely affecting their resistance against the applied loads. This type of openings can also be used for maintenance purposes and usage space of communication lines, pipelines, etc. RC beams are primarily loaded in the plane of the members, which are two-dimensional in a plane stress state and the dominant structural behaviours include bending, shear, or combination of both. In the present study, six numerical models of RC beams with and without openings were simulated by using commercial finite element software ANSYS to evaluate the structural behaviours of those b