Due to economic reasons or need for environmental conservatism or also preserve the natural resources; there has been an increasing shift towards the use of reclaimed asphalt pavement (RAP) materials in the pavement construction industry. Therefore, use the Reclaimed Asphalt Pavement (RAP) has been enormously increased in pavement construction and has been become common practice in many countries. Nevertheless, this is a relatively new concept in Iraq, and has to be remarked that is not used RAP in the production of HMA and this valuable material is mostly degraded. For this purpose, the reclaimed materials were collected from deteriorated pavement segments. The components of asphalt mixtures consist of: two asphalt penetration grades (40-5

This study aims to analyze the spectral properties of plasma produced from rice husk(Rh) using the laser breakdown spectroscopy (LIBS) method. The plasma generation process used the fundamental harmonic (1064 nm) of a Q-switched Nd:YAG laser. Yttrium aluminum garnet (YAG) is a man-made crystalline material. The laser fired pulses with a duration of 10 ns and a repetition rate of 6 Hz. Thus, the energy outputs achieved were 50–200 mJ at the wavelength of 1064 (nm). The silica content in the rice hulls was verified using an XRF measurement, which revealed the presence of silica in the rice hulls in a high percentage. Precise beam focusing was achieved by focusing the laser on the target material. This target material is placed with

Collective C2 transitions in 32S are discussed for higher
energy configurations by comparing the calculations of transition
strength B(CJ  )with the experimental data. These configurations
are taken into account through a microscopic theory including
excitations from the core orbits and the model space orbits with nħω
excitations.
Excitations up to n=10 are considered. However n=6 seems to
be large enough for a sufficient convergence. The calculations
include the lowest seven 2+0 states of 32S.

This study aims to highlight the role of strategic leadership in adopting the intelligent organization model. The study was conducted on 7 economic organizations in Algeria. The study population consisted of 354 leaders, of whom a random sample of 176 leaders (managers, department heads, division heads, engineers) was selected. The researcher used a questionnaire as the main tool of the study. Statistical analysis and hypothesis testing were conducted using SEM (Structural Equation Modeling) with the aid of SPSS.v26 and AMOS.v24 software. The study concluded with a set of results, most notably: there is a statistically significant direct positive effect between strategic leadership and building intelligent organizations at a significance le

This investigation presents an experimental and analytical study on the behavior of reinforced concrete deep beams before and after repair. The original beams were first loaded under two points load up to failure, then, repaired by epoxy resin and tested again. Three of the test beams contains shear reinforcement and the other two beams have no shear reinforcement. The main variable in these beams was the percentage of longitudinal steel reinforcement (0, 0.707, 1.061, and 1.414%). The main objective of this research is to investigate the possibility of restoring the full load carrying capacity of the reinforced concrete deep beam with and without shear reinforcement by using epoxy resin as the material of repair. All be

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