The Flanagan Aptitude Classification Tests (FACT) assesses aptitudes that are important for successful performance of particular job-related tasks. An individual's aptitude can then be matched to the job tasks. The FACT helps to determine the tasks in which a person has proficiency. Each test measures a specific skill that is important for particular occupations. The FACT battery is designed to provide measures of an individual's aptitude for each of 16 job elements.

The FACT consists of 16 tests used to measure aptitudes that are important for the successful performance of many occupational tasks. The tests provide a broad basis for predicting success in various occupational fields. All are paper and pen

The judgment enjoys many potential sources. If we look at these sources with analytical view that enable us to identify the potentials of strength in them which is the existence of features that distinct each place from the other one, which can be considered opportunities that provide the designer and the planner the possibility to design and planning which is based on subs taken from the reality of the available data of the location.
At this point outstands the role of development sources as one of the processes of re-configuring the place which appear as a result of a reality that requires setting new goals for the society and to achieve these goals it takes observations dealing with the place policies that should be taken, so this

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

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Anew Solar concentrator have been designed in this paper, this concentrators Were based on the total internal reflection in a prism, the prism angles has been calculated by depending on the solar incident ray angle in baghdad for a year. The optical design consist ofa triangular presume, Where the solar cells on one side of the prism Wh?le the prism head towered the south. The results show that there is an increasing in the solar ray concentrators and the cell area is reduced.

The neutron flux in this paper, which is generated as a result of γ incineration of the radioactive fisssion products isotopes has been evaluated .It is obvious from this paper that the neutron flux value depends on the number of incineration nuclei and the nuclear cross-section of the incinerated isotopes, and the neutron flux is directly dependent on γ-ray flux. The neutron flux increases from 1010to 1017n/s.gm as the irradiation flux increases from 1016to 1020 γ/cm2.s. It is concluded that the γ-incineration technique can be used to produce a switchable neutron source of high flux.