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

Background: with the advent of new postmaterial in dentistry, it has become important to measure fitness of post restoration along the horizontal plane of the root space.This study aimed to measure and compare, the cement film thickness of conventional zinc phosphate cement in micrometer between the post and root dentin along horizontal plane at different post space regions (coronal, middle and apical) of four types of posts, by using stereomicroscopy. Material and methods: Thirty-two extracted human maxillary canines, mandibular canines and maxillary central incisors (n=32) were instrumented with ProTaper system files (hand use) and obturated with gutta-percha for ProTaper and AH26® root canal sealer. After 24hrs of incubation at 37ºC, p

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

This paper focused on the stone matrix asphalt (SMA) technology that was developed essentially to guard against rutting distress. For this procedure, fibers play a racy role in stabilizing and preventing the drain down problem caused by the necessity of high binder content coupled with their strengthening effect. A set of specimens with cylindrical and slab shapes were fabricated by inclusions jute, polyester, and carbon fibers. For each type, three contents of 0.25%, 0.5%, and 0.75% by weight of mixture were added by lengths of 5, 7.5, and 10 mm. The prepared mixtures were tested to gain the essential pertained parameters discriminated by the values of drain down, Marshall quotient, rut depth, and dynamic stability. It

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