The Effect of Using Differentiated Instruction Approach in Acquiring some Grammatical Structures and Developing Language Performance of Primary Grade Students

This study aimed to investigate the effect of using the differentiated instruction approach while teaching the Arabic Language for acquiring some linguistic structures and developing the linguistic performance of the primary-stage students. The materials and instruments of study consisted of student worksheets, teacher's guide, linguistic structure test, and the linguistic performance test. The participants in the research groups were selected, then, the experiment of study and the pre-post testing of study instruments were implemented. Data was statistically treated using SPSS.18 program. The findings of study revealed that there were statistically differences between the mean scores of the experimental and the control groups at level 0.5 in the linguistic structures test favoring the experimental group. Moreover, there were significat differences between the mean scores of the experimental and the control groups at level 0.5 in the linguistic performance test favoring the experimental group. A correlated relation has been proved between linguistic structure acquisition and the development of linguistic performance the by post-testing of the participants of experimental group

Evaluating And Testing Of A Potential Dna Vaccine Against Vibrio Cholerae

Although it has been more than 100 years since the first attempt to produce a cholera vaccine was made, an effective cholera vaccine has yet to be developed. In this study, the level of protection produced by a potential DNA vaccine (PVax/ctxB) was tested against the ctxB toxin of Vibrio cholerae on Balb/c mice. First, the intramuscular vaccination method was validated using pCMV plasmid that encodes HbsAg, which was detected 5 days after the injection into the tibial muscle. Next, 4 groups of mice were intramuscularly injected with either the pVax/ctxB vaccine construct or pVaxl as the negative control. The first and second groups received 2 injections spaced 3 weeks apart, while the other two groups were given 3 injections spaced 3 weeks apart. This was then followed by challenging the mice with 105 or 107 cfu/ml/mouse from clinical isolates of V. cholerae after 3 weeks of the last injection. Antibody levels for both IgG and serum IgA were monitored using ELISA, and showed high production of IgG after the first booster injection with no significant change of IgA levels

Machine Learning Algorithm for Wireless Indoor Localization

Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m

Journalists' Perceptions Towards Digital Media Training in Jordanian Media Organizations

The study aimed to reveal the perceptions of Journalists' Perceptions toward digital media Training in Jordanian Media organizations. The study sample consisted of journalists working in Jordanian media organizations. The study used the descriptive exploratory approach and concluded that there is an effect of applying digital media tools according to the perceptions of journalists, and they focused on the reasons why most Jordanian media organizations are not ready to employ digital media techniques. In addition, the study indicated that there were Jordan journalists possessed various skills in digital media, including using social networks for research, writing, publishing news stories, and editing content

Super-linear speedup for real-time condition monitoring using image processing and drones

Real-time inspections for the large-scale solar system may take a long time to get the hazard situations for any failures that may take place in the solar panels normal operations, where prior hazards detection is important. Reducing the execution time and improving the system’s performance are the ultimate goals of multiprocessing or multicore systems. Real-time video processing and analysis from two camcorders, thermal and charge-coupling devices (CCD), mounted on a drone compose the embedded system being proposed for solar panels inspection. The inspection method needs more time for capturing and processing the frames and detecting the faulty panels. The system can determine the longitude and latitude of the defect position information in real-time. In this work, we investigate parallel processing for the image processing operations which reduces the processing time for the inspection systems. The results show a super-linear speedup for real-time condition monitoring in large-scale solar systems. Using the multiprocessing module in Python, we execute fault detection algorithms using streamed frames from both video cameras. The experimental results show a super-linear speedup for thermal and CCD video processing, the execution time is efficiently reduced with an average of 3.1 times and 6.3 times using 2 processes and 4 processes respectively

Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms

Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation