Assessment of Multiple-Form Structure Designs of Multistage Testing Using IRT

The current study investigated multistage testing (MST) as an alternative to classical linear testing (CLT) for the General Aptitude Test (GAT). The aim was to assess the effects of two assembly methods (narrow vs. wide range—NR vs. WR), two routing methods (Defined Population Intervals—DPI— and the Approximate Maximum Information method—AMI), and two panel structures (two-stage and three-stage) on precision of ability estimates and accuracy of classification for both sections of the GAT (Verbal and Mathematics). Thus, eight conditions were examined and compared: 2 (assembly conditions) * 2 (panel structures) * 2 (routing methods). The dataset that included a sample of 9,108 examinees was obtained from the National Center of Assessment, Saudi Arabia. The MST designs were evaluated with the criteria that the more accurate condition was the condition with the smallest standard error mean for ability estimates, and the highest agreement percentage of classification between CLT and MST. Findings revealed trivial differences in the estimated ability and standard error means among all conditions, but the design influenced the correlations between MST and CLT ability estimates. The NR and the WR condition performed equally regarding accuracy of ability estimate and classification. The performance of the DPI and AMI were similar in precision of ability estimates, but the DPI performed better than AMI regarding classification accuracy in all conditions. The results indicated that the number of stages was important. The correlation coefficients between the examinees’ scores on MST-3Stage conditions and CLT were higher than the coefficients between examinees scores on MST-2Stage conditions and CLT. Overall, MST can be an appropriate alternative to CLT and CAT when the MST designs are structured well using an optimal item pool. Factors such as assembling and routing methods did not have a substantial impact on the accuracy of ability estimates. That means there is flexibility to use either method—a simpler method would be as effective as a complex method. The number of stages had some impact on the precision of estimations; however, it is possible that increasing the number of items in the second stage MST-2Stage can compensate for differences. Two main recommendations from this study were: (a) the item pool should be satisfactory in MST regarding the coverage of content and range of item difficulty, and (b) the MST design with the simpler method and simpler panel structure and the complex design can perform equally. Thus, advice is to use a simpler approach and reduce effort and cost. The main limitation of the current study was the small size of the item pool and the lack of hard and easy items. For future research, studies that compare the current combinations of various factors in different conditions of MST, using an optimal item pool, is needed to enhance the results. The influence of other factors, such as different panel structures of MST (1-2-3, 1-2-3-4), different routing, and other cut-scores can be examined to identify the optimal condition for MST and for GAT

Semantic feature reduction and hybrid feature selection for clustering of Arabic Web pages

In the literature, high-dimensional data reduces the efficiency of clustering algorithms. Clustering the Arabic text is challenging because semantics of the text involves deep semantic processing. To overcome the problems, the feature selection and reduction methods have become essential to select and identify the appropriate features in reducing high-dimensional space. There is a need to develop a suitable design for feature selection and reduction methods that would result in a more relevant, meaningful and reduced representation of the Arabic texts to ease the clustering process. The research developed three different methods for analyzing the features of the Arabic Web text. The first method is based on hybrid feature selection that selects the informative term representation within the Arabic Web pages. It incorporates three different feature selection methods known as Chi-square, Mutual Information and Term Frequency–Inverse Document Frequency to build a hybrid model. The second method is a latent document vectorization method used to represent the documents as the probability distribution in the vector space. It overcomes the problems of high-dimension by reducing the dimensional space. To extract the best features, two document vectorizer methods have been implemented, known as the Bayesian vectorizer and semantic vectorizer. The third method is an Arabic semantic feature analysis used to improve the capability of the Arabic Web analysis. It ensures a good design for the clustering method to optimize clustering ability when analysing these Web pages. This is done by overcoming the problems of term representation, semantic modeling and dimensional reduction. Different experiments were carried out with k-means clustering on two different data sets. The methods provided solutions to reduce high-dimensional data and identify the semantic features shared between similar Arabic Web pages that are grouped together in one cluster. These pages were clustered according to the semantic similarities between them whereby they have a small Davies–Bouldin index and high accuracy. This study contributed to research in clustering algorithm by developing three methods to identify the most relevant features of the Arabic Web pages

A Systemic Review of the Clinical Management in Diagnosis and Treatment of the Iron Deficiency Anemia in Adults

With a systematic review, this study aimed at exploring the clinical management in diagnosis and treatment of the iron deficiency anemia in adults, as the iron deficiency is the most frequent cause of anemia worldwide. And it impairs quality of life, increases asthenia and can lead to clinical worsening of patients. In addition, iron deficiency has a complex mechanism whose pathologic pathway is recently becoming better understood. This review summarizes the current knowledge regarding diagnostic algorithms for iron deficiency anemia. The majority of aetiologies occur in the digestive tract, and justify morphological examination of the gut. First line investigations are upper gastrointestinal endoscopy and colonoscopy, and when negative, the small bowel should be explored; newer tools such as video capsule endoscopy have also been developed. The treatment of iron deficiency is aetiological if possible and iron supplementation whether in oral or in parenteral form

Analytical Study of the Effects of Stress on Surgeons and Surgical Performance

This study aimed at exploring the effects of stress on surgeons and surgical performance, as the researchers adopted the methodology of descriptive analytical statistics by conducting a semi structured interviews on fourteen surgeons in Jordan. The aim of this study also was to investigate surgeons’ perceptions of surgical stress, highlight key stressors and their impact on performance, and identify coping strategies. Stress poses a serious risk for training surgeons since their performance and well‐being in reflected in patients' health. This study focuses on measuring the stress on surgeons and at the same time evaluates prospectively the results of practices that uses alternative techniques to combat the effects of stress. The study concluded that these interviews provided valuable insights into stressors, stress responses, and coping strategies used by surgeons and allowed us to categorize sources of stress. Although surgeons characteristically enjoy the stimulating features of their work, high levels of stress can affect performance adversely

Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach

Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance properties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and inflexible image analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application

Botulinum toxin: Non cosmetic and off-label dermatological uses

AbstractBotulinum toxin (BT-A) is a neurotoxin which is produced by the Gram-positive anaerobic bacterium Clostridium botulinum. The efficacy of Botulinum toxin in treating hyperhidrosis and the glabellar lines is well known and FDA approved.Because BT-A inhibits the release of acetylcholine and many other neurotransmitters such as norepinephrine and substance P at the level of nerve ending, this toxin has been used to treat a lot of dermatological disorders which are thought to be triggered by these neurotransmitters.In this article we are discussing the medical off-label uses of BT-A in dermatology

The effect of thermal processing in oil on the macromolecular integrity and acrylamide formation from starch of three potato cultivars organically fertilized

Starches from three organically produced cultivars of potato tuber (Lady Rosetta, Spunta and Voyager) have been studied in relation to (i) acrylamide production (ii) macromolecular integrity after frying with extra virgin olive oil, soybean oil and corn oil. During cultivation, a treatment involving the combination of nitrogen, phosphorus and potassium fertilization under organic farming was applied (N1, P2, K1 where Ν1 = 1.3 g Ν per plant, P2 = 5.2 g P2O5 per plant, Κ1 = 4.0 g K2O per plant). Potatoes fried in olive oil retained the highest glucose concentrations for all cultivars 0.85 ± 0.2 mmol/kg, followed by 0.48 ± 0.2 for those fried in corn oil and 0.40 ± 0.1 mmol/kg for those fried in soybean oil. The highest average fructose concentration was recorded for the samples fried in corn oil as 0.81 ± 0.2, followed by 0.80 ± 0.2 and 0.68 ± 0.3 mmol/kg for the samples fried in olive and soybean oils, respectively. Asparagine was the most abundant free amino acid in the three varieties tested, followed by glutamine and aspartic acid. The mean initial concentration of asparagine in raw potatoes tubers was 42.8 ± 1.6 mmoles kg−1 for Lady Rosetta, 34.6 ± 1.2 mmoles kg−1 (dry weight) for Spunta and 36.2 ± 2.0 mmoles kg−1 for Voyager. Lady Rosetta contained a significantly higher concentration of asparagine compared to the other two varieties (p < 0.05). The greatest quantity of acrylamide was observed in French fries derived from the potato variety Lady Rosetta when fried in soybean oil and it was 2,600 ± 440 μg/kg, followed by Spunta which was 2,280 ± 340 μg/kg and Voyager 1,120 ± 220 μg/kg. There is a significant reduction in the formation of acrylamide in the variety Voyager compared to the others (p = 0.05)

Developing a simulated intelligent instrument to measure user behavior toward cybersecurity policies

Institutions struggle to protect themselves from threats and cybercrime. Therefore, they devote much attention to improving information security infrastructures. Users’ behaviors were explored via a traditional questionnaire research instrument in a data collocate process. The questionnaire explores users’ behaviors theoretically, so the respondents’ answers to the questionnaire are insufficiently reliable, and the responses might not reflect actual behavior based on the human bias when facing theoretical problems. This study aims to solve unreliable responses to the questionnaire by developing a simulated intelligent instrument to measure users’ behaviors toward cybersecurity policies in an experimental study using gamification

Appling tracking game system to measure user behavior toward cybersecurity policies

Institutions wrestle to protect their information from threats and cybercrime. Therefore, it is dedicating a great deal of their concern to improving the information security infrastructure. Users’ behaviors were explored by applying traditional questionnaire as a research instrument in data collocate process. But researchers usually suffer from a lack of respondents' credibility when asking someone to fill out a questionnaire, and the credibility may decline further if the research topic relates to aspects of the use and implementation of information security policies. Therefore, there is insufficient reliability of the respondent's answers to the questionnaire’s questions, and the responses might not reflect the actual behavior based on the human bias when facing the problems theoretically. The current study creates a new idea to track and study the behavior of the respondents by building a tracking game system aligned with the questionnaire whose results are required to be known. The system will allow the respondent to answer the survey questions related to the compliance with the information security policies by tracking their behavior while using the system