The effects of cognitive styles on naive impetus theory application degrees of pre-service science teachers

Cataloged from PDF version of article.The purpose of this study was to determine whether there is a relationship between pre-service science teachers’ Field Dependent or Field Independent (FD/FI) cognitive styles and the application of degrees of naive impetus theory. The sample consisted of 122 preservice science teachers (97 females and 25 males) who were enrolled in the Introductory Physics course required by the Science Education program. Data were collected in two successive years, after the completion of the required Introductory Physics undergraduate courses, in 2008 and 2009. The Group Embedded Figure Test and Impetus Theory Application Test (a two-tier-type test) were administered to assess the FD/FI tendency of students and to determine the degree students applied the naïve impetus theory, respectively. Initial results showed that a majority of students had made use of the native impetus theory repeatedly. The results also indicated that the degree to which students applied the naïve impetus theory was statistically related to their FD/FI cognitive styles. The findings of this research showed that there existed a statistically significant difference between the FI and FD students’ degree of applying the naïve impetus theory in favor of FI students. However, the test score gap between FI and FD students remained almost constant regardless of the testing instruments utilized in this study

Multiphysics simulation of the effect of leaflet thickness inhomogeneity and material anisotropy on the stress-strain distribution on the aortic valve

This study developed a realistic 3D FSI computational model of the aortic valve using the fixed-grid method, which was eventually employed to investigate the effect of the leaflet thickness inhomogeneity and leaflet mechanical nonlinearity and anisotropy on the simulation results. The leaflet anisotropy and thickness inhomogeneity were found to significantly affect the valve stress-strain distribution. However, their effect on valve dynamics and fluid flow through the valve were minor. Comparison of the simulation results against in-vivo and in-vitro data indicated good agreement between the computational models and experimental data. The study highlighted the importance of simulating multi-physics phenomena (such as fluid flow and structural deformation), regional leaflet thickness inhomogeneity and anisotropic nonlinear mechanical properties, to accurately predict the stress-strain distribution on the natural aortic valve

Determination of arsenic leaching from glazed and non-glazed Turkish traditional earthenware

PubMed ID: 21669332Glazed and non-glazed earthenware is traditionally and widely used in Turkey and most of the Mediterranean and the Middle East countries for cooking and conservation of foodstuff. Acid-leaching tests have been carried out to determine whether the use of glazed and non-glazed earthenware may constitute a human health hazard risk to the consumers. Earthenware was leached with 4% acetic acid and 1% citric acid solutions, and arsenic in the leachates was measured using hydride generation atomic absorption spectrometry. Arsenic concentrations in the leach solution of non-glazed potteries varied from 30.9 to 800-gL-1, while the glazed potteries varied generally from below the limit of detection (0.5-gL-1) to 30.6-gL-1, but in one poorly glazed series it reached to 110-gL-1. Therefore, the risk of arsenic poisoning by poorly glazed and non-glazed potteries is high enough to be of concern. It appears that this is the first study reporting arsenic release from earthenware into food. © 2011 Elsevier B.V.The support of the Ege University Research Foundation is acknowledged with gratitude. We would like to thank Tülin Deniz Çiftçi and Onur Yayayürük for their contributions during this study. -

Domain specific conversational intelligent agents: natural language processing in real world applications

Natural language processing (NLP) is the branch of Artificial Intelligence (AI) studies that will shape the future of computing and Human Computer Interaction (HCI). Operational complexity of a conversational intelligent agent (Chat-bot) stems from human-related, linguistic and computational aspects. In this study, we define an architectural description of a chat-bot, address the NLP problems which needs to be solved and provide our proposed solutions for a functional conversational intelligent agent. The importance of Bayesian Statistics and Data mining of domain specific text and expected expansion areas are discussed for future research in the conversational AI development

Could spot urine analysis of calcium and uric acid help predict density of urinary stone in computerized tomography? A preliminary study

WOS: 000384949900005PubMed ID: 26955798BACKGROUD: This research studies if Hounsfield density of urinary stone can be predicted without computerized tomography (CT) caused by because increased radiation exposure in follow-up of patients. METHODS: The records of patients with renal or ureteral stone were analyzed retrospectively for the time period between November 2013 and April 2014. The inclusion criteria defined were: no multiple stones; stone size >= 3 mm; presence of renal and ureteral stones; absence of staghom stone. All CT images were assessed in abdominal windows by a single radiologist. Hounsfield Unit (HU) value of CT was used to interpret the density of the stone. The density of the stone was measured in the longest axis of the stone center (core) and the edges (periphery) of each stone. Biochemical analysis of spot urine calcium (Ca) and uric acid (UA) was done at the time of diagnosis. Correlation and linear regression analysis was performed. RESULTS: Forty patients were included the study and median age of patients is 22 (IQR 21-28). Since the unit was a military hospital, most patients admitted to hospital were young male conscripts with low median age. It has been found that spot urine uric acid and uric acid/Ca ratio is associated with stone density as HU (P=0.004, P<0.001). Although predictive value appeared low, linear regression model statistically predicted stone density as HU (P<0.001 R-2=0.32). Stone size has proved to be positively correlated with stone density (P<0.001). CONCLUSIONS: Despite the predictive value of urine analysis model is low, it may be considered to predict HU attenuation of stone. Spot urine analysis of calcium and uric acid may be helpful for both diagnosis and follow-up. We believe that controlled studies with larger patient populations will provide further insights into this issue