915 research outputs found

    The intelligent estimating of spinal column abnormalities by using artificial neural networks and characteristics vector extracted from image processing of reflective markers

    Get PDF
    Spinal column abnormities such as kyphosis and lordosis are the most common deformity that normally compare to the standard norms. To classify the subjects into the healthy and abnormal groups based on the angle values of the standard norms, the aim of this study was to use the artificial neural network method as a standard way for realizing the spinal column abnormalities. In this way, 40 male students (26 Ā± 2 years old, 72 Ā± 2.5 kg weight, and 169 Ā± 5.5 cm height) volunteered for this research. The lumbar lordosis and thoracic kyphosis angles were analyzed using an image processing of 13 reflective markers set on the spines process of the thoracic and lumbar spine. Therefore, after analyzing the position of these markers, a characteristic vector was extracted from the lateral side of every subject. The artificial neural network was trained by using the characteristic vector extracted from the labeled image of that person to diagnose abnormalities. The results indicate that the high efficiency of this method as the CCR (train) and CCR (test) was about 96 and 93%, respectively. These results show that the neural network can be considered as a standard way to diagnose the spinal abnormalities. Moreover, the most important benefit of this method is the estimation of spinal column abnormalities without considering intermediate quantities, and also the standard norms of these intermediate quantities can be considered as a non-invasive method.Keywords: Abnormality, spinal column, kyphosis, lordosis, neural network, classificationAfrican Journal of Biotechnology Vol. 12(4), pp. 419-42

    Stay at Home: Flight-to-Safety and Home Bias in U.S. ETFs During COVID-19 Pandemic

    Get PDF
    We examine the relations between dollar flows of U.S. traded ETFs with exposure to the U.S., Europe, Asia, and the rest of the world during the COVID-19 crisis utilizing a Markov Switching Model (MSVAR). We find convincing evidence that investors use ETFs to gain exposure to foreign markets. This study differs from the new stream of research on the effects of COVID-19 on financial markets and investorsā€™ reactions in two major ways. First, we follow the money by using actual dollars of fund flows, whereas previous studies use returns. Second, we investigate the existence of two distinct regimes during this pandemic: (1) a ā€˜ā€˜normalā€ regime when all ETFs receive positive flows and (2) a ā€˜ā€˜panicā€ regime which emerges when the number of infected people surges in a global location and investors shift their funds from non-U.S. ETFs to U.S.-exposed ETFs. This portfolio rebalancing away from international funds toward U.S. ETFs, is consistent with the flight-to-safety effect and surge in ā€œhome biasā€ investing during the adverse economic shock. Furthermore, we find evidence of rapid portfolio adjustments of U.S. investors in response to the COVID-19 outbreak in a given geographic location.https://digitalcommons.odu.edu/gradposters2021_business/1008/thumbnail.jp

    The Effect of Keyword Method on Vocabulary Retension of Senior High School EFL Learners in Iran

    Get PDF
    This study aimed at investigating the effect of keyword method, as one of the mnemonic strategies, on vocabulary retention of Iranian senior high school EFL learners. Following a quasi-experimental design, the study used thirty eight (n=38) female senior high school students in grade four from two intact classes at a public high school. The students were randomly assigned to experimental and control groups. The experimental group was instructed through the keyword method and the control group learned vocabulary through the traditional method. To analyze the data, paired-samples t-test and independent samples t-tests were run. It was found that students in the experimental group significantly outperformed the students in the control group in vocabulary retention by keyword method. Also, a significant difference was found between the performance of the keyword group and traditional group in delay recall posttest. Overall, this study illustrated that the use of keyword method can largely reduce learners' problems in the acquisition and retention of L2 words. The findings of this research may have pedagogical implications for teachers and learners. Keywords: Keyword method, mnemonic strategies, vocabulary retention, EFL learner

    Iranian Intermediate EFL Learners' Vocabulary Inferencing Strategies: A Qualitative Study

    Get PDF
    The present qualitative and interpretative study aims to investigate Iranian EFL learners' L2 vocabulary strategies. The distribution of strategy types and what factors contribute to the success of the inferencing strategies are the two main purposes of the study. Using think-aloud procedures with 15 Iranian EFL learners, the present study explored L2 learners' inferencing strategies and the relationship with their success. Sixteen types of inferential strategies were revealed to be employed by the participants and two types of inferences were identified: successful and less successful inferences. The results of the study are discussed in the light of the similar studies and the suggestions for future research are made. The study has a number of pedagogical implications for L2 research and practice, L2 teachers, syllabus designers, and educational psychology

    Effects of different tumors on the steady-state heat distribution in the human eye using the 3D finite element method

    Full text link
    In this paper, a three-dimensional finite element method is developed to simulate the heat distribution in the human eye with different types of tumors to understand the effect of tumors on heat distribution in the human eye. The human eye is modeled as a composition of several homogeneous regions and the physical and thermal properties of each region used in this study are more accurate than the models used in previous studies. By considering the exact and complicated geometry of all parts, the finite element method is a proper solution for solving the heat equation inside the human eye. There are two kinds of boundary conditions called the radiation condition and the Robin condition. The radiation boundary condition is modeled as a Robin boundary condition. For modeling eye tumors and their effect on heat distribution, we need information about eye tumor properties such as heat conductivity, density, specific heat, and so on. Thanks to no accurate reported information about eye tumor properties, the properties of other types of tumors such as skin, and bowel tumors are used. Simulation results with different parameters of eye tumors show the effect of eye tumors on heat distribution in the human eye.Comment: 15 pages, 6 Figures, 5 Table

    Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform

    Full text link
    Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological information about the activity of the human brain which can be used to diagnose epilepsy. However, visual inspection of a large number of electroencephalogram signals is very time-consuming and can often lead to inconsistencies in physicians' diagnoses. Quantification of abnormalities in brain signals can indicate brain conditions and pathology so the electroencephalogram (EEG) signal plays a key role in the diagnosis of epilepsy. In this article, an attempt has been made to create a single instruction for diagnosing epilepsy, which consists of two steps. In the first step, a low-pass filter was used to preprocess the data and three separate mid-pass filters for different frequency bands and a multilayer neural network were designed. In the second step, the wavelet transform technique was used to process data. In particular, this paper proposes a multilayer perceptron neural network classifier for the diagnosis of epilepsy, that requires normal data and epilepsy data for education, but this classifier can recognize normal disorders, epilepsy, and even other disorders taught in educational examples. Also, the value of using electroencephalogram signal has been evaluated in two ways: using wavelet transform and non-using wavelet transform. Finally, the evaluation results indicate a relatively uniform impact factor on the use or non-use of wavelet transform on the improvement of epilepsy data functions, but in the end, it was shown that the use of perceptron multilayer neural network can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure

    Production of Bioactive Peptides in Milk Using Two Native Strains of Levilactobacillus brevis

    Get PDF
    Background and Objective: Milk proteins are precursors of several biologically active peptides. One of the methods of producing these peptides is fermentation using lactic acid bacteria. The aim of this study was to investigate production of antioxidant and angiotensin-I converting enzyme inhibitory bioactive peptides in cow milk fermented by two strains of Levilactobacillus brevis. Material and Methods: Two strains of Levilactobacillus brevis KX572376 (M2) and Levilactobacillus brevis KX572382 (M8) were used in fermentation of low-fat cow milk. Moreover, pH changes, proteolytic activity, water-soluble extract biological activity (antioxidant activity and angiotensin-I converting enzyme inhibition) of the samples and peptide fraction less than 3 kDa were investigated at 24 and 48 h of fermentation (30 Ā°C). Peptide profile of the superior sample was analyzed as well. Statistical analysis was carried out using one-way of variance, Tukey test and SPSS software v.25. Results and Conclusion: The two strains decreased milk pH to a similar level in the first 24 h. Quantities of free amine groups in the samples treated with M2 and M8 strains within 24 and 48 h of fermentation were significantly different (pā‰¤0.05), compared to the control sample. In the first 24 h of fermentation, no difference was observed in the quantity of free amines of M2 and M8 samples. In the second 24 h, further free amine groups were produced due to the activity of M8 strain in milk. Antioxidant activity of the water-soluble extracts of M2 and M8 samples was significantly (pā‰¤0.05) higher than that of the control sample during fermentation. Antioxidant activity in fractions less than 3 kDa did not show significant differences in M2 and M8 samples at 24 and 48 h of fermentation. In the control sample, no antioxidant activity was observed in fractions less than 3 kDa. The highest ACE inhibitory activity in fractions less than 3 kDa of M8 was observed after 48 h. No angiotensin-I converting enzyme inhibition was seen in fractions less than 3 kDa of M2 and control sample. The RP-HPLC peptide patterns of the fraction less than 3 kDa of M8 and control sample were different, which was a justification for the biological activity in this sample. Conflict of interest: The authors declare no conflict of interest
    • ā€¦
    corecore