This case study aims to evaluate the associate degree curriculum in child development. In the curriculum evaluation process, Stufflebeam's CIPP curriculum evaluation model was chosen. In the process ...of collecting data, interviews were conducted with 64 associate degree students and six instructors working in this programme. The data obtained from the interviews were analyzed by content analysis method. The results suggest that the content of the most effective courses in the program includes important and new information for students’ professional development, and includes the information needed in daily life. In addition, the students prefer to be taught practically and effectively with different methods/techniques in the teaching and learning processes. They also suggest that the program should include formative assesment and extracurricular activities such as visits to institutions, conferences/ seminars, and internships. On the other hand, the instructors stated that the problems arising from the students and the deficiencies related to the physical facilities (infrastructure), such as students' lack of interest/willingness to the course, reduce the effectiveness of the curriculum. Based on the results of the study, 21st-century skills should be included and students' professional, academic and social development should be supported in the program. Different methods/techniques, materials and extracurricular activities should be used to ensure that students engage in the course with interest and enthusiasm, and multiple assessment methods/tools should be used in the assessment and evaluation process.
Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task due to the non-linearity and non-stationarity nature of EEG signals. Existing feature extraction methods cannot ...extract the deep concealed characteristics of EEG signals from different layers for efficient classification scheme and also hard to select appropriate and effective feature extraction methods for different types of EEG data. Hence this study intends to develop an efficient deep feature extraction based method to automatically classify emotion status of people. In order to discover reliable deep features, five deep convolutional neural networks (CNN) models are considered: AlexNet, VGG16, ResNet50, SqueezeNet and MobilNetv2. Pre-processing, Wavelet Transform (WT), and Continuous Wavelet Transform (CWT) are employed to convert the EEG signals into EEG rhythm images then five well-known pretrained CNN models are employed for feature extraction. Finally, the proposed method puts the obtained features as input to the support vector machine (SVM) method for classifying them into binary emotion classes: valence and arousal classes. The DEAP dataset was used in experimental works. The experimental results demonstrate that the AlexNet features with Alpha rhythm produces better accuracy scores (91.07% in channel Oz) than the other deep features for the valence discrimination, and the MobilNetv2 features yields the highest accuracy score (98.93% in Delta rhythm (with channel C3) for arousal discrimination.
Treatment of lung diseases, which are the third most common cause of death in the world, is of great importance in the medical field. Many studies using lung sounds recorded with stethoscope have ...been conducted in the literature in order to diagnose the lung diseases with artificial intelligence-compatible devices and to assist the experts in their diagnosis. In this paper, ICBHI 2017 database which includes different sample frequencies, noise and background sounds was used for the classification of lung sounds. The lung sound signals were initially converted to spectrogram images by using time–frequency method. The short time Fourier transform (STFT) method was considered as time–frequency transformation. Two deep learning based approaches were used for lung sound classification. In the first approach, a pre-trained deep convolutional neural networks (CNN) model was used for feature extraction and a support vector machine (SVM) classifier was used in classification of the lung sounds. In the second approach, the pre-trained deep CNN model was fine-tuned (transfer learning) via spectrogram images for lung sound classification. The accuracies of the proposed methods were tested by using the ten-fold cross validation. The accuracies for the first and second proposed methods were 65.5% and 63.09%, respectively. The obtained accuracies were then compared with some of the existing results and it was seen that obtained scores were better than the other results.
The new coronavirus, known as COVID-19, first emerged in Wuhan, China, and since then has been transmitted to the whole world. Around 34 million people have been infected with COVID-19 virus so far, ...and nearly 1 million have died as a result of the virus. Resource shortages such as test kits and ventilator have arisen in many countries as the number of cases have increased beyond the control. Therefore, it has become very important to develop deep learning-based applications that automatically detect COVID-19 cases using chest X-ray images to assist specialists and radiologists in diagnosis. In this study, we propose a new approach based on deep LSTM model to automatically identify COVID-19 cases from X-ray images. Contrary to the transfer learning and deep feature extraction approaches, the deep LSTM model is an architecture, which is learned from scratch. Besides, the Sobel gradient and marker-controlled watershed segmentation operations are applied to raw images for increasing the performance of proposed model in the pre-processing stage. The experimental studies were performed on a combined public dataset constituted by gathering COVID-19, pneumonia and normal (healthy) chest X-ray images. The dataset was randomly separated into two sections as training and testing data. For training and testing, these separations were performed with the rates of 80%–20%, 70%–30% and 60%–40%, respectively. The best performance was achieved with 80% training and 20% testing rate. Moreover, the success rate was 100% for all performance criteria, which composed of accuracy, sensitivity, specificity and F-score. Consequently, the proposed model with pre-processing images ensured promising results on a small dataset compared to big data. Generally, the proposed model can significantly improve the present radiology based approaches and it can be very useful application for radiologists and specialists to help them in detection, quantity determination and tracing of COVID-19 cases throughout the pandemic.
Flow field and heat transfer of an impinging swirling jet at low nozzle-to-plate distances have been investigated numerically for three different cases with six different turbulence models. The ...effects of Reynolds number (2100, 4100, 6100, 8100) and dimensionless nozzle-to-plate distance (
H
/
D
= 0.25, 0.5, 0.75, 1) on flow field and heat transfer of the swirling jet are studied parametrically. It is noted that the results of the cases employed exhibit sensitivity to the height of the computational domain defined on the impingement plate, particularly at low nozzle-to-plate distances. It is also seen that one of the cases used is in good agreement with the experimental results by employing Realizable
k
–
ε
turbulence model. Parametric analysis results show that the theoretical swirl number decreases with increasing Reynolds number at constant
H
/
D
and raises for
H
/
D
< 0.75. With the decrease in the Reynolds number from 8100 to 2100, although the
H
/
D
loses gradually effect on the heat transfer,
H
/
D
= 0.25 continues its effect. It is observed that the pressure peaks and the subatmospheric pressure on the impingement plate change with the nozzle-to-plate distance and Reynolds number.
The recognition of various lung sounds recorded using electronic stethoscopes plays a significant role in the early diagnoses of respiratory diseases. To increase the accuracy of specialist ...evaluations, machine learning techniques have been intensely employed during the past 30 years. In the current study, a new pretrained Convolutional Neural Network (CNN) model is proposed for the extraction of deep features. In the CNN architecture, an average-pooling layer and a max-pooling layer are connected in parallel in order to boost classification performance. The deep features are utilized as the input of the Linear Discriminant Analysis (LDA) classifier using the Random Subspace Ensembles (RSE) method. The proposed method was evaluated against a challenge dataset known as ICBHI 2017. The deep features and the LDA with RSE method provided the best accuracy score when compared to other existing methods using the same dataset, improving the classification accuracy by 5.75%.
Propylthiouracil (PTU)-induced vasculitis is a rare antineutrophilic cytoplasmic antibody-associated vasculitis involving small vessels. The patients are often present with constitutional symptoms ...such as skin rash, fever, sore throat, and joint pain, and rarely with systemic symptoms such as muscle pain, weakness, weight loss, conjunctival, and mucosal irritation. Early diagnosis with the help of clinical findings, laboratory and serological markers, discontinuation of PTU, and immunosuppressive treatments are beneficial. In this study, a case of necrotizing vasculitis after PTU is presented, and the literature is reviewed.
Ustilago maydis is a biotrophic fungus causing corn smut disease in maize. The secreted effector protein Pit2 is an inhibitor of papain-like cysteine proteases (PLCPs) essential for virulence. Pit2 ...inhibitory function relies on a conserved 14 amino acids motif (PID14). Here we show that synthetic PID14 peptides act more efficiently as PLCP inhibitors than the full-length Pit2 effector. Mass spectrometry shows processing of Pit2 by maize PLCPs, which releases an inhibitory core motif from the PID14 sequence. Mutational analysis demonstrates that two conserved residues are essential for Pit2 function. We propose that the Pit2 effector functions as a substrate mimicking molecule: Pit2 is a suitable substrate for apoplastic PLCPs and its processing releases the embedded inhibitor peptide, which in turn blocks PLCPs to modulate host immunity. Remarkably, the PID14 core motif is present in several plant associated fungi and bacteria, indicating the existence of a conserved microbial inhibitor of proteases (cMIP).
The new type of coronavirus disease, which has spread from Wuhan, China since the beginning of 2020 called COVID-19, has caused many deaths and cases in most countries and has reached a global ...pandemic scale. In addition to test kits, imaging techniques with X-rays used in lung patients have been frequently used in the detection of COVID-19 cases. In the proposed method, a novel approach based on a deep learning model named DeepCovNet was utilized to classify chest X-ray images containing COVID-19, normal (healthy), and pneumonia classes. The convolutional-autoencoder model, which had convolutional layers in encoder and decoder blocks, was trained by using the processed chest X-ray images from scratch for deep feature extraction. The distinctive features were selected with a novel and robust algorithm named SDAR from the deep feature set. In the classification stage, an SVM classifier with various kernel functions was used to evaluate the classification performance of the proposed method. Also, hyperparameters of the SVM classifier were optimized with the Bayesian algorithm for increasing classification accuracy. Specificity, sensitivity, precision, and F-score, were also used as performance metrics in addition to accuracy which was used as the main criterion. The proposed method with an accuracy of 99.75 outperformed the other approaches based on deep learning.
Cognitive prediction in the complicated and active environments is of great importance role in artificial learning. Classification accuracy of sound events has a robust relation with the feature ...extraction. In this paper, deep features are used in the environmental sound classification (ESC) problem. The deep features are extracted by using the fully connected layers of a newly developed Convolutional Neural Networks (CNN) model, which is trained in the end-to-end fashion with the spectrogram images. The feature vector is constituted with concatenating of the fully connected layers of the proposed CNN model. For testing the performance of the proposed method, the feature set is conveyed as input to the random subspaces K Nearest Neighbor (KNN) ensembles classifier. The experimental studies, which are carried out on the DCASE-2017 ASC and the UrbanSound8K datasets, show that the proposed CNN model achieves classification accuracies 96.23% and 86.70%, respectively.
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