Breakup of Liquid Feedstock in Plasma Spraying

Suspension plasma spray is an emerging technology to produce functional nanostructured coatings at moderate cost. In general, in this technique, the liquid is injected radially into a high-velocity high-temperature plasma flow. After liquid breakup and evaporation, solid particles remain in the field and impact the substrate. Preliminary studies have shown that liquid jet atomization is the primary phenomenon that controls the coating quality. However, due to the complex thermophysical properties of plasma and its intricate flow physics, the breakup processes of liquid jets in plasma crossflows have not been investigated comprehensively yet. In general, the gaseous Reynolds number and the liquid-to-gas density ratio in this process are around 50 and 10,000, respectively, which are far outside the limits commonly observed in engines and wind tunnels. In this regard, detailed features of the breakup phenomena of the liquid jets injected in plasma and air crossflow are provided. Moreover, a case study has been established to analyze the effect of changing the surface tension of the liquid in the plasma spray process. The finite volume scheme is used to solve the incompressible variable-density Navier-Stokes equations. In addition, the volume of fluid (VOF) approach is utilized to track the gas-liquid interfaces. Finally, qualitative results such as instantaneous snapshots and shape of the liquid jet cross-sections, in company with quantitative data like including fracture point location, length of surface waves and size of the droplets have been presented

cerebellar infarction in a 9 years old child presenting with fever and ataxia

Background: Cerebellar acute ischemic stroke (AIS) can be a complication of minor head trauma, vertebral artery dissection, vasospasm or systemic hypoperfusion. Computed Tomography (CT) scan usually is negative few hours after acute infarction. Magnetic resonance imaging (MRI) is superior to CT scan for posterior fossa lesions and also in acute phase of cerebellar stroke specially in children. Case summary: The patient was a 9 years Old girl presenting with sudden onset of headache and recurrent vomiting, ataxia and history of 3 consecutive days of fever and malaise. In the report of MRI there were abnormal low T1 and high T2 signal intensity in left cerebellar hemisphere involving superior and middle cerebellar peduncles. After 4 days of admission, the patient became drowsy, symptoms progressed and transfered to the pediatric intensive care unit (PICU). The patient underwent hemispherectomy surgery of the left cerebellar hemisphere because of acute obstructive hydrocephaly. After 5 months of occupational therapy the force of her extremities were normal and the ataxia completely disappeared. Conclusion: Childhood acute ischemic stroke although rare can happen with cerebellar involvement. Because in our patient the first brain CT scan was nearly normal and a false negative rate for initial computed tomography (CT) scanning of 60-80 percent also contributes to missed and delayed diagnosis of childhood AIS, we conclude that for every child presenting with acute ataxia without identified cause in addition to CT scan, MRI also being ordered and from the beginning beside other causes, stroke be contemplated as a cause of ataxia

Instantaneous 3D EEG Signal Analysis Based on Empirical Mode Decomposition and the Hilbert–Huang Transform Applied to Depth of Anaesthesia

Depth of anaesthesia (DoA) is an important measure for assessing the degree to which the central nervous system of a patient is depressed by a general anaesthetic agent, depending on the potency and concentration with which anaesthesia is administered during surgery. We can monitor the DoA by observing the patient’s electroencephalography (EEG) signals during the surgical procedure. Typically high frequency EEG signals indicates the patient is conscious, while low frequency signals mean the patient is in a general anaesthetic state. If the anaesthetist is able to observe the instantaneous frequency changes of the patient’s EEG signals during surgery this can help to better regulate and monitor DoA, reducing surgical and post-operative risks. This paper describes an approach towards the development of a 3D real-time visualization application which can show the instantaneous frequency and instantaneous amplitude of EEG simultaneously by using empirical mode decomposition (EMD) and the Hilbert–Huang transform (HHT). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions (IMFs). The Hilbert spectral analysis method is then used to obtain instantaneous frequency data. The HHT provides a new method of analyzing non-stationary and nonlinear time series data. We investigate this approach by analyzing EEG data collected from patients undergoing surgical procedures. The results show that the EEG differences between three distinct surgical stages computed by using sample entropy (SampEn) are consistent with the expected differences between these stages based on the bispectral index (BIS), which has been shown to be quantifiable measure of the effect of anaesthetics on the central nervous system. Also, the proposed filtering approach is more effective compared to the standard filtering method in filtering out signal noise resulting in more consistent results than those provided by the BIS. The proposed approach is therefore able to distinguish between key operational stages related to DoA, which is consistent with the clinical observations. SampEn can also be viewed as a useful index for evaluating and monitoring the DoA of a patient when used in combination with this approach

Binomial Thinning Integer-Valued AR (1) with Poisson – α Fold Zero Modified Geometric Innovations

Real count data time series often show the phenomenon of the overdispersion. In this paper, we introduce the first-order integer-valued autoregressive process. The univariate marginal distribution is derived from the Delaporte distribution and the innovations are convolution of Poisson with -fold zero modified geometric distribution, based on binomial thinning operator, for modelling integer-valued time series with overdispersion. Some properties of the model are derived. The methods of Yule–Walker, conditional lea st squares and conditional maximum likelihood are used for estimating of the parameters, and their asymptotic properties are established. The Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples. The model is fitted to time series of the weekly number of syphilis cases that are overdispersed count data

Symmetric Duality in Mathematical Programming

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Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out cross-validation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pre-trained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification. © 2013 IEEE

Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images

Background: Nonalcoholic Fatty Liver Disease (NAFLD) as a prevalent condition can significantly have health implications. Early detection and accurate grading of NAFLD are essential for effective management and treatment of the disease.Objective: The current study aimed to develop an advanced hybrid machine-learning model to classify NAFLD grades using ultrasound images.Material and Methods: In this analytical study, ultrasound images were obtained from 55 highly obese individuals, who had undergone bariatric surgery and used histological results from liver biopsies as a reference for NAFLD grading. The features were extracted from the ultrasound images using popular pretrained Convolutional Neural Network (CNN) models, including VGG19, MobileNet, Xception, Inception-V3, ResNet-101, DenseNet-121, and EfficientNet-B7. The fully connected layers were removed from the CNN models and also used the remaining structure as a feature extractor. The most relevant features were then selected using the minimum Redundancy Maximum Relevance (mRMR) method. We then used four classification algorithms: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Multilayer Perceptron (MLP) neural network, and Random Forest (RF) classifiers, to categorize the ultrasound images into four groups based on liver fat level (healthy liver, low fat liver, moderate fat liver, and high-fat liver).Results: Among the different CNN models and classification methods, EfficientNet-B7 and RF achieved the highest accuracy. The average accuracies of the LDA, MLP, SVM, and RF classifiers for the feature extraction method with EfficientNet-B7 were 88.48%, 93.15%, 95.47%, and 96.83%, respectively. The proposed automatic model can classify NAFLD grades with a remarkable accuracy of 96.83%. Conclusion: The proposed automatic classification model using EfficientNet-B7 for feature extraction and a Random Forest classifier can improve NAFLD diagnosis, especially in regions, in which access to professional and experienced medical experts is limited

Cerebellar infarction in a 9 year old child presenting with fever and ataxia: A case report

Cerebellar acute ischemic stroke (CAIS) can be a complication of minor head trauma, vertebral artery dissection, vasospasm or systemic hypoperfusion. CT scan usually is negative few hours after acute infarction. Magnetic resonance imaging (MRI) is superior to CT scan for posterior fossa lesions and also in acute phase of cerebellar stroke especially in children. Here we report a 9-year-old girl referred to the Pediatric Emergency Room, Moosavi Hospital, Zanjan, Iran in January 2017 presenting with sudden onset of headache and recurrent vomiting, ataxia, and history of 3 consecutive days of fever and malaise. In the report of MRI, there were abnormal low T1 and high T2 signal intensity in left cerebellar hemisphere involving superior and middle cerebellar peduncles. After 4 days of admission, the patient became drowsy, symptoms progressed and transferred to the pediatric intensive care unit (PICU). The patient underwent hemispherectomy surgery of the left cerebellar hemisphere because of acute obstructive hydrocephaly. After 5 months of occupational therapy, the force of her extremities was normal and the ataxia completely disappeared. Childhood acute ischemic stroke although rare can happen with cerebellar involvement. Because in our patient the first brain CT scan was nearly normal and a false negative rate for initial computed tomography (CT) scanning of 60-80 also contributes to missed and delayed diagnosis of childhood AIS, for every child presenting with acute ataxia without identified cause in addition to CT scan, MRI also being ordered and from the beginning besides other causes, stroke be contemplated as a cause of ataxia. © 2019, Iranian Child Neurology Society. All rights reserved