TAP block, needle through introducer approach

AbstractSince introduction of transversus abdominis plane (TAP) block to pediatric practice, ultrasonography is considered the standard of care to perform such block in pediatric age group.In spite of the rarity of reported complication of the block in literatures, many practitioners still avoid performing such block in pediatric age group giving the shallow depth of plane and probability of intra-abdominal organs trauma.I am explaining a new approach entailed using Ultrasound (US) guided spinal needle through introducer in 3years old child.This technique has a potential to make the block more approachable as it decreases the probability of intra-abdominal organs trauma

Refinery Scheme’s Mass Targeting and Bottom Section Synthesis for Heavy Oil

This work evaluates the introduction of heavy oil in a refinery as a first step. The first step will yield an increase in the production of the bottom products (vacuum residue, gas oil and diesel). It will also reduce the production of the light products (gases, LPG and naphtha) from ADU/VDU for oil with API above 20. However, we showed that if the heavy oil is below 20 API, the vacuum residue will be the only increasing product. This also reflects on the unit capital cost. The power and steam required by the refinery should also increase as crude oil becomes heavier due to the high amount of steam used in the delayed coker unit. Nevertheless, the fuel for the fire heaters does not show the expected change as compared to the model. The report goes to a further step by replacing bottom product processes with gasification and syngas routes. This step results to reduce the total production of fuel. Therefore, the fuel gasification paths MTG, DME (direct and indirect), and FT are more valuable than other gasification paths. All fuel paths showed a similar amount of fuel production, yielding extra production around 100,000 lb/hr compared to the base case. Moreover, the direct path of DME provided the lowest estimated cost compared to other fuel gasification paths. The MTG path and indirect DME path have a similar cost. The final step is to investigate two challenges related to the gasification cases: water balance and fuel demand. The investigation shows that more than 95% of used water can be recovered by recycling water (both direct and indirect recycling). Furthermore, the study shows that MTG and DME-indirect paths demand less fuel when compared to the base case

Analyze the Human Movements to Help CNS to Shape the Synergy using CNMF and Pattern Recognition

© 2017 The Authors. The Biomedical Signals have been studied for developing human control systems to improving the quality of life. The EMG signal is one of the main types of biomedical signals. It is a convoluted signal. This signal (EMG signal) controlled by the Central nervous system (CNS). It has been a long time expected that the human central nervous system (CNS) uses flexible combinations of some muscles synergy (MS) to solve and control redundant movements. Synergy muscles activities are different in a single muscle. In the concept of Synergy muscle, the CNS does not directly control the activation of a large number of muscles. There are two main movements can help CNS to shape the synergy. The automatic body response and the voluntary actions. These activities remain not too bright. Some studies support the hypothesis that the automatic body responses could be used as a reference to familiarize the voluntary efforts. It has been validating by analyzing the human voluntary movement and the automatic mechanical motions from the muscle synergy. Based on the validation, there was a proposition that the automatic synergy motion may express some features which could support the CNS to shape the voluntary synergy motion using the nonnegative matrix factorization (NMF). Thus the target of the presenting work is to analyses the human movements from the muscle synergy to help CNS shapes the synergy movement by suggestion using the concatenated non-negative matrix factorization (CNMF) method and the pattern recognition method. Then compare the two results and see if that help CNS to shape the synergy movements and which method has more accuracy

A Case Study Of Attitudes And Motivations Of Students And Lecturers Toward Language-In-Education Policies In Al-Aqsa University, Palestine

Tujuan kajian ini adalah untuk menentukan sikap para pensyarah dan pelajar di atas dasar pendidikan Bahasa Inggeris dan peranannya di Fakulti Sains, Universiti AlAqsa. Kajian ini telah menganalisis data yang diperoleh daripada empat instrumen yang diguna pakai untuk mengesan sikap para pensyarah dan pelajar terhadap polisi kerajaan di dalam bidang bahasa dan juga bidang pendidikan semasa. The overreaching aim of this research is to determine students` and lecturers` attitudes and motivation towards the current English education policies and the role of English in science faculties in Al-Aqsa University. Accordingly, this research intended to analyze the data collected from the four instruments utilized in this study to detect the attitudes towards current language policy in education, and attitudes towards English in science faculties. The sample for the survey consisted of 400 students, and lecturers

A Case Study Of Attitudes And Motivations Of Students And Lecturers Toward Language-In-Education Policies In Al-Aqsa University, Palestine

Tujuan kajian ini adalah untuk menentukan sikap para pensyarah dan pelajar di atas dasar pendidikan Bahasa Inggeris dan peranannya di Fakulti Sains, Universiti AlAqsa. The overreaching aim of this research is to determine students` and lecturers` attitudes and motivation towards the current English education policies and the role of English in science faculties in Al-Aqsa University

Upper limb recovery prediction after stroke rehabilitation based on regression method

© Springer Nature Switzerland AG 2019. In this paper, we investigate the possibility of a machine-learning algorithm using the Support Victor Machine Regression (SVMR) to predict the motor functional recovery of moderate post stroke patients during their rehabilitation program. To train the model, we used the recorded electromyography (EMG) signals from the upper limb muscles of the patients during their initial rehabilitation sessions. Then we tested the trained model to predict the later muscles performance of the patient during the same sessions. The results of this pilot study were promising; data were, to some extent, predictable. We believe such research direction could be essential to motivate the patient to complete the designed rehabilitation program and can assist the therapist to innovate proper rehabilitation menu for individual patients

Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. Computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recently, deep learning with a facial image-based ASD classification has gained interest due to its ease of collection and non-invasiveness. We observed that the existing approaches utilized either local or global features of facial images to diagnose ASD. However, its important to consider both local and global features to obtain fine-grained details and larger contextual information for accurate detection and classification. This paper proposes a sequencer-based patch-wise Local Feature Extractor along with a Global Feature Extractor. Finally, the features from these modules are aggregated to obtain the final feature for the classification of ASD. Experiments on a publicly available Autism Facial Image Dataset demonstrate that our proposed framework achieves state-of-the-art performance. We achieved accuracy, precision, recall, and F1-score of 94.7%, 94.0%, 95.3%, and 94.6%, respectively

Label self-advised support vector machine (LSA-SVM)-automated classification of foot drop rehabilitation case study

© 2019 Veterinary World. All rights reserved. Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures

E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis

Rice is one of the most important crops worldwide. Diseases of the rice plant can drastically reduce crop yield and even lead to complete loss of production. Early diagnosis can reduce the severity and help efforts to establish effective treatment and reduce the usage of pesticides. Traditional machine learning approaches have already been employed for automatic diagnosis. However, they heavily rely on manual preprocessing of images and handcrafted features, which is challenging, time-consuming, and may require domain expertise. Recently, a single end-to-end deep learning (DL)-based approach was employed to diagnose rice diseases. However, it is not highly robust, nor is it generalizable to every dataset. Hence, we propose a novel end-to-end training of convolutional neural network (CNN) and attention (E2ETCA) ensemble framework that fuses the features of two CNN-based state-of-the-art (SOTA) models along with those of an attention-based vision transformer model. These fused features are utilized for diagnosis by the addition of an extra fully connected layer with softmax. The whole procedure is performed end-to-end, which is very important for real-world applications. Additionally, we feed the extracted features into a traditional machine learning approach support vector machine for classification and further analysis. To verify the effectiveness of our proposed E2ETCA framework, we demonstrate it on three publicly available datasets: the Mendeley Rice Leaf Disease Image Samples dataset, the Kaggle Rice Diseases Image dataset, the Bangladesh Rice Research Institute dataset, and a combination of these three datasets. On the basis of various evaluation metrics (accuracy, precision, recall, and F1-score), our proposed E2ETCA framework exhibits superior performance to existing SOTA approaches for rice disease diagnosis, which can also be generalizable in similar other domains