Thermosyphon Heat Pipe Technology

Heat pipes play vital roles in increasing heat transfer performance of many engineering systems such as solar collectors and this leads to an increase in their usage. Investigation on the performance of heat pipes under different operation conditions and inclination angles is required for effective utilization. In this chapter, a general overview on the construction, operation, advantages, and classifications of heat pipes is presented. Particular attention is given to the heat pipe without wick material in the inner diameter (thermosyphon). Intensive discussions are presented on the construction, operations, advantages and applications of thermosyphon heat pipe. The experimental and numerical approaches on the performance evaluation and characterization of thermosyphon are discussed. A detailed procedure on how experimental work is carried out on thermosyphon is discussed including instrumentation and calibration of the devices. Modelling and simulation of the performance of thermosyphon are discussed, including the model set-up procedure. Factors affecting the performance of thermosyphon such as fill ratio, working fluid, heat input, inclination angles, are analysed based on the overall thermal resistance and thermosyphon performance. Current researches on the effects of major factors affecting the operation of thermosyphon are presented, as well as their current development and various applications in engineering systems

A transfer learning with deep neural network approach for diabetic retinopathy classification

Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently

Using deep learning models for learning semantic text similarity of Arabic questions

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%

Transfer deep learning approach for detecting coronavirus disease in X-ray images

Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task

Cardiopulmonary resuscitation in low-resource settings: a statement by the International Liaison Committee on Resuscitation, supported by the AFEM, EUSEM, IFEM, and IFRC.

Most recommendations on cardiopulmonary resuscitation were developed from the perspective of high-resource settings with the aim of applying them in these settings. These so-called international guidelines are often not applicable in low-resource settings. Organisations including the International Liaison Committee on Resuscitation (ILCOR) have not sufficiently addressed this problem. We formed a collaborative group of experts from various settings including low-income, middle-income, and high-income countries, and conducted a prospective, multiphase consensus process to formulate this ILCOR Task Force statement. We highlight the discrepancy between current cardiopulmonary resuscitation guidelines and their applicability in low-resource settings. Successful existing initiatives such as the Helping Babies Breathe programme and the WHO Emergency Care Systems Framework are acknowledged. The concept of the chainmail of survival as an adaptive approach towards a framework of resuscitation, the potential enablers of and barriers to this framework, and gaps in the knowledge are discussed, focusing on low-resource settings. Action points are proposed, which might be expanded into future recommendations and suggestions, addressing a large diversity of addressees from caregivers to stakeholders. This statement serves as a stepping-stone to developing a truly global approach to guide resuscitation care and science, including in health-care systems worldwide