Neurofeedback training (NFT) is a non-invasive, safe, and effective method of regulating the nerve state of the brain. Presently, NFT is widely used to prevent and rehabilitate brain diseases and improve an individual's external performance. Among the various NFT methods, NFT to improve sport performance (SP-NFT) has become an important research and application focus worldwide. Several studies have shown that the method is effective in improving brain function and motor control performance. However, appropriate reviews and prospective directions for this technology are lacking. This paper proposes an SP-NFT classification method based on user experience, classifies and discusses various SP-NFT research schemes reported in the existing literature, and reviews the technical principles, application scenarios, and usage characteristics of different SP-NFT schemes. Several key issues in SP-NFT development, including the factors involved in neural mechanisms, scheme selection, learning basis, and experimental implementation, are discussed. Finally, directions for the future development of SP-NFT, including SP-NFT based on other electroencephalograph characteristics, SP-NFT integrated with other technologies, and SP-NFT commercialization, are suggested. These discussions are expected to provide some valuable ideas to researchers in related fields.After 35 rounds of talks over the past seven years, the negotiations on the China-EU Comprehensive Agreement on Investment (CAI) passed the finishing line at the end of 2020, a timely gift for the 45th anniversary of the establishment of China-EU diplomatic ties. As a most comprehensive and significant economic and trade agreement between China and the EU, CAI marks a highly relevant step to meet the expectations of different sectors and should be cherished by both sides.
  In this paper, the transfer learning method has been implemented to chest X-ray (CXR) and computed tomography (CT) bio-images of diverse kinds of lungs maladies, including CORONAVIRUS 2019 (COVID-19). COVID-19 identification is a difficult assignment that constantly demands a careful analysis of a patient's clinical images, as COVID-19 is found to be very alike to pneumonic viral lung infection. In this paper, a transfer learning model to accelerate prediction processes and to assist medical professionals is proposed. Finally, the main purpose is to do an accurate classification between Covid-19, pneumonia and, healthy lungs using CXR and CT images.
 
  Learning transfer gives the possibility to find out about this new illness COVID-19, using the knowledge we have about the pneumonia virus. This demonstrates the apprehensiveness achieved from a new architecture trained to detect virus-related pneumonia that must be transferred for COVID-19 detection. Transfer learning presents a considerable dissimilarity in nsidering other existing models because the obtained classification accuracy is over the recently obtained results. It is a belief that the new architecture that is implemented in this study, delivers a petite step in building refined Coronavirus 2019 diagnosis architecture using CXR and CT bio-images.The dual pandemics of coronavirus disease-19 (COVID-19) and diabetes among patients are associated with 2- to 3-times higher intensive care admissions and higher mortality rates. Whether sheltering at home, quarantined with a positive COVID-19 test, or hospitalized, the person living with diabetes needs special considerations for successful management.  https://www.selleckchem.com/products/ipi-145-ink1197.html  Having diabetes and being COVID-19-positive increases the risk of poor outcomes and death. Providers need to give anticipatory pharmacologic guidance to patients with diabetes during COVID-19 lockdown. Patients with diabetes need to be more observant than others and to use self-protective actions. This review (1) discusses the clinical observations of COVID-19, diabetes and underlying mechanisms, (2) describes special considerations in caring for patients with diabetes in a COVID-19 environment, and (3) reviews clinical implications for the health care provider. This review highlights current evidenced-based knowledge. Additional research regarding clinical management is warranted.These guidelines of the European Resuscitation Council (ERC) Cardiac Arrest under Special Circumstances are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. This section provides guidelines on the modifications required for basic and advanced life support for the prevention and treatment of cardiac arrest under special circumstances; in particular, specific causes (hypoxia, trauma, anaphylaxis, sepsis, hypo-/hyperkalaemia and other electrolyte disorders, hypothermia, avalanche, hyperthermia and malignant hyperthermia, pulmonary embolism, coronary thrombosis, cardiac tamponade, tension pneumothorax, toxic agents), specific settings (operating room, cardiac surgery, cardiac catheterization laboratory, dialysis unit, dental clinics, transportation [in-flight, cruise ships], sport, drowning, mass casualty incidents), and specific patient groups (asthma and chronic obstructive pulmonary disease, neurological disease, morbid obesity, pregnancy).Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue.