DESARROLLO DE UNA METODOLOGÍA DE ENSEÑANZA PARA EL PLAN MAESTRO DE PRODUCCIÓN EN EDUCACION A DISTANCIA, MEDIANTE EL USO DE LENGUAJE R EN TIEMPOS DE COVID-19

The severity of the SARS-CoV-2 virus that causes the infectious disease of Covid-19, affected the traditional face-to-face educational system;which has been forced to rethink teaching through technological elements in various careers of Higher Education Institutions (IES) in areas such as: social, economic, administrative, arts, etc., being the area of industrial engineering and logistics, the most complicated to rethink, for which, it has had to enhance the ability, to favor the teaching-learning of logistical problem solving, through the use of open-source programs such as the R programming language, evidencing its accessibility, reliability and flexibility to modify its code, by personnel who do not need to be specialists in the area of programming or software development;in addition to allowing the effective management and storage of data, as well as the analysis of large volumes and also provides tools that add to the presentation of high quality graphics. © 2023 Universidad de La Habana. All rights reserved.

Use of Deep Learning Application in Medical Devices

Artificial Intelligence (AI) is a system that helps machines to march with human abilities within daily lifestyles. Deep learning supported by AI can be an effective application within healthcare sector. This research has explained various aspects of Deep learning application that can be a major area of concern for pushing the development process of Indian medical sector that have lack of infrastructure and lack of capacity, to take less time to optimise the medical diagnosis process. This research has also investigated the advantages and disadvantages that medical sector might face while using deep learning applications. Deep learning applications under AI systems are used to classify objects. CNN model, Machine-learning tools, and other tools that use deep learning approach are effective to diagnose any disease and in medical image analysis process. Deep learning techniques are also used to detect heart disease and manage the data regarding the patients of heart diseases. Secondary data collection method has been used and a thematic analysis has been conducted in this research to describe and find various challenges that might have been engaged within deep learning process used in medical sectors of India. It has been found that, Deep Learning is used widely for COVID-19 medical image processing through a fully connected CNN model. As a result, the main finding states that deep learning application creates a major scope for the improvement in Indian medical sector. © 2022 IEEE.

Green Supply Chain Coordination During the COVID-19 Pandemic Based on Consignment Contract

COVID-19 has further strengthened consumers’ online consumption habits and brought a new boom in which enterprises can use online transactions and green products to avoid risk and gain profits in the pandemic. A green supply chain model is established containing one e-commerce platform and one manufacturer. The Nash bargaining contract and Rubinstein bargaining contract are applied to mitigate conflicts of profits in the model. From the coordination analysis, we show that both Nash and Rubinstein bargaining contracts can achieve coordination and mitigate conflict of profits through the adjustment of platform usage rates. According to each member’s bargaining power and patience, the optimal platform usage rate is determined, the supply chain profits of both sides are allocated, the green production’s research and development are promoted, and a win-win situation is realized. Specifically, in the Nash bargaining contract, the excess profit of each member depends on their bargaining power. The stronger the bargaining power, the more excess profits will go to the e-commerce platform, and the less excess profits will go to the manufacturer. In the Rubinstein bargaining contract, the excess profit of each member depends on the lowest profit bound and bargaining patience. The higher the manufacturer’s (or e-commerce platform’s) patience, the higher his profit. When the patience of both is high (or low), the e-commerce platform (or the manufacturer) plays a leading role and obtains more profits. Copyright © 2022 Wang, Reivan Ortiz, Dextre-Martinez and Zhang.

Research on the Impact of COVID-19 on Import and Export Strategies

Due to the spread of COVID-19, the public health crisis is bound to have a huge impact on the world economy and international trade. How to study the import and export strategies under the coronavirus pandemic has become a major issue that many scholars need to solve urgently. Therefore, a two-stage game model is constructed, and the reverse solution method is used to obtain the optimal output of enterprises in importing countries and exporting countries before and after the outbreak of pandemic, as well as the optimal subsidies for enterprises from exporting countries and the optimal import quarantine rate for importing countries. Based on the game between the two countries without the pandemic outbreak, the impact of the pandemic on the output, profits, and social welfare of enterprises in the two countries was compared. Enterprises in exporting countries face double threats from the pandemic and import quarantine fees. The increase in import quarantine fees reduces the social welfare of exporting countries. In order to effectively control the spread of the pandemic, subsidies are an effective means to restore exports to normal. Reasonable collection of import quarantine fees by importing countries can promote bilateral trade, but an excessive collection will be counterproductive. The governments of exporting countries should establish emergency mechanisms and relevant subsidy policies, and enterprises should continuously improve their competitiveness. At the same time, countries should abandon the concept of trade protection and negotiate and cooperate to jointly deal with the pandemic.

Clinical and para clinical variables predicting prognosis in patients with covid-19: Systematic review

Currently, multiple clinical and paraclinical variables have been found to predict prognosis in patients with VOC-19. The clinical presentation may be mild, moderate, or even present as acute respiratory distress syndrome (ARDS) and multisystem organ failure (MSF) with fatal outcome. The identification of prognostic factors, both clinical and para-clinical, will allow the grading of patients’ risk and the active control of the severity of the disease. The main objective was to determine the clinical and paraclinical variables predicting prognosis in patients with FOM-19. A systematic review will be conducted. The search will be performed in the following databases: PubMed, Taylor and Francis, LILACS, Springer, Scopus, Web of Science and CINAHL. The search will use keywords based on DeCs and MeSH categorization, using the Boolean AND and OR connections. The variables (clini-cal history) for this study will be as follows: Age, signs and symptoms, history, mortality, laboratory tests, habits and treatments. The aim is to provide scientific evidence and cre-ate national statistics to facilitate the use of prognostic variables in the evolution of the disease;in this way, we will be able to get a close look at this new disease we are facing, according to our population reality. © 2020, Venezuelan Society of Pharmacology and Clinical and Therapeutic Pharmacology. All rights reserved.