ABSTRACT
The Covid-19 pandemic has deepened the existing gender inequalities. In particular, it has dealt a significant blow to women entrepreneurs, as it has magnified the pre-pandemic disadvantages women have faced in the economic, social, financial and regulatory ecosystems they operate in, particularly due to the nature and size of their businesses. The article outlines three main reasons that explain why women entrepreneurs have been disproportionately impacted during this health pandemic. It then explores how trade agreements can help women overcome the barriers that impede their entrepreneurial potential and help their businesses sustain the pandemic-inflicted market disruptions.
ABSTRACT
At World Trade Organization (WTO), nothing is agreed until everything is agreed and until everyone agrees at the negotiating tables, and that ‘magic' moment has been difficult to arrive at. Some WTO Members have argued that if all Members cannot move ahead together with the acceptance of new rules, the Members who are able and willing to move ahead should be provided with the required space to do so. Some Members have indeed chosen to push ahead as they have recently sought progress in negotiations through the Joint Statement Initiatives (JSIs). The JSI proponents claim that JSIs can contribute to building a more responsive and relevant WTO – which will be critical to restoring global trade and economic growth in the wake of the COVID-19 crisis. Others have staunchly opposed such plurilateral attempts at trade liberalization on various grounds, often labelling them as attempts to circumvent the WTO's core tenets of multilateralism. The article contributes to this debate, as the authors assess different routes through which JSIs can be added to the WTO acquis and the WTO-compatibility of each of these routes. It then assesses the possible detrimental impact that JSIs can have on the essence and fabric of the multilateral trading system (MTS). © 2023 Kluwer Law International BV, The Netherlands.
ABSTRACT
The global impact of COVID-19 has been significant and several vaccines have been developed to combat this virus. However, these vaccines have varying levels of efficacy and effectiveness in preventing illness and providing immunity. As the world continues to grapple with the ongoing pandemic, the development and distribution of effective vaccines remains a top priority, making monitoring prevention strategies mandatory and necessary to mitigate the spread of the disease. These vaccines have raised a huge debate on social networks and in the media about their effectiveness and secondary effects. This has generated big data, requiring intelligent tools capable of analyzing these data in depth and extracting the underlying knowledge and feelings. There is a scarcity of works that analyze feelings and the prediction of these feelings based on their estimated polarities at the same time. In this work, first, we use big data and Natural Language Processing (NLP) tools to extract the entities expressed in tweets about AstraZeneca and Pfizer and estimate their polarities;second, we use a Long Short-Term Memory (LSTM) neural network to predict the polarities of these two vaccines in the future. To ensure parallel data treatment for large-scale processing via clustered systems, we use the Apache Spark Framework (ASF) which enables the treatment of massive amounts of data in a distributed way. Results showed that the Pfizer vaccine is more popular and trustworthy than AstraZeneca. Additionally, according to the predictions generated by Long Short-Term Memory (LSTM) model, it is likely that Pfizer will continue to maintain its strong market position in the foreseeable future. These predictive analytics, which uses advanced machine learning techniques, have proven to be accurate in forecasting trends and identifying patterns in data. As such, we have confidence in the LSTM's prediction of Pfizer's ongoing dominance in the industry. © Hassan Badi, Imad Badi, Karim El Moutaouakil, Aziz Khamjane, Abdelkhalek Bahri 2022
ABSTRACT
According to the World Health Organization, there are approximately 17.9 million people in the world who will die under the cause of Cardiovascular diseases (CVDs) in 2019. Heart and Brain are both related to Cardiovascular diseases. Even if the patients do not pass away due to the disease, the post-effect of this illness burdens the patients and their families. Also, the outbreak of COVID-19 makes the patients take a risk of undergoing rehabilitation in the hospital. Thus, a smart healthcare solution which is a Smart Healthcare Tracker through the Internet of Things is designed. The system consists of an EMG sensor, accelerometer, gyroscope, and heart rate/pulse oximeter connected to ESP 32 with an interface of NodeMCU to study the patients' health condition for arms and legs strength by sending the data to the caregivers or physicians. The project aimed to obtain a consistent and accurate reading for each of the features for arms and legs strength analysis and sleeping disturbance analysis. The BLYNK app is also applied to the project design as a platform to display the analysis result to the caregivers/physicians on the gadgets at any time and anywhere. The prototype has been constructed and the data collection is built successfully. The prototype is trusted to obtain accurate and consistent results and can provide a sustainable way for the rehabilitation to indicate the health condition and the recovery stage of the patients. © 2021 IEEE.
ABSTRACT
Background: Lockdown during the COVID-19 pandemic can affect the behavior of daily habits among children and adolescents. This study aims to determine the effect of the COVID-19 pandemic lockdown on the physical activity and sleeping habits of children and adolescents. Subjects and Method: This study is a systematic review by searching for articles using the online database PubMed and BASE. The dependent variable is the COVID-19 lockdown. The independent variables are physical activity and sleep habits.