ABSTRACT
For decades, societies have been planting the seed of their own destruction. The environmental degradation catastrophe has become so voluminous and complex, seen in many forms and extending across various dimensions of nature. Air pollution, water pollution, and soil pollution have caused tremendous amounts of damage. Species extinction and the loss of various forms of life have been massively increasing at an unprecedented rate. It is calculated that approximately 0.01–0.1% of all known species will become extinct each year. This raises a major concern: Could biodiversity loss affect the wellbeing of nations through hindering economic growth? If so, to what extent? This is the question that this study aims to investigate. The case of COVID-19 has been a powerful example enabling the world to witness how biodiversity loss could affect economic growth, which has posed as an economic threat to all nations. This study, therefore, investigates the relationship between biodiversity and economic growth utilizing a fixed effects panel regression conducted using a selected sample of OECD countries. Findings of this study indicate that biodiversity does in fact hinder GDP growth in the long run. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID19 chest X-ray has been used as supplementary tools to support COVID19 severity level diagnosis. However, there are challenges that required to face by researchers around the world in order to implement these chest X-ray samples to be very helpful to detect the disease. Here, this paper presents a review of COVID19 chest X-ray classification using deep learning approach. This study is conducted to discuss the source of images and deep learning models as well as its performances. At the end of this paper, the challenges and future work on COVID19 chest X-ray are discussed and proposed. © 2021 Institute of Physics Publishing. All rights reserved.