"1998 onwards, a span reporting 1000s of studies depicts the ever-increasing Schiff bases and their complexes applicability; this study genetically tests the research of the last 20 years. The variety of these molecules structural has made them obtainable for a so broad ambit for implementations of biological. They are eminent and because of this unique feature they find their position in the quantitative and qualitative calculation of metals in the aqueous medium. It demonstrated to be prominent catalysts and showed an enjoyable effect of fluorescence. Definitively, Schiff base fissures gotten situation of a unique during bio-experiments and in vitro to develop drugs with a large number of biological structures containing parasites

The emergence of such widespread pharmaceuticals as a pollutant has become one of the world's critical environmental problems that may lead to both the public's health and biodiversity deterioration. This article provides an exhaustive account of the current understanding of the environmental persistence of pharmaceutical contaminants following in-depth analysis of the additive effects of existing natural biodegradation pathways on the human health impact of these drugs. Paying special attention to biodegradation decomposing agents such as bacteria, fungi, and algae the paper estimates their ability to convert drug ingredients to compound that is eventually less toxic. Although these biologic systems contain an enormous potential fo

Currently and under the COVID-19 which is considered as a kind of disaster or even any other natural or manmade disasters, this study was confirmed to be important especially when the society is proceeding to recover and reduce the risks of as possible as injuries. These disasters are leading somehow to paralyze the activities of society as what happened in the period of COVID-19, therefore, more efforts were to be focused for the management of disasters in different ways to reduce their risks such as working from distance or planning solutions digitally and send them to the source of control and hence how most countries overcame this stage of disaster (COVID-19) and collapse. Artificial intelligence should be used when there is no practica

In light of accelerating environmental degradation, the transition to a green economy is an imperative for achieving sustainable development. This study provides a critical analysis of the international legal and institutional framework governing this transition, revealing a significant gap between normative developments and the institutional framework on one hand, and their practical implementation on the other. The transition faces legal obstacles, including reliance on non-binding voluntary commitments and conflicts between environmental obligations and global trade and investment rules. It also reveals a significant financing gap, as financial flows to developing countries continue to lag behind commitments, in add

Arabic language processing with artificial intelligence has evolved significantly in the past decades, from traditional rule- and dictionary-based techniques, through statistical models to modern deep and transformer models. This review intends to present an overview of the most well-known Arabic models as well as datasets used for its training, and the main practical applications such as sentiment analysis, machine translation, speech recognition, and smart assistant. AI-based Arabic NLP has had good progress in the previous decades, from rule and dictionary-based approaches to statistical methods and deep transformative learning models nowadays. In addition to it, the most popular state-of-the-art models that are fine-tuned for the Arabi

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise