This critical review highlights recent advances in the structurally modified (thio)urea-based receptors for anion complexation and sensing. Modifications of the (thio)urea structure are aimed at a better anion binding in terms of higher binding constant, anion selectivity and feasibility. Major (thio)urea receptors are reviewed as N-alkyl, N-aryl and N-amido/N-amino (thio)ureas. Hints for designing (thio)urea-based receptors for anions are discussed (102 references).

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

This study examines the relationships between government interventions, risk perception, and the public's adoption of protective action recommendations (PARs) during the COVID-19 coronavirus disease emergency in mainland China. We conducted quota sampling based on the proportion of the population in each province and gender ratios in the Sixth Census and obtained a sample size of 3837. Government intervention was divided into government communication, government prevention and control, and government rescue. We used multiple regression and a bootstrap mediation effect test to study the mechanism of these three forms of government intervention on the public's adoption of PARs. The results show that government prevention and control and government rescue significantly increased the likelihood of the public adopting PARs. Risk perception was significantly associated with the public's adoption of PARs. The effects of government interventions and risk perception on the public's adoption of PARs was not found to vary by region. Risk perception is identified as an important mediating factor between government intervention and the public's adoption of PARs. These results indicate that increasing the public's risk perception is an effective strategy for governments seeking to encourage the public to adopt PARs during the COVID-19 pandemic.