A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

A Comprehensive Taxonomy of Dynamic Texture Representation

Representing dynamic textures (DTs) plays an important role in many real implementations in the computer vision community. Due to the turbulent and non-directional motions of DTs along with the negative impacts of different factors (e.g., environmental changes, noise, illumination, etc.), efficiently analyzing DTs has raised considerable challenges for the state-of-the-art approaches. For 20 years, many different techniques have been introduced to handle the above well-known issues for enhancing the performance. Those methods have shown valuable contributions, but the problems have been incompletely dealt with, particularly recognizing DTs on large-scale datasets. In this article, we present a comprehensive taxonomy of DT representation in order to purposefully give a thorough overview of the existing methods along with overall evaluations of their obtained performances. Accordingly, we arrange the methods into six canonical categories. Each of them is then taken in a brief presentation of its principal methodology stream and various related variants. The effectiveness levels of the state-of-the-art methods are then investigated and thoroughly discussed with respect to quantitative and qualitative evaluations in classifying DTs on benchmark datasets. Finally, we point out several potential applications and the remaining challenges that should be addressed in further directions. In comparison with two existing shallow DT surveys (i.e., the first one is out of date as it was made in 2005, while the newer one (published in 2016) is an inadequate overview), we believe that our proposed comprehensive taxonomy not only provides a better view of DT representation for the target readers but also stimulates future research activities.

Acid-in-clay electrolyte for wide-temperature-range and long-cycle proton batteries

Proton conduction underlies many important electrochemical technologies. We report a series of new proton electrolytes: acid-in-clay electrolyte termed AiCE, prepared by integrating fast proton carriers in a natural phyllosilicate clay network, that can be made into thin-film (tens of microns) fluid-impervious membranes. The chosen example systems (sepiolite-phosphoric acid) rank top among the solid proton conductors in consideration of proton conductivities (15 mS cm−1 at 25 °C, 0.023 mS cm−1 at −82 °C), the stability window (3.35 V), and reduced chemical activity. A solid-state proton battery was assembled using AiCE as the electrolyte to demonstrate the performance of these electrolytes. Benefitting from the wider electrochemical stability window, reduced corrosivity, and excellent ionic selectivity of AiCE, the two main problems (gasification and cyclability) of proton batteries have been successfully solved. This work also draws the attention of elemental cross-over in proton batteries and illustrates a simple “acid-in-clay” approach to synthesize a series of solid proton electrolytes with a superfast proton permeability, outstanding selectivity, and improved stability for many potential applications associated with protons.

An Aza-Piancatelli Templated Reaction Manifold of 4-Aminocyclopentenones Enables Access to Complex Carbocyclic Assemblies

Capitalizing on the propensity of 1, 2-amino group migration in γ-aminocyclopentenone with a suitable promoter, gem-diaryl equipped systems unfolded an unprecedented avenue for the Lewis acid promoted displacement of γ-aniline group with nucleophiles such as indole. Such transformation, besides providing a means for direct γ-functionalization of cyclopentenones, presented innumerable scope for β, γ-annulation. Various tailored indolo bis-nucleophiles were explored in the current study that rendered an array of indole alkaloid-like compounds in excellent yields and selectivity through one-pot operation. Analysis of collective experimental observation along with designed control experiments strongly suggested the possibility of a retro aza-Piancatelli rearrangement, which is hitherto unknown in the context. Such repertoire could find potential applications in the synthesis of complex assemblies from the Piancatelli rearrangement and related processes. 1. Introduction 2. Aza-Piancatelli rearrangement and related domino processes 3. An unprecedented aza-Piancatelli-templated strategy for polycyclic assemblies 4. Summary and Outlook

Recent advances in dual-filler mixed matrix membranes

Mixed matrix membranes (MMMs) have been widely developed as an attractive solution to overcome the drawbacks found in most polymer membranes, such as permeability-selectivity trade-off and low physicochemical stability. Numerous fillers based on inorganic, organic, and hybrid materials with various structures including porous or nonporous, and two-dimensional or three-dimensional, have been used. Demanded to further improve the characteristics and performances of the MMMs, the use of dual-filler instead of a single filler has then been proposed, from which multiple effects could be obtained. This article aims to review the recent development of MMMs with dual filler and discuss their performances in diverse potential applications. Challenges in this emerging field and outlook for future research are finally provided.

SWOT-SOR Analysis of Activated Carbon-Based Technologies and O3/UV Process as Polishing Treatments for Hospital Effluent

The management and treatment of hospital wastewater are issues of great concern worldwide. Both in the case of a dedicated treatment or co-treatment with urban wastewater, hospital effluent is generally subjected to pre-treatments followed by a biological step. A polishing treatment is suggested to promote (and guarantee) the removal of micropollutants still present and to reduce the total pollutant load released. Activated carbon-based technologies and advanced oxidation processes have been widely investigated from technical and economic viewpoints and applied in many cases. In this study, the potential exploitation of these technologies for the polishing treatment of hospital effluent is investigated by combining a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis with a Strategic Orientation (SOR) analysis. This approach allows a coherent strategy to be extracted from the SWOT-SOR data, increasing the chances of success of each technology. It emerges that both technologies present relevant and sometimes similar strengths and can present opportunities. At the same time, activated carbon-based technologies are more likely to contain the main identified threats than O3/UV technology. The study also finds that, for both technologies, further research and development could improve their potential applications in the treatment of hospital wastewater.