Unrolling a rain-guided detail recovery network for single-image deraining

Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. Method To resolve this issue, we propose a novel unrolling rain-guided detail recovery network(URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. Result Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks;thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details.Experiments on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.

Medical Waste Treatment Station Selection Based on Linguistic q-Rung Orthopair Fuzzy Numbers

During the COVID-19 outbreak,the use of single-use medical supplies increased significantly.It is essential to select suitable sites for establishing medical waste treatment stations.It is a big challenge to solve the medical waste treatment station selection problem due to some conflicting factors.This paper proposes a multi-attribute decision-making(MADM)method based on the partitioned Maclaurin symmetric mean(PMSM)operator.For the medical waste treatment station selection problem,the factors or attributes(these two terms can be interchanged.)in the same clusters are closely related,and the attributes in different clusters have no relationships.The partitioned Maclaurin symmetric mean function(PMSMF)can handle these complex attribute relationships.Hence,we extend the PMSM operator to process the linguistic q-rung orthopair fuzzy numbers(Lq-ROFNs)and propose the linguistic q-rung orthopair fuzzy partitioned Maclaurin symmetric mean(Lq-ROFPMSM)operator and its weighted form(Lq-ROFWPMSM).To reduce the negative impact of unreasonable data on the final output results,we propose the linguistic q-rung orthopair fuzzy partitioned dual Maclaurin symmetric mean(Lq-ROFPDMSM)operator and its weighted form(Lq-ROFWPDMSM).We also discuss the characteristics and typical examples of the above operators.A novel MADM method uses the Lq-ROFWPMSM operator and the Lq-ROFWPDMSM operator to solve the medical waste treatment station selection problem.Finally,the usability and superiority of the proposed method are verified by comparing it with previous methods.

Risk management research in East Asia: a bibliometric analysis

Purpose–The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends,hotspots,and directions for future research.Design/methodology/approach–The data source for this paper is the Web of Science Core Collection,and 7,154 publications and related information have been derived.We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools(VOS Viewer and CiteSpace).Findings–The analysis results show that China is the most productive and influential country/region.East Asia countries have strong cooperation with each other and also have cooperation with other countries.The study shows that risk management has been involved in various fields such as credit,supply chain,health emergency and disaster especially in the background of COVID-19.We also found that machine learning,especially deep learning,has been playing an increasingly important role in risk management due to its excellent performance.Originality/value–This paper focuses on studying risk management in East Asia,exploring its publication’s fundamental information,citation and cooperation networks,hotspots,and research trends.It provides some reference value for scholars who are interested or further research in this field.