Abstract To develop efficient electrochemiluminescence (ECL) of activated silole chromophores, the relative ECL efficiencies of eight thiophene‐containing compounds are firstly studied in a coreactant pathway. The experimental results show that the extended π‐conjugated systems and donor groups of the silole emitters affect both the radical stability and emission efficiencies. It is found that the 1,1‐di‐ tert ‐butyl‐2,5‐bis[(2,2′‐bithiophen)‐5‐yl]‐3,4‐diphenylsilole (2c) compound with benzoyl peroxide (BPO) as a coreactant exhibits the highest relative ECL efficiencies among the studied systems due to its structural properties. Moreover, the absolute ECL efficiency of the potential pulsing/ECL experiment in the coreactant pathway is 6‐fold larger than that in the potentiodynamic experiment due to the short time interval for the radical species to meet and react. This work provides a guidance for structural modification of silole compounds to tune ECL performance.
Negative association rules are always hidden in the huge infrequent items,but they also have strong correlation and contain important information.This paper presents an effective method based on the correlation and coefficient to estimate NAR and a proposal.The experiment results show that this algorithm is effective in improving the mining efficiency.
Cloud computing provides users with convenient data storage services, which simultaneously poses various security concerns, the integrity of outsourced data has been termed as one of the most concerning security issues. Certificateless public auditing not only enables a third-party auditor (TPA) to check data integrity, but also avoids complex certificates management and inherent drawbacks of key escrow. Up to date, a few certificateless public auditing schemes have been proposed, without providing fast data dynamics or protecting the identity privacy of users. In this paper, we propose a lightweight conditional anonymous certificateless public auditing (CACPA) scheme, supporting much faster data dynamics in cloud storage systems. Based on a homomorphic hash function, we design a certificateless signature and integrate it into the construction of CACPA, reducing the computational costs of TPA substantially. CACPA achieves conditional identity privacy preservation, anyone cannot infer the real identity of a user based on outsourced data, only the private key generator (PKG) can revoke the users when some misbehaviors occur. We provide security analysis of CACPA, and conduct performance evaluation demonstrating the lightweight advantages of CACPA, and therefore it is suitable for auditors with resource-constrained mobile devices.
With the outbreak of the epidemic, it has had a major impact on the economy, society, and people's lives. The entity mining of network public opinion is important, which is helpful for theme mining, subsequent emotion analysis, knowledge graph construction, entity relationship extraction and other prediction tasks, and can find useful knowledge and key information. However, existing named entity recognition (NER) datasets that are available publicly or used by other existing works mainly focus on simple entity forms, such as places, organizations, people, with less focus on medical entities related to COVID-19 in the media. Additionally, there are very limited Chinese datasets that address COVID-19-related NER. Therefore, in this paper, we create a Chinese dataset called CoV-Ch, which is derived from online Weibo news and comments about COVID-19. We define 10 entity types, including 4 general entity types (Person, Organization, Location, Time), 6 medical and COVID-19-related entity types (Disease, Symptom, Medicine, Treatment, Tool, Policy). CoV-Ch contains 8000 sentences, 10735 entities. These ten entity types contain key information related to COVID-19 public opinion, which help to monitor the development of the pandemic. By observing that these entity types appear relatively frequently in epidemic web posts, we can conclude that entity types should be useful and available in the text. We benchmark the performance of classical deep learning models on our dataset for the NER task with extensive experiments. Results show the performance of the BERT-based methods is better. But, the dataset has vast room for improvement for the specific NER task.
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