Research on Risk Prevention and Resolution Strategies of College Network Ideology in the Context of the Internet

With the rapid development of the Internet,the network ideology of colleges and universities is facing severe challenges.This paper deeply analyzes the root of the risk of network ideology and makes a specific investigation of the status quo of network public opinion in colleges and universities.On this basis,the study explores and puts forward a series of targeted risk prevention and resolution strategies,aiming at providing a systematic solution for the network ideology security of colleges and universities.In this paper,with the combination of theory and practice as the path,we verify the effectiveness and applicability of the proposed strategy through the analysis of the implementation effect of the strategy.This study also provides theoretical support and practical guidance for the prevention and control of ideological risks and public opinion guidance in universities under the network environment,which has important practical significance.With the continuous progress of network technology,the threats to the network ideology of colleges and universities are increasing.For example,the spread of false information has become a serious problem affecting the security of college network ideology.

Preparative Resolution of Gatifloxacin Enantiomers with Pre-Column Esterification Strategy and Comparing Their Enantioselectivity to Bacteria and Antibody

Gatifloxacin （GFX） is a kind of chiral fluoroquinolones compound due to the methyl group at the C-3 position of the piperazine ring[1]. Although the enantiomers of GFX show similar levels of antimicrobial activity and pharmacokinetics[2], the other biological activities （i.e., toxicity or enantioselective recognition to various receptors in vivo） of GFX enantiomers have not yet been studied. With this in mind, we developed a rapid and cost-effective high performance liquid chromatographic （HPLC） separation procedure for GFX enantiomers with a pre-column esterification strategy.

Online conflict resolution strategies for human activity recognition in smart homes

DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, distributed learning agents with diverse classifiers, detect sensorstream data, make local predictions, communicate and collaborate to identify current activities.Then, they learn from their collaborations to improve their own performance in activity recognition.Conflict resolution strategies are applied to generate one final predicted activity when thelocal predicted activity of an agent is different from received predicted activities of other agents.In this paper, two conflict resolution strategies using online learning, w-max-trust and w-maxfreq,are proposed. We experimentally test these strategies by performing an evaluation studyon the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and Fmeasuremetrics compared to the offline strategies max-trust and max-freq and also to the onlineexisting one max-wPerf .