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Observation of resonance modes is the most straightforward way of studying mechanical oscillations because these modes have maximum response to stimuli. However, a deeper understanding of mechanical motion can be obtained by also looking at modal responses at frequencies in between resonances. Here, an imaging of the modal responses for a nanomechanical drum driven off resonance is presented. By using the frequency modal analysis, these shapes are described as a superposition of resonance modes. It is found that the spatial distribution of the oscillating component of the driving force, which is affected by both the shape of the actuating electrode and inherent device properties such as asymmetry and initial slack, greatly influences the modal weight or participation. This modal superposition analysis elucidates the dynamics of any nanomechanical system through modal weights. This aids in optimizing mode-specific designs for force sensing and integration with other systems.

Background Following the discovery and emergence of the plasmid-mediated colistin resistance gene, mcr-1, the Chinese government formally banned colistin as an animal growth promoter on April 30, 2017. Herein, we report patterns in colistin resistance and mcr-1 abundance in Escherichia coli from animals and humans between 2015 and 2019, to evaluate the effects of the colistin withdrawal. Methods We did an epidemiology comparative study to investigate: annual production and sales of colistin in agriculture across mainland China according to data from the China Veterinary Drug Association from 2015 to 2018; the prevalence of colistin-resistant E coli (CREC) in pigs and chickens in 23 Chinese provinces and municipalities as reported in the China Surveillance on Antimicrobial Resistance of Animal Origin database from Jan 1, 2015, to Dec 31, 2016, and Jan 1, 2017, to Dec 31, 2018; the presence of residual colistin and mcr-1 in faeces from 118 animal farms (60 pig, 29 chicken, and 29 cattle) across four provinces over July 1, 2017, to August 31, 2017, and July 1, 2018 to August 31, 2018; the prevalence of mcr-1-positive E coli (MCRPEC) carriage in healthy individuals attending routine hospital examinations across 24 provinces and municipalities from June 1 to July 30, 2019, comparing with equivalent 2016 data (June 1 to September 30) from our previous study in the same hospitals; and the patterns in CREC prevalence among hospital E coli infections across 26 provinces and municipalities from Jan 1, 2015, to Dec 31, 2016, and Jan 1, 2018, to Dec 31, 2019, reported on the China Antimicrobial Surveillance Network. Findings After the ban on colistin as a growth promoter, marked reductions were observed in the production (27 170 tonnes in 2015 vs 2497 tonnes in 2018) and sale (US$71·5 million in 2015 vs US$8·0 million in 2018) of colistin sulfate premix. Across 118 farms in four provinces, mean colistin residue concentration was 191·1 μg/kg (SD 934·1) in 2017 versus 7·5 μg/kg (

© 2019 IEEE. Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.