sj-docx-1-nms-10.1177_14614448231179941 – Supplemental material for Not all skepticism is “healthy” skepticism: Theorizing accuracy- and identity-motivated skepticism toward social media misinformation

Supplemental material, sj-docx-1-nms-10.1177_14614448231179941 for Not all skepticism is “healthy” skepticism: Theorizing accuracy- and identity-motivated skepticism toward social media misinformation by Jianing Li in New Media & Society</p

Supplementary Material

Supplementar

Characterization of the odorous constituents and chemical structure of thermally modified rubberwood

Rubberwood is a sustainable timber from tropical plantations, and is widely used in human’s daily routine like furniture and interior decoration. The thermal modification technology can effectively make rubberwood preservative, and improve its dimensional stability, while the odorants emitted from thermally modified rubberwood limited its application and recently has attracted people’s attention. In this study, the effects of thermal modification on odor and key odorous constituents of rubberwood were studied. The volatile odorants were identified by sensory evaluation via gas chromatography–mass spectrometry/olfactometry. To gain profound insights into the contribution of single odorants to the overall odor of heat-treated rubberwood, the odor-active constituents were calculated using relative odor activity values (ROAV) on the basis of odor thresholds. It’s revealed that with increasing temperature, the ROAV of 5-methyl-2-furancarboxaldehyde increased from 0.01 to 2.6 and then decreased to 0.7, and 5-methyl-2-furancarboxaldehyde became the important odorant of thermally modified rubberwood ranged from 155°C to 185°C. The odor of 5-methyl-2-furancarboxaldehyde smelled like chocolate and burnt. The identification of odorants from thermally modified rubberwood could provide theoretical guidelines for further controlling the production technology to produce the low odor thermally modified rubberwood.</p

Potential disruptive technology topics.

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, this paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the Latent Dirichlet Allocation (LDA) topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil unmanned aerial vehicles (UAVs) as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the data acquisition, main equipment, and ground platform intelligence.</div

The first stage of the technology network.

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, this paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the Latent Dirichlet Allocation (LDA) topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil unmanned aerial vehicles (UAVs) as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the data acquisition, main equipment, and ground platform intelligence.</div

Intertopic distance map.

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, this paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the Latent Dirichlet Allocation (LDA) topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil unmanned aerial vehicles (UAVs) as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the data acquisition, main equipment, and ground platform intelligence.</div

The number of topics perplexity curve.

Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, this paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the Latent Dirichlet Allocation (LDA) topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil unmanned aerial vehicles (UAVs) as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the data acquisition, main equipment, and ground platform intelligence.</div

Potential disruptive technology identification flow chart.

Potential disruptive technology identification flow chart.</p

Multiscale Simulations to Discover Self-Assembled Oligopeptides: A Benchmarking Study

Peptide self-assembly is critical for biomedical and material discovery and production. While it is costly to experimentally test every possible peptide design, computational assessment provides an affordable solution to evaluate many designs and prioritize synthesis and characterization. Following a theoretical investigation, we present a systematic analysis of all-atom and coarse-grained simulations to predict peptide self-assembly. Benchmarking studies of two model dipeptides allow us to assess the impacts of intrinsic properties (such as amino acids and terminal modifications) and external environment (such as salinity) on the simulated aggregation. Further examination of 20 oligopeptides containing two to five amino acids shows good agreement among our theory, simulations, and prior experimental observations. The success rate of our prediction is 90%. Therefore, our theory, simulation, and analysis can be useful to identify peptide designs that can self-assemble and predict the potential nanostructures. These findings lay the ground for future virtual screening of peptide-assembled nanostructures and computer-aided biologics design

The calculation results of each IPC node location index.

The calculation results of each IPC node location index.</p