Scientometric engineering: Exploring citation dynamics via arXiv eprints

Scholarly communications have been rapidly integrated into digitised and networked open ecosystems, where preprint servers have played a pivotal role in accelerating the knowledge transfer processes. However, quantitative evidence is scarce regarding how this paradigm shift beyond the traditional journal publication system has affected the dynamics of collective attention on science. To address this issue, we investigate the citation data of more than 1.5 million eprints on arXiv (https://arxiv.org) and analyse the long-term citation trend for each discipline involved. We find that the typical growth and obsolescence patterns vary across disciplines, reflecting different publication and communication practices. The results provide unique evidence on the attention dynamics shaped by the research community today, including the dramatic growth and fast obsolescence of Computer Science eprints, which has not been captured in previous studies relying on the citation data of journal papers. Subsequently, we develop a quantitatively-and-temporally normalised citation index with an approximately normal distribution, which is useful for comparing citational attention across disciplines and time periods. Further, we derive a stochastic model consistent with the observed quantitative and temporal characteristics of citation growth and obsolescence. The findings and the developed framework open a new avenue for understanding the nature of citation dynamics. Peer Review https://publons.com/publon/10.1162/qss_a_00174

Smart Folding and Floating Shelter Design for Disaster Mitigation with Natural Ventilation and UVC System

The global COVID-19 outbreak has hit the world in the last two years. Indonesia itself recorded positive cases of COVID-19 of approximately 4 million cases as of September 15, 2021. In addition, the frequency of occurrence of natural disasters in Indonesia, which is relatively high every year, requires our collective attention. In early 2021, there have been several natural disasters, including floods in South Kalimantan, earthquakes in West Sulawesi, and others. If the impact of the natural disaster makes residents must do the evacuation, a proper shelter (evacuee camp) and prioritizes health protocols are needed. Therefore, this study discusses the design innovation of disaster response shelters in the form of smart folding and floating shelters designed for a shelter with a capacity of one family (4-5 people). This capacity limitation is to maintain health protocols and suppress the transmission of the Coronavirus in evacuation areas. Our designed shelter prepared in a compact form to facilitate evacuation mobility and can be implemented in all types of disasters with a folding and floating structure system (the shelter can float and be folded). The material used is light steel as the main structure and cork wall as a material that allows the shelter to float. We designed natural ventilation to regulate air circulation, integrated with an ultraviolet C (UVC) lamp. The UVC lamp is intended as a disinfectant against the Coronavirus. Thus, the application of natural ventilation and disinfection using UVC can provide a cleaner air supply. This air supply and circulation are shown in our simulation results using ANSYS Fluent. These results show that smart folding and floating shelter designs can be used for disaster mitigation.

Advances in the Supramolecular Chemistry of Tetracoordinate Boron-Containing Organic Molecules into Organogels and Mesogens

Boron-containing organic compounds are well accepted as a class of compounds having excellent photophysical properties. In addition to the unique photophysical properties, the ease of synthesis and structural robustness make tetracoordinate boron complexes ideal for a variety of applications. While significant light has been thrown on their luminescence properties, there is no collective attention to their supramolecular chemistry. In this mini review, we discuss the progress made in the supramolecular chemistry of these compounds which includes their utility as building blocks for liquid crystalline materials and gels largely driven by various non-covalent interactions like H-bonding, CH-π interactions, BF-π interactions and Van der Waals forces. The organoboron compounds presented here are prepared from easy-to-synthesize chelating units such as imines, diiminates, ketoiminates and diketonates. Moreover, the presence of heteroatoms such as nitrogen, oxygen and sulfur, and the presence of aromatic rings facilitate non-covalent interactions which not only favor their formation but also helps to stabilize the self-assembled structures.

Upscaling human activity data: A statistical ecology approach

Big data require new techniques to handle the information they come with. Here we consider four datasets (email communication, Twitter posts, Wikipedia articles and Gutenberg books) and propose a novel statistical framework to predict global statistics from random samples. More precisely, we infer the number of senders, hashtags and words of the whole dataset and how their abundances (i.e. the popularity of a hashtag) change through scales from a small sample of sent emails per sender, posts per hashtag and word occurrences. Our approach is grounded on statistical ecology as we map inference of human activities into the unseen species problem in biodiversity. Our findings may have applications to resource management in emails, collective attention monitoring in Twitter and language learning process in word databases.

Hurricanes and hashtags: Characterizing online collective attention for natural disasters

We study collective attention paid towards hurricanes through the lens of n-grams on Twitter, a social media platform with global reach. Using hurricane name mentions as a proxy for awareness, we find that the exogenous temporal dynamics are remarkably similar across storms, but that overall collective attention varies widely even among storms causing comparable deaths and damage. We construct ‘hurricane attention maps’ and observe that hurricanes causing deaths on (or economic damage to) the continental United States generate substantially more attention in English language tweets than those that do not. We find that a hurricane’s Saffir-Simpson wind scale category assignment is strongly associated with the amount of attention it receives. Higher category storms receive higher proportional increases of attention per proportional increases in number of deaths or dollars of damage, than lower category storms. The most damaging and deadly storms of the 2010s, Hurricanes Harvey and Maria, generated the most attention and were remembered the longest, respectively. On average, a category 5 storm receives 4.6 times more attention than a category 1 storm causing the same number of deaths and economic damage.

Changes in public response associated with various COVID-19 restrictions in Ontario, Canada: an observational study using social media time series data

Background: News media coverage of anti-mask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views, but does little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policymakers to craft better public health messages in anticipation of poor reactions to controversial restrictions. Objective: Using data from social media, this study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (e.g. business and school closure, regional lockdown differences, additional public health restrictions such as social distancing and masking). Methods: COVID-related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 to Oct 31 2020. Sentiment scores were calculated using the VADER algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites, and dynamic regression models with ARIMA errors were used to examine the association between public health restrictions and changes in public opinion over time (i.e. collective attention, aggregate positive sentiment, and level of disagreement) controlling for the effects of confounders (i.e. daily COVID-19 case counts, holidays, COVID-related official updates). Results: In addition to expected direct effects (e.g. business closure led to decreased positive sentiment and increased disagreements), the impact of restriction on public opinion is contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closure and other restrictions (e.g. masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (i.e. sentiment polarization). Partial (region-targeted) lockdowns were associated with better public response (i.e. higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. Conclusions: Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policymakers anticipate public response to future pandemic restrictions, and ensure adequate

Changes in public response associated with various COVID-19 restrictions in Ontario, Canada: an observational study using social media time series data (Preprint)

BACKGROUND News media coverage of anti-mask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views, but does little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policymakers to craft better public health messages in anticipation of poor reactions to controversial restrictions. OBJECTIVE Using data from social media, this study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (e.g. business and school closure, regional lockdown differences, additional public health restrictions such as social distancing and masking). METHODS COVID-related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 to Oct 31 2020. Sentiment scores were calculated using the VADER algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites, and dynamic regression models with ARIMA errors were used to examine the association between public health restrictions and changes in public opinion over time (i.e. collective attention, aggregate positive sentiment, and level of disagreement) controlling for the effects of confounders (i.e. daily COVID-19 case counts, holidays, COVID-related official updates). RESULTS In addition to expected direct effects (e.g. business closure led to decreased positive sentiment and increased disagreements), the impact of restriction on public opinion is contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closure and other restrictions (e.g. masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (i.e. sentiment polarization). Partial (region-targeted) lockdowns were associated with better public response (i.e. higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns. CONCLUSIONS Our study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policymakers anticipate public response to future pandemic restrictions, and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.