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Climate change has led to a variety of disasters that have caused damage to infrastructure and the economy with societal impacts to human living. Understanding people’s emotions and stressors during disaster times will enable preparation strategies for mitigating further consequences. In this paper, we mine emotions and stressors encountered by people and shared on Twitter during Hurricane Harvey in 2017 as a showcase. In this work, we acquired a dataset of tweets from Twitter on Hurricane Harvey from 20 August 2017 to 30 August 2017. The dataset consists of around 400,000 tweets and is available on Kaggle. Next, a BERT-based model is employed to predict emotions associated with tweets posted by users. Then, natural language processing (NLP) techniques are utilized on negative-emotion tweets to explore the trends and prevalence of the topics discussed during the disaster event. Using Latent Dirichlet Allocation (LDA) topic modeling, we identified themes, enabling us to manually extract stressors termed as climate-change-related stressors. Results show that 20 climate-change-related stressors were extracted and that emotions peaked during the deadliest phase of the disaster. This indicates that tracking emotions may be a useful approach for studying environmentally determined well-being outcomes in light of understanding climate change impacts. 

Sodium-ion batteries exhibit significant promise as a viable alternative to current lithium-ion technologies owing to their sustainability, low cost per energy density, reliability, and safety. 

Abstract In this work, the dynamics of the spread of COVID-19 is considered in the presence of both human-to-human transmission as well as environment-to-human transmission. Specifically, we expand and modify traditional epidemiological model for COVID-19 by incorporating a compartment to study the dynamics of pathogen concentration in the environmental reservoir, for instance concentration of droplets in closed spaces. We perform a mathematical analysis for the model proposed including an endemic equilibrium analysis as well as a next-generation approach both of which help to derive the basic reproduction number. We also study the e˚cacy of wearing a facemask through this model. Another important contribution of this work is the introduction to physics informed deep learning methods (PINNs) to study the dynamics. We propose this as an alternative to traditional numerical methods for solving system of differential equations used to describe dynamics of infectious diseases. Our results show that the proposed PINNs approach is a reliable candidate for both solving such systems and for helping identify important parameters that control the disease dynamics. 

Abstract Manufacturing sustainable sodium ion batteries with high energy density and cyclability requires a uniquely tailored technology and a close attention to the economical and environmental factors. In this work, we summarized the most important design metrics in sodium ion batteries with the emphasis on cathode materials and outlined a transparent data reporting approach based on common metrics for performance evaluation of future technologies. Sodium-ion batteries are considered as one of the most promising alternatives to lithium-based battery technologies. Despite the growing research in this field, the implementation of this technology has been practically hindered due to a lack of high energy density cathode materials with a long cycle-life. In this perspective, we first provide an overview of the milestones in the development of Na-ion battery (NIB) systems over time. Next, we discuss critical metrics in extraction of key elements used in NIB cathode materials which may impact the supply chain in near future. Finally, in the quest of most promising cathode materials for the next generation of NIBs, we overlay an extensive perspective on the main findings in design and test of more than 295 reports in the past 10 years, exhibiting that layered oxides, Prussian blue analogs (PBAs) and polyanions are leading candidates for cathode materials. An in-depth comparison of energy density and capacity retention of all the currently available cathode materials is also provided. In this perspective, we also highlight the importance of large data analysis for sustainable material design based on available datasets. The insights provided in this perspective, along with a more transparent data reporting approach and an implementation of common metrics for performance evaluation of NIBs can help accelerate future cathode materials design in the NIB field. Graphical abstract 

Surveys are an important vehicle for advancing research on urban policy and governance. The introduction of online tools eased survey-based data collection, making it cheaper and easier to obtain data from key informants like local elected officials or public administrators. However, the utility of web-based survey administration may be diminishing. To investigate this dynamic and search for strategies to support survey research in urban studies, we perform a systematic review of survey research in urban policy and administration scholarship and conduct an original survey follow-up experiment. Our findings identify a clear downward trend in survey response rates that was accentuated during the COVID-19 pandemic. Results from our survey experiment show distinctly different costs per solicitation and per completed survey, depending on administration mode. These findings stimulate discussion on how scholars may continue to use surveys to generate high-quality, empirically rigorous research on urban affairs in light of recent trends. 

Polysaccharides have been shown to have immunomodulatory properties. Modulation of the immune system plays a crucial role in physiological processes as well as in the treatment and/or prevention of autoimmune and infectious diseases. Cellulose nanocrystals (CNCs) are derived from cellulose, the most abundant polysaccharide on the earth. CNCs are an emerging class of crystalline nanomaterials with exceptional physico-chemical properties for high-end applications and commercialization prospects. The aim of this study was to design, synthesize, and evaluate the cytotoxicity of a series of biocompatible, wood-based, cationic CNCs as potential immunomodulators. The anionic CNCs were rendered cationic by grafting with cationic polymers having pendant +NMe3 and +NH3 moieties. The success of the synthesis of the cationic CNCs was evidenced by Fourier transform infrared spectroscopy, dynamic light scattering, zeta potential, and elemental analysis. No modification in the nanocrystals rod-like shape was observed in transmission electron microscopy and atomic force microscopy analyses. Cytotoxicity studies using three different cell-based assays (MTT, Neutral Red, and LIVE/DEAD®) and three relevant mouse and human immune cells indicated very low cytotoxicity of the cationic CNCs in all tested experimental conditions. Overall, our results showed that cationic CNCs are suitable to be further investigated as immunomodulators and potential vaccine nanoadjuvants.