Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g., number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.

In forward osmosis (FO), an osmotic pressure gradient generated across a semi-permeable membrane is used to generate water transport from a dilute feed solution into a concentrated draw solution. This principle has shown great promise in the areas of water purification, wastewater treatment, seawater desalination and power generation. To ease optimization and increase understanding of membrane systems, it is desirable to have a comprehensive model that allows for easy investigation of all the major parameters in the separation process. Here we present experimental validation of a computational fluid dynamics (CFD) model developed to simulate FO experiments with asymmetric membranes. Simulations are compared with experimental results obtained from using two distinctly different complex three-dimensional membrane chambers. It is found that the CFD model accurately describes the solute separation process and water permeation through membranes under various flow conditions. It is furthermore demonstrated how the CFD model can be used to optimize membrane geometry in such as way as to promote the mass transfer.

Understanding the characteristics of public attention and perception is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and perception. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.

Forward osmosis (FO) membranes have gained interest in several disciplines for the rejection and concentration of various molecules. One application area for FO membranes that is becoming increasingly popular is the use of the membranes to concentrate or dilute high value compound solutions such as pharmaceuticals. It is crucial in such settings to control the transport over the membrane to avoid losses of valuable compounds, but little is known about the rejection and transport mechanisms of larger biomolecules with often flexible conformations. In this study, transport of two chemically similar peptides with molecular weight (M_w) of 375 and 692 Da across a thin film composite Aquaporin Inside™ Membrane (AIM) FO membrane was investigated. Despite the relative large size, both peptides were able to permeate the dense active layer of the AIM membrane and the transport mechanism was determined to be diffusion-based. Interestingly, the membrane permeability increased 3.65 times for the 692 Da peptide (1.39 × 10⁻¹² m²·s⁻¹) compared to the 375 Da peptide (0.38 × 10⁻¹² m²·s⁻¹). This increase thus occurs for an 85% increase in Mw but only for a 34% increase in peptide radius of gyration (R_g) as determined from molecular dynamics (MD) simulations. This suggests that R_g is a strong influencing factor for membrane permeability. Thus, an increased R_g reflects the larger peptide chains ability to sample a larger conformational space when interacting with the nanostructured active layer increasing the likelihood for permeation.

Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.