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  1. Free, publicly-accessible full text available June 1, 2023
  2. Free, publicly-accessible full text available December 16, 2022
  3. Rauch, W Liu (Ed.)
    Water contamination by nitrogen and phosphorus ions has a direct consequence of eutrophication to the ecosystem. The objective of this study is to investigate the production of hydrochars by acetic acid or sodium hydroxide assisted hydrothermal carbonization (HTC), various activation methods, and the potential of hydrochar as an adsorbent to remove NH4+-N and PO43--P from wastewater. The results showed that acetic acid or sodium hydroxide assisted HTC and activation with magnesium chloride or air could improve the surface properties of hydrochar. Acetic acid modification generated extensive oxygenated functional groups, while sodium hydroxide modification produced hydrochar with a high N/C ratiomore »and surface aromaticity. Treating hydrochar with magnesium chloride could impregnate nano-magnesium particles on the hydrochar, thereby improving the ability to remove N and P. Air activation of hydrochar resulted in more oxygen containing functional groups. The NH4+-N and PO43--P adsorption capacities of these hydrochars ranged from 92.6 to 122.4mg/g and 1.6 to 15.8mg/g, respectively. The adsorption capacity of hydrochars in swine wastewater is similar to the results of artificial wastewater. The results suggested that Mg-nanoparticle dispersion and oxygen-containing functional groups played a major role in adsorption than ion exchange and physisorption.« less
    Free, publicly-accessible full text available October 1, 2022
  4. Free, publicly-accessible full text available August 27, 2022
  5. The Mumbai Suburban Railways, locals, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. Due to high density during transit, the potential risk of disease transmission is high, and the government has taken a wait and see approach to resume normal operations. To reduce disease transmission, policymakers can enforce reduced crowding and mandate wearing of masks. Cohorting – forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on locals without severe restrictions. Cohorting allows us to: (𝑖) form traveler bubbles, thereby decreasing the number of distinctmore »interactions over time; (𝑖𝑖) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (𝑖𝑖𝑖) target cohorts for testing and early detection of symptomatic as well as asymptomatic cases. Studying impact of cohorts using compartmental models is challenging because of the ensuing representational complexity. Agent-based models provide a natural way to represent cohorts along with the representation of the cohort members with the larger social network. This paper describes a novel multi-scale agent-based model to study the impact of cohorting strategies on COVID-19 dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a detailed agent-based model comprising of 12.4 million agents. Individual cohorts and their inter-cohort interactions as they travel on locals are modeled using local mean field approximations. The resulting multi-scale model in conjunction with a detailed disease transmission and intervention simulator is used to assess various cohorting strategies. The results provide a quantitative trade-off between cohort size and its impact on disease dynamics and well being. The results show that cohorts can provide significant benefit in terms of reduced transmission without significantly impacting ridership and or economic & social activity.« less
  6. Engineering design decisions have non-trivial implications, and empathic approaches are one way that engineers can understand and translate the perspectives of diverse stakeholders. Prior literature demonstrates that students must develop empathic skills and beliefs that these skills are important to embody empathic approaches in meaningful ways. However, we have limited understanding of the relationship between students’ beliefs about the value of empathy in engineering decision making and how they describe their reported use of empathic approaches. We collected qualitative data through interviews with ten undergraduate engineering students in capstone design. We found that our participants espoused a belief that empathicmore »approaches are valuable in engineering design decisions. However, while students considered diverse perspectives when describing how they made design decisions, their reported behaviour during design decisions did not demonstrate translation of their empathic understanding. Based on these findings, we provide recommendations to educators and researchers.« less
  7. The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatiotemporal forecasting of epidemic dynamics is crucial. Deep learning-based time series models for forecasting have recently gained popularity and have been successfully used for epidemic forecasting. Here we focus on the design and analysis of deep learning-based models for COVID-19 forecasting. We implement multiple recurrent neural network-based deep learning models and combine them using the stacking ensemble technique. In order to incorporate the effects of multiple factors in COVID-19 spread, we consider multiple sources such as COVID-19more »confirmed and death case count data and testing data for better predictions. To overcome the sparsity of training data and to address the dynamic correlation of the disease, we propose clustering-based training for high-resolution forecasting. The methods help us to identify the similar trends of certain groups of regions due to various spatio-temporal effects. We examine the proposed method for forecasting weekly COVID-19 new confirmed cases at county-, state-, and country-level. A comprehensive comparison between different time series models in COVID-19 context is conducted and analyzed. The results show that simple deep learning models can achieve comparable or better performance when compared with more complicated models. We are currently integrating our methods as a part of our weekly forecasts that we provide state and federal authorities.« less