Modeling and design optimization for membrane filters

Membrane filtration is widely used in many applications, ranging from industrial processes to everyday living activities. With growing interest from both industrial and academic sectors in understanding the various types of filtration processes in use, and in improving filter performance, the past few decades have seen significant research activity in this area. Experimental studies can be very valuable, but are expensive and time-consuming, therefore theoretical studies offer potential as a cost-effective and predictive way to improve on current filter designs. In this work, mathematical models, derived from first principles and simplified using asymptotic analysis, are proposed for: (1) pleated membrane filters, where the macroscale flow problem of Darcy flow through a pleated porous medium is coupled to the microscale fouling problem of particle transport and deposition within individual pores of the membrane; (2) dead-end membrane filtration with feed containing multiple species of physicochemically-distinct particles, which interact with the membrane differently; and (3) filtration with reactive particle removal using porous media composed of chemically active granular materials. Asymptotically-simplified models are used to describe and evaluate the membrane performance numerically and filter design optimization problems are formulated and solved for a number of industrially-relevant scenarios. This study demonstrates the potential of such modeling to guide industrial membrane filter design for a range of applications involving purification and separation

Chinese University Students’ Digital Engagement in Post-COVID: A Sociomaterial Approach

Many scholars internationally conducted research on teaching and learning during the pandemic. Some scholars identified positive changes in student academic performance and student satisfaction while others found negative impacts on students’ mental health. My observation is that Covid-19 and technology (non-human actors) have shaped students’ cognition (way of thinking), behavior (digital practices), and affection (mental health). In fact, in China, digital engagement has become an important part of the lives of university students even in the post-Covid era. This qualitative study aims to investigate two Chinese universities with focus on their students’ digital engagement from a sociomaterial perspective. This research focuses on three questions: 1) What are Chinese university students’ perceptions and practices of their digital study (2022-2023)? 2) What dilemmas or conflicts did they confront when they configured a digitally mediated learning space that constitutes messy assemblages of material and social practices? 3) How did they respond to new challenges (human actors and nonhuman actors)? In order to answer these questions, the study adopts interviews and visual analysis of individual digital learning environment (including imaging and texts). It offers insights on reframing student digital engagement as constantly shifting sociomaterial assemblages from which different forms of agency emerge as effects of connections and activity. These assemblages can be closely read and interpreted from the students’ dynamic study practices, which are not only social but also at times private and unobservable. The study will contribute to a more nuanced understanding of the human-material-digital practice and enactments for the Chinese academic community and beyond

Deep Neural Network Regression and Sobol Sensitivity Analysis for Daily Solar Energy Prediction Given Weather Data

Solar energy forecasting plays an important role in both solar power plants and electricity grid. The effective forecasting is essential for efficient usage and management of the electricity grid, as well as for the solar energy trading. However, many of the existing models or algorithms are based on real physical laws, where tons of calculations, step-by-step modification, and many inputs are required. In this research, a novel deep Multi-layer Perceptron (MLP) based regression approach for predicting solar energy is proposed, in which the inputs are only ensemble weather forecasting data. The results demonstrate that our proposed deep Multi-layer Perceptron based regression approach for solar energy forecasting is efficient as well as accurate enough. A Sobol sensitivity analysis is performed over the trained model, determining the most important variables in the weather forecasting model data. The first-order and the total order Sobol sensitivity indices for quantifying feature importance, are calculated for each model input parameter. With using the process of feature removal, the result of Sobol sensitivity analysis is verified

MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents

Significant advancements have occurred in the application of Large Language Models (LLMs) for various tasks and social simulations. Despite this, their capacities to coordinate within task-oriented social contexts are under-explored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behavior and produce meaningful results. To bridge this gap, we introduce collaborative generative agents, endowing LLM-based Agents with consistent behavior patterns and task-solving abilities. We situate these agents in a simulated job fair environment as a case study to scrutinize their coordination skills. We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills. Our evaluation demonstrates that these agents show promising performance. However, we also uncover limitations that hinder their effectiveness in more complex coordination tasks. Our work provides valuable insights into the role and evolution of LLMs in task-oriented social simulations

Human Mobility Trends during the COVID-19 Pandemic in the United States

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.Comment: 11 pages, 9 figure

DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems

This paper develops a Deep Graph Operator Network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g. the power grid or traffic) with an underlying sub-graph structure. We build our DeepGraphONet by fusing the ability of (i) Graph Neural Networks (GNN) to exploit spatially correlated graph information and (ii) Deep Operator Networks~(DeepONet) to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution-independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different sub-graph. Finally, empirical results on the (i) transient stability prediction problem of power grids and (ii) traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents in highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models -- random forest, k-nearest neighbors, and a support vector machine ensemble -- and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Overall, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08)