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1. CarbonCast: multi-day forecasting of grid carbon intensity

   https://doi.org/10.1145/3563357.3564079  

   Maji, Diptyaroop; Shenoy, Prashant; Sitaraman, Ramesh K. (November 2022, Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’22),)
2. Multi‐vehicle multi‐sensor occupancy grid map fusion in vehicular networks

   https://doi.org/10.1049/cmu2.12314  

   Meng, Xi; Duan, Dongliang; Feng, Tao (December 2021, IET Communications)

   Abstract Sensing is an essential part in autonomous driving and intelligent transportation systems. It enables the vehicle to better understand itself and its surrounding environment. Vehicular networks support information sharing among different vehicles and hence enable the multi‐vehicle multi‐sensor cooperative sensing, which can greatly improve the sensing performance. However, there are a couple of issues to be addressed. First, the multi‐sensor data fusion needs to deal with heterogeneous data formats. Second, the cooperative sensing process needs to deal with low data quality and perception blind spots for some vehicles. In order to solve the above problems, in this paper the occupancy grid map is adopted to facilitate the fusion of multi‐vehicle and multi‐sensor data. The dynamic target detection frame and pixel information of the camera data are mapped to the static environment of the LiDAR point cloud, and the space‐based occupancy probability distribution kernel density estimation characterization fusion data is designed , and the occupancy grid map based on the probability level and the spatial level is generated. Real‐world experiments show that the proposed fusion framework is better compatible with the data information of different sensors and expands the sensing range by involving the collaborations among multiple vehicles in vehicular networks. 

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3. Multi-Day Forecasting of Electric Grid Carbon Intensity Using Machine Learning

   https://doi.org/10.1145/3607114.3607117  

   Maji, Diptyaroop; Shenoy, Prashant; Sitaraman, Ramesh K. (June 2023, ACM SIGEnergy Energy Informatics Review)

   The ever-increasing demand for energy is resulting in considerable carbon emissions from the electricity grid. In recent years, there has been growing attention on demand-side optimizations to reduce carbon emissions from electricity usage. A vital component of these optimizations is short-term forecasting of the carbon intensity of the grid-supplied electricity. Many recent forecasting techniques focus on day-ahead forecasts, but obtaining such forecasts for longer periods, such as multiple days, while useful, has not gotten much attention. In this paper, we present CarbonCast, a machine-learning-based hierarchical approach that provides multi-day forecasts of the grid's carbon intensity. CarbonCast uses neural networks to first generate production forecasts for all the electricity-generating sources. It then uses a hybrid CNN-LSTM approach to combine these first-tier forecasts with historical carbon intensity data and weather forecasts to generate a carbon intensity forecast for up to four days. Our results show that such a hierarchical design improves the robustness of the predictions against the uncertainty associated with a longer multi-day forecasting period. We analyze which factors most influence the carbon intensity forecasts of any region with a specific mixture of electricity-generating sources and also show that accurate source production forecasts are vital in obtaining precise carbon intensity forecasts. CarbonCast's 4-day forecasts have a MAPE of 3.42--19.95% across 13 geographically distributed regions while outperforming state-of-the-art methods. Importantly, CarbonCast is the first open-sourced tool for multi-day carbon intensity forecasts where the code and data are freely available to the research community. 

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4. Map Connectivity and Empirical Hardness of Grid-based Multi-Agent Pathfinding Problem

   https://doi.org/10.1609/icaps.v34i1.31508  

   Ren, Jingyao; Ewing, Eric; Kumar, TK Satish; Koenig, Sven; Ayanian, Nora (May 2024, Proceedings of the International Conference on Automated Planning and Scheduling)

   We present an empirical study of the relationship between map connectivity and the empirical hardness of the multi-agent pathfinding (MAPF) problem. By analyzing the second smallest eigenvalue (commonly known as lambda2) of the normalized Laplacian matrix of different maps, our initial study indicates that maps with smaller lambda2 tend to create more challenging instances when agents are generated uniformly randomly. Additionally, we introduce a map generator based on Quality Diversity (QD) that is capable of producing maps with specified lambda2 ranges, offering a possible way for generating challenging MAPF instances. Despite the absence of a strict monotonic correlation with lambda2 and the empirical hardness of MAPF, this study serves as a valuable initial investigation for gaining a deeper understanding of what makes a MAPF instance hard to solve. 

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5. Multi-scale model of clogging in microfluidic devices with grid-like geometries

   https://doi.org/10.1098/rspa.2022.0119  

   Kelly, Gess; Fai, Thomas G. (May 2022, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   We propose a coarse-grained theoretical model to capture the ageing of microfluidic devices under different conditions including constant applied flow rate and constant applied pressure gradient. Microfluidic devices that sort cells by their deformability hold significant promise for medical applications. However, clogging in these microfluidic systems causes their properties to change over time and potentially limits their reliability. We compare the results of the coarse-grained model with those of stochastic simulations and with existing theoretical studies. Lastly, we apply the model to experimental data on the clogging of sickle red blood cells and discuss its wider applicability. 

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