Our goal is to create a user friendly dashboard with data related to the current COVID-19 pandemic. This includes an interactive map, charts, and numbers presented to the user in a simplified manner. The data spans every county in the United States. Beyond just being a COVID-19 Tracker, our tool will be available as an API that can be used with any other state and county specific data.

Identifying new and cutting-edge investment strategies is a crucial step in establishing any large business within its relative industry. Fort Capital, whose primary investment focus is on industrial-grade buildings, is taking an innovative and insightful approach to geographic understanding. Fort Capital aims to identify trade routes used by major market players, such as Amazon and Walmart, to find the areas where industrial warehouses and large-scale distribution centers are in highest demand. To locate such trade routes, identifying the main travelers on these routes is essential, and Truck Detective aims to do exactly that. Using machine learning and artificial intelligence models such as a deep neural network, Truck Detective enables Fort Capital to detect, with high accuracy, the location of big rig trucks, and can additionally help identify where they came from or where they are heading. This, in turn, illuminates geographically important areas with promising investment opportunities for Fort Capital.

Vehicle Re-identification, which aims to retrieve matching vehicles across different cameras, is a challenging problem in Intelligent Transport System due to different factors such as illumination conditions, occlusions, and video resolution. Numerous studies are proposing the use of Deep Neural Networks, a recent advance in Artificial Intelligence, thanks to their exceptional feature embedding extraction. However, Deep Neural Networks perform poorly on cross-domain settings. Furthermore, vehicle re-identification training data is relatively limited because public videos are only accessible to the authority only. Our study tackles the above challenges by utilizing several state-of-the-art techniques on domain learning to expand the model's generalization capability. Our research shows that we can outperform other state-of-the-art models by large margins on popular vehicle re-identification benchmarks.

The purpose of this project is to create a closed loop system that will enable a continuous drying cycle of mined limestone through a rotating cylindrical dryer. Our client, Lhoist North America, has tasked us with designing this system, and our biggest issue has been putting together the system on a limited budget. We have determine that the most efficient method of designing the system is to used scrapped equipment that Lhoist has available and reconfiguring it for our design, rather than buying a new system. Another challenge we have faced is the method of transporting the mined limestone due to its sand-like qualities. We believe that the most effective method of designing the system will be by altering scrapped material from Lhoist’s scrapyard to complete a closed loop system of the limestone for the rotary dryer.

Dryer Testing
The parameters which were used to test the dryer was that the incline was set at 5 degrees, and the dryer rpm was at 5 and 10. Further, we used four rows of 90-degree lifters followed by four rows of radial lifters. We tested using a small grain limestone sample to be a middle of the road test. Originally, we started testing with one scoop (one quart) inside the cylinder, started the motor and turned to the 10 rpm, and added one quart every ten seconds until 4 total scoops were through the cylinder. The time this took was consistently right around the 90 second mark. However, when the volume was turned up, the findings were more interesting. When we started with a full five gallons inside of the cylinder, turned the motor up until 10 rpm, and added another five gallons at the 30 second mark, the time that it took for all of the material to exit the cylinder was right around the 90 second mark, the same time as when only a gallon of material went through the dryer.