Cost-Effective, Time-Efficient Passenger Rail System for the Eastern United States

A program was developed using a genetic algorithm and automated lookup features to design an efficient passenger rail system for the eastern-half of the United States connecting large cities, metropolitan populations greater than two million, with overnight rail service. The results of the program predicted a passenger starting at the farthest point of the system boards the train at 16:02 on average and arrives at a different point of the system at 07:57 on average the following day, assuming the train travels an average speed of 70 mph. The design used actual distances by train track where possible. The system was modeled with six trains that meet at a hub and exchange passengers and continue on to their destination.The optimal solution had a total one-way minimum distance of 4334 km (2693 miles). Assuming the same ridership that currently exists on a popular train route, ticket prices would average $62 (USD) for a one-way ticket. For this system to be feasible, the government would need to own or lease one set of tracks for all the routes determined, build a hub for passengers to transfer trains near Charleston,WV, and ensure the trains are unimpeded by other trains. Installing tracks that go around cities that the trains do not stop at would be a great benefit also. With advances in communication, GPS, and train control technology, this article points out the benefits of publically available tracks to form a transportation network similar to that found in road, air, and water traffic

Estimating animal pose using deep learning a trained deep learning model outperforms morphological analysis

INTRODUCTION: Analyzing animal behavior helps researchers understand their decision-making process and helper tools are rapidly becoming an indispensable part of many interdisciplinary studies. However, researchers are often challenged to estimate animal pose because of the limitation of the tools and its vulnerability to a specific environment. Over the years, deep learning has been introduced as an alternative solution to overcome these challenges. OBJECTIVES: This study investigates how deep learning models can be applied for the accurate prediction of animal behavior, comparing with traditional morphological analysis based on image pixels. METHODS: Transparent Omnidirectional Locomotion Compensator (TOLC), a tracking device, is used to record videos with a wide range of animal behavior. Recorded videos contain two insects: a walking red imported fire ant (Solenopsis invicta) and a walking fruit fly (Drosophila melanogaster). Body parts such as the head, legs, and thorax, are estimated by using an open-source deep-learning toolbox. A deep learning model, ResNet-50, is trained to predict the body parts of the fire ant and the fruit fly respectively. 500 image frames for each insect were annotated by humans and then compared with the predictions of the deep learning model as well as the points generated from the morphological analysis. RESULTS: The experimental results show that the average distance between the deep learning-predicted centroids and the human-annotated centroids is 2.54, while the average distance between the morphological analysis-generated centroids and the human-annotated centroids is 6.41 over the 500 frames of the fire ant. For the fruit fly, the average distance of the centroids between the deep learning- predicted and the human-annotated is 2.43, while the average distance of the centroids between the morphological analysis-generated and the human-annotated is 5.06 over the 477 image frames. CONCLUSION: In this paper, we demonstrate that the deep learning model outperforms traditional morphological analysis in terms of estimating animal pose in a series of video frames

Single Cell Manipulation using Ferromagnetic Composite Microtransporters

For biomedical applications, such as single cell manipulation, it is important to fabricate microstructures that can be powered and controlled wirelessly in fluidic environments. In this letter, we describe the construction and operation of truly micron-sized, biocompatible ferromagnetic microtransporters driven by external magnetic fields. Microtransporters were fabricated using a simple, single step fabrication method and can be produced in large numbers. We demonstrate that they can be navigated to manipulate single cells with micron-size precision without disturbing the local environment