The Interoceanic corridor of Mexico stands as a pivotal infrastructure project poised to significantly enhance Mexico's national and regional economy. Anticipated to start the operations in 2025 under the auspice of the national government, this corridor represents a strategic counterpart to the Panama Canal, which faces capacity constraints due to climate change and environmental impacts. Positioned as a promising alternative for transporting goods from Asia to North America, this corridor will offer a new transport route, yet its real operational capacity and spatial impacts remains uncertain. In this paper, the authors undertake a preliminary, informed analysis leveraging publicly available data and other specific information about infrastructure capacities and economic environment to forecast the potential throughput of this corridor upon full operationalization and in the future. Applying simulation techniques, the authors simulate the future operations of the corridor according to different scenarios to offer insights into its potential capacity and impacts. Furthermore, the paper delves into the opportunities and challenges that are inherent in this project and gives a comprehensive analysis of its potential impact and implications.
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In this paper, we focus on how the qualitative vocabulary of Dynalearn, which is used for describing dynamic systems, corresponds to the mathematical equations used in quantitative modeling. Then, we demonstrate the translation of a qualitative model into a quantitative model, using the example of an object falling with air resistance.
The performance of human-robot collaboration tasks can be improved by incorporating predictions of the human collaborator's movement intentions. These predictions allow a collaborative robot to both provide appropriate assistance and plan its own motion so it does not interfere with the human. In the specific case of human reach intent prediction, prior work has divided the task into two pieces: recognition of human activities and prediction of reach intent. In this work, we propose a joint model for simultaneous recognition of human activities and prediction of reach intent based on skeletal pose. Since future reach intent is tightly linked to the action a person is performing at present, we hypothesize that this joint model will produce better performance on the recognition and prediction tasks than past approaches. In addition, our approach incorporates a simple human kinematic model which allows us to generate features that compactly capture the reachability of objects in the environment and the motion cost to reach those objects, which we anticipate will improve performance. Experiments using the CAD-120 benchmark dataset show that both the joint modeling approach and the human kinematic features give improved F1 scores versus the previous state of the art.
