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This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type collect high-fidelity measurements at a slow time scale and robots of another type collect low-fidelity measurements at a fast time scale, for the purpose of fusing measurements together. The multiscale measurements are fused to create a model of a complex, nonlinear spatiotemporal process. The model helps determine optimal sensing locations and predict the evolution of the process. Key contributions are: i) consolidation of multiple types of data into one cohesive model, ii) fast determination of optimal sensing locations for mobile robots, and iii) adaptation of models online for various monitoring scenarios. We illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. We test our approach using physical marine robots adaptively sampling a process in a water tank.

Although sex education in Taiwan has been conducted for many years and there has been increased attention to gender equality and the prevention of gender-based violence, traditional gender norms still deeply influence society. As a result, students face challenges in navigating sexual communication and making informed decisions about their sexuality. This research is carried out to empirically analyze the issue of authoritative oppression and gender equality through a textual analysis of the renowned book “Fang Si-Qi’s First Love Paradise”. The findings of this analysis shed light on various contemporary sex education issues in Taiwan, particularly those related to gender equality. These issues are deconstructed into key points for reforming sex and gender education. We propose five key points for discussion and suggest the adoption of a sex-positive framework as a guiding principle for curriculum design

This paper studies how global dynamics can inform path planning and decision-making for robots. Specifically, we investigate how coherent sets, an environmental feature found in flow-like environments, informs robot awareness within these scenarios. We compute coherent sets online with techniques from machine learning, and design a framework for robot behavior that uses coherent sets. We demonstrate the effectiveness of online methods over offline methods. Notably, we apply these online methods for robot monitoring of urban environments and robot navigation through water. Environmental features such as coherent sets provide rich context to robots for smarter, more efficient behavior.