The study examines how scientists can better acknowledge, credit and utilize environmental data collected by Indigenous ...

Accurate forest volume estimation is crucial for sustainable forest management, but the most commonly used methods often rely on models that may not always be applicable across different tree species ...

In this blog post, we shall explore the methodology of 3D reconstruction on the multi-view object scenes, used for volumetric ...

The high point of 2024 was the commencement of the first widespread and prolonged exploration campaign in modern times on ...

Abstract: We present a sampling-based model predictive control method that uses a generic physics simulator as the dynamical model. In particular, we propose a Model Predictive Path Integral ...

Discussion: We provide recommendations for optimal eDNA metabarcoding sampling design based on our observations. The study underscores the importance of understanding biological and physical factors ...

Python implementation of MPPI (Model Predictive Path-Integral) controller to understand the basic idea. Mandatory dependencies are numpy and matplotlib only.

Over the years, enhanced sampling methods and ... Deep learning-based surrogate models have recently emerged as an alternative. Prior approaches focused on learning either the transition density of MD ...

3) addresses the challenges with a full-range soft-switching operation and magnetic integration, based on a dual-decoupling idea, for power-density optimization and efficiency. Figure 4 shows the ...

This R package (Hahsler, Piekenbrock, and Doran 2019) provides a fast C++ (re)implementation of several density-based algorithms with a focus on the DBSCAN family for clustering spatial data. The ...