EELS: Autonomous snake-like robot with task and motion planning capabilities for ice world exploration.

Ice worlds are at the forefront of astrobiological interest because of the evidence of subsurface oceans. Enceladus in particular is unique among the icy moons because there are known vent systems that are likely connected to a subsurface ocean, through which the ocean water is ejected to space. An existing study has shown that sending small robots into the vents and directly sampling the ocean water is likely possible.

Isomeric stability of indaziflam and major degradation products in the environment.

Understanding the isomeric behavior of active ingredients in the soil and water environment is the first and a major part of deriving an exposure assessment. Whilst a variety of approaches have been taken previously, with the new regulatory framework for the risk assessment of isomeric plant protection compounds recently published by EFSA, (European Food Safety Authority) there will in future be a more consistent approach which has been taken here. For indaziflam (IAF), the alkylazine, cross spectrum residual herbicide which has a cellulose biosynthesis inhibition mode of action, there was no published data on the isomeric degradation behavior in soil and water.

Selected Applications of the Theory of Connections: A Technique for Analytical Constraint Embedding.

In this paper, we consider several new applications of the recently introduced mathematical framework of the Theory of Connections (ToC). This framework transforms constrained problems into unconstrained problems by introducing constraint-free variables. Using this transformation, various ordinary differential equations (ODEs), partial differential equations (PDEs) and variational problems can be formulated where the constraints are always satisfied.

Deep Theory of Functional Connections: A New Method for Estimating the Solutions of Partial Differential Equations.

This article presents a new methodology called Deep Theory of Functional Connections (TFC) that estimates the solutions of partial differential equations (PDEs) by combining neural networks with the TFC. The TFC is used to transform PDEs into unconstrained optimization problems by analytically embedding the PDE's constraints into a "constrained expression" containing a free function. In this research, the free function is chosen to be a neural network, which is used to solve the now unconstrained optimization problem.

Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines Using the Theory of Functional Connections.

Differential equations (DEs) are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. While there are many other techniques for finding approximate solutions to these equations, this paper looks to compare the application of the (TFC) with one based on least-squares support vector machines (LS-SVM). The TFC method uses a constrained expression, an expression that always satisfies the DE constraints, which transforms the process of solving a DE into solving an unconstrained optimization problem that is ultimately solved via least-squares (LS).