Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models.

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot's proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.

Geocolloidal interactions and relaxation dynamics under nanoconfinement: Effects of salinity and particle concentration.

HYPOTHESIS: Energy-related contaminants are frequently associated with geocolloids that translocate in underground fissures with dimensions comparable with geocolloids. To assess the transport and impact of energy-related contaminants in geological systems, fundamental understandings of interfacial behaviors of nanoparticles under confinement is imperative. We hypothesize that the dynamic properties of geocolloids, as well as their dependence on aqueous medium conditions would deviate from bulk behaviors under nanoconfinement. EXPERIMENTS: Force profiles and rheological properties of 50 nm silica nanoparticles in aqueous media confined between mica surfaces as a function of surface separation, particle concentrations, and salinity were measured utilizing the surface forces apparatus. FINDINGS: Force profiles revealed the critical surface separation for nonlinear rheological behaviors coincides with the onset of exponential repulsion between mica surfaces. When salts were absent, the normal forces and viscosity values of colloidal suspensions resembled pure water. In contrast, with salts, the force profiles and corresponding critical length scales were found to be highly sensitive to the particle concentration and the degree of confinement. A Newtonian to shear-thinning transition was captured with increasing degrees of confinement. Our results show that the interplay among confinement, particle, and ionic concentrations can alter the interparticle forces and rheological responses of true nanosized-colloidal suspensions and thus their transport behaviors under nanoconfinement for the first time.

The effect of nanoconfinement on the glass transition temperature of ionic liquids.

This work is concerned with investigating the glass transition behavior of ionic liquids as a function of nanoconfinement. The glass transition temperature was found to increase with a decrease in confinement length, below a critical confinement of 40-50 nm and 80-90 nm for 1-butyl-3-methylimidazolium tetrafluoro-borate and 1-methyl-3-octylimidazolium tetrafluoro-borate between alumina surfaces, respectively.

Preparation of Palladium/Silver-Coated Polyimide Nanotubes: Flexible, Electrically Conductive Fibers.

A simple and practical method for coating palladium/silver nanoparticles on polyimide (PI) nanotubes is developed. The key steps involved in the process are silver ion exchange/reduction and displacement reactions between silver and palladium ions. With the addition of silver, the conductivity of the PI nanotubes is greatly enhanced. Further, the polyimide nanotubes with a dense, homogeneous coating of palladium nanoparticles remain flexible after heat treatment and show the possibility for use as highly efficient catalysts. The approach developed here is applicable for coating various noble metals on a wide range of polymer matrices, and can be used for obtaining polyimide nanotubes with metal loaded on both the inner and outer surface.

Optimum Leaf Removal Increases Nitrogen Accumulation in Kernels of Maize Grown at High Density.

Increasing plant density is one of the main approaches of achieving higher yields for modern maize crop. However, there exists leaf redundancy for high-density maize, and leaves of the upper canopy shade more competent leaves at the middle strata. In a two-year field experiments, Jinhai5, a semi-compact corn cultivar, was grown at a density of 105,000 plants ha-1 grown until 3 days after silking (3DAS), when plants were subjected to removal of the uppermost two leaves (S2), four leaves (S4) or six leaves (S6), with no leaf removal as control (S0). We evaluated the effects of leaf removal on N remobilization, photosynthetic capacity of the remaining leaves for N uptake, and N accumulation in kernels. Our present results concluded that, under high plant density, excising the uppermost two leaves promoted N remobilization from vegetative organs to kernels and enhanced photosynthetic capacity for N uptake, leading to an increased N accumulation in kernels (19.6% higher than control). However, four or six uppermost leaves removal reduced N remobilization from stem and photosynthesis for poor N uptake, resulting in 37.5 and 50.2% significantly reduced N accumulation in kernels, respectively.