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Monte Carlo Tree Search (MCTS) faces a severe scalability bottleneck in Multi-Agent Planning (MAP) due to the combinatorial explosion of joint action spaces. In this position paper, we argue that the key to tractable planning lies in leveraging low-dimensional representational structures to guide the tree search, rather than enumerating the raw action space. Specifically, under a linear approximation of joint-action returns, we demonstrate that the node expansion problem can be effectively cast and solved as a linear contextual bandit, providing theoretical regret guarantees. Empirical results on complex benchmarks confirm that this structure-aware search significantly outperforms state-of-the-art baselines, offering a scalable path for neuro-symbolic multi-agent planning. We further discuss promising extensions, including integrating dynamic agent grouping and coalition formation mechanisms to further reduce the effective branching factor. 

Abstract Amorphous carbons can have drastically different physical properties depending on synthetic methods. Among these, hydrogenated diamond-like carbon (HDLC) produced via plasma-enhanced chemical vapor deposition is unique in that it exhibits superlubricity with a coefficient of friction (COF) less than 0.01 in proper environmental conditions. It is known that HDLC undergoes friction-induced graphitization at the shear interface and forms a highly hydrogenated transfer film at the counter-surface sliding against it. In contrast, glassy carbon (GC) produced via pyrolysis of organic precursors rarely exhibits superlubricious behavior even though the graphitic nature probed with Raman spectroscopy is similar to that of the transfer film formed from HDLC. This study addresses this drastic difference in friction of HDLC and GC and identifies key parameters that can be tuned to achieve (nearly) superlubricious behaviors with GC. The factors influencing the superlubricity of amorphous carbon include the composition and structure of the initial carbon coating, which strongly depend on the synthetic method, and the coating failure and transfer film stability, which depend on the surface chemistry of the substrate. 

Although the relationship between plastics and their embedded small molecules has been previously hypothesized, direct and systematic evidence remains limited. Herein, we introduced an innovative approach to validate this relationship by screening specific small molecules as markers to decode plastic information. Given the mature techniques available for polymer identification, enabling subsequent validation, this study focused on screening polymer-specific small molecule markers. Specifically, plastic samples of various polymer types─including raw plastic pellets and postprocessed plastic products─were collected, extracted, and analyzed with a nontargeted method. Distinct polymer-based features were observed in raw plastic pellets: 21 in polyethylene (PE), 69 in polypropylene (PP), 119 in poly(ethylene terephthalate) (PET), and 14 in polystyrene (PS). Of these, 2, 28, 101, and 10 features were also detected in postprocessed plastic products of the same polymer, indicating these co-occurring features could serve as polymer-specific markers. Representative markers were identified, including Irganox 1010 transformation products in PP-based plastics, PET oligomers in PET-based plastics, and dibenzoylmethane in PS-based plastics. These markers were then used to identify the polymer type of two additional plastic bottles as PET, consistent with results obtained from pyrolysis-gas chromatography/mass spectrometry. This work provides a proof-of-concept for employing small molecule markers to decode plastic-related information. 

While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent drones. Specifically, this paper contributes a new data set,sEMGCommands forPilotingDrones (sCPD), and ansEMG‐basedCross‐subjectClassificationNetwork (sXCNet), for both command keyword recognition and inspector identification. sXCNet acquires the desired functions and performance through a synergetic effort of sEMG signal processing, spatial‐temporal‐frequency deep feature extraction, and multitasking‐enabled cross‐subject representation learning. The cross‐subject design permits deploying one unified model across all authorized inspectors, eliminating the need for subject‐dependent models tailored to individual users. sXCNet achieves notable classification accuracies of 98.1% on the sCPD data set and 86.1% on the public Ninapro db1 data set, demonstrating strong potential for advancing sEMG‐enabled human–drone collaboration in road infrastructure inspection. 

Abstract Measuring the diffusion coefficient of clay-based liner materials is important in estimating and predicting long-term barrier performance in waste containment facilities. Various theoretical models, including the finite cylindrical model, have been commonly used to determine the diffusion properties of clay-based liner materials in leaching tests. However, the assumption of zero-concentration boundary conditions of the traditional finite cylindrical model contradicts the measured variation of concentration in real leaching tests, likely resulting in (1) underestimated and unconservative diffusion coefficient, or (2) requirement of a relatively large liquid-to-soil ratio and frequent leachate replacement in the experiment to maintain the zero-concentration boundary condition. In this study, a theoretical model was developed to evaluate the solute diffusion process within a soil specimen under arbitrary, time-dependent concentration boundary conditions. The proposed model, incorporating the time-dependent boundary conditions, provides efficient calculations of the concentration distribution and the cumulative fraction leached of solute across the soil specimen. The example application of the proposed model to experimental data demonstrates the capability of the proposed model to determine apparent diffusion coefficients of clay-based liner materials without introducing errors associated with the assumption of a zero concentration boundary condition. The proposed model provides a comprehensive method to investigate the dynamic transport behaviors of solutes through clay-based liner materials in future studies. 

Abstract Mechanical stress can directly activate chemical reactions by reducing the reaction energy barrier. A possible mechanism of such mechanochemical activation is structural deformation of the reactant species. However, the effect of deformation on the reaction energetics is unclear, especially, for shear stress-driven reactions. Here, we investigated shear stress-driven oligomerization reactions of cyclohexene on silica using a combination of reactive molecular dynamics simulations and ball-on-flat tribometer experiments. Both simulations and experiments captured an exponential increase in reaction yield with shear stress. Elemental analysis of ball-on-flat reaction products revealed the presence of oxygen in the polymers, a trend corroborated by the simulations, highlighting the critical role of surface oxygen atoms in oligomerization reactions. Structural analysis of the reacting molecules in simulations indicated the reactants were deformed just before a reaction occurred. Quantitative evidence of shear-induced deformation was established by comparing bond lengths in cyclohexene molecules in equilibrium and prior to reactions. Nudged elastic band calculations showed that the deformation had a small effect on the transition state energy but notably increased the reactant state energy, ultimately leading to a reduction in the energy barrier. Finally, a quantitative relationship was developed between molecular deformation and energy barrier reduction by mechanical stress.