arXiv subjects

Ye Xiao

Publications and source records attributed to Ye Xiao.

3 recordsLinked to original sources

arXiv2026

Mechanics of hierarchical twisted and coiled polymer artificial muscles: Decoupling force from kinematic limits

Thermally actuated twisted and coiled polymer (TCP) artificial muscles exhibit exceptional specific work capacities but are limited by an inherent competition between load-bearing capacity and actuation stroke. To address this limitation, we investigate a hierarchical helical structure designed to decouple force generation from kinematic limits. We propose a coupled thermo-mechanical model incorporating inter-filamentary contact mechanics and geometric nonlinearities to predict the assembly's equilibrium response. The results indicate that this hierarchical topology significantly amplifies isometric actuation stress compared to monofilament baselines, while maintaining a biological-like contraction stroke of approximately 22%. A critical topological threshold governed by the balance between cooperative load-sharing and geometric confinement is identified. Beyond an optimal bundle complexity, the geometric jamming dominates, as excessive inter-filamentary friction hinders actuation. Furthermore, we elucidate a stiffness-stroke synergy in homochiral configurations, where high helical angles amplify the thermal untwisting torque to overcome increased structural rigidity. Crucially, the volumetric energy density exhibits scale invariance regarding the hierarchical radius, implying that absolute force output can be linearly scaled through geometric upsizing without compromising efficiency. These findings provide a mechanics-based rationale for the structural programming, demonstrating that soft actuator performance limits are dictated by topological order rather than intrinsic material properties.

cond-mat.soft↗

arXiv2025

Evaluating an LLM-Powered Chatbot for Cognitive Restructuring: Insights from Mental Health Professionals

Recent advancements in large language models (LLMs) promise to expand mental health interventions by emulating therapeutic techniques, potentially easing barriers to care. Yet there is a lack of real-world empirical evidence evaluating the strengths and limitations of LLM-enabled psychotherapy interventions. In this work, we evaluate an LLM-powered chatbot, designed via prompt engineering to deliver cognitive restructuring (CR), with 19 users. Mental health professionals then examined the resulting conversation logs to uncover potential benefits and pitfalls. Our findings indicate that an LLM-based CR approach has the capability to adhere to core CR protocols, prompt Socratic questioning, and provide empathetic validation. However, issues of power imbalances, advice-giving, misunderstood cues, and excessive positivity reveal deeper challenges, including the potential to erode therapeutic rapport and ethical concerns. We also discuss design implications for leveraging LLMs in psychotherapy and underscore the importance of expert oversight to mitigate these concerns, which are critical steps toward safer, more effective AI-assisted interventions.

cs.HC↗

arXiv2023

FRA-LSTM: A Vessel Trajectory Prediction Method Based on Fusion of the Forward and Reverse Sub-Network

In order to improve the vessel's capacity and ensure maritime traffic safety, vessel intelligent trajectory prediction plays an essential role in the vessel's smart navigation and intelligent collision avoidance system. However, current researchers only focus on short-term or long-term vessel trajectory prediction, which leads to insufficient accuracy of trajectory prediction and lack of in-depth mining of comprehensive historical trajectory data. This paper proposes an Automatic Identification System (AIS) data-driven long short-term memory (LSTM) method based on the fusion of the forward sub-network and the reverse sub-network (termed as FRA-LSTM) to predict the vessel trajectory. The forward sub-network in our method combines LSTM and attention mechanism to mine features of forward historical trajectory data. Simultaneously, the reverse sub-network combines bi-directional LSTM (BiLSTM) and attention mechanism to mine features of backward historical trajectory data. Finally, the final predicted trajectory is generated by fusing output features of the forward and reverse sub-network. Based on plenty of experiments, we prove that the accuracy of our proposed method in predicting short-term and mid-term trajectories has increased by 96.8% and 86.5% on average compared with the BiLSTM and Seq2seq. Furthermore, the average accuracy of our method is 90.1% higher than that of compared the BiLSTM and Seq2seq in predicting long-term trajectories.

cs.CY↗