Curvature-Specific Coupling Electrode Design for a Stretchable Three-Dimensional Inorganic Piezoelectric Nanogenerator

Structures such as 3D buckling have been widely used to impart stretchability to devices. However, these structures have limitations when applied to piezoelectric devices due to the uneven distribution of internal strain during deformation. When strains with opposite directions simultaneously affect piezoelectric materials, the electric output can decrease due to cancellation. Here, we report an electrode design tailored to the direction of strain and a circuit configuration that prevents electric output cancellation. These designs not only provide stretchability to piezoelectric nanogenerators (PENGs) but also effectively minimize electric output loss, achieving stretchable PENGs with minimal energy loss. These improvements were demonstrated using an inorganic piezoelectric material (PZT thin film) with a high piezoelectric coefficient, achieving a substantial maximum output power of 8.34 mW/cm3. Theoretical modeling of the coupling between mechanical and electrical properties demonstrates the dynamics of energy harvesting, emphasizing the electrode design. In vitro and in vivo experiments validate the device’s effectiveness in biomechanical energy harvesting. These results represent a significant advancement in stretchable PENGs, offering robust and efficient solutions for wearable electronics and biomedical devices. © 2024 The Authors. Published by American Chemical Society.TRUEsciescopu

Vialess heterogeneous skin patch for multimodal monitoring and stimulation

System-level wearable electronics require to be flexible to ensure conformal contact with the skin, but they also need to integrate rigid and bulky functional components to achieve system-level functionality. As one of integration methods, folding integration offers simplified processing and enhanced functionality through rigid-soft region separation, but so far, it has mainly been applied to modality of electrical sensing and stimulation. This paper introduces a vialess heterogeneous skin patch with multi modalities that separates the soft region and strain-robust region through folded structure. Our system includes electrical and optical modalities for hemodynamic and cardiovascular monitoring, and a force-electrically driven micropump for drug delivery. Each modality is demonstrated through on-demand drug delivery, flexible waveguide-based PPG monitoring, and ECG and body movement monitoring. Wireless data transmission and real-time measurement validate the feedback operation for multi-modalities. This engineered closed-loop platform offers the possibility for broad applications, including cardiovascular monitoring and chronic disease management. © 2025. The Author(s).TRUEsciescopu

HyperCLOVA X Technical Report

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs.Comment: 44 pages; updated authors list and fixed author name

Hydrodynamic modeling of a robotic surface vehicle using representation learning for long-term prediction

The hydrodynamic modeling of a surface vehicle in an aquatic environment is known to be a challenging problem. In particular, it is difficult to calculate the hydrodynamic forces due to the intricate coupling of the vehicle with water. In recent years, deep dynamics models have been utilized to improve the accuracy of dynamics modeling precision. However, it is well known that data-driven approaches do not extrapolate well and are vulnerable to out-of-distribution data. In this paper, we argue that the naive use of neural networks may reduce the accuracy of long-term predictions. In order to address this issue, we employ representation learning techniques that facilitate the learning of the valid data space, so that long-term predictions can be made within the valid data space. In addition, hallucinated replay is incorporated into the prediction network to further improve the accuracy of long-term predictions. We validate the proposed method on experimental data using a robotic surface vehicle and demonstrate its application to path tracking control.

Mission Planning for Underwater Survey with Autonomous Marine Vehicles

With the advancement of intelligent vehicles and unmanned systems, there is a growing interest in underwater surveys using autonomous marine vehicles (AMVs). This study presents an automated planning strategy for a long-term survey mission using a fleet of AMVs consisting of autonomous surface vehicles and autonomous underwater vehicles. Due to the complex nature of the mission, the actions of the vehicle must be of high-level abstraction, which means that the actions indicate not only motion of the vehicle but also symbols and semantics, such as those corresponding to deploy, charge, and survey. For automated planning, the planning domain definition language (PDDL) was employed to construct a mission planner for realizing a powerful and flexible planning system. Despite being able to handle abstract actions, such high-level planners have difficulty in efficiently optimizing numerical objectives such as obtaining the shortest route given multiple destinations. To alleviate this issue, a widely known technique in operations research was additionally employed, which limited the solution space so that the high-level planner could devise efficient plans. For a comprehensive evaluation of the proposed method, various PDDL-based planners with different parameter settings were implemented, and their performances were compared through simulation. The simulation result shows that the proposed method outperformed the baseline solutions by yielding plans that completed the missions more quickly, thereby demonstrating the efficacy of the proposed methodology.</jats:p