Odd elasticity in Hamiltonian formalism

A host of elastic systems consisting of active components exhibit path-dependent elastic behaviors not found in classical elasticity, which is known as odd elasticity. Odd elasticity is characterized by antisymmetric (odd) elastic modulus tensor. Here, from the perspective of geometry, we construct the Hamiltonian formalism to show the origin of the antisymmetry of the elastic moduli. Furthermore, both non-conservative stress and the associated nonlinear constitutive relation naturally arise. This work also opens the promising possibility of exploring the physics of odd elasticity in dynamical regime by Hamiltonian formalism.Comment: 9 pages, 2 figure

Development of a mechatronic sorting system for removing contaminants from wool

Automated visual inspection (AVI) systems have been extended to many fields, such as agriculture and the food, plastic and textile industries. Generally, most visual systems only inspect product defects, and then analyze and grade them due to the lack of any sorting function. This main reason rests with the difficulty of using the image data in real time. However, it is increasingly important to either sort good products from bad or grade products into separate groups usingAVI systems. This article describes the development of a mechatronic sorting system and its integration with a vision system for automatically removing contaminants from wool in real time. The integration is implemented by a personal computer, which continuously processes live images under the Windows 2000 operating system. The developed real-time sorting approach is also applicable to many other AVI systems

SageFormer: Series-Aware Graph-Enhanced Transformers for Multivariate Time Series Forecasting

Multivariate time series forecasting plays a critical role in diverse domains. While recent advancements in deep learning methods, especially Transformers, have shown promise, there remains a gap in addressing the significance of inter-series dependencies. This paper introduces SageFormer, a Series-aware Graph-enhanced Transformer model designed to effectively capture and model dependencies between series using graph structures. SageFormer tackles two key challenges: effectively representing diverse temporal patterns across series and mitigating redundant information among series. Importantly, the proposed series-aware framework seamlessly integrates with existing Transformer-based models, augmenting their ability to model inter-series dependencies. Through extensive experiments on real-world and synthetic datasets, we showcase the superior performance of SageFormer compared to previous state-of-the-art approaches