Touch and deformation perception of soft manipulators with capacitive e-skins and deep learning

Tactile sensing in soft robots remains particularly challenging because of the coupling between contact and deformation information which the sensor is subject to during actuation and interaction with the environment. This often results in severe interference and makes disentangling tactile sensing and geometric deformation difficult. To address this problem, this paper proposes a soft capacitive e-skin with a sparse electrode distribution and deep learning for information decoupling. Our approach successfully separates tactile sensing from geometric deformation, enabling touch recognition on a soft pneumatic actuator subject to both internal (actuation) and external (manual handling) forces. Using a multi-layer perceptron, the proposed e-skin achieves 99.88\% accuracy in touch recognition across a range of deformations. When complemented with prior knowledge, a transformer-based architecture effectively tracks the deformation of the soft actuator. The average distance error in positional reconstruction of the manipulator is as low as 2.905$\pm$2.207 mm, even under operative conditions with different inflation states and physical contacts which lead to additional signal variations and consequently interfere with deformation tracking. These findings represent a tangible way forward in the development of e-skins that can endow soft robots with proprioception and exteroception

Structural, Elastic, Electronic and Optical Properties of a New Layered-Ternary Ta4SiC3 Compound

We propose a new layered-ternary Ta4SiC3 with two different stacking sequences ({\alpha}- and {\beta}-phases) of the metal atoms along c axis and study their structural stability. The mechanical, electronic and optical properties are then calculated and compared with those of other compounds M4AX3 (M = V, Nb, Ta; A = Al, Si and X = C). The predicted compound in the {\alpha}-phase is found to possess higher hardness than any of these compounds. The independent elastic constants of the two phases are also evaluated and the results discussed. The electronic band structures for {\alpha}- and {\beta}-Ta4SiC3 show metallic conductivity. Ta 5d electrons are mainly contributing to the total density of states (DOS). We see that the hybridization peak of Ta 5d and C 2p lies lower in energy and the Ta 5d-C 2p bond is stronger than Ta 5d-Si 3p bond. Further an analysis of the different optical properties shows the compound to possess improved behavior compared to similar types of compounds.Comment: 9 pages, 5 figures; PACS: 60.20.Dc; 62.20.-x; 71.15.Mb; 78.20.Ci; Keywords: Ta4SiC3, First-principles; Elastic properties; Electronic properties; Optical propertie

The genome of broomcorn millet

Broomcorn millet (Panicum miliaceum L.) is the most water-efficient cereal and one of the earliest domesticated plants. Here we report its high-quality, chromosome-scale genome assembly using a combination of short-read sequencing, single-molecule real-time sequencing, Hi-C, and a high-density genetic map. Phylogenetic analyses reveal two sets of homologous chromosomes that may have merged ~5.6 million years ago, both of which exhibit strong synteny with other grass species. Broomcorn millet contains 55,930 proteincoding genes and 339 microRNA genes. We find Paniceae-specific expansion in several subfamilies of the BTB (broad complex/tramtrack/bric-a-brac) subunit of ubiquitin E3 ligases, suggesting enhanced regulation of protein dynamics may have contributed to the evolution of broomcorn millet. In addition, we identify the coexistence of all three C4 subtypes of carbon fixation candidate genes. The genome sequence is a valuable resource for breeders and will provide the foundation for studying the exceptional stress tolerance as well as C4 biology

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions