Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey.

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application.

Machine intelligence accelerated design of conductive MXene aerogels with programmable properties.

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Effect of Far-Red Light on Biomass Accumulation, Plant Morphology, and Phytonutrient Composition of Ruby Streaks Mustard at Microgreen, Baby Leaf, and Flowering Stages.

Far-red (FR) light influences plant development significantly through shade avoidance response and photosynthetic modulation, but there is limited knowledge on how FR treatments influence the growth and nutrition of vegetables at different maturity stages in controlled environment agriculture (CEA). Here, we comprehensively investigated the impacts of FR on the yield, morphology, and phytonutrients of ruby streaks mustard (RS) at microgreen, baby leaf, and flowering stages. Treatments including white control, white with supplementary FR, white followed by singularly applied FR, and enhanced white (WE) matching the extended daily light integral (eDLI) of FR were designed for separating the effects of light intensity and quality. Results showed that singular and supplemental FR affected plant development and nutrition similarly throughout the growth cycle, with light intensity and quality playing varying roles at different stages. Specifically, FR did not affect the fresh and dry weight of microgreens but increased those values for baby leaves, although not as effectively as WE. Meanwhile, FR caused significant morphological change and accelerated the development of leaves, flowers, and seedpods more dramatically than WE. With regard to phytonutrients, light treatments affected the metabolomic profiles for baby leaves more dramatically than microgreens and flowers. FR decreased the glucosinolate and anthocyanin contents in microgreens and baby leaves, while WE increased the contents of those compounds in baby leaves. This study illustrates the complex impacts of FR on RS and provides valuable information for selecting optimal lighting conditions in CEA.