Investigation on the Compressive Behavior of Hybrid Polyurethane(PU)-Foam-Filled Hyperbolic Chiral Lattice Metamaterial.

In this study, a novel hybrid metamaterial has been developed via fulfilling hyperbolic chiral lattice with polyurethane (PU) foam. Initially, both the hyperbolic and typical body-centered cubic (BCC) lattices are fabricated by 3D printing technique. These lattices are infiltrated in a thermoplastic polyurethane (TPU) solution dissolved in 1,4-Dioxane, and then freeze casting technique is applied to achieve the PU-foam-filling. Intermediate (IM) layers possessing irregular pores, are formed neighboring to the lattice-foam interface. While, the foam far from the lattice exhibits a multi-layered structure. The mechanical behavior of the hybrid lattice metamaterials has been investigated by monotonic and cyclic compressive tests. The experimental monotonic tests indicate that, the filling foam is able to soften the BCC lattice but to stiffen the hyperbolic one, further to raise the stress plateau and to accelerate the densification for both lattices. The foam hybridization also benefits the hyperbolic lattice to prohibit the property degradation under the cyclic compression. Furthermore, the failure modes of the hybrid hyperbolic lattice are identified as the interface splitting and foam collapse via microscopic analysis. Finally, a parametric study has been performed to reveal the effects of different parameters on the compressive properties of the hybrid hyperbolic lattice metamaterial.

Effect of L-calcium lactate, zinc lactate, and ferric sodium EDTA on the physicochemical and functional properties of liquid whole egg.

The study aimed to evaluate the physicochemical and functional properties of liquid whole egg (LWE) with L-calcium lactate (L-Ca), zinc lactate (L-Zn), and sodium ferric EDTA (NaFeEDTA), and to compare with NaCl addition to determine the application potential of these mineral supplements. Results showed that salts addition significantly influenced the foaming, emulsifying, and gelling properties of LWE, which was possible through affecting the pH, particle size, surface hydrophobicity, apparent viscosity, and solubility. The addition of all the four salts reduced pH but increased the d4,3 diameter of LWE. Additionally, the addition of 200 mM L-Ca and 6 mM L-Zn significantly improved the emulsifying capacity by 41.73% and 13.6%, the foaming capacity by 26.57% and 10%, and the protein solubility by 13.89% and 12.70%, respectively. In the meantime, mineral supplements tend to produce lower hardness gel, especially with 25 mM L-Ca and 8 mM L-Zn, and the hardness was decreased from 2401.13 to 1138.29 and 1175.59 g, respectively. A relative decrease in hardness was desirable in gelled egg products. Moreover, the addition of NaCl and L-Ca showed a higher redness and yellowness, but the addition of NaFeEDTA showed an undesirable color in dark brown, which may be not accepted by the public. In summary, L-Ca and L-Zn had great potential for application in LWE, which was more appropriate than adding NaCl. This study provides a basis for improving the functional properties of LWE products in the future. PRACTICAL APPLICATION: The addition of L-Ca and L-Zn to liquid whole egg (LWE) could improve the foaming and emulsifying capacity of LWE as well as produce a lower hardness gel, which may be more conducive to the production of cake, custards, and meat products. Meantime, it is more in line with people's pursuit of a healthy diet.

A deep learning fusion model for brain disorder classification: Application to distinguishing schizophrenia and autism spectrum disorder.

Deep learning has shown a great promise in classifying brain disorders due to its powerful ability in learning optimal features by nonlinear transformation. However, given the high-dimension property of neuroimaging data, how to jointly exploit complementary information from multimodal neuroimaging data in deep learning is difficult. In this paper, we propose a novel multilevel convolutional neural network (CNN) fusion method that can effectively combine different types of neuroimage-derived features. Importantly, we incorporate a sequential feature selection into the CNN model to increase the feature interpretability. To evaluate our method, we classified two symptom-related brain disorders using large-sample multi-site data from 335 schizophrenia (SZ) patients and 380 autism spectrum disorder (ASD) patients within a cross-validation procedure. Brain functional networks, functional network connectivity, and brain structural morphology were employed to provide possible features. As expected, our fusion method outperformed the CNN model using only single type of features, as our method yielded higher classification accuracy (with mean accuracy >85%) and was more reliable across multiple runs in differentiating the two groups. We found that the default mode, cognitive control, and subcortical regions contributed more in their distinction. Taken together, our method provides an effective means to fuse multimodal features for the diagnosis of different psychiatric and neurological disorders.