Abstract In this paper, firstly, on the basis of the traditional hand weaving technique in Lingnan area, modern digital technology is used to integrate it systematically, so as to construct the three-dimensional weaving technique. Secondly, the structural properties of the three-dimensional woven fabrics are investigated through the two-step and four-step weaving methods in terms of the movement trajectories of the three-dimensional woven fabrics within the cross-section. Finally, the basic properties of bamboo weaving materials were analyzed in order to facilitate the force simulation analysis of the obtained morphological model samples of parametric Lingnan region bamboo weaving craft products using the structural simulation analysis software SIM-SOLID. The results show that in the structural force study of bamboo weaving craft products, the equivalent viscous damping coefficient is calculated by taking the first hysteresis loop of each stage of the loading cycle, and its maximum deformation is 0.428 mm, which is 0.003 mm smaller than that of the model with unfixed joints, which indicates that the performance of the Lingnan region weaving technique is optimized obviously by adopting the three-dimensional weaving technique. This paper provides theoretical references for the digital integration of traditional hand-woven techniques and the enhancement of structural properties of woven materials in the Lingnan region.
<a>The growth of three-dimensional covalent organic frameworks (3D COFs) with new topologies is still considered as a great challenge due to limited availability of high-connectivity building units. Here we report the design and synthesis of novel 3D triptycene-based COFs, </a><a></a><a>termed</a> JUC-568 and JUC-569, following the deliberate symmetry-guided design principle. By combining a triangular prism (6-connected) node with a planar triangle (3-connected) or another triangular prism node, the targeted COFs adopt unreported <b>ceq </b>or non-interpenetrated <b>acs</b> topology, respectively. <a>Both materials</a> show permanent porosity and impressive performance <a>in the adsorption of CO<sub>2</sub></a> (~ 98 cm<sup>3</sup>/g at 273 K and 1 bar), CH<sub>4</sub> (~ 48 cm<sup>3</sup>/g at 273 K and 1 bar), and especially H<sub>2</sub> (up to 274 cm<sup>3</sup>/g or 2.45 wt% at 77 K and 1 bar), which is <a>highest </a>among <a>porous organic materials</a> reported to date. This research thus provides a promising strategy for diversifying 3D COFs based on complex building blocks and promotes their <a></a><a>potential applications</a> <a>in</a><a></a><a> energy storage and environment-related field</a>s.
A facile method to prepare Pt-Cu nanowires (NWs) was introduced. Structural characterization such as high-resolution transmission electron microscope (HR-TEM), selected-area electron diffraction (SAED), EDS element mapping, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and inductively coupled plasma mass spectrometry (ICP-MS) showed the formation of Pt-Cu alloy, with a width of 4.5 nm on average. The formation process of Pt-Cu NWs was studied; it was found that bromine ion, who has preferential adsorption on Pt (100) face, served as a growth-directing agent; Brij58 not only served as a protector but also played an important role in forming Pt-Cu NWs; the mechanism was proposed. Their electrocatalytic activity towards methanol oxidation was investigated; we found that the current density of Pt-Cu NWs was 295 mA·mg -1 when the ratio of Pt/Cu is 1 : 1, which is 11.5 and 2.35 times higher than that of pure Pt (26 mA·mg -1 ) and commercial Pt/C (126 mA·mg -1 ). The high electrocatalytic activity is attributed to the presence of abundant structural defects and surface active sites on the synthesized Pt-Cu NWs.
Motion Estimation (ME) is the most computationally intensive part in the whole video compression process. The ME algorithms can be divided into full search ME (FS) and fast ME (FME). The FS is not suitable for high definition (HD) frame size videos because its relevant high computation load and hard to deal with complex motions in limited search range. A lot of FME algorithms have been proposed which can significantly reduce the computation load compared to FS. Though many kinds of hardware implementations of ME have been proposed, almost all of them fail to consider about the motion vector field (MVF) coherence and rate-distortion (RD) cost which have significant impact to the coding efficiency. In this paper, we propose a hardware friendly ME algorithm and corresponding highly data reusable hardware architecture. Simulation results show that the proposed ME algorithm performs better RD performance than conventional FME algorithm. The proposed reconfigurable ME hardware is implemented in VHDL and mapped to a low cost Xilinx XC3S1500 FPGA. It works at 100MHz and is capable to process 1920 × 1080 of 30fps video format in real time and have very high data reuse ratio.
Neural networks (NNs) are widely employed as effective equalizers in intensity-modulated direct-detection (IM/DD) links due to their excellent ability in dealing with nonlinear channel impairments. However, the complexity concern impedes the real-time application of NN-based receivers. To address this issue, we propose mixed-precision quantization of recurrent NN (RNN)-based equalizers in a 100-Gb/s 15-km C-band IM/DD system, which saves about 73.3% and 22.4% memory compared with traditional floating-point-based and fixed-precision quantized RNN. A simple and effective neuron clustering approach is proposed to realize mixed-precision quantization of RNN without degrading system performance.
The original data for the paper titled "A pH-responsive amphiphilic hydrogel based on pseudo-peptides and poly(ethylene glycol) for oral drug delivery"
Background and Aims: Accurately predicting the response to methotrexate (MTX) in juvenile idiopathic arthritis (JIA) patients before administration is the key point to improve the treatment outcome. However, no simple and reliable prediction model has been identified. Here, we aimed to develop and validate predictive models for the MTX response to JIA using machine learning based on electronic medical record (EMR) before and after administering MTX. Materials and Methods: Data of 362 JIA patients with MTX mono-therapy were retrospectively collected from EMR between January 2008 and October 2018. DAS44/ESR-3 simplified standard was used to evaluate the MTX response. Extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and logistic regression (LR) algorithms were applied to develop and validate models with 5-fold cross-validation on the randomly split training and test set. Data of 13 patients additionally collected were used for external validation. Results: The XGBoost screened out the optimal 10 pre-administration features and 6 mix-variables. The XGBoost established the best model based on the 10 pre-administration variables. The performances were accuracy 91.78%, sensitivity 90.70%, specificity 93.33%, AUC 97.00%, respectively. Similarly, the XGBoost developed a better model based on the 6 mix-variables, whose performances were accuracy 94.52%, sensitivity 95.35%, specificity 93.33%, AUC 99.00%, respectively. Conclusion: Based on common EMR data, we developed two MTX response predictive models with excellent performance in JIA using machine learning. These models can predict the MTX efficacy early and accurately, which provides powerful decision support for doctors to make or adjust therapeutic scheme before or after treatment.
'/%3e%3cuse xlink:href='%23a' transform='translate(288.889 -214.211)'/%3e%3cuse xlink:href='%23a' transform='translate(108 342.749)'/%3e%3cuse xlink:href='%23a' transform='translate(469.189 342.749)'/%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)