Investigation on thermal conductivity and mechanical properties of Si3N4 ceramics via one-step sintering

High thermal conductivity Si₃N₄ ceramics were fabricated using a one-step method consisting of reaction-bonded Si₃N₄ (RBSN) and post-sintering. The influence of Si content on nitridation rate, β/(α+β) phase rate, thermal conductivity and mechanical properties was investigated in this work. It is of special interest to note that the thermal conductivity showed a tendency to increase first and then decrease with increasing Si content. This experimental result shows that the optimal thermal conductivity and fracture toughness were obtained to be 66 W (m K)^-1 and 12.0 MPa m^1/2, respectively. As a comparison, the nitridation rate and β/(α+β) phase rate in a static pressure nitriding system, i.e., 97% (MS10), 97% (MS15), 97% (MS20) and 8.3% (MS10), 8.3% (MS15), 8.9% (MS20), respectively, have obvious advantages over those in a flowing nitriding system, i.e., 91% (MS10), 91% (MS15), 93% (MS20) and 3.1% (MS10), 3.3% (MS15), 3.3% (MS20), respectively. Moreover, high lattice integrity of the β-Si₃N₄ phase was observed, which can effectively confine O atoms into the β-Si₃N₄ lattice using MgO as a sintering additive. This result indicates that one-step sintering can provide a new route to prepare Si₃N₄ ceramics with a good combination of thermal conductivity and mechanical properties.     

微波辅助孵育法富集麦胚多肽的工艺优化

目的 麦胚孵育过程中蛋白酶将蛋白质水解成肽和氨基酸，多肽具有明确的生理活性和营养调节作用。本研究以麦胚为试验原料，采用微波辅助预处理的方法，研究孵育的温度、时间、pH及料液比4种因素对孵育后的蛋白酶活力及多肽含量的影响。 方法 通过单因素和响应曲面试验进行工艺条件优化。 结果 与未进行微波辅助预处理的样品相比，微波辅助处理（600 W, 10 s）能显著提高孵育后样品中的蛋白酶酶活力（约9.4倍）和肽含量（约3.1倍）。经过微波辅助处理后，孵育温度为51.5℃、pH为4.0、时间为6.33 h、料液比为1:7时，蛋白酶活力达最高为3826.24 U/g；孵育温度为45.0 ℃、pH为4.8、时间为8 h、料液比为1:7时，肽含量达最高为262.63 mg/g。 结论 微波辅助处理能有效的激活麦胚孵育液中的内源性蛋白酶活性，促进蛋白水解反应，显著提高孵育后的肽含量。该研究结果为麦胚多肽的制备新工艺开发提供了研究基础。     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.     

Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

Phase composition,microstructure, and properties of Al4O4C–(Al2OC)1-x(AlN)x –Zr2Al3C4–Al2O3 refractories prepared at high temperatures in nitrogen

The novel Al₄O₄C–(Al₂OC)_1-x(AlN)_x–Zr₂Al₃C₄–Al₂O₃ refractories with ultra-low carbon content have been successfully prepared by constructing the core-shell structure of aluminum at 1300–1700°C in nitrogen. The phase composition, microstructure, and properties of the novel refractories are deeply investigated. The cracking temperature on the core-shell structure of aluminum is further explored and the reaction mechanism of Zr₂Al₃C₄ has also added explanation. The results show that the novel refractories have excellent physical properties and cannot be corroded by molten iron. There exist two different Al₂OC solid solutions in the novel refractories, Al₂OC-rich (Al₂OC)_1-x(AlN)_x and AlN-rich (Al₂OC)_1-x(AlN)_x. The temperatures affect their relative content. When temperatures are less than 1600°C, the relative content of Al₂OC-rich (Al₂OC)_1-x(AlN)_x is more than that of AlN-rich (Al₂OC)_1-x(AlN)_x. When temperatures are above 1700°C, the relative content of AlN-rich (Al₂OC)_1-x(AlN)_x is more than that of Al₂OC-rich (Al₂OC)_1-x(AlN)_x. The core-shell structure of aluminum fully ruptures at about 1200°C. Zr₂Al₃C₄ begins to form at about 1000°C and generates in large at 1200°C.     

A general framework to study the price‐color relationship in paintings with an application to Mark Rothko rectangular series

We propose a general framework to study the relationship between the price of a painting and its color‐related attributes. To this end we focus on four key aspects: dominant colors, features of the color palette, color harmony, and color emotions. We demonstrate the usefulness of this approach with an example based on Mark Rothko's post‐1950 paintings (the “rectangular” series), and auction data from the 1994 to 2018 period. We identify two distinct price‐color regimes in Rothko's market: (a) [1994‐2005], a period in which prices are explained mainly by the growing popularity of the artist regardless of the color attributes of the paintings sold; and (b) [2006‐2018], a period in which color‐related attributes explain most of the prices. Furthermore, we find that in this second period, the dominant colors and the diversity of the color palette, are by far the most relevant attributes that influence the price; color harmony and color emotions hold almost no explanatory power during this period. Finally, we propose a new metric based on the Herfindahl Index to describe color diversity; this metric seems to be promising at characterizing the effect of the color palette on the price of a painting.