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Muhammad Yazid Samatra Nor Qhairul Izzreen Mohd Noor Umi Hartina Mohamad Razali Jamilah Bakar Sharifudin Md. Shaarani 《Comprehensive Reviews in Food Science and Food Safety》2022,21(4):3153-3176
Gelatin is one of the most important multifunctional biopolymers and is widely used as an essential ingredient in food, pharmaceutical, and cosmetics. Porcine gelatin is regarded as the leading source of gelatin globally then followed by bovine gelatin. Porcine sources are favored over other sources since they are less expensive. However, porcine gelatin is religiously prohibited to be consumed by Muslims and the Jewish community. It is predicted that the global demand for gelatin will increase significantly in the future. Therefore, a sustainable source of gelatin with efficient production and free of disease transmission must be developed. The highest quality of Bovidae-based gelatin (BG) was acquired through alkaline pretreatment, which displayed excellent physicochemical and rheological properties. The utilization of mammalian- and plant-based enzyme significantly increased the gelatin yield. The emulsifying and foaming properties of BG also showed good stability when incorporated into food and pharmaceutical products. Manipulation of extraction conditions has enabled the development of custom-made gelatin with desired properties. This review highlighted the various modifications of extraction and processing methods to improve the physicochemical and functional properties of Bovidae-based gelatin. An in-depth analysis of the crucial stage of collagen breakdown is also discussed, which involved acid, alkaline, and enzyme pretreatment, respectively. In addition, the unique characteristics and primary qualities of BG including protein content, amphoteric property, gel strength, emulsifying and viscosity properties, and foaming ability were presented. Finally, the applications and prospects of BG as the preferred gelatin source globally were outlined. 相似文献
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《International Journal of Hydrogen Energy》2022,47(79):33765-33780
This work investigates selective Ni locations over Ni/CeZrOx–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 CeZrOx–Al2O3, resulting in the stepwise locations of Ni over CeZrOx–Al2O3. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrOx for 10-wt.% Ni generates more oxygen vacancies in the CeZrOx lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/gcat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al2O3 after the saturation of Ni–CeZrOx 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 mgc/gcat·h (20Ni/CZ-Al). 相似文献
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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. 相似文献
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To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity. 相似文献
6.
Shilin Du Yichen Dong Feiqiang Guo Beile Tian Songbo Mao Lin Qian Chengyun Xin 《International Journal of Hydrogen Energy》2021,46(27):14138-14147
The potential of using high metals containing coal gangue and lignite to prepare high-activity coal char-based catalysts is investigated for effective biomass tar decomposition. Loose structure and rough surface are formed for these char-based catalysts with heterogeneous distribution of a large number of inorganic particles. In the biomass tar decomposition, the performance of the coal char-based catalysts is significantly influenced by the content of the metals in the raw materials and coal gangue char (GC) with the ash content as high as 50.80% exhibits the highest activity in this work. A high biomass tar conversion efficiency of 93.5% is achieved at 800 °C along with a significant increase in the fuel gas product. During the five-time consecutive tests, the catalytic performance of GC increases a little at the second or third times reuse and remains relatively stable, showing the remarkable stability of the catalyst in biomass tar decomposition applications. 相似文献
7.
目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题,提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式,保证数据的安全性。然后利用面元素重排,完全不会造成三维模型失真的性质,通过不同嵌入模式规则对三角面元素进行重排,以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明,该算法不会对三维模型造成任何失真,嵌入容量为每顶点2比特,且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真,容量大、安全性高、鲁棒性强,适用于三维载体不容修改的情形,如军事、医学、秘密通信和版权保护等。 相似文献
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Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely. 相似文献
10.
《Ceramics International》2021,47(21):30298-30309
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. 相似文献