Bovidae-based gelatin: Extractions method,physicochemical and functional properties,applications, and future trends

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.     

Achieving superior hydrogen storage properties via in-situ formed nanostructures: A high-capacity Mg–Ni alloy with La microalloying

Mg-based hydride is a promising hydrogen storage material, but its capacity is hindered by the kinetic properties. In this study, Mg–Mg₂Ni–LaH_x nanocomposite is formed from the H-induced decomposition of Mg₉₈Ni_1·67La_0.33 alloy. The hydrogen capacity of 7.19 wt % is reached at 325 °C under 3 MPa H₂, attributed to the ultrahigh hydrogenation capacity in Stage I. The hydrogen capacity of 5.59 wt % is achieved at 175 °C under 1 MPa H₂. The apparent activation energies for hydrogen absorption and desorption are calculated as 57.99 and 107.26 kJ/mol, which are owing to the modified microstructure with LaH_x and Mg₂Ni nanophases embedding in eutectic, and tubular nanostructure adjacent to eutectic. The LaH_2.49 nanophase can catalyze H₂ molecules to dissociate and H atoms to permeate due to its stronger affinity with H atoms. The interfaces of these nanophases provide preferential nucleation sites and alleviate the “blocking effect” together with tubular nanostructure by providing H atoms diffusion paths after the impingement of MgH₂ colonies. Therefore, the superior hydrogenation properties are achieved because of the rapid absorption process of Stage I. The efficient synthesis of nano-catalysts and corresponding mechanisms for improving hydrogen storage properties have important reference to related researches.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

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.     

Porous agarose/Gd-hydroxyapatite composite bone fillers with promoted osteogenesis and antibacterial activity

Artificial bone fillers are essentially required for repairing bone defects, and developing the fillers with synergistic biocompatibility and anti-bacterial activity persists as one of the critical challenges. In this work, a new agarose/gadolinium-doped hydroxyapatite filler with three-dimensional porous structures was fabricated. For the composite filler, agarose provides three-dimensional skeleton and endows porosity, workability, and high specific surface area, hydroxyapatite (HA) offers the biocompatibility, and the rare earth element gadolinium (Gd) acts as the antibacterial agent. X-ray photoelectron spectroscopy detection showed the doping of Gd in HA lattice with the formation of Gd-HA interstitial solid solution. Attenuated total reflection Fourier transform infrared spectroscopy imaging suggested chemical interactions between agarose and Gd-HA, and the physical structure of agarose was tuned by the Gd-doped HA. Cytotoxicity testing and alizarin red staining experiments using mouse pro-osteoblasts (MC3T3-E1) revealed remarkable bioactivity and osteogenic properties of the composite fillers, and proliferation and growth rates of the cells increased in proportion to Gd content in the composites. Antibacterial testing using the gram-positive bacteria S. aureus and the gram-negative bacteria E. coli indicated promising antibacterial properties of the fillers. Meanwhile, the antibacterial properties of composite filles were enhanced with the increase of Gd content. The antibacterial fillers with porous structure and excellent physicomechanical properties show inspiring potential for bone defect repair.     

Effects of non-thermal plasma treating wheat kernel on the physicochemical properties of wheat flour and the quality of fresh wet noodles

The effects of non-thermal plasma (NTP) on the physicochemical properties of wheat flour and the quality of fresh wet noodles ( FWN) were investigated. The results showed that NTP effectively decreased the total plate count (TPC), yeast and mould count (YMC) and Bacillus spp. in wheat flour. Wet gluten contents and the stability time reached the maximum when treated for 20 s. The viscosity of starch increased significantly after treatment due to the increased of damaged starch. The contents of secondary structure were altered to some extent, which was because that the ordered network structure of gluten protein broken. Furthermore, compared with the control, texture properties of FWN were enhanced significantly at 20 s, and the darkening rate of FWN was greatly inhibited due to the low polyphenol oxidase (PPO) activity. Consequently, the most suitable treatment was 500 W for 20 s, providing a basis for the application of NTP in flour products.     

基于面元素重排的三维网格模型数据隐藏算法

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。     

Mathematical law of size effect on the flexural property of ceramics

Brittle materials generally exhibit size effects, and the mechanical properties of these materials degrade significantly with an increase in size. However, the mathematical law governing the attenuation degree of mechanical properties with the increase in size is still unknown. In this study, maximum loads of differently sized ceramic test strips were subjected to three point bending tests under two working conditions of equal spans and span amplifications, respectively. Subsequently, the theoretical maximum loads of materials were calculated using the finite element method (FEM). By calculating the difference between the calculated values and the actual maximum loads, the attenuation of mechanical properties of ceramic samples were observed. The results show that the theoretical mechanical properties and the performance attenuation caused by the size effect tend to increase according to the following equation: y=ax³+bx²+cx+d. Therefore, mechanical properties and performance attenuation of any sample exhibiting a size within the experimental range can be predicted by a mathematical law, which was obtained through mechanical tests results of four samples with different sizes. The obtained mathematical law holds great significance for predicting the mechanical properties of materials under size effects.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

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.