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.     

Porous CoNiOOH nanosheets derived from the well-mixed MOFs as stable and highly efficient electrocatalysts for water oxidation

The development of efficient and stable electrocatalysts is of great significance for improving water splitting. Among them, transition metal oxyhydroxides show excellent performance in oxygen evolution reactions (OER), but there are certain difficulties in direct preparation. Recently, Metal–organic frameworks (MOFs) as precatalysts or precursors have shown promising catalytic performance in OER and can be decomposed under alkaline conditions. Therefore, using a mild and controllable way to convert MOFs into oxyhydroxides and retaining the original structural advantages is crucial for improving the catalytic activity. Herein, a rapid electrochemical strategy is used to activate well-mixed MOFs to prepare Co/Ni oxyhydroxide nanosheets for efficient OER catalysts, and the structural transformation in this process was investigated in detail by using scanning electron microscope, X-ray diffraction, Raman, X-ray photoelectron spectroscopy and electrochemical methods. It is discovered that electrochemical activation can promote ligand substitution of well-mixed MOFs to form porous oxyhydroxide nanosheets and tune the electronic structure of the metal (Co and Ni), which can lead to more active site exposure and accelerate charge transfer. In addition, the change of structure also improves hydrophilicity, as well as benefiting from the strong synergistic effect between multiple species, the optimal a-MCoNi–MOF/NF has excellent OER performance and long-term stability. More obviously, the porous CoNiOOH nanosheets are formed in situ during electrochemical activation process through structural transformation and acts as the active centers. This work provides new insights for mild synthesis of MOFs derivatives and also provides ideas for the preparation of highly efficient catalysts.     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

From modification to mechanism: Supercritical hydrothermal synthesis of nano-zirconia

Nano-zirconia has been widely applied due to its excellent physical and chemical properties (e.g., high strength, corrosion resistance, oxygen ion conductivity). Existing preparation methods of nano-zirconia tend to require long reaction time, and the sizes of final particles are large with uneven distributions. Sub-/supercritical hydrothermal synthesis of nanoparticles is favored by researchers owing to controllable reaction process, uniform particle size distribution, good reproducibility, short reaction time, high conversion rate and harmlessness to environment. In this paper, the characteristics and mechanisms of dissolution, crystallization and growth of nano-zirconia during sub-/supercritical hydrothermal synthesis are systematically reviewed. The influences of process and material parameters on the size and purity of particles are analyzed. Then, the reaction mechanism and product phase transition mechanism during hydrothermal synthesis of zirconia are summarized to provide a theoretical reference for the oriented preparation. Finally, the improvement and commercialization of sub-/supercritical hydrothermal synthesis technology are evaluated, and the future research topics are proposed.     

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.     

Efficient Flexible M-Tree Bulk Loading Using FastMap and Space-Filling Curves

Many database applications currently deal with objects in a metric space. Examples of such objects include unstructured multimedia objects and points of interest (POIs) in a road network. The M-tree is a dynamic index structure that facilitates an efficient search for objects in a metric space. Studies have been conducted on the bulk loading of large datasets in an M-tree. However, because previous algorithms involve excessive distance computations and disk accesses, they perform poorly in terms of their index construction and search capability. This study proposes two efficient M-tree bulk loading algorithms. Our algorithms minimize the number of distance computations and disk accesses using FastMap and a space-filling curve, thereby significantly improving the index construction and search performance. Our second algorithm is an extension of the first, and it incorporates a partitioning clustering technique and flexible node architecture to further improve the search performance. Through the use of various synthetic and real-world datasets, the experimental results demonstrated that our algorithms improved the index construction performance by up to three orders of magnitude and the search performance by up to 20.3 times over the previous algorithm.     

Polishing of zirconia ceramics by chemically-induced micro-nano bubbles

This study introduces micro-nano bubbles (MNBs) in the process of polishing zirconia ceramics through sodium borohydride hydrolysis to assist in polishing yttria-stabilized zirconia (YSZ). Compared with conventional silica sol, the material removal rate using this MNB-assisted technology is increased by 261.4%, and a lower surface roughness of 1.28 nm can be obtained. Raman, X-ray diffraction, and X-ray photoelectron spectroscopy are used to study the structural changes and phase stability of the YSZ during different polishing periods. The results show that MNBs are the key factor promoting the transformation from the tetragonal phase to the monoclinic phase on the surface of the YSZ during polishing. The H₂O molecules (or OH^? ions) on the surface of the YSZ are driven by the thermal kinetic energy of the micro-jets formed by the collapse of micro-bubbles, and they permeate to occupy more oxygen vacancies in the crystal lattice. Atomic force microscopy and nano-indentation tests show that the micro-protrusions on the surface of the YSZ preferentially undergo phase transformation, and their hardness decreases. This promotes abrasives to preferentially remove rough spots on the surface and achieve more efficient polishing. We believe this work adds valuable insights regarding low-temperature degradation and ultra-precise machining of YSZ ceramic materials.     

Characterization of the structure and properties of processed alumina-graphene and alumina-zirconia composites

The onset of hybrid alumina-based composites, which combines two or more nano-particles within the alumina matrix has already shown promising improvements in the matrix material. However, variations in mechanical properties including the optimum compositions that give improved properties faced with the development of alumina-based composites require further studies to understand the underlying mechanisms and synergistic effects of the nano-particle additions on the alumina matrix. In the current study, the structure and properties of Al?O?-graphene (0.5 wt%) and Al?O?–ZrO? (4 wt% and 10 wt%) composites fabricated via hot-pressing was studied as a baseline for multiple combinations. Even though the addition of 10 wt%ZrO? resulted in a 23% reduction in the grain size of the alumina matrix, the 4 wt%ZrO? addition resulted in a 14% increase in grain size as compared to the parent alumina matrix. X-ray diffraction analysis revealed that there was approximately 85% monoclinic (m-ZrO₂) vs. 15% tetragonal (t-ZrO₂) crystal structures in the A4ZrO? sample whilst the A₁₀ZrO? had approximately 93% m-ZrO₂ vs. 7% t-ZrO₂. The high-volume fraction of the monoclinic crystal structures in the A₁₀ZrO? accounts for the induced microcracks in the sample since the transition from the ductile-tetragonal to brittle-monoclinic is associated with the exertion of compressive stresses on the alumina matrix by the associated elastic volume expansion of m-ZrO₂. Also, the addition of 0.5 wt%graphene resulted in about 37% reduction in the grain size of the alumina matrix, and approximately 10% increase in hardness as a result of the distribution of graphene along the grain boundaries of the parent alumina matrix, which restricts grain coalescence and growth during processing. Furthermore, an increase up to 115% and 164% were observed in the fracture toughness (K_IC) with the inclusion of 0.5 wt%graphene and 10 wt%ZrO? respectively, which was primarily ascribed to the fine-grained microstructures and toughening mechanisms of the intergranular graphene and ZrO? particles.     

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。