Flash cosintering of a lanthanum strontium cobalt ferrite nanofibre/Gd-doped ceria bilayer structure

For solid oxide fuel cells, an important structural requirement is that the electrolyte layer needs to be dense and the electrode layer porous, which is difficult to obtain by conventional cosintering. In this work, flash cosintering of a double layer structure consisting of a Gd-doped ceria substrate with a lanthanum strontium cobalt ferrite nanofibre coating is investigated. Experimental and finite element modelling results reveal that when the LSCF layer is connected to the electrode, the heat is concentrated in the LSCF layer, which leads to a huge temperature gradient and introduces severe cracking. When the LSCF layer is electrically isolated from the electrode, the heat is concentrated in the GDC layer, and the temperature gradient is dramatically reduced. In this situation, the density of GDC can reach 92.86% while a high porosity of 52.26% is maintained in the LSCF layer, which is higher than that of the conventional cosintered sample.     

成型温度对PBX装药内部质量及性能的影响

针对压制成型的PBX炸药装药，选择CT无损检测、巴西实验和扫描电镜检测等技术，对比研究了室温和加热两种温度下压制成型的炸药装药内部质量、静态力学性能和细观破坏形式。结果表明，加热压制有利于改善炸药装药的内部质量，可避免产生初始损伤，且提高了装药的力学性能。细观尺度上，室温压制成型的装药主要发生界面脱黏破坏，加热压制成型装药的主要破坏形式是穿晶断裂。     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Insights from proteome to phosphorylated proteome: deciphering different regulatory mechanisms in goat muscles with high- and low-meat quality

To investigate the inner mechanisms of meat quality differences between high-quality (HQ) and low-quality (LQ), a comparative quantitative study between longissimus thoracis and external intercostals of goat muscle was performed from proteome to phosphorylated proteome using RP-HPLC in combination with the ‘isobaric tag’ for relative and absolute quantitation (iTRAQ) labelling strategy. Altogether, 1441 proteins were identified in our study, of which 673 were phosphoproteins, and a total of twenty were common differentially expressed proteins. Myosin, carbonic anhydrase, and phosphoglucomutase could be used as proteins marker for HQ and LQ meat. Bioinformatics analysis showed that these proteins exhibited different rates for glycolysis and oxidative phosphorylation reaction, thus causing the different pH and NADH change rates, and resulting in better colour, tenderness, and water retention in HQ meat. The release of Ca²⁺ and adenosine triphosphate changed the meat quality through calcium signalling. Our finding provides a comprehensive view of proteome changes and their phosphorylation levels in goat muscle, involved in producing meats of different muscle parts. It also gives a better understanding of the regulation of protein on various biological processes that determine the final meat quality attributes.     

Special Issue on Artificial Intelligence and Technology: Trend,Challenges, and Opportunities

Mobile Networks and Applications -     

A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Water Resources Management - Urban river not only has the important function in urban hydrological environment, but also is an area for entertainment. Water quality assessment is the core technique...     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.