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γ-TiAl alloy is a typically difficult material to machine, and machining defects such as grain pull-out and material spalling are common during machining, resulting in component scrap. As a result, it is critical to investigate the real-time monitoring method of surface roughness during milling. A new model for predicting surface roughness is proposed in this article. Based on the idea of deep learning, the prediction problem of surface roughness is turned into a classification problem. The features of the force signal are extracted using a continuous wavelet transform, and the one-dimensional signal is converted into a two-dimensional time-frequency diagram to obtain more information. The transfer learning mechanism is introduced to make the model run faster and improve prediction accuracy. The overfitting problem is solved and the model's generalization ability is improved through batch normalization and dropout layer, and the prediction accuracy is increased by 3%. The results show that the method has high recognition accuracy, with a maximum classification accuracy of 98% and an average accuracy of 96.12%, a 6% improvement over the traditional model, allowing it to accurately realize real-time monitoring of surface quality in milling processing.

Icariin (ICA) is the main active component of the traditional Chinese medicine Epimedium, which has been confirmed to play a neuroprotective role in spinal cord injury (SCI) models. Astrocyte activation is a key link in “inflammatory storms” after SCI, and eventually form glial scar to inhibit neurological recovery. Studies have shown that icariin can effectively inhibit inflammatory response and promote the recovery of nerve function after SCI, but the specific mechanism is still unclear. This study applied network pharmacology and in vivo experimental verification to explore the molecular mechanism of ICA in the treatment of SCI. A total of 37 targets were screened out, mainly enriched in response to hypoxia, response to abiotic stimulus, regulation of cellular response to stress, and the TGF-beta signaling pathway etc. In the animal experiments, the Basso, Beattie and Bresnahan scores and Hematoxylin Eosin staining revealed that ICA could effectively improve the behavioral and histological recovery of SCI rats. Then ICA was confirmed to reduce astrocyte GFAP, Vimentin, and the expression of Ki-67 after SCI. Furthermore, ICA could inhibit the TGF-beta signaling pathway. ICA could also inhibit the expression of Yap protein. However, when Yap protein was activated, ICA inhibited the TGF-beta signaling pathway and reactive astrocyte activation was reversed. To sum up, the mechanism of ICA treatment for SCI may involve the inhibition of reactive astrocyte activation by inhibiting the expression of Yap protein and down-regulating the TGF-beta signaling pathway.

In order to solve the problems of timeliness and accuracy in the performance analysis of complex product assembly, taking the performance analysis of the array antenna assembly process as the research object, and taking the assembly process performance "measurable" as the requirement, a digital twin modeling method for the assembly performance analysis of the array antenna is proposed. This method integrates finite element, mesh model simplification and surrogate model. In order to realize the real-time prediction and rapid visualization of the assembly performance in the assembly process, a mesh simplification algorithm for finite element mesh model based on edge collapse is proposed and a Gaussian process regression surrogate model for product assembly performance is constructed in order to complete the construction of a digital twin model for assembly performance analysis. Finally, the proposed method is verified by an example of the assembly performance analysis of a typical array antenna. The result shows that the digital twin model proposed has high response speed and fidelity in the analysis of assembly performance, and has certain reference value and significance for the application of digital twin technology in the product assembly process.

In order to solve the problem of unmeasurable assembly performance of complex product, the digital twin-driven assembly quality control and prediction of complex product is studied by means of cyber-physical fusion in the assembly workshop. The connotation of digital twin intelligent assembly is introduced, the current research status of complex product assembly quality is compared and analyzed, and three main key technologies for the assembly quality control of complex product are proposed: (1) multidimensional, multi-scale, multidisciplinary modeling and simulating of digital twin-driven assembly; (2) multi-source heterogeneous data collection, sensing and fusion for assembly processes; (3) data-driven decision making, feedback and optimization technology. Finally, the application of digital twin technology in the field of assembly quality control of complex product is prospected.

Nowadays it is commonly accepted that knowledge, as a resource has become increasingly important in the drive toward knowledge-driven innovation in the manufacturing industry, and which is especially critical for the research and development of complex equipment. In China, there are several fields of manufacturing, such as aviation, ship, and electronics. Although each of them has its own professional technology, they also take advantage of the basic common manufacturing technology. Thus, it is meant for sharing manufacturing technology knowledge. In order to promote innovation, a manufacturing technology knowledge retrieval system is developed to make more efficient use of manufacturing technology knowledge. Manufacturing technology knowledge is a phrase with vast meanings, which may include knowledge of different manufacturing process means, machine, and process capabilities or the latest technology developments. In this system, the manufacturing technology knowledge is defined and organized. A knowledge retrieval method based on the weighted fusion of ontology-based semantic extension (OSE) and user-based collaborative filtering (UCF) is proposed and described detailed. On this basis, the prototype system of manufacturing technology knowledge retrieval system is developed. Its architecture and main functions are elaborated. The results of this system facilitate the sharing of manufacturing knowledge, and it is expected to accelerate the process of knowledge transfer, to improve the level of the overall manufacturing technology.

In this paper, the simulation and key link characterization of the complex assembly model step-down process are studied and analysed in depth using the digital twin approach, and the method is used in the practical process. The physical model step-down method MORA algorithm and its physical interpretation in various simplified cases are given, and the MORA method is improved on this basis. The concept of local activeness based on knot structure is introduced, and the process of model transformation and downscaling and decomposition based on local activeness is explained in detail. The high-fidelity mapping of solid equipment is completed in virtual space, which can accurately reproduce and predict the health state of engineering equipment throughout its life cycle, effectively avoiding the huge property losses and safety risks caused by early failure of vulnerable structures and providing a safe and stable working environment for offshore oil and gas production. With the prototype monitoring data as reference, the response surface method is used to identify the parameters of the finite element model of the hinge node, which improves the fidelity of the virtual model of the hinge node. Considering the friction coefficient changes and load characteristics during the degradation of the hinge node, the dynamics simulation conditions are set, and the operating states of the hinge node at different stages of its whole life cycle are simulated by using the high-fidelity virtual model of the hinge node, and the prediction model of the hot spot stress of the hinge node is established to monitor its in-position state in real time, and the operation and maintenance overhaul method based on the health state of the hinge node is proposed. The system is divided into four modules: multilevel inverse modelling of the assembly twin, statistical shape characterization and analysis of batch parts, optimization of fixture positioning and flexible assembly of thin-walled parts, and optimization of low-stress assembly of bolted joint structure, which verifies the feasibility of the method and provides guidance for the actual product forming process.

With the exhibition sector becoming increasingly important for the hospitality and tourism industry, it is critical to understand what drives the success of an exhibition. Through a comprehensive literature review, we first identified two broad categories of factors, namely exhibition attributes and exhibitor perceptions, that influence exhibitor satisfaction and loyalty. Next, a meta-analysis was conducted based on 26 empirical papers studying the Chinese exhibition market to quantitatively evaluate these relationships. The results show that among the exhibition attributes, booth management, service personnel, and exhibition environment are the most important factors affecting exhibitor satisfaction, while exhibition brand is the most important factor affecting loyalty. Among exhibitor perceptions, service quality is more important for satisfaction, while perceived value is more meaningful to loyalty. This study offers insights into strategies for exhibition organizers to cultivate long-term relationships, and to better cope with the challenges of emerging forces such as the Internet. Published version

Prior work investigated how foreign language impacts decision making by either reducing access to emotion or imposing additional cognitive demands. In this fMRI study, we employed a cross-task design to assess at the neural level whether and how the interaction between cognitive loading and emotional involvement is affected by language (native L1 vs. foreign L2). Participants completed a Lexico-semantic task where in each trial they were presented with either a neutrally or a negatively valenced word either in L1 or L2, and either under cognitive loading or not. After each trial, they had to decide whether to take a risky decision within a gambling game. During the Gamling task, left amygdala and right insula were more activated after having processed a negative word under cognitive loading. However, this was true for L1 but not for L2, suggesting that cognitive loading was germane and facilitated rather than hindered access to emotion only in L1. Further suggesting that cognitive load can enhance emotional sensitivity in L1 but not in L2, we found that functional connectivity between reward-related striatum and right insula increased under cognitive loading only in L1. Overall, results suggest that cognitive loading in L1 can favor access to emotion and lead to a more impulsive decision making, whereas cognitive loading in L2 can attenuate access to emotion and lead to a more rational decision making.

Three-dimensional ordered macroporous NiFe2O4 (3DOM-NFO) powder was synthesized through the direct templating method combined with the sol–gel combustion technique. Polymethyl methacrylate (PMMA) spheres with different sizes were used as the hard templates. In order to understand the effect of PMMA spheres mean size on the structure and catalytic activity of synthesized 3DOM-NFO, the detailed characterization of the material was carried out by XRD, SEM, BET, XPS, UV–VIS, and DRS techniques. Direct hydrogeneration production of CH4 from CO2 and H2O was used to evaluate the catalysis performance of 3DOM-NFOs. The production of CH4 evolution can reach 1040.8 μmol/g at 350 °C and ambient pressure when 300 nm template was used. It was concluded that the specific surface area and moderate concentration of oxygen vacancies are the crucial factors affecting the catalysis properties. Reasonably high turnover number of 0.244 and high CH4 conversion efficiency of 0.897% were obtained in this study. 3DOM-NFO can be a promising catalyst for hydrocarbon fuel production by directly using CO2 and H2O as the safe and cheap feedstocks.

Embryonic stem (ES) cells provide an invaluable tool for molecular analysis of vertebrate development and a bridge linking genomic manipulations in vitro and functional analysis of target genes in vivo. Work towards fish ES cells so far has focused on zebrafish (Danio renio) and medaka (Oryzias latipes). Here we describe the derivation, pluripotency, differentiation and growth responses of ES cell lines from Nile tilapia (Oreochromis niloticus), a world-wide commercial farmed fish. These cell lines, designated as TES1-3, were initiated from blastomeres of Nile tilapia middle blastula embryos (MBE). One representative line, TES1, showed stable growth and phenotypic characteristics of ES cells over 200 days of culture with more than 59 passages under feeder-free conditions. They exhibited high alkaline phosphatase activity and expression of pluripotency genes including pou5f3 (the pou5f1/oct4 homologue), sox2, myc and klf4. In suspension culture together with retinoic acid treatment, TES1 cells formed embryoid bodies, which exhibited expression profile of differentiation genes characteristics of all three germ cell layers. Notably, PKH26-labeled TES1 cells introduced into Nile tilapia MBE could contribute to body compartment development and led to hatched chimera formation with an efficacy of 13%. These results suggest that TES1 cells have pluripotency and differentiation potential in vitro and in vivo. In the conditioned DMEM, all of the supplements including the fetal bovine serum, fish embryonic extract, fish serum, basic fibroblast growth factor and non-protein supplement combination 5N were mitogenic for TES1 cell growth. This study will promote ES-based biotechnology in commercial fish.

The study aims to present architecture and technology solution for collaborative manufacturing execution system oriented to discrete manufacturing enterprises. Under the networked manufacturing environment, the enterprises are facing challenges including the increasing manufacturing process complexity, the shorter product delivery, the higher customer satisfaction requirements, and so on. To deal with these challenges, the manufacturing enterprises must implement information management, while collaborative manufacturing execution system is one of the key technologies to realize informationization. Thus, in this study, the authors analyze production characteristics, typical production flow, and collaborative management requirements of multi-workshop production in the discrete manufacturing enterprise and propose an architecture of collaborative manufacturing execution system oriented to discrete manufacturing enterprises. The main functions of this system and key technologies to realize the system is also analyzed. Finally, a prototype system is developed and a case study is executed in Henan Pingyuan Optics Electronics Co., LTD in OPTO-Electronic Group to demonstrate the implementation and potentials of the system.

NiFe2O4 nanoparticles with a high concentration of oxygen vacancies were synthesized via a solvothermal route. The morphology and oxygen vacancies of the catalyst was changed by adjusting the ratio of ethylene glycol (EG) in the mixture solvent. Water was used as a hydrogen source, the thermal catalytic performance of NiFe2O4 was up to 357.6 μmol g−1 of CH4 evolution under low temperature conditions. These findings may further broaden the horizon for CO2 reduction using the oxygen nonstoichiometry strategy.

Different morphologies of SrTiO3/TiO2 heterostructures like nanocubes, nanoparticles, nanospheres, and nanofibers were synthesized via a facile hydrothermal process using TiO2 as both a template and precursor in Sr(OH)2 solution. Their structure, interface and composition can be rationally tailored by simply adjusting the Sr(OH)2/TiO2 (Sr/Ti) mole ratios and the morphology of SrTiO3/TiO2 heterostructures can be controlled easily using TiO2 with different morphologies. A SrTiO3 crystal thin layer was grown on an anatase TiO2 substrate to fabricate a heterostructure interface contact between SrTiO3 and TiO2 and the lattice mismatch had an effect on the electrical transport properties. The SrTiO3/TiO2 heterostructures are beneficial for the fast separation of photogenerated electrons and holes so as to suppress the recombination of photogenerated electrons and holes at the interface of SrTiO3 and TiO2. Besides this, the different morphologies of the SrTiO3/TiO2 heterostructures allowing facile electron transfer, the hierarchical structure promoting mass transfer and allowing more light reflection and absorption, and the large specific surface area providing more reaction sites to facilitate the reactants to the desired oxidation places all together create a synergistic effect to improve the photocatalytic activity of the hierarchical SrTiO3/TiO2 heterostructures. Under the irradiation of UV light, in a water/methanol sacrificial reagent system, the SrTiO3/TiO2 NP heterostructures at a Sr/Ti mole ratio of 40% with the highest BET and smallest crystallite size achieve the highest photocatalytic activity generating 0.731 mmol of H2. The SrTiO3/TiO2 heterostructures exhibit better photocatalytic activity by generating three times more H2 than bare TiO2 and pure SrTiO3.

The ultrafast optical nonlinearity of a pyrazine-based N-heteroacene (Py-0-TQ) is systematically investigated via Z-scan, optical limiting and transient absorption spectrum. Reverse saturable absorption at visible wavelengths is observed. Further study on excited-state kinetics indicates that the strong nonlinear optical (NLO) absorption originates from the cooperating effect between different NLO mechanisms including excited-state absorption (ESA) and two-photon absorption (TPA). Structure-property relation is analyzed, result indicates that the central π-system in Py-0-TQ plays a key role in the cooperating effect via providing a broadband, long-lived ESA and an enhanced TPA as trigger. The experimental and theoretical studies suggest that pyrazine-based N-heteroacene molecules with proper design may have great potential in NLO applications.