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Continuous SiC fiber reinforced titanium matrix（SiCf/Ti）composite exhibiting high specific strength，high specific modulus and high temperature resistance，shows important application prospects in the aerospace field. In this paper，the development of SiCf/Ti composite's application，preparation，property control and testing technology is summarized，and the bottleneck problems required to be broken through are raised. SiCf/Ti composite shows the unidirectional performance advantages，making it suitable for rotating ring parts（bling，turbine disk，etc），rod parts（turboshaft，connecting rod，fastener，etc）and plate parts（aircraft skin，etc）. The commonly used preparation methods of SiCf/Ti composite materials are foil-fiber-foil（FFF）method and matrix coating technology（MCT）. FFF is suitable for preparing plate structural parts，and MCT is suitable for winding structural parts，such as rings，disks and shafts. The properties of SiCf/Ti composite mainly depend on SiC fiber，titanium alloy matrix and fiber/matrix interface. The microstructure and properties of SiC fibers are highly sensitive to the preparation process，and it is one of the research focuses to obtain stable SiC fibers by regulating the reactor structure and deposition conditions. The titanium alloy matrix can be coated on the fiber surface by physical vapor deposition to prepare the titanium alloy precursor wire，which is the key to the subsequent preparation of high-quality components. The interfacial microstructure，thermal stability and mechanical properties of SiCf/Ti composites are closely related to the coating on the fiber surface，so the control of coating type and structure is an important means to control the interfacial properties of SiCf/Ti composites. The application of SiCf/Ti composite materials has promoted the development of non-destructive testing technology，so researchers have carried out the basic research of ultrasonic testing，X-ray testing and acoustic emission in application of composite material testing. In order to realize the wide application of SiCf/Ti composites，further research work should be carried out in the structural design，low-cost manufacturing，failure analysis and life prediction of composite materials in the future.

Continuous SiC fiber reinforced titanium matrix composite（SiCf/Ti composite）has good specific strength and comprehensive mechanical properties, it is a lightweight high temperature structural material that attracts much attention in the new generation of equipment development. SiCf/Ti composites can be prepared by the methods of foil fiber foil（FFF）and matrix coating fiber（MCF）. In order to compare the effects of the two methods on the growth of interfacial reaction, SiCf/TC17 composites were prepared by FFF and MCF respectively. By study the interfacial reaction layer thickness of composites prepared by the two processes after thermal exposure at the temperature from 800℃ to 900 ℃, the microstructure and the thickness of interfacial layer were analyzed by scanning electron microscope, further the interfacial reaction rate at high temperature was obtained, and the interfacial reaction kinetic parameters of different materials were attained. The results showed that the interfacial reaction rate of SiCf/TC17 composite prepared by MCF method was higher than that of the composites prepared by FFF method at the same temperature. The interfacial reaction rate factor k0 is 4.942×10−3 m/s1/2 and 8.149×10−3 m/s1/2 for the composites prepared by MCF and FFF method, respectively. Additionally, the reaction activation energy Q is 276.3 kJ/mol and 291.7 kJ/mol for those, respectively. This is because the titanium alloy matrix prepared by MCF method has smaller phase structure and higher element diffusion rate at high temperature. Therefore, the composite prepared by MCF has lower reaction activation energy at high temperature.

Metacognition is regarded as a retrospective skill promoting learners' learning performance, deep thinking, and academic well-being. Stimulated Recall (SR) is regarded as a reliable approach to inspiring learners' metacognition in the classroom. However, the outbreak of COVID-19, causing widespread class suspension, may impair the effect of SR on cultivating distance learners' metacognition. The current study, employing multi-mode stimuli of learners' eye movements and feedforward, aimed to develop the effect of SR on activating learners' metacognition in remote settings. Forty-eight university graduates were recruited to participate in an eye-tracking experiment using digital dictionaries. Their feedforward and eye movements were collected as multi-mode stimuli. By reviewing the consistency and discrepancies between their feedforward and eye movements, participants were invited to conduct an SR interview, which stimulated them to retrospect on their prior cognitive behaviors. The results of the metacognition scale pre-post test showed that learners' metacognitive skills were significantly improved by the stimulated recall with multi-mode stimuli. The findings theoretically enrich the metacognition strategy in the Cognitive Theories of Multimedia Learning, and practically extend the implementation of stimulated recall in distance learning contexts.

Background: Semiliquidambar cathayensis Chang is an extremely valuable and endangered medicinal plant. To investigate the exploitation and rational utilization of S. cathayensis, this study conducted metabolomics analysis of the leaves and bark of artificially cultivated S. cathayensis at different developmental stages. Methods: These metabolites were detected and identified by Ultra-Performance Liquid Chromatography–tandem Mass Spectrometry (UPLC-MS/MS) technology, and then univariate statistical analyses, multivariate pattern analyses, and pathway analyses were carried out. Results: As a result, a total of 801 metabolites were detected in S. cathayensis; differential metabolites in leaves at different developmental stages were mainly enriched in pathways related to flavonoids, whereas differential metabolites in bark at different developmental stages were mainly aromatic compounds, amino acids, and flavonoids, among others. This study revealed that young leaves are ideal for use in treating rheumatism, regulating blood pressure, and lowering blood glucose, while old leaves are better suited for skincare products and extracting materials to prevent neurodegenerative diseases and support women's ovarian health. As for bark, four-year-old S. cathayensis bark is optimal for extracting myricetin. If the pharmaceutical, chemical, food, and industrial fields require extensive extraction of L-phenylalanine, trans-3-hydroxycinnamate, and 4-hydroxyphenylacetate, and if the medical field needs to extract anti-allergy, liver protection, and anti-coagulant ingredients, the two-year-old S. cathayensis bark is the best choice. Conclusions: Thus, this study established a solid theoretical framework for the rational, effective, and sustainable utilization of S. cathayensis leaves and bark. [ABSTRACT FROM AUTHOR]

In order to count soybean seeds quickly and accurately, improve the speed of seed test and the level of soybean breeding, a method of soybean seed counting based on VGG-Two (VGG-T) was developed in this research. Firstly, in view of the lack of available image dataset in the field of soybean seed counting, a fast target point labeling method of combining pre-annotation based on digital image processing technology with manual correction annotation was proposed to speed up the establishment of publicly available soybean seed image dataset with annotation. Only 197 min were taken to mark 37,563 seeds when using this method, which saved 1592 min than ordinary manual marking and could reduce 96% of manual workload. At the same time, the dataset in this research is the largest annotated data set for soybean seed counting so far. Secondly, a method that combined the density estimation-based and the convolution neural network (CNN) was developed to accurately estimate the seed count from an individual threshed seed image with a single perspective. Thereinto, a CNN architecture consisting of two columns of the same network structure was used to learn the mapping from the original pixel to the density map. Due to the very limited number of training samples and the effect of vanishing gradients on deep neural networks, it is not easy for the network to learn all parameters at the same time. Inspired by the success of pre-training, this research pre-trained the CNN in each column by directly mapping the output of the fourth convolutional layer to the density map. Then these pre-trained CNNs were used to initialize CNNs in these two columns and fine-tune all parameters. Finally, the model was tested, and the effectiveness of the algorithm through three comparative experiments (with and without data enhancement, VGG16 and VGG-T, multiple sets of test set) was verified, which respectively provided 0.6 and 0.2 mean absolute error (MAE) in the original image and patch cases, while mean squared error (MSE) were 0.6 and 0.3. Compared with traditional image morphology operations, ResNet18, ResNet18-T and VGG16, the method proposed improving the accuracy of soybean seed counting. In the testset containing soybean seeds of different densities, the error fluctuation was small, and it still had excellent counting performance. At the same time, compared with manual counting and photoelectric seed counter, it saved about 2.493 h and 0.203 h respectively for counting 11,350 soybean seeds, realizing rapid soybean seeds counting.

Mobile cloud learning, a combination of mobile learning and cloud computing, is a relatively new concept that holds considerable promise for future development and delivery in the education sectors. Cloud computing helps mobile learning overcome obstacles related to mobile computing. The main focus of this paper is to explore how cloud computing changes traditional mobile learning. A case study of the usage of Moodle in the cloud via mobile learning in Khalifa University was conducted.

This paper reports on a new pedagogy for Chinese language teaching and learning at elementary schools through exploratory classroom instruction using Information and Communication Technologies. The study used quantitative method to collect data from two elementary schools of China. The results showed that: (1) the three-in-one pedagogy of "character recognition, in-class reading and in-class writing" is a new approach for enhancing the efficiency of Chinese language learning at elementary schools, and (2) the new pedagogy provides a new perspective of integrating ICT into Classroom-based Chinese Language Learning (CCLL). (Contains 7 figures and 6 tables.)

Smart agricultural is a new form of agriculture that makes full use of human wisdom to develop agriculture. It is a new stage, new model and new pattern of agricultural development. The development of agricultural information technology is an inevitable requirement for smart agricultural. The new generation of core information technology, such as agricultural big data, cloud computing, Internet of things, artificial intelligence, can enable the innovative development of smart agricultural. It can provide new technologies, new methods and new solutions for the healthy development of smart agricultural. Agricultural informationization standardization is the premise to guide the progress and innovation of agricultural science and technology. It can lead the progress of agricultural science and technology and standardize the process of agricultural production. It is an urgent need for the development of smart agricultural. Agricultural Internet of things and agricultural application-specific chip are the core technologies and equipment for the development of smart agricultural. The application demand of agricultural Internet of things can promote the development of agricultural application-specific chip technology. The technological innovation of agricultural application-specific chip will promote the technological upgrading of agricultural Internet of things. Agricultural big data and cloud computing are powerful technical support for massive and complex agricultural information processing. The computing requirements of big data algorithms can promote the innovation and development of cloud computing technology. The improvement of cloud computing capability is more convenient for the application of big data algorithms and applications. Agricultural information security and blockchain are the key to guarantee the security of agricultural information, agricultural product quality certification system and agricultural. Agricultural artificial intelligence is the inevitable choice to improve agricultural labor productivity, reduce resource consumption, and efficient production. The innovation and application of artificial intelligence algorithm is an effective measure to realize smart agricultural. Agricultural plasma technology provides a new technological means for smart agricultural to produce more safer and more reassuring green organic agricultural products. It can be used in different stages of agricultural production, includes before, during and after production, to protect the healthy development of the whole agricultural production chain. The original innovation and autonomous control of the key technologies of smart agricultural will surely lead the healthy development of smart agricultural.

This paper investigates the impact of big data on firms' environmental, social, and governance (ESG) performance. We provide quasi-experimental estimates using a unique quasi-natural experiment: the national policy of introducing Big Data Pilot Zones (BDPZs) in Chinese cities. Our analysis exploits data on firm-level ESG performance with a standard difference-in-differences empirical strategy. We find that big data has a significant positive impact on corporate ESG performance. The effect is more prominent for companies in non-heavily polluting industries, with a lower level of digital transformation, and those not rigorously audited. Analysis of the mechanisms shows that big data enhances the transparency of corporate information. Facing external supervisory pressure, companies tend to enhance their ESG performance to mitigate reputational risks. [ABSTRACT FROM AUTHOR]

This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a semantic gene (knowledge ontology) into the model, Learning Call can flexibly describe the internal structure and external relations of learning resources, allowing the evolution of learning resources to occur in an orderly way. Furthermore, by employing a computational model of a social cognition network, Learning Cell enables not only materialize resource sharing but also the sharing of social cognition networks. Finally, by separately deploying resource structures and resource content in the cloud storage model, Learning Cell achieves context awareness of ubiquitous learning (u-learning) resources. Learning Cell represents a resource aggregation model that is different from the learning object model. It makes up for the defects of existing learning technologies in the following areas: the sharing of process information and social cognition networks, the intelligence of resources, and the evolution of content. Learning Cell provides a theoretical framework of u-learning resource organization.

As mobile devices with wireless access become more readily available, learning delivered via mobile devices of all types must be designed to ensure successful learning. This paper first examines three questions related to the design of mobile learning: 1) what mobile learning (m-learning) guidelines can be identified in the current literature, 2) what insights can be gained from attempts to bring m-learning to developing countries, and 3) what m-learning frameworks exist and what do they claim about the design of m-learning. Based on an examination of these questions, we aim to synthesize existing guidelines, understand these guidelines in light of the theory of transactional distance and cloud computing, and present a preliminary discussion on why m-learning is not merely an evolution of e-learning. We also identify areas where additional research is needed to further develop guidelines and frameworks for mobile learning supported by cloud computing.

The gradual development of intelligent learning (iLearning) systems has prompted the changes of teaching and learning. This paper presents the architecture of an intelligent learning (iLearning) system built upon the recursive iLearning model and the key technologies associated with this model. Based on this model and the technical structure of a cloud-based intelligent system, the authors developed an exemplary iLearning system-"Mobile Class", accessible from the Shanghai Lifelong Learning Network, an online platform for the continuing education of Shanghai residents. This cloud-based intelligent learning (C-iLearning) system can adopt the cloud management model and synchronize users' learning process in the clouds, so as to support users' continuous learning with different devices. Formal testing with target users revealed the effectiveness of this system in supporting anytime anywhere learning.

The computer science classes in China's institutions of higher education often have large numbers of students. In addition, many institutions offer "blended" classes that include both on-campus and online students. These large blended classrooms have long suffered from a lack of interactivity. Many online classes simply provide recorded instructor lectures to which distance students listen after downloading. This format only reinforces the negative effects of passive nonparticipatory learning. At a major university in Shanghai, researchers and developers actively seek technological interventions that can greatly increase interactivity in blended classes. They have developed a cutting-edge mobile learning system that can deliver live broadcasts of real-time classroom teaching to online students with mobile devices. Their system allows students to customize their means of content-reception, based on when and where the students are tuning into the broadcast. The system also supports short text-messaging and instant polls. Through these features, students can ask questions and make suggestions in real time, and the instructor can respond immediately. This paper describes this system in detail and also reports results from a formal implementation of the system with a blended classroom of 562 students (of whom 90% were online). (Contains 5 tables and 2 figures.)