Research Progress and Prospect of Intelligent Tea Garden Technology Application

The development and application of internet plus modern tea industry technology is more and more extensive.As an important part of the development process of tea industry,intelligent tea garden plays an important role in the development of the whole industry.At present,intelligent tea garden technology is widely used in many fields such as intelligent monitoring,water and fertilizer integration,green prevention and control,quality and safety traceability.In this paper,the application of intelligent tea garden technology in tea gardens was reviewed.On this basis,the development trend of new information technology and tea industry was prospected,in order to provide some reference and thinking for the innovative research of new technology in tea garden in the future.

Gasification of Organic Waste:Parameters,Mechanism and Prediction with the Machine Learning Approach

Gasification of organic waste represents one of the most effective valorization pathways for renewable energy and resources recovery,while this process can be affected by multi-factors like temperature,feedstock,and steam content,making the product’s prediction problematic.With the popularization and promotion of artificial intelligence such as machine learning(ML),traditional artificial neural networks have been paid more attention by researchers from the data science field,which provides scientific and engineering communities with flexible and rapid prediction frameworks in the field of organic waste gasification.In this work,critical parameters including temperature,steam ratio,and feedstock during gasification of organic waste were reviewed in three scenarios including steam gasification,air gasification,and oxygen-riched gasification,and the product distribution and involved mechanism were elaborated.Moreover,we presented the details of ML methods like regression analysis,artificial neural networks,decision trees,and related methods,which are expected to revolutionize data analysis and modeling of the gasification of organic waste.Typical outputs including the syngas yield,composition,and HHVs were discussed with a better understanding of the gasification process and ML application.This review focused on the combination of gasification and ML,and it is of immediate significance for the resource and energy utilization of organic waste.

Normally-off AlGaN/GaN heterojunction field-effect transistors with in-situ AlN gate insulator

AlGaN/GaN heterojunction field-effect transistors(HFETs)with p-GaN cap layer are developed for normally-off operation,in which an in-situ grown AlN layer is utilized as the gate insulator.Compared with the SiNxgate insulator,the AlN/p-GaN interface presents a more obvious energy band bending and a wider depletion region,which helps to positively shift the threshold voltage.In addition,the relatively large conduction band offset of AlN/p-GaN is beneficial to suppress the gate leakage current and enhance the gate breakdown voltage.Owing to the introduction of AlN layer,normally-off p-GaN capped AlGaN/GaN HFET with a threshold voltage of 4 V and a gate swing of 13 V is realized.Furthermore,the field-effect mobility is approximately 1500 cm^(2)·V^(-1)·s^(-1)in the 2DEG channel,implying a good device performance.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

Multi-componential metal intercalated graphite hybrids synthesized by co-intercalation polymerization towards highefficient microwave absorptions

Magnetic expanded graphite(EG)hybrids were synthesized by co-intercalation polymerization of aniline together with transition metal ions.Experimental results show that metal ions(Fe,Co,Ni,Cu)and even their mixtures can co-intercalate into graphite interlayers with flexibly controllable ratios and contents.Among these co-intercalation compounds,Fe/Ni-intercalated graphite with a predesigned mole ratio of 1:3 transforms into NiFe_(2)O_(4)/FeNi_(3)@EG during the annealing process.The synthesized magnetic EG hybrids present multiband microwave absorption in C and X bands due to improved impedance match as well as significantly enhanced interfacial polarization relaxation induced by multi-componential metals.The reflection values of−39.1 dB at 6.95 GHz and−25.7 dB at 9.4 GHz are achieved with an ultra-low loading of 5 wt.%.This work provides a flexible approach for tuning the components and structures of magnetic EG hybrids,which may contribute to the development of microwave absorption materials with superior performances.

Natural polymer-based adhesive hydrogel for biomedical applications

Hydrogel is a polymer network system that can form a hydrophilic three-dimensional network structure through different cross-linking methods.In recent years,hydrogels have received considerable attention due to their good biocompatibility and biodegradability by introducing different cross-linking mechanisms and functional components.Compared with synthetic hydrogels,natural polymer-based hydrogels have low biotoxicity,high cell affinity,and great potential for biomedical fields;however,their mechanical properties and tissue adhesion capabilities have been unable to meet clinical requirements.In recent years,many efforts have been made to solve these issues.In this review,the recent progress in the field of natural polymer-based adhesive hydrogels is highlighted.The authors first introduce the general design principles for the natural polymer-based adhesive hydrogels being used as excellent tissue adhesives and the challenges associated with their design.Next,their usages in biomedical applications are summarised,such as wound healing,haemostasis,nerve repair,bone tissue repair,cartilage tissue repair,electronic devices,and other tissue repairs.Finally,the potential challenges of natural polymer-based adhesive hydrogels are presented.