Design and Implementation of Fresh Vegetable Sales Volume Trend Forecasting System Based on Improved SVR

The forecast of sales volume trend of fresh vegetables has significant referential function for government dominant departments,producers and consumers.In order to evaluate the e-commerce sales information of fresh vegetables scientifically and accurately,the sales volume information of such four common vegetables as baby cabbage,potatoes,bok choy and tomatoes,from Anhui Jinghui Vegetable E-commerce Co.,Ltd.was selected as the research object to establish the sales trend prediction system.Taking the improved SVR as an example,we introduced the overall architecture,detailed design and function realization of the system.The system can reflect the short-term sales volume trend of fresh vegetables,and also can provide guidance for the realization of e-commerce order-oriented management and scientific production.

Relation Extraction Based on Dual Attention Mechanism

The traditional deep learning model has problems that the longdistance dependent information cannot be learned, and the correlation between the input and output of the model is not considered. And the information processing on the sentence set is still insufficient. Aiming at the above problems, a relation extraction method combining bidirectional GRU network and multiattention mechanism is proposed. The word-level attention mechanism was used to extract the word-level features from the sentence, and the sentence-level attention mechanism was used to focus on the characteristics of sentence sets. The experimental verification in the NYT dataset was conducted. The experimental results show that the proposed method can effectively improve the F1 value of the relationship extraction.

Tea picking point detection and location based on Mask-RCNN

The accurate identification,detection,and segmentation of tea buds and leaves are important factors for realizing intelligent tea picking.A tea picking point location method based on the region-based convolutional neural network(R-CNN)Mask-RCNN is proposed,and a tea bud and leaf and picking point recognition model is established.First,tea buds and leaf pictures are collected in a complex environment,the Resnet50 residual network and a feature pyramid network(FPN)are used to extract bud and leaf features,and preliminary classification and preselection box regression training-performed on the feature maps through a regional proposal network(RPN).Second,the regional feature aggregation method(RoIAlign)is used to eliminate the quantization error,and the feature map of the preselected region of interest(ROI)is converted into a fixed-size feature map.The output module of the model realizes the functions of classification,regression and segmentation.Finally,through the output mask image and the positioning algorithm the positioning of the picking points of tea buds and leaves is determined.One hundred tea tree bud and leaf pictures in a complex environment are selected for testing.The experimental results show that the average detection accuracy rate reaches 93.95%and that the recall rate reaches 92.48%.The tea picking location method presented in this paper is more versatile and robust in complex environments.

Solid-state Li-air batteries:Fundamentals,challenges,and strategies

The landmark Net Zero Emissions by 2050 Scenario requires the revolution of today's energy system for realizing nonenergy-related global economy.Advanced batteries with high energy density and safety are expected to realize the shift of end-use sectors toward renewable and clean sources of electricity.Present Li-ion technologies have dominated the modern energy market but face with looming challenges of limited theoretical specific capacity and high cost.Li-air(O2)battery,characterized by energy-rich redox chemistry of Li stripping/plating and oxygen conversion,emerges as a promising“beyond Li-ion”strategy.In view of the superior stability and inherent safety,a solid-state Li-air battery is regarded as a more practical choice compared to the liquid-state counterpart.However,there remain many challenges that retard the development of solid-state Li-air batteries.In this review,we provide an in-depth understanding of fundamental science from both thermodynamics and kinetics of solid-state Li-air batteries and give a comprehensive assessment of the main challenges.The discussion of effective strategies along with authoritative demonstrations for achieving highperformance solid-state Li-air batteries is presented,including the improvement of cathode kinetics and durability,solid electrolyte design,Li anode optimization and protection,as well as interfacial engineering.

Interfacial engineering of Ni/V2O3 for hydrogen evolution reaction

Electrocatalytic water splitting offers a sustainable route for hydrogen production,enabling the clean and renewable alternative energy system of hydrogen economy.The scarcity and high-cost of platinum-group-metal(PGM)materials urge the exploration of high-performance non-PGM electrocatalysts.Herein,a unique hierarchical structure of NiA/2O3 with extraordinary electrocatalytic performance(e.g.t overpotentials as low as 22 mV at 20 mA·cm^-2 and 94 mV at 100 mA·cm^-2)toward hydrogen evolution reaction in alkaline electrolyte(1 M KOH)is reported.The investigation on the hierarchical NiA/2O3 with a bimodal size-distribution also offers insight of interfacial engineering that only proper NiA/2O3 interface can effectively improve H20 adsorption,H20 dissociation as well as H adsorption,for an efficient hydrogen production.

Enhanced bioavailability and biosafety of cannabidiol nanomicelles for effective anti-inflammatory therapy

Cannabidiol(CBD)shows great anti-inflammatory potential;however,the hydrophobicity and strong first-pass effect of CBD leads to its extremely low oral bioavailability.Poloxamer 407(P407)is a tri-block copolymer composed of(poly)ethylene oxide(PEO)and(poly)propylene oxide(PPO)sections.It has a PEO-PPO-PEO structure,which is widely used in the preparation of drug delivery systems that are highly biocompatible.When it reaches a certain concentration in water,P407 can self-assemble into a micelle structure containing a hydrophobic core and a hydrophilic shell.A potential approach to enhanc-ing the oral bioavailability of hydrophobic drugs incorporating them into the hydrophilic carrier.We prepared CBD nanomicelles with a drug loading of 14.29%by a cosolvent evaporation method using P407 with appropriate antioxidants.Cell experiments indicated that anti-inflammatory markers(IL-4 and IL-10)increased,while inflammatory markers(TNF-αand IL-6)decreased.Moreover,animal experiments showed that inflammatory cells were inhibited by CBD nanomicelles,and the anti-inflammatory effect of micelles was better than that of CBD,while no obvious evidence indicated toxicity to the liver and kidney.

Content-Based Hybrid Deep Neural Network Citation Recommendation Method

The rapid growth of scientific papers makes it difficult to query related papers efficiently,accurately and with high coverage.Traditional citation recommendation algorithms rely heavily on the metadata of query documents,which leads to the low quality of recommendation results.In this paper,DeepCite,a content-based hybrid neural network citation recommendation method is proposed.First,the BERT model was used to extract the high-level semantic representation vectors in the text,then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence,and the text vectors were matched in depth to generate candidate sets.Further,the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multisource features.In the reranking stage,a variety of Metapath features were extracted from the citation network,and added to the deep neural network to learn,and the ranking of recommendation results were optimized.Compared with PWFC,ClusCite,BM25,RW,NNRank models,the results of the Deepcite algorithm presented in the ANN datasets show that the precision(P@20),recall rate(R@20),MRR and MAP indexesrise by 2.3%,3.9%,2.4%and 2.1%respectively.Experimental results on DBLP datasets show that the improvement is 2.4%,4.3%,1.8%and 1.2%respectively.Therefore,the algorithm proposed in this paper effectively improves the quality of citation recommendation.