Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm.

It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO-XGBOOST-CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO-XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO-XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO-XGBOOST-CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices.

Design of Semiautomatic Digital Creation System for Electronic Music Based on Recurrent Neural Network.

Semiautomated digital creation is increasingly important in the manipulation of electronic music. How to realize the learning of local effective features of audio data is a difficult point in the current research field. Based on recurrent neural network theory, this paper designs a semiautomatic digital creation system for electronic music for digital manipulation and genre classification. The recurrent neural network improves the transmission of electronic music information between the input and output of the network by adopting dense connections consistent with DenseNet and adopts an inception-like structure for the autonomous selection of effective recursive nuclear electronic music categories. In the simulation process, the prediction method based on semiautomatic digital audio clips is also adopted, which pays more attention to the learning of local effective features of audio data, which gives the model the ability to create audio samples of different lengths and improves the model's support for creative tasks in different scenarios. It includes the determination of the number of neurons, the selection of the function of neurons, the determination of the connection method, and the specific learning algorithm rules, and then the training samples are formed. The experimental results show that the recurrent neural network exhibits powerful feature extraction ability and classification ability of music information. The 10-fold cross-validation on GTZAN dataset and ISMIR2004 dataset has obtained 88.7% and 87.68%, surpassing similar ones. The model has reached a leading level. After further use of the MSD (Million Song Dataset) dataset for pre-semiautomatic training, the model effect has been further greatly improved. The accuracy rate on the dataset has been increased to 91.0% and 89.91%, respectively, which has improved the semiautomatic number and creative advancement.

[Investigation of medical journal website construction in Guangdong province].

OBJECTIVE: To investigate the current status of internet-based publication of biomedical journals and website construction in Guangdong province. METHODS: On the platform of the currently available databases for academic science journals (Wanfang Datebase, China Acdemic Journal Network, PubMed and SCI) with the assistance general searching engines, the websites of the medical journals in Guangdong province were evaluated and the editorial offices of the core medical journals in Guangdong province were interviewed. RESULTS: At present, the internet-based publication of the journals was executed predominantly by 3 means, namely utilization of the access to the websites of the direct sponsor, authoritative websites of science and technology, or independent websites. CONCLUSION: Relying on the database system of Wanfang and China Academic Journal Network, for instance, most of the journals can provide access to their full-text resources, but this internet-based publication is somehow delayed in comparison with the print publication. At present, only a few medical journals in Guangdong have built their independent websites, and among those which already have, Journal of First Military Medical University and Cancer are indexed by IM and PubMed, therefore they provide online abstracts of the papers ahead of the print copy publication.

[Manipulative reduction and splint fixation for the treatment of middle and lower segment fracture of ulnar and radius].

OBJECTIVE: To investigate the outcome of manipulative reduction and splint fixation for treatment of middle and lower fractures of ulnar and radius. METHODS: Sixty-eight patients with lower segment fractures of ulnar and radius included 46 male and 22 female, aged from 1 to 26 years. The course was from 10 min to 1 week. There were 39 cases in right and 29 in left; 45 in inferior segment and 23 in middle segment. All cases were closed fractures. According to fracture displacement, different methods of manipulation and splint or plaster splint fixation were used to reduction and fixation. RESULTS: Sixty-eight patient were followed-up for 0.5 to 1 year (mean 8 months), the fractures were all healing. According to the effective evaluation criteria, the results were excellent in 57 cases, good in 11. X-ray film showed anatomic reduction in 38 cases, similar to anatomic reduction in 20, functional reduction in 10. CONCLUSIONS: Manipulative reduction and splint fixation for the treatment of the ulnar and radial bone fractures is a method of simple, minimally invasive, effective and functional advantages of quick recovery.