The Transporter Classification Database (TCDB; tcdb.org) is a freely accessible reference resource, which provides functional, structural, mechanistic, medical and biotechnological information about transporters from organisms of all types. TCDB is the only transport protein classification database adopted by the International Union of Biochemistry and Molecular Biology (IUBMB) and now (October 1, 2020) consists of 20 653 proteins classified in 15 528 non-redundant transport systems with 1567 tabulated 3D structures, 18 336 reference citations describing 1536 transporter families, of which 26% are members of 82 recognized superfamilies. Overall, this is an increase of over 50% since the last published update of the database in 2016. This comprehensive update of the database contents and features include (i) adoption of a chemical ontology for substrates of transporters, (ii) inclusion of new superfamilies, (iii) a domain-based characterization of transporter families for the identification of new members as well as functional and evolutionary relationships between families, (iv) development of novel software to facilitate curation and use of the database, (v) addition of new subclasses of transport systems including 11 novel types of channels and 3 types of group translocators and (vi) the inclusion of many man-made (artificial) transmembrane pores/channels and carriers.

No abstract

Traffic data is the premise of the number of call center seats. The corresponding agents can be arranged for different traffic volumes to achieve optimal configuration of call center human resources. In this paper, ARIMA model and LSTM neural network model based on time series are used to predict traffic. The traffic of the power call center in Hebei Province is taken as an example to conduct experiments on Python software. The results show that LSTM neural network model has higher prediction accuracy than ARIMA model.

In the accurate investment evaluation of power grid, the existing model does not consider the uncertainty of the actual evaluation object, and the evaluation subject mainly relies on subjective judgment, resulting in low information utilization rate. Therefore, the accurate investment evaluation model of power grid is designed based on Improved Fuzzy Neural Inference. The evaluation index system is established from three aspects of power supply capacity, power supply quality and power grid benefit of power grid investment, so as to achieve the purpose of giving consideration to both economic and social benefits. Based on the Improved Fuzzy Neural Inference, the membership function algorithm is designed, and the parameters are adjusted to obtain the best fuzzy inference results. According to the membership function and index improvement value, the standardized data table is established. The indicators of the same level are quantified, and the weight attributes are solved to complete the construction of accurate investment evaluation model of power grid. The experimental results show that the design model has certain advantages in the utilization rate of investment information, which is 10.59%, 15.40% and 9.95% higher than the existing models. It proves that the accurate investment evaluation model of power grid constructed in this paper is closer to the actual situation and has good application effect.

No abstract