Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems

The Recommender system is a vital information service on today's Internet. Recently, graph neural networks have emerged as the leading approach for recommender systems. We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems. We also analyze challenges and open problems in graph construction, embedding propagation and aggregation, and computation efficiency. This guides us to better explore the future directions and developments in this domain.Comment: arXiv admin note: text overlap with arXiv:2103.08976 by other author

Knowledge, Attitudes, and Social Responsiveness Toward Corona Virus Disease 2019 (COVID-19) Among Chinese Medical Students—Thoughts on Medical Education

Purpose: To assess knowledge, attitudes, and social responsiveness toward COVID-19 among Chinese medical students.Methods: Self-administered questionnaires were used to collect data from 889 medical students in three well-known Chinese medical universities. The questionnaire was comprised of three domains which consisted of demographic characteristic collection, seven items for knowledge, and eight items for attitudes and social responsiveness toward COVID-19. Data from different universities were lumped together and were divided into different groups to compare the differences, including (1) students at the clinical learning stage (Group A) or those at the basic-medicine stage (Group B) and (2) students who have graduated and worked (Group C) or those newly enrolled (Group D).Results: Medical students at group B had a weaker knowledge toward COVID-19 than did students at group A, especially in the question of clinical manifestations (p < 0.001). The percentage of totally correct answers of COVID-19 knowledge in group C was higher than that in Group D (p < 0.001). There were significant differences between groups C and D in the attitudes and social responsiveness toward COVID-19. Surprisingly, we found that the idea of newly enrolled medical students could be easily affected by interventions.Conclusions: In light of this information, medical education should pay attention not only to the cultivation of professional knowledge and clinical skills but also to the positive interventions to better the comprehensive qualities including communicative abilities and empathy

Yi Qi Qing Re Gao Attenuates Podocyte Injury and Inhibits Vascular Endothelial Growth Factor Overexpression in Puromycin Aminonucleoside Rat Model

Proteinuria is the hallmark of chronic kidney disease. Podocyte damage underlies the formation of proteinuria, and vascular endothelial growth factor (VEGF) functions as an autocrine/paracrine regulator. Yi Qi Qing Re Gao (YQQRG) has been used to treat proteinuria for more than two decades. The objective of this study was to investigate the protective effect and possible mechanisms of YQQRG on puromycin aminonucleoside (PAN) rat model. Eighty male Sprague-Dawley rats were randomized into sham group, PAN group, PAN + YQQRG group, and PAN + fosinopril group. Treatments were started 7 days before induction of nephrosis (a single intravenous injection of 40 mg/kg PAN) until day 15. 24 h urinary samples were collected on days 5, 9, and 14. The animals were sacrificed on days 3, 10, and 15, respectively. Blood samples and renal tissues were obtained for detection of biochemical and molecular biological parameters. YQQRG significantly reduced proteinuria, elevated serum albumin, and alleviated renal pathological lesions. YQQRG inhibited VEGF-A, nephrin, podocin, and CD2AP mRNA expression and elevated nephrin, podocin, and CD2AP protein levels starting on day 3. In conclusion, YQQRG attenuates podocyte injury in the rat PAN model through downregulation of VEGF-A and restoration of nephrin, podocin, and CD2AP protein expression

Investigation and Validation of the Time-Varying Characteristic for the GPS Differential Code Bias

The time-varying characteristic of the bias in the GPS code observation is investigated using triple-frequency observations. The method for estimating the combined code bias is presented and the twelve-month (1 January⁻31 December 2016) triple-frequency GPS data set from 114 International GNSS Service (IGS) stations is processed to analyze the characteristic of the combined code bias. The results show that the main periods of the combined code bias are 12, 8, 6, 4, 4.8 and 2.67 h. The time-varying characteristic of the combined code bias, which is the combination of differential code bias (DCB) (P1⁻P5) and DCB (P1⁻P2), shows that the real satellite DCBs are also time-varying. The difference between the two sets of the computed constant parts of the combined code bias, with the IGS DCB products of DCB (P1⁻P2) and DCB (P1⁻P2) and the mean of the estimated 24-h combined code bias series, further show that the combined code bias cannot be replaced by the DCB (P1⁻P2) and DCB (P1⁻P5) products. The time-varying part of inter-frequency clock bias (IFCB) can be estimated by the phase and code observations and the phase based IFCB is the combinations of the triple-frequency satellite uncalibrated phase delays (UPDs) and the code-based IFCB is the function of the DCBs. The performances of the computed the IFCB with different methods in single point positioning indicate that the accuracy for the constant part of the combined code bias is reduced, when the IGS DCB products are used to compute. These performances also show that the time-varying part of IFCB estimated with phase observation is better than that of code observation. The predicted results show that 98% of the predicted constant part of the combined code bias can be corrected and the attenuation of the predicted accuracy is much less evident. However, the accuracy of the predicted time-varying part decreases significantly with the predicted time

Synthesis Load Forecasting Method Based on Artificial Immune System for Power System

Electric power load forecasting is not only the sticking point of the safely, operation of whole system, but also the key part of the economical and healthy development of electric power system. The intrinsic single models have shortage, so the synthesis forecasting model making better use of all information will be pursued. It combines those single models property to take full advantage of their information to improve the precision. The most important part of the combination forecasting model is how to confirm the weight. In AIS, antigen and antibody are the parallelism of aim function and doable result. The appetency between antigen and antibody is regarded as the matching degree between feasible result and the objective function. Because of its good property on global searching, it can find the optimal solutions, some synthetic forecasting models based on AIS are set up in this paper, which combine AIS and load forecasting. The attempter average synthetic model and power geometry average synthetical model proposed in this paper, has been applied to a certain area mid-long term load forecasting. It is showed that the synthetic forecasting model based on AIS could provide high forecasting precision

Short-term Load Forecasting Model Considering Meteorological Factors

Because of the limitation of basic data and processing methods, the traditional load characteristic analysis method can not achieve user-level refined prediction. This paper builds a user-level short-term load forecasting model based on algorithms such as decision trees and neural networks in big data technology. Firstly, based on the grey relational analysis method, the influence of meteorological factors on load characteristics is quantitatively analyzed. The key factors are selected as input vectors of decision tree algorithm. This paper builds a category label for each daily load curve after clustering the user’s historical load data. The decision tree algorithm is used to establish classification rules and classify the days to be predicted. Finally, Elman neural network is used to predict the short-term load of a user, and the validity of the model is verified

Artificial Neural Network Model for Temperature Prediction and Regulation during Molten Steel Transportation Process

With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tested using large amounts of field data to predict the end-point steel temperature of basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The prediction model was applied to predict the degree of influence of different operating parameters on steel temperature. A comparison of the prediction results with the production data shows that the prediction system has good prediction accuracy, with a hit rate of over 90% for steel temperature deviations within 20 °C. Compared with the traditional steel temperature management model, the prediction system in this paper has higher management efficiency and a faster response time and is more practical and generalizable in the thermal management of steel