Small and medium-sized enterprises (SMEs) are now growing rapidly and playing an important role in the development of the national economy. As the economy grows, the contradiction between the credit risk of SMEs and the credit risk early warning mechanism of traditional supply chain financing has become increasingly important. In response to the issues of a single source of business information, the high investment cost of the existing early risk early warning mechanism, etc., from a commercial bank credit risk management perspective, this paper proposes to build an SMEs credit risk early warning system based on reliable blockchain data. The reliability of the data obtained is assessed utilising a hierarchical analysis and a vague overall judgement method. The results show that the use of blockchain technology can enhance the credibility and accuracy of the data, which provides a data guarantee for more rapid risk alert.
Introduction: Nowadays, physical activities have become an important way for people to improve their physical fitness. Running is considered to be an exercise option with low investment, low consumption, and no space restrictions. It can play a role in physical fitness, entertainment, and communication. Objective: Analyze the impact of running activities on athletes’ physical fitness. Methods: Volunteer athletes were randomly divided into two groups (experimental and control groups). The experimental group performed running activities for eight weeks while the control group did not participate in running. Key physiological and biochemical indicators such as height, weight, blood pressure, blood lipids, etc. were recorded. The data were collected and processed according to the mathematical statistics method to analyze the data. Results: There is no significant difference in height after the experiment (P>0.05). The value of body weight curve was changed after the increment of running exercise (P<0.05). There was no significant difference in bust size between the two tests (P>0.05). Waist circumference, hip circumference and upper limb circumference were significantly different after the test (P<0.05). The subjects’ blood pressure and heart rate before and after the test were significantly different, proportional to the changes in mean vital capacity (P<0.01). Conclusion: Long-term running training impacts athletes’ physical and mental health. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
With the non-stop merchandising and improvement of bodily training instructing reform, the institution of a couple of getting to know the assessment and evaluation mannequin of bodily training different educating impact can stimulate the enthusiasm and activity in sports activities and learning, which is useful to domesticate students’ cognizance of lifelong bodily exercise. In the educating process, through the integration technique of varied instructing methods, this paper explores the issues present in the method of bodily training reform, places ahead a diverse bodily training instructing impact assessment mannequin that meets the wants of customized intelligence education and construction, and makes use of the technique of empirical evaluation to affirm the comparison model. In order to reflect the effect of physical training effectively and accurately, a convolution neural neighbourhood model based totally completely on the random multivariate matrix is proposed to reflect on consideration on the excellent of bodily education. The overall performance of the prediction accuracy assessment mannequin is evaluated via the simulation test and in contrast with the standard approach model. The experimental information is that the average assessment accuracy of the single convolution neural community mannequin is 82.15%, whilst the common comparison accuracy of the convolution neural community mannequin based totally on the random multivariate matrix is 97.58%, and the prediction accuracy is increased by means of 15.43%. The common prediction error of the single convolution neural community mannequin is 0.97, and the common error is 0.91, the common error is decreased by using 0.05. It shows that the random multivariate matrix convolution neural neighbourhood model can efficaciously realize the evaluation of instructing quality.
One of the biggest challenges for Internet of Things (IoT) systems is traffic congestion in large networks. For this reason, the bandwidth should be increased in such systems. In addition, the issue of routing is raised in sending packets from the origin to the destination. Therefore, if there are many IoT devices in the network, it will increase the traffic, which makes faultless routing important in these networks. In this paper, a novel routing method based on Routing Protocol for Low-Power (RPL) is presented to minimize the energy consumption of the Internet of Things. Using the backward method based on the A* method to reduce energy consumption in a large graph, promising nodes are selected. A coordinate node is used to manage packets and transfer them. The selection of the coordinator node helps to receive packets with less energy and less delay from its neighbors, and the head node selects the best coordinator node with the shortest distance and the highest residual energy. The proposed method improves the energy consumption criteria, the delay between nodes, and the network overhead criterion by considering the estimated energy to the destination with the A* method.
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