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Cloud storage provides convenience in managing data for users. Data integrity becomes important because data owner (DO) loses control of their data once it is uploaded to the cloud server (CS). Public auditing is used to check data integrity in cloud storage. Traditional public auditing schemes introduce a third-party auditor (TPA) to help users check their data. However, TPA is assumed to be trusted in these schemes, which may not be practical. A dishonest TPA may provide a good report to DO without executing the auditing task timely. If the data loss could not be detected timely, it may cause a great loss to DO. In this paper we aim to solve these problems using blockchain technique. In our scheme DO, TPA and CS interact with blockchain via smart contracts. We utilize a time-locked deposit smart contract to incentive TPA and CS for their fulfillment in the auditing task honestly. Otherwise, they would be amerced. We use storage smart contracts to ensure the auditing process transparency, and utilize zero-knowledge proof to protect DO’s privacy. The scheme is extended to support batch auditing to reduce the user’s cost. Experimental results show that our scheme is efficient and practical. &nbsp

The Underground transportation system plays a vital role in public transportation and exhibits complicated dynamics. The model predictive control approach for underground train dispatching is propo...

AIM: To investigate the effects of baffle and intraocular pressure (IOP) on the aerosols generated in the noncontact tonometer (NCT) measurement and provide recommendations for the standardized use of the NCT during coronavirus disease 2019 (COVID-19). METHODS: This clinical trial included 252 subjects (312 eyes) in The Eye Hospital, Wenzhou Medical University from March 7, 2020, to March 28, 2020. Sixty subjects (120 eyes) with normal IOP were divided into two groups. One group used an NCT without a baffle, another group used an NCT with a baffle. Another 192 subjects (192 eyes) were divided into four groups: Group A(1) (without a baffle+normal IOP), Group A(2) (without a baffle+high IOP), Group B(1) (with a baffle+normal IOP) and Group B(2) (with a baffle+high IOP). Particulate matter (PM) 2.5 and PM10 generated by all subjects were quantified during the NCT measurement. The IOP values were recorded simultaneously. Effects of baffle and IOP on aerosols generated during the NCT measurement were analyzed. RESULTS: In the normal eye group with a baffle, the aerosol density decreased in a wave-like shape near the NCT with the increase in the number of people measured for IOP, demonstrating no cumulative effect. However, in the normal eye group without a baffle, there was a cumulative effect. PM2.5 and PM10 in Group A(2) were higher than Group A(1) (both P

AIM: To investigate the effects of school-based comprehensive intervention on myopia development in elementary school children. METHODS: As a part of the Wenzhou Epidemiology of Refraction Error Study, there were 1524 participating elementary students (730 girls, 47.9%) in grades 1 to 3 from three campuses of one school, aged 7.3±0.9y, who were examined twice every year for a 2.5y follow up period. Comprehensive intervention and other reminders were given at school every semester for the intervention group. The control group did not receive comprehensive intervention and did not have reminders of it. RESULTS: There were 651 students in the intervention group [mean age 7.3±0.9y; 294 (45.2%) girls] and 737 students in the control group [mean age 7.2±0.9y; 346 (46.9%) girls]. Overall mean myopia progression during the 2.5y follow-up was -0.49±1.04 diopters (D) in the intervention group and -0.65±1.08 D in the control group (P=0.004). The majority that not get myopia at baseline spherical equivalent (SE≤-1.0 D). Their mean myopia progression during the 2.5y follow-up was -0.37±0.89 D in the intervention group and -0.51±0.93 D in the control group (27.5% reduction, P=0.009); Overall, mean axial length elongation was less in the intervention group (0.56±0.32 mm) than in the control group (0.61±0.38 mm, 10.5% reduction, P=0.009). The percentage of close reading distance (

More and more high-power impact loads in the power grid are put into use. When the power is impacted, these loads will cause the power grid bus voltage fluctuations and reduce the power quality. Therefore, in order to accurately analyze the changes in the power system, it is particularly important to establish a reasonable impact load model. This paper takes the impact load data generated by the CT machine during exposure as an example to analyze the characteristics of the impact load. Based on the active power characteristics of the impact load, machine learning methods (such as support vector machines and long and short-term memory networks) are introduced into the impact load modeling, and the power waveform change law of the impact load is accurately established by this method. Finally, the effectiveness of the method is verified by simulation.

The city waste production forecasting is one of the important prerequisites to develop disposal measures for public services. The city waste systematic management started relatively late in china, the influence factors of waste output are relative larger, and the relationships among the factors are sometimes difficult to accurately described, moreover, the forecasting data sample is seldom. So the errors of the prediction were over large always. Based on the analysis of the traditional prediction GM (1,1) model, the application of background value optimizing GM(1,1)model to city waste production forecasting is proposed in this paper. Comparative analysis the waste production forecasting results of the traditional model and the optimization model in Shanghai between the year 2004 to 2009, it got verified the feasibility and effectiveness of the optimal of the background value in GM (1,1) model. Finally, the optimized GM (1,1) model is used to forecast the future waste production in Shanghai.