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Hydraulic fracturing of shale gas reservoirs is characterized by large fracturing fluid consumption, long working cycle and low flowback efficiency. Huge amounts of fracturing fluid retained in shale reservoirs for a long time would definitely cause formation damage and reduce the gas production efficiency. In this work, a pressure decay method was conducted in order to measure the amount of fracturing fluid imbibition and sample permeability under the conditions of formation temperature, pressure and adsorbed methane in real time. Experimental results show that (1) the mass of imbibed fracturing fluid per unit mass of shale sample is 0.00021–0.00439 g/g considering the in-situ pressure, temperature and adsorbed methane. (2) The imbibition and flowback behavior of fracturing fluid are affected by the imbibition or flowback pressure difference, pore structure, pore surface properties, mechanical properties of shale and mineral contents. (3) 0.01 mD and 0.001 mD are the critical initial permeability of shales, which could be used to determine the relationship between the formation damage degree and the flowback pressure difference. This work is beneficial for a real experimental evaluation of shale formation damage induced by fracturing fluid.

Abstract In order to strengthen the scientific management of saline-alkali land and the accurate management of agricultural production, a scientific data governance platform for saline-alkali land was developed. Based on the data accumulation of big data platform, the relationship between wheat growth and meteorology was taken as the research object. Thirteen variables including atmospheric pressure, temperature, light, and precipitation were extracted from surface meteorological data for correlation analysis, and temperature, precipitation, and sunshine were selected as the characteristic variables; through discretization processing, we have finally determined the three indicators that can be categorized: accumulative temperature, sunshine hours, and temperature. Finally, the three indicators are combined with months to build a model of farming season and weather based on Apriori. The results show that when judging the farming season with the month as the index, the accuracy of the model is between 78.81 and 100%. When the temperature or accumulated temperature is taken as the index to judge the winter wheat farming time, the accuracy of the model is above 90%. This shows that accurate analysis of farming season can be achieved through big data correlation analysis, which provides technical support for the timely adoption of agricultural production.

Abstract In view of the problems existing in the processing of duck products, such as complicated technology, difficulties in information collection and information linkage, and lack of dedicated information collection equipment, the duck product traceability information of the Institute of Things automatic collection system was developed. Acquisition system is mainly composed of sensing terminals, bus structure, and host computer management system. Perceiving the terminal can automatically perceive the information of each link and transmit the perception information to the upper computer management system through the bus. The upper computer management system realizes the functions of storing the sensing information, intelligent analysis, and alarm prompting. The long-term operation results show that the system performance is stable and reliable; the collection of data is efficient, complete, and accurate; and the degree of automation of the system is high, which significantly improves the product quality and safety supervision capabilities of the company.

Wireless sensor networks (WSNs) have been pushed to the forefront in the past decade, owing to the advert of the Internet of Things. Our research suggests that the reliability and lifetime performance of a typical application in WSNs depends crucially on a set of parameters. In this paper, we implemented our experiments on the nearest closer protocol with the J-Sim simulation tool. We then analyze the closure relationships among the density, reliability and lifetime, and reveal the trade-off among them based on our analysis on the experiment results. Next, we propose five intelligent evaluation models that are applicable to such situations. Our research allows the WSN users to predict the significant evaluation parameters directly from the settings while costly simulations are no longer necessary.

Trend testing is essential for time sequence analysis. However, the existing trend testing methods mainly study the trends of the sequence as a whole, while there is a lack of feasible research tools for the internal trends of the sequence. Therefore, a non-parametric method was proposed to study the overall and internal trends of the sequence using the ideas of set pair, Cox-Stuart, Innovative Trend Analysis Methodology, and Mann-Kendall and applied to temperature and precipitation sequences. The applied study indicated that the overall and internal trends for global temperature were significantly increasing at the confidence level of α = 0.05. For precipitation, the trends (both overall and internal) of Laifeng and Leibo were increasing and decreasing, respectively, and some of the internal trends were significant (α = 0.05); however, the overall and internal trends of Pingbian and Sangzhi were not exactly the same, i.e., the trend of high (low) values in Pingbian (Sangzhi) was different from the other trends of the rest. In general, the method successfully tested not only the overall trends in temperature and precipitation, but also their internal (divided into low, middle, and high values) trends. These results agreed with the linear slope, Sen's slope, Mann-Kendall, and its improved. Therefore, the method can be used for trend analysis of the sequences.

Drought is a natural phenomenon caused by a shortage of water resources and has a great impact on agriculture, the economy, and the environment. To study the Spatio-temporal distribution and explore the zonal patterns of drought, this paper took the Yunnan-Guizhou Plateau (YGP) as the research region, selected the air temperature, relative humidity, sunshine duration, wind speed, and precipitation data from 47 meteorological stations on the YGP. First, the standardized precipitation evapotranspiration index (SPEI) was calculated, and then the Spatio-temporal distribution of YGP drought was analyzed with the SPEI, Mann–Kendall test, and principal component analysis (PCA). Finally, the correlations between the average SPEI, drought characteristics extracted from the SPEI, and longitude/elevation/latitude were analyzed with the linear regression method, and then the zonal patterns of the YGP drought were obtained. The results revealed that the annual and seasonal SPEI values mainly decreased. Because the first component of the SPEI (the largest eigenvalue makes it the most important component) in annual and winter had a poor relationship with longitude/elevation, its correlation was weak, while the average SPEI values in other seasons were significantly correlated with longitude/elevation (α = 0.001), and the absolute value of the correlation coefficient was between 0.6879–0.9453. Except for PC1 in annual and winter, PC1 and PC2 were significantly correlated with longitude/elevation (α = 0.001), and the absolute value of the correlation coefficient was between 0.5087–0.9501. The duration, severity, intensity, frequency of drought were significantly correlated with longitude/elevation (α = 0.001) in most situations. The average SPEI values and drought characteristics showed a good multivariate linear correlation with longitude, latitude, and elevation, indicating that drought exhibited strong zonal patterns. This study will provide new ideas for drought research and technical support for regional industrial layouts, planting structure adjustments, and drought and disaster reduction.

Background. Kawasaki disease (KD) is a self-limited vasculitis with unknown etiologies, and coronary artery lesions (CALs) are the most common and serious complications. Retinol-binding protein 4 (RBP4) has been confirmed effects on vasodilation, platelet activation inhibition, and cardiovascular diseases by researches. Therefore, this study was aimed at investigating the relationship between RBP4 and inflammation as well as thrombogenesis in children with KD. Methods. 79 subjects were from 62 children with KD and 17 healthy controls (HCs). The KD group was divided into KD with CALs (KD-CALs) and KD without CALs (KD-NCALs), and the serum RBP4 levels were measured by enzyme-linked immunosorbent assay (ELISA). Results. Compared with the HC group, serum RBP4 levels in the KD group were significantly decreased (p