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The shear behavior of large-scale weak intercalation shear zones (WISZs) often governs the stability of foundations, rock slopes, and underground structures. However, due to their wide distribution, undulating morphology, complex fabrics, and varying degrees of contact states, characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging. This study proposes an analytical method to address this issue, based on geological fieldwork and relevant experimental results. The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters. The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC, enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station. The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5–1 times the main powerhouse span, showing local activation. Furthermore, the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

Abstract：In this study, a total of 911 soil samples were collected throughout Qingyang county in Anhui province and the content of organic matter, total nitrogen, alkali-hydrolysable nitrogen, available potassium, and available phosphorus in the selenium-rich soil picked out was examined. According to the classification standard of selenium-rich soil in Anhui province, the characteristics of selenium content, grading and spatial distribution of selenium-rich soil were studied by using Kriging interpolation technique in GIS and moreover, the soil fertility level of selenium-rich soil was evaluated by index sum method to study soil selenium content and nutrient status in selenium-rich soil in Qingyang county and provide scientific basis for development of local selenium-rich. The results showed that the selenium content in Qingyang county ranged from 0.144 mg/kg to 13.989 mg/kg, and the average value was 0.368 mg/kg, which was 1.57 times of the background value of 0.234 mg/kg in Anhui province. For the soil in this county, selenium-rich soil with selenium content higher than 0.29mg/kg accounted for about 64% of soil of Qingyang and the selenium-rich soil was mainly distributed in the townships such as Xinhe, Muzhen, Dingqiao, Youhua. The spatial distribution showed a flake characteristic with gradual decreasing trend from east to west and north to south. The index sum method indicated that the IFI value of the fertility level of selenium-rich soil in Qingyang county was at the medium to low level of 79.97-80.80. In conclusion, Qingyang county in Anhui province had abundant selenium rich soil resources, and it was necessary to strengthen the fertility management of selenium-rich soil and improve the fertility level. [ABSTRACT FROM AUTHOR]

Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This study presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, the degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that the proposed algorithm delivers approximately 98% prediction accuracy. [ABSTRACT FROM AUTHOR]

The problem of noise levels in the home is of increasing interest for reasons of health and psychological well-being, among others. This study analyses in detail the meta-data compiled in the Spanish Census of Population and Housing, which provided data on a large number of environmental variables, including noise pollution in the home. A geostatistical study is conducted from the data provided by the latter survey, in which spatial autocorrelation is measured by analytical techniques by using R statistical software and the ESRI geographic information system. We study the empirical variogram, analize different theoretical models (gaussian, exponential and spherical) and estimate the parameters from different perspectives: constant or linear trend, weighted least squares, maximum likelihood and restricted maximum likelihood. The noise level is estimated through the kriging interpolation technique, using the different parameters of each model. [ABSTRACT FROM AUTHOR]