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AbstractIn this paper, we investigate systems subject to random shocks that are classified into critical and noncritical categories, and develop two novel critical shock models. Classical extreme shock models and run shock models are special cases of our developed models. The system fails when the total number of critical shocks reaches a predetermined threshold, or when the system stays in an environment that induces critical shocks for a preset threshold time, corresponding to failure mechanisms of the developed two critical shock models respectively. Markov renewal processes are employed to capture the magnitude and interarrival time dependency of environment‐induced shocks. Explicit formulas for systems under the two critical shock models are derived, including the reliability function, the mean time to failure and so on. Furthermore, the two critical shock models are extended to the random threshold case and the integrated case where formulas of the reliability indexes of the systems are provided. Finally, a case study of a lithium‐ion battery system is conducted to illustrate the proposed models and the obtained results.

In ancient China, where was frequently troubled by invaders, the government set up many beacon towers for alerting and transmitting military information along the border and the coast. Many beacon sites still exist in some areas, which are generally located in dangerous places with high mountains and rough terrain, bringing great difficulties to archaeological discovery. Therefore, it is particularly important to develop a predictive model applicable to the distribution of mountain beacon sites. Taking 68 beacon sites found in Wenzhou as research samples, this study used the superimposed method of logistic regression and viewshed analysis, forming a high-precision, scientific and operational predictive model for the distribution of beacon sites, which was verified by the cross-validation method. The results showed that the beacon site predictive model simulated in this study could reduce the probability scope of site location by 90% compared with the common logistic regression predictive model, which greatly improved the accuracy and ability of site prediction. At the same time, it could also be used to understand the relationship between the known sites and their surroundings to assist in decision-making about conservation and management.

Wu B, Guo M, Chi I, Plassman BL. Social network and health: a comparison of Chinese older adults in Shanghai and Chinese elderly immigrants in BostonInt J Soc Welfare 2011: 20: S59–S71 © 2011 The Author(s), International Journal of Social Welfare © 2011 Blackwell Publishing Ltd and the International Journal of Social Welfare.The association between social network and health in older Chinese immigrants in the USA and their counterparts in China was examined using data from 177 Chinese immigrants in Boston, USA and 428 respondents in Shanghai, China. The results showed that Chinese immigrants had a significantly better self‐rated health status but also that they reported a greater number of functional limitations. Although the level of the social network differed among respondents in Shanghai and in Boston, the impact of the social network on health status was similar for the two samples. The impact of the social network diminished when covariates such as health behaviors and health problems were added to the models. The conclusion, therefore, is that much of the association between a strong social network and better self‐reported health in later life may be explained by other factors such as socio‐economic status, health behaviors, health status, and access to health care.

AbstractLong-term phosphorus (P) fertilizer application can lead to an accumulation of uranium (U) in agricultural soil, potentially posing risks on the environment and human health. In this study, we found that such risks could be severe in two long-term grasslands (Andosol) in Iceland (Sámstaðir and Geitasandur) after about 50 years of P fertilization. At Sámstaðir, where P fertilizers were applied at an annual rate of 39.3 kg ha−1year−1, the soil U concentration increased from 0.65 mg kg−1in the unfertilized soil to 6.9 mg kg−1in the fertilized surface soil (0–5 cm). At Geitasandur with P fertilization rate at 78.6 kg ha−1year−1, the soil U concentration reached 15 mg kg−1. The average annual U accumulation rates were 130 and 310 µg kg−1year−1, respectively. These values were larger, by up to a factor of ten, than any previously reported rates of fertilizer-derived U accumulation. However, the U concentration in one of the applied P fertilizers was 95 mg U kg−1fertilizer, similar to the median value of those reported in previous studies, and thus unlikely to be the only factor leading to the high U accumulation rates. By contrast, as our Andosols had low bulk density within a range of 0.2 to 0.5 g cm−3, the annual U inputs to the 0–5 cm soil were 19 g ha−1year−1and 32 g ha−1year−1at the two sites, respectively, within the range of to-date reported values in agricultural systems. In addition, we found that U was mostly retained in the surface soil rather than mobilizing to deeper soil. This was likely due to the fact that the Andosols were rich in organic matter which promoted U retention. Therefore, the observed high U accumulation rates were a result of the combination of (i) the large amounts of the applied P fertilizers and (ii) the soil properties of the Andosols with low bulk density and elevated organic matter content concentrating U in the upper surface soil. Our study shows that agricultural production systems on Andosols may have already suffered from severe U contamination due to P fertilization. We are therefore calling for future checks and regulations on P fertilizer-related soil U accumulation in these and certain comparable agroecosystems.