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This paper aims to investigate the connection between overall environmental quality and human development. Based on China’s provincial panel data from 2004 to 2017, this study constructed the Environment Degradation Index (EDI) and Human Development Index (HDI) to measure environmental pollution and human development, respectively, and it used the Simultaneous Equations Model (SEM) to assess the relationship between them. The results showed that there was an inverted U-shaped relationship found between EDI and HDI, and the coefficients of the first and second power of HDI were 5.2781 and -2.3476, respectively. Meanwhile, the results also confirmed that environmental pollution, in turn, delayed regional economic growth, and every 0.01 unit increase in EDI was correlated with a 3.15% decrease in GDP per capita. It is recommended that the government should speed up human development to surpass the turning point of the inverted U-shaped curve soonest possible.

Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly faster in computing attractors for empirical experimental systems.AvailabilityThe software package is available at https://sites.google.com/site/desheng619/download.

Drought disaster space agglomeration assessment is one of the important components of meteorological disaster prevention and mitigation. Agriculture affected by drought disaster is not only a serious threat to world food security, but also an obstacle to sustainable development. Additionally, China is an important agricultural import and export country in the world. Therefore, we used the global Moran’s I and the local indicators of spatial autocorrelation (LISA) to reveal the spatial agglomeration of agricultural drought disaster in China from1978 to 2016, respectively. The results showed that China’s agricultural drought disaster presents local spatial autocorrelation of geographical agglomeration at national level during the study period. The spatial agglomeration regions of China’s agricultural drought disaster were in Inner Mongolia, Jilin province, Heilongjiang province, Liaoning province, Shanxi province, Hebei province, Shandong province, Shaanxi province and Henan province, indicating that agricultural drought disaster mainly distributed in North and Northwest China, especially occurred in the Yellow River Basin and its north areas. We also found that the overall movement direction of agricultural drought disaster agglomeration regions was northwest, and the maximum moving distance was 722.16 km. Our results might provide insight in early warning and prevention for drought disaster.