Effect of Investment in Environmental Protection on Green Development of Industrial Enterprises: Evidence from Central China

China\u27s industrialization and urbanization process is advancing rapidly. While using natural resources, the country also generates a large amount of waste, causing serious environmental pollution and affecting further development of human society. Expanding the scale of investment in environmental protection has gradually become an effective means to address the problem. China is continuously increasing investment in environmental protection, actively improving environmental conditions, and achieving the dual goals of promoting high-quality development and environmental protection. Six provinces in central China were taken as research objects and the regional differences in their investments in environmental protection were analyzed. A panel entropy weight model was used to calculate the green development level of industrial enterprises, and a panel regression model was employed to calculate the impact of investment in environmental protection on the degree of influence of the green development of industrial enterprises. Results show that the six provinces in central China have significant differences in their investment in industrial environmental pollution control. The unreasonable allocation of environmental protection investment funds has led to the insignificant improvement of environmental pollution caused by industrial enterprises in the six central provinces of investment in environmental protection. R&D expenditure of industrial enterprises, the total import, and export volume of foreign-invested enterprises, and the fixed asset investment of the entire society have a positive role in promoting the green development of industrial enterprises. The added value of the secondary industry has a significant negative effect on the green development of industrial enterprises. Conclusions can be used as a reference for encouraging industrial enterprises to increase investment in environmental protection and promote green development

Exploiting family history in genetic analysis of rare variants

Genetic association analyses have successfully identified thousands of genetic variants contributing to complex disease susceptibility. However, these discoveries do not explain the full heritability of many diseases, due to the limited statistical power to detect loci with small effects, especially in regions with rare variants. The development of new and powerful methods is necessary to fully characterize the underlying genetic basis of complex diseases. Family history (FH) contains information on the disease status of un-genotyped relatives, which is related to the genotypes of probands at disease loci. Exploiting available FH in relatives could potentially enhance the ability to identify associations by increasing sample size. Many studies have very low power for genetic research in late-onset diseases because younger participants do not contribute a sufficient number of cases and older patients are more likely deceased without genotypes. Genetic association studies relying on cases and controls need to progress by incorporating additional information from FH to expand genetic research. This dissertation overcomes these challenges and opens up a new paradigm in genetic research. The first chapter summarizes relevant methods used in this dissertation. In the second chapter, we develop novel methods to exploit the availability of FH in aggregation unit-based test, which have greater power than other existing methods that do not incorporate FH, while maintaining a correct type I error. In the third chapter, we develop methods to exploit FH while adjusting for relatedness using the generalized linear mixed effect models. Such adjustment allows the methods to have well-controlled type I error and maintain the highest sample size because there is no need to restrict the analysis to an unrelated subset in family studies. We demonstrate the flexibility and validity of the methods to incorporate FH from various relatives. The methods presented in the fourth chapter overcome the issue of inflated type I error caused by extremely unbalanced case-control ratio. We propose robust versions of the methods developed in the second and third chapters, which can provide more accurate results for unbalanced study designs. Availability of these novel methods will facilitate the identification of rare variants associated with complex traits

Technology Research of Large Underwater Ultra-deep Curtain Grouting in Zhong-guan Iron Ore

AbstractProblems in Zhong-guan Iron Ore are complicated hydrogeological conditions, larger water inflow in mine ore, all ore bodies buried under the water table, ordovician limestone aquifer in the system directly to the roof for the ore body. Paper used ring-type single-row curtain grouting closed ground plan. This has not only achieved the safety of mining, but also protected ground water resources and hydro-geological environment. Study has shown that: the elevation of purdah base is -96 m ∼ -568 m, the average drilling depth is 523.92 m, the minimum hole depth is 321 m, and the maximum is 810 m, holes depth greater than 600 m take up about 30.8 A single slurry material can allow seepage gradient and the curtain can withstand the maximum head difference design curtain thickness T > 10 m, grouting hole spacing is designed to 12 m; curtain grouting pressure is 2 times of the head pressure. Research improves reference for similar mines

Estimating adaptive cruise control model parameters from on-board radar units

Two new methods are presented for estimating car-following model parameters using data collected from the Adaptive Cruise Control (ACC) enabled vehicles. The vehicle is assumed to follow a constant time headway relative velocity model in which the parameters are unknown and to be determined. The first technique is a batch method that uses a least-squares approach to estimate the parameters from time series data of the vehicle speed, space gap, and relative velocity of a lead vehicle. The second method is an online approach that uses a particle filter to simultaneously estimate both the state of the system and the model parameters. Numerical experiments demonstrate the accuracy and computational performance of the methods relative to a commonly used simulation-based optimization approach. The methods are also assessed on empirical data collected from a 2019 model year ACC vehicle driven in a highway environment. Speed, space gap, and relative velocity data are recorded directly from the factory-installed radar unit via the vehicle's CAN bus. All three methods return similar mean absolute error values in speed and spacing compared to the recorded data. The least-squares method has the fastest run-time performance, and is up to 3 orders of magnitude faster than other methods. The particle filter is faster than real-time, and therefore is suitable in streaming applications in which the datasets can grow arbitrarily large.Comment: Accepted for poster presentation at the Transportation Research Board 2020 Annual Meeting, Washington D.