Big data analysis of water quality monitoring results from the Xiang River and an impact analysis of pollution management policies

Water pollution prevention and control of the Xiang River has become an issue of great concern to China's central and local governments. To further analyze the effects of central and local governmental policies on water pollution prevention and control for the Xiang River, this study performs a big data analysis of 16 water quality parameters from 42 sections of the mainstream and major tributaries of the Xiang River, Hunan Province, China from 2005 to 2016. This study uses an evidential reasoning-based integrated assessment of water quality and principal component analysis, identifying the spatiotemporal changes in the primary pollutants of the Xiang River and exploring the correlations between potentially relevant factors. The analysis showed that a series of environmental protection policies implemented by Hunan Province since 2008 have had a significant and targeted impact on annual water quality pollutants in the mainstream and tributaries. In addition, regional industrial structures and management policies also have had a significant impact on regional water quality. The results showed that, when examining the changes in water quality and the effects of pollution control policies, a big data analysis of water quality monitoring results can accurately reveal the detailed relationships between management policies and water quality changes in the Xiang River. Compared with policy impact evaluation methods primarily based on econometric models, such a big data analysis has its own advantages and disadvantages, effectively complementing the traditional methods of policy impact evaluations. Policy impact evaluations based on big data analysis can further improve the level of refined management by governments and provide a more specific and targeted reference for improving water pollution management policies for the Xiang River

A \u3cem\u3eLIN28B\u3c/em\u3e Tumor-Specific Transcript in Cancer

The diversity and complexity of the cancer transcriptome may contain transcripts unique to the tumor environment. Here, we report a LIN28B variant, LIN28B-TST, which is specifically expressed in hepatocellular carcinoma (HCC) and many other cancer types. Expression of LIN28B-TST is associated with significantly poor prognosis in HCC patients. LIN28B-TST initiates from a de novo alternative transcription initiation site that harbors a strong promoter regulated by NFYA but not c-Myc. Demethylation of the LIN28B-TST promoter might be a prerequisite for its transcription and transcriptional regulation. LIN28B-TST encodes a protein isoform with additional N-terminal amino acids and is critical for cancer cell proliferation and tumorigenesis. Our findings reveal a mechanism of LIN28B activation in cancer and the potential utility of LIN28B-TST for clinical purposes

Three Typical Mental Disorders Associated With Behavioral Genetics And Environment

The purpose of the paper is to review the studies on family and identify the major factor contribute to these psychiatric problems. The basic psychiatric problem was range from bipolar disorder to antisocial personality disorder with the addition of reading disability. In the study of using the principle of animal behavior to research three different types of the psychiatric problem were being used to find out the influence of genetic and environment on both the history and current condition of the family and the impact on people’s future behavior with the experiment of how family study and twin study. This is a common psychiatric disease among current society due to the heavy pressure around people after reviewing different types of articles related to this psychiatric problem. The study was based on several datasets from the previous study, including 3 family studies and 6 twin studies with several different types of DSM questionnaires and interview information from thousands of twins with various in the category. With an analysis of all the statistical information, we conclude that the psychiatric problem is closely related. Both genetic and environmental are differentiated in the percentage of effect to the cause of the related problem

Random Characteristics of Hydraulic Gradient through Three-Dimensional Multilayer Embankment

The distribution characteristics of hydraulic gradient in embankment are closely related to seepage failure. Seepage failures such as flowing soil and piping will lead to serious damage and even the overall failure of embankment. The hydraulic conductivity has strong spatial variability, which changes the distribution of hydraulic gradient in embankment and increases the difficulty for predicting the embankment seepage instability. In this study, the distribution of soil hydraulic conductivity in a section of Shijiu Lake embankment was obtained by the permeability test. Based on Local Average Subdivision technique, a three-dimensional multilayer random field of embankment hydraulic conductivity was generated. Then, the mean and standard deviation of overflow point height and hydraulic gradient were calculated by the Monte Carlo method, which combined the generated three-dimensional random model and the deterministic analysis method of seepage field. Finally, the coefficient of variation (COV) of hydraulic conductivity (0.1, 0.3, 0.5, 0.7, 1.0, 2.0, and 3.0), the fluctuation scale in vertical direction (3 m) and the fluctuation scale in horizontal plane (3 m, 6 m, 12 m, 24 m, 36 m, and 48 m) were selected respectively for analyzing the random characteristics of embankment overflow point height and hydraulic gradient under the influence of different COV and fluctuation scale of embankment soil hydraulic conductivity

Application of Artificial Intelligence Large Language Model for Smart Environmental Judicial Adjudication

Environmental judicial adjudication is an essential component of the eco-environment governance system. The artificial intelligence large language model (AI-LLM), developed based on generative artificial intelligence, has offered significant opportunities for the environmental judicial adjudication to develop toward a higher level of smart adjudication. This study aims to promote the integration of AI-LLM technology with environmental judicial adjudication and promote the intelligent development of the environmental judicial adjudication. It explores the role and practical applications of AI-LLM in environmental judicial adjudication and summarizes prominent problems such as poor data quality, bias caused by algorithmic opacity, and limited capabilities for deep application. Using eco-environmental protection cases as an example, this study establishes a smart environmental judicial adjudication system based on AI-LLM and elaborates on the architecture design of the system and the technical elements involved. Furthermore, it proposes the following suggestions to promote the intelligent development of environment judicial adjudication: (1) emphasizing toplevel design and establishing a high-end think tank for environmental justice; (2) building an environmental justice data center to improve the judicial data standards system; (3) establishing an algorithmic governance mechanism to promote the fairness and justice in environmental judicial adjudication; and (4) improving the multiple accountability mechanism for environmental justice to strengthen the judicial supervision and management system