回归分析与灰色系统耦合用于水环境预测研究

探讨了将多元回归分析模型和灰色系统（ＧＭ（１,１））耦合应用于武汉市东湖水体污染预测的方法和技术。首先根据多年污染因子浓度监测资料分别建立了ＣＯＤ、ＢＯＤ、ＴＮ、ＴＰ与人口、经济和捕鱼量之间的多元线性回归方程;之后根据人口、经济和鱼产量的历史资料,用ＧＭ（１,１）模型拟合,再反推得到预测值;最后,将人口、经济和鱼产量的预测值代入多元线性回归方程得到污染物浓度的未来值。这样将环境污染与社会经济联系起来更能反映环境质量的变化趋势。结果表明,到２１ 世纪,东湖水体的ＴＮ、ＴＰ、ＣＯＤ、ＢＯＤ 的浓度将分别是１９８２ 年的２．５８、３．７０、２．４５ 和７．８９ 倍;比１９９２年分别增长３３．５％ 、６８．１８％ 、６８．２８％ 和２２９．２４％ ,将处于超富营养状态,如不及时治理,生态环境将进一步恶化。     

Nonparametric spatial covariance functions: Estimation and testing

Spatial autocorrelation techniques are commonly used to describe genetic and ecological patterns. To improve statistical inference about spatial covariance, we propose a continuous nonparametric estimator of the covariance function in place of the spatial correlogram. The spline correlogram is an adaptation of a recent development in spatial statistics and is a generalization of the commonly used correlogram. We propose a bootstrap algorithm to erect a confidence envelope around the entire covariance function. The meaning of this envelope is discussed. Not all functions that can be drawn inside the envelope are candidate covariance functions, as they may not be positive semidefinite. However, covariance functions that do not fit, are not supported by the data. A direct estimate of the L₀ spatial correlation length with associated confidence interval is offered and its interpretation is discussed. The spline correlogram is found to have high precision when applied to synthetic data. For illustration, the method is applied to electrophoretic data of an alpine grass (Poa alpina).     

东南沿海地区集装箱内部温-湿度环境条件研究

以东南沿海地区军用物资开展集装箱运输为背景,在福州地区对集装箱内部温度、湿度和大气温度等数据进行测试,对数据进行回归分析后,建立集装箱内部温度模型,预测出该地区集装箱内部最恶劣的温度及湿度范围,为危险军用物资集装箱安全储运提供理论依据.     

外卖配送公共交通违法行为影响因素分析

通过问卷调查研究外卖配送公共交通违法行为影响因素，将外卖配送员分为曾经发生过和未发生过公共交通违法行为2组样本。通过Logistic回归对比分析，发现影响外卖配送员是否会发生公共交通违法行为的因素为平均每月送餐收入和平均每单配送距离；接着对其中发生过公共交通违法行为的1组样本，运用灰色关联分析法判断外卖配送员发生公共交通违法行为的主要驱动因素是平均每日接单数量、平均每月送餐收入以及平均每单配送时长，并根据影响因素提出了解决该问题的针对性长期建议。     

焊接作业厂房烟尘扩散规律研究与响应面分析

为研究焊接烟尘在多因素耦合作用下的扩散规律，通过在某小型焊接车间进行实验，将不同温度、湿度、风速3种影响因素进行耦合分析，测定不同环境条件下焊接车间内的烟尘浓度。采用Design Expert软件进行Box Behnken实验设计，构建电焊焊接烟尘浓度在3因素3水平条件下的二次回归响应曲面模型，通过研究二次模型实验因素间交互作用，了解二次项对烟尘浓度影响因素的排序，并简要分析电焊烟尘的治理措施。研究结果表明:温度、湿度、风速3个因素均为显著影响；二次项显示因素影响RhV（相对湿度与风速）>TRh（环境温度与相对湿度）>TV（环境温度与风速）。     

Heterogeneous effects of urbanization and environment Kuznets curve hypothesis in Africa

This work explores the heterogeneous effect of urbanization and nonrenewable energy consumption on the environment in 54 African nations. Panel data were used from 1996 to 2019. For estimation, panel quantile regression analysis, augmented mean group, panel threshold regression, and the environment Kuznets curve hypothesis were applied to check the relationship between income and carbon emissions. The study's outcome demonstrates that urbanization and nonrenewable energy consumption degrade the environment in Africa. Furthermore, an inverted U-shape relationship exists between economic growth and CO₂ emissions, confirming the environmental Kuznets curve hypothesis. The findings indicate that urbanization should be planned; otherwise, urbanization can cause environmental degradation. African countries must adopt green urbanization and use renewable energy and clean manufacturing technologies. The institutions are encouraged to execute the standard, regulatory environment, and policies to reduce carbon emissions. Countries throughout the African continent should actively respond to the issues by charting a separate and diverse route for urban development.     

“路怒症”的内外生逻辑及影响因素研究

为探索“路怒症”的内外生逻辑，运用调查问卷数据，采用灰色关联分析法从即时和长期2方面对引发路怒的原因进行分析。结果表明:在引发路怒的即时原因中，前车或其他车随意变换车道或调头、其他车辆插队或加塞、夜间行驶开远光灯是最重要的3个原因；在引发路怒的长期原因中，工作或生活压力大最易引发路怒。运用Logistic回归分析各主要路怒原因的易发驾驶人群，据此提出针对性建议，以促进城市交通安全有效治理。     

Future prediction & estimation of faults occurrences in oil pipelines by using data clustering with time series forecasting

The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.     

Analysis of factors affecting individual participation in carbon market

China will set up a national carbon emissions trading market by the end of 2017, which is initially open to individual investors from the initial market for business and institutional investors. In this article, the main influencing factors and mechanism of individual participation in carbon trading market are studied by establishing multiple linear regression model. The study found that age, education level, length of account opening time, and risk attitude are the main factors influencing the participation of individual investors. Environmental awareness and environmental impact are less affected; information transparency and transaction risk also have an impact on the degree of individual investor participation; investment experience does not affect the participation of individual investors in the carbon trading market.