基于Logistic回归模型的呼伦贝尔草原火险预测研究

目前国内外还没有对不同火险条件下草原火险时空发生概率的研究,而这方面研究对草原火灾管理对策和防火救助应急预案的制定具有重要意义.根据呼伦贝尔草原火灾统计月报表和相关气象、社会经济资料,利用Logistic回归模型建立草原火险预测模型,对草原火险进行了空间上的预测.结果表明,日平均风速、日降水量对草原火险影响较大. 以2005年所有火灾案例对草原火险预测模型进行检验,研究表明,该预测方法具有较高的可靠性,可为火灾管理和减灾决策的制定提供指导.     

阴/非离子化学剂与糖脂类生物剂复配对煤尘润湿效果影响

为解决糖脂类生物表面活性剂润湿性能差的问题,选取4种化学表面活性剂和3种生物表面活性剂,通过对化学表面活性剂与生物表面活性剂的溶液的表面张力、沉降速度和红外光谱分析,研究化学表面活性剂与生物表面活性剂复配对煤尘润湿效果的影响。研究结果表明：化学表面活性剂与生物表面活性剂复配对煤尘的润湿性存在增强作用,且化学表面活性剂润湿性能越强,复配后对煤尘润湿性的增强作用越好,当二者为阴/非组合时这种增效作用更强,当质量分数达到一定值时,影响减弱。单体与复配的表面张力的线性回归模型表明,在对复配表面张力的影响中化学表面活性剂占主要影响因素。其中化学表面活性剂SDS与生物表面活性剂槐糖脂复配时增效作用最好,表面张力最低为23.04 mN/m,煤尘沉降速度最快为7.70 mg/s。     

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。     

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.     

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）。     

Statistical modeling of cargo tank truck crashes: Rollover and release of hazardous materials

Introduction: Cargo Tank Trucks (CTTs) are a primary surface transportation carrier of hazardous materials (hazmat) in the United States and CTT rollover crashes are the leading cause of injuries and fatalities from hazmat transportation incidents. CTTs are susceptible to rollover crashes because of their size, distribution of weight, a higher center of gravity, and the surging and sloshing of liquid cargo during transportation. This study identified and quantified the effects of various factors on the probability of rollover and release of hazmat in traffic crashes where a CTT was involved. Method: Bayesian Model Averaging (BMA)-based logistic regression models were estimated with rollover and hazmat release as the binary response variables, and crash, truck, roadway, environment, and driver characteristics as the explanatory variables. 2010–2016 police-reported CTT-involved crash data from Nebraska and Kansas was utilized. Receiver Operating Characteristic (ROC) curves confirmed appropriateness of the modeling approach for inference and prediction on the crash dataset. Results: CTTs are more likely to rollover in crashes while turning and changing lanes relative to going straight; side impacts (side collisions) and severe crosswinds increased the likelihood of rollovers; tractor and semi-trailer body style decreased the probability of rollover, while truck tractors are more prone to rollovers; collisions with fixed objects and higher posted speeds increased the rollover probability; rollovers and intersection crash locations increased the likelihood of hazmat release. Conclusions: The findings can assist stakeholders (policy-makers, private shippers, and CTT drivers) in restricting CTTs’ operations for safety; scheduling, routing, and fleet planning; and low-level decision-making (e.g., emergency stopping or local routing). Practical Applications: This study identified and quantified the effects of different factors on the conditional probability of rollover and release of hazmat in CTT-involved crashes. The findings may assist stakeholders in decision-making towards safe operations of CTTs for transportation of hazmat.     

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.     

Carbon productivity convergence club and its initial conditions: China’s construction industry

A three-stage method is proposed to study the convergence clubs for the dynamic total factor carbon productivity (DCP) and the initial conditions. The first stage is to measure the DCP that reflects the initial difference. The second stage is to identify the convergence club of DCP. The last stage is to examine the initial factors that may affect the formation of the convergence club. Construction industry data from 30 provinces in mainland China from 2005 to 2016 were adopted to conduct an empirical study. The empirical results showed that (1) the arithmetic mean value of China’s provincial DCP showed an upward trend and the standard deviation showed an expanding trend. (2) There are five convergence clubs, but 13 provinces failed to converge to any club. (3) The higher the degree of construction industry marketization in 2005, the greater the probability that the provinces belong to a club with higher DCP. To improve the DCP, the effective diffusion of low-carbon construction technologies and the market-oriented reform of state-owned construction companies should be promoted. The three-stage method can also be applied to study different industries in different countries or regions.     

“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.