A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

In estimating spatial means of environmental variables of a region from datacollected by convenience or purposive sampling, validity of the results canbe ensured by collecting additional data through probability sampling. Theprecision of the estimator that uses the probability sample can beincreased by interpolating the values at the nonprobability sample points tothe probability sample points, and using these interpolated values as anauxiliary variable in the difference or regression estimator. Theseestimators are (approximately) unbiased, even when the nonprobability sampleis severely biased such as in preferential samples. The gain in precisioncompared to the estimator in combination with Simple Random Samplingis controlled by the correlation between the target variable andinterpolated variable. This correlation is determined by the size (density)and spatial coverage of the nonprobability sample, and the spatialcontinuity of the target variable. In a case study the average ratio of thevariances of the simple regression estimator and estimator was 0.68for preferential samples of size 150 with moderate spatial clustering, and0.80 for preferential samples of similar size with strong spatialclustering. In the latter case the simple regression estimator wassubstantially more precise than the simple difference estimator.     

鲁南经济带城乡绿色发展效率时空分异及驱动因素识别

城乡绿色发展效率对高效低耗绿色发展模式构建、社会经济可持续发展以及实现城乡互动融合具有重要意义。基于鲁南经济带35个县（市、区）13 a面板数据,综合采用DEA-Malmquist指数法、城乡绿色发展效率脱钩状态模型、地理探测器等多种计量模型,对2005—2017年鲁南经济带城乡绿色发展效率时空分异特征与驱动因素进行综合研究,结果表明：（1）技术进步是鲁南经济带绿色TFP增长的主要来源,但其贡献度不断下降,而纯技术效率与规模效率对于绿色TFP增长的促进作用逐年增强;（2）研究期内鲁南经济带绿色TFP增长具有空间分异性,绿色TFP的高效率区和次高效率区有向东北和西南地区集中的趋势;（3）鲁南经济带城乡绿色发展效率的脱钩关系出现反复,由负脱钩（耦合）到脱钩再到负脱钩,整体上城乡绿色发展处于动态变化与非协调（或低级耦合）阶段;（4）普通中学在校学生数、粮食产量、城乡居民储蓄存款余额是城乡绿色发展效率的高作用力影响指标,因子交互作用后对城乡绿色发展效率解释力远超单因子,反映出鲁南经济带城乡绿色发展效率的驱动因素具有复杂性特征。     

A Parallel Computation Tool to Enable Dynamic Sensitivity and Model Performance Analysis of APEX: Evapotranspiration Modeling

Global sensitivity analysis can be used for assessing the relative importance of model parameters on model outputs. The sensitivity of parameters usually indicates a temporal variation due to variation in the environmental conditions (e.g., variation in weather or plant growth). In addition, the size of averaging window by which the outputs of a model are aggregated or averaged may impact parameter sensitivities. In this study, temporal variation of parameters sensitives, model performance, as well as the impact of the size of time‐averaging window on evapotranspiration (ET) prediction using the Agricultural Policy/Environmental eXtender (APEX) model are investigated. To achieve these goals, an open‐source package named PARAPEX was developed in R and used to perform dynamic sensitivity and model performance analysis of APEX using parallel computation. PARAPEX reduced the computation time from 5,939 to 379 s (using 20 and 1 computation nodes, respectively). The sensitivity analysis results indicated the parameters accounting for the reducing effect of plant cover on evaporation from the soil surface, the effect of soil on the plant root growth, and the effect of cycling and transformation dynamics of organic matter at the top soil layer as the top sensitive parameters based on the mean daily simulated ET and the Nash–Sutcliffe model performance measure. The dynamic performance analysis indicated poor ET predictions by APEX during the growing seasons. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities

Objective: Road traffic injuries (RTIs) are a major global health issue causing a global burden of mortality and morbidity. Half of all fatalities on the world’s roads are vulnerable road users (VRUs). The targeted intervention strategies based on fatality analysis focusing on VRUs can effectively contribute to reducing RTIs. This study aimed to compare VRUs and motor vehicle occupants (MVOs) in terms of epidemiology and injury profile.

Methods: We utilized a nationwide, prospective database of RTI-related mortality cases for patients who visited 23 emergency departments between January 2011 and December 2015. All fatalities due to RTIs in the prehospital phase or in-hospital were eligible, excluding patients with unknown mode of transport and those admitted to general wards. The primary and secondary outcomes were fracture injuries and visceral injuries diagnosed using the International Classification of Diseases, Tenth Revision (ICD-10). We compared fracture injuries between VRUs and MVOs using Abbreviated Injury Scale (AIS) 2? and 2+ classification.

Results: Among a total 3,694 road traffic fatalities (RTFs), 43.3% were pedestrians, followed by MVOs (27.0%), motorcyclists (18.9), bicyclists (6.6%), and agricultural vehicle users (4.2%). The elderly (>60 years old) accounted for 54.9% of VRU fatalities. RTFs occurred most frequently in the autumn and the VRU group and the MVO group showed significant differences in weekly and diurnal variation in RTFs. The injury severities (AIS 2+) of the head, neck, and thorax were significantly different between the 2 groups (P?Conclusions: Elderly pedestrians should be targeted for decreases in RTFs, and road traffic safety interventions for VRUs should be made based on the analysis of temporal epidemiology and injury profiles of RTFs.     

Sleepy driving and risk of obstructive sleep apnea among truck drivers in Saudi Arabia

Objective: Truck drivers represent a group at a particularly higher risk of motor vehicle accidents (MVAs). Sleepy driving and obstructive sleep apnea (OSA) among truck drivers are major risk factors for MVAs. No study has assessed the prevalence of sleepy driving and risk of OSA among truck drivers in Saudi Arabia. Therefore, this study aimed to assess sleepy driving and risk of OSA among these truck drivers.

Methods: This study included 338 male truck drivers working in Saudi Arabia. A validated questionnaire regarding sleepy driving and OSA was used. The questionnaire included sociodemographic assessment, the Epworth Sleepiness Scale (ESS), the Berlin Questionnaire (BQ), and driving-related items.

Results: The drivers had a mean age of 42.9?±?9.7 years. The majority (94.7%) drove more than 5?h a day. A history of MVAs during the last 6 months was reported by 6.5%. Approximately 95% of the participants reported that they had accidentally fallen asleep at least once while driving over the past 6 months, and 49.7% stated that this had happened more than 5 times during the last 6 months. Based on the BQ score, a high risk of OSA was detected in 29% of the drivers. “Not getting good-quality sleep” (odds ratio [OR]?=?2.89; 95% confidence interval [CI], 1.08–7.75; P = .014) and driving experience from 6 to 10 years (OR = 3.37; 95% CI, 1.28–8.91; P = .034) were the only independent predictors of MVAs in the past 6 months.

Conclusions: Sleepy driving and a high risk of OSA was prevalent among the study population of male truck drivers in Saudi Arabia. Not getting good-quality sleep and driving experience from 6 to 10 years contributes to the accident risk among these truck drivers.     

基于电力历史应急大数据的应急情景规则分析与发现方法研究

为了解决电力突发事件演化过程难于预测,进而导致事件应急缺少针对性,较为被动的问题,提出在现有应急预案指导、事故模拟仿真、事故案例经验总结等方法基础上,引入基于电力历史大数据分析与预测的应急情景规则分析与发现方法,将电力应急突发事件情景构建的结果与相关大数据预测模型（汇总统计、分类与预测）相结合,提出科学、合理且具有良好操作性的情景演化规则的获取方法与技术路线,为进一步提高复杂电力突发事件的应急处置与指挥能力提供技术支撑。     

全国非煤矿山总量情况统计分析及监管建议

为研究全国非煤矿山基本情况并为非煤矿山安全监管部门提供决策支撑,采用统计、对比分析等方法,以各地上报的2017年统计数据及历年安全生产年鉴为依据,对我国“十三五”期间非煤矿山总量、类型、分布以及“头顶库”、高边坡露天矿山等重点监管对象进行分析,并在此基础上提出强化重点对象精准监管、严格安全生产条件、严控建设项目准入、规范尾矿库闭库注销制度4点监管建议。分析汇总了截至2017年底我国非煤矿山总量情况的统计数据,可为非煤矿山行业的深入分析研究提供重要支撑。     

贵州省闪电定位资料中小幅值地闪范围研究

采用2006—2015年贵州省ADTD闪电定位资料,应用Matlab软件中的曲线拟合工具箱对数据进行对数正态分布拟合,利用非参数正态分布检验中的Kolmogorov—Smirnov检验方法(简称K-S检验方法)分析各区间拟合效果,最终确定贵州省闪电定位系统资料的"小幅值地闪"干扰区间为-10~2k A,该结论为贵州省闪电资料修正提供理论依据,在使用ADTD闪电定位资料时可先将此区间误差数据剔除。该研究为利用闪电定位资料确定雷电灾害风险区划提供重要参考。     

丹江口水库水体氮的时空分布及入库通量

为解析丹江口水库水体氮的时空分布特征及主要污染来源,以丹江口库区及主要入库河流为研究对象,分析了水体氮的空间分布、季节及年际变化;利用回归分析,解析了氮污染驱动因素;并估算了入库河流氮通量及对库区氮污染负荷的贡献率. 结果表明:研究区水体ρ(TN)、ρ(NH₄+-N)分别在0.07~16.73和0.01~10.65 mg/L之间,年均值分别为2.34和0.71 mg/L,空间分布呈入库河流高于库区的趋势;季节特征表现为春季、冬季>秋季>夏季. 2005—2014年库区水体ρ(TN)整体呈先升后降的趋势,其中取水口陶岔断面ρ(TN)上升较快,2012年较2007年增加了1.5倍,2013年开始ρ(TN)有所下降,但仍然维持在较高水平. 近10年来库区水体ρ(NH₄+-N)始终维持在较低水平. 神定河、犟河、泗河、剑河等环库支流河口氮污染最严重,城镇化是造成流域水体氮污染的主要驱动力. 汉江TN入库量贡献最大,占63.0%,其中境外来水TN入库量占总量的59.2%,为达到丹江口水库生态环境保护要求的Ⅲ类水质(GB 3838—2002《地表水环境质量标准》)目标,汉江TN需削减量为16 715.0 t/a. 境内河流中,环库河流的TN入库量最大,其中泗河、老灌河、神定河、金钱河、犟河和天河等TN入库量较大,TN需削减量分别为 2 286.7、2 197.7、1 493.6、1 106.9、979.1和728.9 t/a.      

The use and safety of medications known to affect driving in Jordan: A cross-sectional study

Objectives: The aim of this study was to estimate the main driving-impairing medications used by drivers in Jordan, the reported frequency of medication side effects, the frequency of motor vehicle crashes (MVCs) while using driving-impairing medicines, as well as factors associated with MVCs.

Methods: A cross-sectional study involving 1,049 individuals (age 18–75 years) who are actively driving vehicles and taking at least one medication known to affect driving (anxiolytics, antidepressants, hypnotics, antiepileptics, opioids, sedating antihistamines, hypoglycemic agents, antihypertensives, central nervous system [CNS] stimulants, and herbals with CNS-related effects) was conducted in Amman, Jordan, over a period of 8 months (September 2013–May 2014) using a structured validated questionnaire.

Results: Sixty-three percent of participants noticed a link between a medicine taken and feeling sleepy and 57% stated that they experience at least one adverse effect other than sleepiness from their medication. About 22% of the participants reported having a MVC while on medication. Multiple logistic regression analysis showed that among the participants who reported having a crash while taking a driving-impairing medication, the odds ratios were significantly higher for the use of inhalant substance (odds ratio [OR] = 2.787, P = .014), having chronic conditions (OR = 1.869, P = .001), and use of antiepileptic medications (OR = 2.348, P = .008) and significantly lower for the use of antihypertensives (OR = 0.533, P = .008).

Conclusion: The study results show high prevalence of adverse effects of medications with potential for driving impairment, including involvement in MVCs. Our findings highlight the types of patient-related and medication-related factors associated with MVCs in Jordan, such as inhalant use, presence of chronic conditions, and use of antiepileptics.