基于模糊聚类关联分析法的煤与瓦斯突出程度分析

运用模糊聚类分析方法对煤与瓦斯突出的样本集合进行分类,建立了不同突出程度的模糊模式.用关联分析确定待分析样本与模式的关联程度,以此预测预报样本的煤与瓦斯突出危险程度.实例分析表明,与模糊聚类分类后、将模式与待预报样本组成新样本集合进行聚类分析并以此分类结果进行预报的方法相比,这种预报方法不仅可靠程度高,而且能定量描述待报样本与模式的亲和程度.     

东洞庭湖湖滨带土壤酸碱度的分布及对重金属含量的影响

用统计学方法研究了东洞庭湖湖滨带土壤pH 值的分布特征及与重金属污染特征关系。研究表明：在东洞庭湖湖滨带67个采样点中,7.46％的土样为酸性土壤,多分布在居民地和旅游区;13.43％的土样为中性土壤,多分布在湿地保护区;79.10％的土壤为碱性土壤,主要分布在农业区和工业区;无强酸性和强碱性样品。不同土壤pH范围的重金属平均含量不同,随着土壤pH值由酸性、中性到碱性的升高,Hg、Cd、Pb、Zn含量先降低后增加;As、Cu、Ni、Cr含量先增加后降低。     

近10年流域江湖关系变化作用下鄱阳湖水动力及水质特征模拟

鄱阳湖水文情势受流域来水及长江共同影响,近10年流域、长江和鄱阳湖间关系发生了较大变化,导致了新的水文节律调整,进一步使得湖区水质环境发生变化.对近10年序列(2003—2012年)和1956—2002年序列的多年平均的日水文过程进行对比,分析了鄱阳湖的流域入流、湖口出流及湖区水位的年内变化过程;近10年鄱阳湖最高水位降低,湖相时间变短,河相时间增长;通过构建鄱阳湖的二维水动力水质模型,并采用实测2010年湖区水动力及水质数据对模型进行率定验证,在此基础上着重研究流域、江湖水文情势变化条件下,湖区的水动力和水质发生的变化.模拟结果显示,由于4—6月间湖区丰水期滞后13 d,8—10月间枯水期提前21 d,导致TN浓度在两个时间段内分别上升10.6%和12.4%,TP浓度在两期间内分别升高11.7%和13.6%.在8—10月期间,湖区水位下降速率增加,南部与西部的碟型湖提前与主湖区分离,形成相对静水的水塘,加剧了碟型湖的富营养化风险.     

The dyadic level of conceptualization and analysis: A missing link in multilevel OB research?

Despite burgeoning multilevel research in organizational behavior over the past two decades, our understanding of dyadic relationships at work remains underdeveloped. Focusing on leader–member exchange, we discuss conceptual and methodological challenges that have hampered research at this level and illustrate how and why such analysis might provide new insights. Copyright © 2015 John Wiley & Sons, Ltd.     

基于可靠性理论的事件树分析方法研究

事件树分析过程中事件树的编制和分析方法缺乏系统性,导致实际运用中事件树的编制缺乏可操作性。从目标系统的选择、环节事件逻辑关系的确定、定量分析中基本数据的来源和确定方法等三个问题进行了系统研究,并以可靠性的相关理论为基础,提出"从控制和影响初始事件发展演化的角度,调查分析对初始事件做出响应的安全功能,按系统工作原理对目标系统进行重构,构建目标系统的功能结构图,画出系统的可靠性框图"入手,对"传统的事件树绘制程序"进行修正,解决了事件树分析过程中目标系统的选择、环节事件逻辑关系的确定以及定量分析基本数据的确定方法等问题。修正后的方案对实现事件树的正确编制和系统分析逻辑更清晰,操作指导性更强。     

新常态下要科学认识环境管理六个量的关系

针对新常态下环境管理面临的机遇与挑战,本文提出了环境管理工作要树立新的思维,并在此基础上深入剖析了总量与质量、容量与流量、存量与增量的关系。现行的总量控制制度在实施过程中带来的污染减排潜力越来越小,因此,环境管理的目标应从以污染排放控制为主向以环境质量改善为主转变,通过采取具有针对性的差异化容量总量控制管理手段,实现污染排放流量管控,同时,应充分考虑现有环境本底负荷的问题,实现污染存量和可能增量的协同控制,这对于推进环境管理工作的战略转型具有借鉴意义。     

Development of machine learning based prediction models for hazardous properties of chemical mixtures

Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.     

Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning

● A novel framework integrating quantile regression with machine learning is proposed. ● It aims to identify factors driving observations to upper boundary of relationship. ● Increasing N:P and TN concentration help fulfill the effect of TP on CHL. ● Wetter and warmer decrease potential and increase eutrophication control difficulty. ● The framework advances applications of quantile regression and machine learning. The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N׃P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N׃P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.     

1949年以来中国环境与发展关系的演变

从IPAT方程出发,发现了环境影响随着经济发展或时间的演变依次遵循三个"倒U型"曲线规律,即环境影响强度的倒U型曲线、人均环境影响的倒U型曲线和环境影响总量的倒U型曲线。根据此规律,可以将该演化过程划分为四个阶段即:环境影响强度高峰前阶段、环境影响强度高峰到人均环境影响量高峰阶段、人均环境影响量高峰到环境影响总量高峰阶段以及环境影响总量稳定下降阶段。在环境演变的不同阶段,主要驱动力存在着明显的差异。在环境影响强度高峰前阶段,资源消耗或污染物排放增长更多地由资源或污染密集型技术进步驱动;在资源消耗或污染物强度高峰到人均资源消耗或污染物排放高峰阶段,主要由经济增长驱动;而在人均资源消耗或污染物排放高峰到资源消耗或污染物排放总量高峰阶段以及总量高峰以后的发展阶段,则主要由节约高效技术或污染减排技术进步来驱动。实证分析表明,中国目前环境与发展关系基本上处于经济增长主要驱动的环境影响强度高峰向人均环境影响高峰过渡阶段,这同时意味着中国要在短期内实现人均环境影响和环境影响总量高峰的跨越是异常困难的。