The impact of privatization approaches on the productivity growth of the water industry: A case study of Chile

The water industry faces the challenge of implementing privatization reforms. This process mainly adopts the following two approaches: the privatization of public water and sewerage services (WSS) and the privatization of water companies’ ownership. This paper investigates the impact of both privatization approaches on changes in productivity in the water industry. In doing so, the Luenberger productivity indicator (LPI) was computed for a sample of Chilean water and sewerage companies (WaSCs) for the period 1997–2013. Unlike the most commonly applied index (Malmquist productivity index), the LPI simultaneously takes into account output expansion and input contraction. The results evidenced that productivity in the Chilean water industry decreased after its privatization. However, in the sub-periods in which the ownership of WaSCs was privatized, the productivity of the water industry increased due to technical improvements. On the other hand, the concession of WWS to private WaSCs involved a regression in productivity. From a policy perspective, regulators and managers of WaSCs at the international level can learn important lessons from the Chilean case to improve the productivity of the water industries in their countries.     

遥感指数在湖泊叶绿素a反演研究中的应用

借鉴陆地植被指数的算法,利用MODIS数据的波段组合对太湖叶绿素8浓度进行反演。探讨了差值植被指数DVI、比值植被指数RVI、归一化植被指数NDVI这三种遥感指数形式的反演效果及各自特点。MODIS数据蓝光波段和红外波段的组合是反演太湖叶绿素浓度的最佳波段组合,DVI适合于反演叶绿素浓度高的情况,RVI的反演结果两极分化明显而普适性较差,NDVI适合于反演浓度较低的情况。     

基于GPRS网络的环境自动监控数据传输系统的设计

分析了江苏省环境自动监控系统数据传输的现状，提出了一种基于GPKS网络的环境自动监控数据传输系统，并进行了详细的设计。     

Assessing the efficiency of Chilean water and sewerage companies accounting for uncertainty

Efficiency assessment of water and sewerage companies (WaSCs) has attracted considerable attention both for water company managers and water regulators. Within the methodological approaches that can be applied to estimate efficiency scores, data envelopment analysis (DEA) is the most widely applied technique. In spite of the positive features of DEA, it presents a major drawback which is its deterministic nature. In other words, conventional DEA models do not account for uncertainty in the data. To overcome this limitation, we assess, for the first time, the efficiency of a sample of Chilean WaSCs by using a DEA model with statistical tolerance in the data. Hence, 81 efficiency scores are estimated for each WaSC rather than a single score as with conventional DEA models. The results illustrate that outputs exhibit larger uncertainty than inputs. Moreover, WaSCs efficiency scores change significantly under the best-case and worst-case scenarios. The ranking of the WaSCs allows for the identification of which of them has the highest performance based on their efficiency scores. This information is essential to enhance efficiency and innovation in the water industry. Moreover, the introduction of uncertainty in the efficiency assessment allows for the prediction and ranking of future performance of WaSCs.     

Development of novel combustion risk index for flammable liquids based on unsupervised clustering algorithms

Current liquid flammability classification mainly relies on flash point and its risk is largely dependent on consequence and probability. However, combustions of liquefied marine fuels have their uniqueness, leading to a less consistent with the common classification. This work aims at classifying flammable liquids in compression ignition engines for further safety evaluation. Besides liquid flammability characteristics, flame propagation and aerosol formulation are considered. Two unsupervised machine learning clustering algorithms, k-means and spectral clustering, are applied to the collected liquid compounds database. To consider both cluster cohesion and separation, the global mean silhouette value is used to find the optimal number of clusters and to evaluate the clustering performance. The results show that the spectral clustering outperforms k-means on classifying the risk ratings for all proposed models, while the clustering accuracy of the optimal model has been doubled by employing spectral clustering algorithm. Moreover, principal component analysis and star coordinate diagrams are presented to visualize high dimensional data to 2-D graphs. Finally, the overall liquid safety performance is evaluated by a novel combustion risk index via the weight values determined by the information entropy approach. This index can be used to explore inherently safer fuels in the process industries.     

Development of fire risk visualization tool based on heat map

At present, relatively few tools are able to visualize regional fire risks both dynamically and efficiently. As a general method of density clustering and visualization, heat maps can be used to analyze the spatial distribution characteristics of fire risks. In this study, based on combining a transformed sigmoid function with a heat map method, a transformed sigmoid risk model (TSRM) was proposed. A numerical mapping method between fire risk values and colors was established based on an RGB color model. By using HTML 5 and JavaScript, a fire risk visualization tool (FRVT) was developed based on the TSRM. Baidu Map was used as the geographic information system engine for the FRVT by calling its JavaScript application program interface. To validate the TSRM and FRVT with case studies, 16,448 historical fires from 2013 to 2017 in Changsha, China, were gathered and visualized. The visualization results indicated that the high-risk areas were mainly distributed in urban areas, and that the medium-risk and low-risk areas were primarily distributed in rural areas. The FRVT can also be applied to visualize and analyze process-related fire risks from a regional perspective. In further research, it will be important to update the FRVT, e.g., to visualize the fire risks posed by the transportation of hazardous materials.     

Safety performance measurement in collectivized oil companies in China: Contribution of leading indicators to lagging indicators

With the collectivization of the Chinese oil industry, oil companies have been expanding in size. However, the intensified differences in the safety performance of subsidiaries have severely hindered the collaborative management of the headquarters. Understanding the safety status of each member is urgent for parent companies and their subsidiaries to identify gaps and make improvements. A unified set of safety performance indicators and a practical measurement tool are essential for the Chinese oil industry. Hence, this study identified a set of safety performance indicators encompassing both leading and lagging indicators using data envelopment analysis (DEA) and entropy weight method (EWM) to reveal the critical factors affecting the safety performance of the oil industry. A total of 300 front-line workers from eight subsidiaries of an oil company participated in the survey. The identified indicators were preliminarily weighted using EWM. Then, DEA was employed to measure the safety performance of the eight subsidiaries, demonstrating that management commitment was the most crucial factor in distinguishing safety performance; safety culture was more differentiated than risk management. Safety performance was not entirely positively correlated with safety investments, but the reasonable allocation of safety resources played a vital role. In addition, the weaknesses in each subsidiary's safety management were identified, and the quantitative effects of each leading indicator on safety performance were obtained.     

一种基于数据融合的林火火线动态蔓延预测方法的研究

准确预测林火蔓延对于有效防治野火危害具有重要意义。传统林火蔓延速度场经验模型需要可燃物特性、坡度、温度及湿度等众多实测参数,由于参数均具有不同的时空分布,限制了模型的实际应用。因此提出速度场实时测量及水平集法模拟林火蔓延两种技术相结合的方法,通过测定火蔓延锋面的位置获取当前时刻锋面处的速度场分布并对此速度场在未燃烧区域进行延拓,结合水平集法预测林火蔓延,并使用数值模拟及实验数据验证了该方法能够有效预测短期内林火蔓延趋势,得出基于速度场的数据融合方法可满足实际林火蔓延建模的需求。     

基于电网防台抗台系统的应急主题数据仓库分析与设计

为解决电网防台抗台过程中缺少应急辅助支持的问题,提出基于现有电网防台抗台系统中的业务数据,采用星型结构分析与设计“台风气象灾情”、“电网因灾（台风）损失”、“电网因灾（台风）应急资源投入”等应急主题数据仓库,充分利用、开发相关的数据探查与ETL工具实现相关的数据集成,构建相关应急主题下的多维数据模型,并开展联机分析处理（OLAP）研究工作,为相关企业和部门开展电网应急管理工作提供应急辅助支持。     

Measuring and analyzing adaptive capacity at management levels of resilient systems

Resilience engineering (RE) is capable of handling disruptive events and controlling their consequences in process industries such as petrochemical plants. This study aims at analyzing the level of adaptive capacity and identifying effective factors on developing adaptive capacity in the organizational structure of process industries. The data of this study were obtained through direct observation and a structured questionnaire in a petrochemical plant. Managers at all levels participated in the survey. Data envelopment analysis (DEA), which is a mathematical approach, was used to compute and analyze the role of the factors contributing to adaptive capacity. The results indicated that reporting safety issues played a central role in enhancing adaptive capacity at all management levels. Both middle management and low-level management emphasized the importance of management commitment, whereas top-level management considered flexibility as a vital factor in managing disruptions and reducing accidents. The findings of this study could be useful for managers and other decision-makers to improve safety in process industries.