Evaluating the efficiency of local municipalities in providing traffic safety using the Data Envelopment Analysis

The purpose of this study was to estimate the relative efficiency of 197 local municipalities in traffic safety in Israel during 2004–2009, using Data Envelopment Analysis (DEA). DEA efficiency is based on multiple inputs and multiple outputs, when their weights are unknown. We used here inputs reflecting the resources allocated to the local municipalities (such as funding), outputs include measures that reflect reductions in accidents (such as accidents per population), and intermediate variables known as safety performance indicators (SPI): measures that are theoretically linked to crash and injury reductions (such as use of safety belts). Some of the outputs are undesirable. Using DEA, the local municipalities were rank-scaled from the most efficient to the least efficient and required improvements for inefficient municipalities were calculated. We found that most of the improvements were required in two intermediate variables related to citations for traffic violations. Several DEA versions were used including a two-stage model where in the first stage the intermediate variables are the outputs, and in the second stage they are the inputs. Further analyses utilizing multiple regressions were performed to verify the effect of various demographic parameters on the efficiency of the municipalities. The demographic parameters tested for each local municipality were related to the size, age, and socio-economic level of the population. The most significant environmental variable affecting the efficiency of local municipalities in preventing road accidents is the population size of the local authority; the size has a negative effect on the efficiency. As far as we could determine, this is the first time that the DEA is used to measure the efficiency of local municipalities in improving traffic safety.     

Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Data augmentation and parameter expansion can lead to improved iterative sampling algorithms for Markov chain Monte Carlo (MCMC). Data augmentation allows for simpler and more feasible simulation from a posterior distribution. Parameter expansion accelerates convergence of iterative sampling algorithms by increasing the parameter space. Data augmentation and parameter-expanded data augmentation MCMC algorithms are proposed for fitting probit models for independent ordinal response data. The algorithms are extended for fitting probit linear mixed models for spatially correlated ordinal data. The effectiveness of data augmentation and parameter-expanded data augmentation is illustrated using the probit model and ordinal response data, however, the approach can be used broadly across model and data types.     

Privacy protection in pervasive systems: State of the art and technical challenges

Pervasive and mobile computing applications are dramatically increasing the amount of personal data released to service providers as well as to third parties. Data includes geographical and indoor positions of individuals, their movement patterns as well as sensor-acquired data that may reveal individuals’ physical conditions, habits, and, in general, information that may lead to undesired consequences like unsolicited advertisement or more serious ones like discrimination and stalking.In this survey paper, at first we consider representative classes of pervasive applications, and identify the requirements they impose in terms of privacy and trade-off with service quality. Then, we review the most prominent privacy preservation approaches, we discuss and summarize them in terms of the requirements.Finally, we take a more holistic view of the privacy problem by discussing other aspects that turn out to be crucial for the widespread adoption of privacy enhancing technologies. We discuss technical challenges like the need for tools augmenting the awareness of individuals and to capture their privacy preferences, as well as legal and economic challenges. Indeed, on one side privacy solutions must comply to ethical and legal requirements, and not prevent profitable business models, while on the other side it is unlikely that privacy preserving solutions will become practical and effective without new regulations.     

Feasibility and effort estimation models for medium and small size information mining projects

Information mining is a sub-discipline of Information Systems which provides the non-trivial knowledge needed for making decisions inside an organization. Although such projects have different features from Software Engineering ones, they share some of their problems. Among these problems two are highlighted: unmanaged risks and inaccurate estimations of necessary resources to complete the project. In this context, this paper presents two ad-hoc models to be applied in Small and Medium-sized Enterprises: one for assessing project feasibility and the other for estimating the resources and time required to carry out the project. Both models should be applied at the beginning of the project.     

A geometric framework for data fusion in information retrieval

Data fusion in information retrieval has been investigated by many researchers and a number of data fusion methods have been proposed. However, problems such as why data fusion can increase effectiveness and favorable conditions for the use of data fusion methods are poorly resolved at best. In this paper, we formally describe data fusion under a geometric framework, in which each component result returned from an information retrieval system for a given query is represented as a point in a multi-dimensional space. The Euclidean distance is the measure by which the effectiveness and similarity of search results are judged. This allows us to explain all component results and fused results using geometrical principles. In such a framework, score-based data fusion becomes a deterministic problem. Several interesting features of the centroid-based data fusion method and the linear combination method are discussed. Nevertheless, in retrieval evaluation, ranking-based measures are the most popular. Therefore, this paper investigates the relation and correlation between the Euclidean distance and several typical ranking-based measures. We indeed find that a very strong correlation exists between these. It means that the theorems and observations obtained using the Euclidean distance remain valid when ranking-based measures are used. The proposed framework enables us to have a better understanding of score-based data fusion and use score-based data fusion methods more precisely and effectively in various ways.     

Supporting SMEs' IP capabilities: Impact study of INPI pre-diagnosis through the use of the AIDA approach

This article aims to evaluate the impact of a regional SME IP support programme in France led by the INPI (National Institute of IP) and the Lorraine regional government. The panel includes 150 regional SMEs. The data were collected through the use of the AIDA approach and a data analysis was carried out, including principal components analysis (PCA) and a hierarchical ascending classification (HAC). The data analysis shows that the INPI pre-diagnosis allowed enterprises to progress in their IP-management. The knowledge of IP and its issues increase very significantly and appear to influence the IP-related management of the companies studied.     

What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors

Crashes at highway-rail grade crossings can result in severe injuries and fatalities to vehicle occupants. Using a crash database from the Federal Railroad Administration (N = 15,639 for 2004–2013), this study explores differences in safety outcomes from crashes between passive controls (Crossbucks and STOP signs) and active controls (flashing lights, gates, audible warnings and highway signals). To address missing data, an imputation model is developed, creating a complete dataset for estimation. Path analysis is used to quantify the direct and indirect associations of passive and active controls with pre-crash behaviors and crash outcomes in terms of injury severity. The framework untangles direct and indirect associations of controls by estimating two models, one for pre-crash driving behaviors (e.g., driving around active controls), and another model for injury severity. The results show that while the presence of gates is not directly associated with injury severity, the indirect effect through stopping behavior is statistically significant (95% confidence level) and substantial. Drivers are more likely to stop at gates that also have flashing lights and audible warnings, and stopping at gates is associated with lower injury severity. This indirect association lowers the chances of injury by 16%, compared with crashes at crossings without gates. Similar relationships between other controls and injury severity are explored. Generally, crashes occurring at active controls are less severe than crashes at passive controls. The results of study can be used to modify Crash Modification Factors (CMFs) to account for crash injury severity. The study contributes to enhancing the understanding of safety by incorporating pre-crash behaviors in a broader framework that quantifies correlates of crash injury severity at active and passive crossings.     

富营养化评价中数据分析的研究进展

数据处理方法是富营养化评价的关键因素。简要阐述富营养化的定义,比较详细地评述富营养化评价过程中数据处理方法的研究现状及国内外常用的几种富营养化评价方法。重点介绍几种定量评价水质富营养化的方法:综合指数法、统计分析方法(主成分、聚类和判别分析),以及遥感技术、多标准评价分析、模型分析等。指出富营养评价技术的发展将为水质管理提供可靠的依据,并对富营养化评价技术的研究做出了展望。     

Statistical methods for reservoir fluid composition distribution analysis

This paper presents statistical methods for analyzing the molar distribution of components in petroleum fluids. Reservoir fluid characterization is very significant in Equation of State modeling and in some flow assurance- related problems. Heptanes plus components (C₇₊) are usually lumped together as a pseudo-component, hence, characterizing it improves modeling. Three statistical methods were assumed for analyzing the distribution of components using two reservoir fluid data. The statistical methods selected were validated for best fit of the molar distribution data using methods including the Kolmogorov–Smirnov test. The goodness of fit for each test is presented. A new model showing the relationship while using data of Kolmogorov–Smirnov test is presented with high r².