Efficiency, Conflicting Goals and Trade-Offs: A Nonparametric Analysis of the Water and Wastewater Service Industry in Italy

This paper presents a benchmarking study of the water and wastewater industry in Italy. A three-stage modeling approach was implemented to measure the efficiency of 53 utility operators. This approach is based on the implementation of network and conventional data envelopment analysis (DEA) to model the production process of the water service utility operators. In comparison to the conventional black-box or one-stage production model generally adopted in previous studies, the proposed approach provides information relative to the different efficiency components of the stages and blocks of the water service production process and its overall efficiency. Further, by shifting the efficiency analysis to a two-dimensional performance space, i.e., resource and market-efficiency, it offers a more comprehensive view of the water service industry and allows accounting for different business goals at the same time and for an investigation of industry trade-offs. Results show that the operators’ efficiencies in the Italian water service industry are generally variable and low. There are no water service utilities which are 100% efficient from the resource-efficiency perspective, and the maximum efficiency score is 0.545. Efficiency measurements suggest that there is a general orientation of the Italian water industry to not invest in upgrading and improving the infrastructure assets, and achieving an acceptable efficiency in the operations is critical to delivering water services to market in an efficient way. Only one utility operator is 100% efficient from the market-efficiency perspective. The low tariffs adopted by the water service operators do not allow the gaining of satisfactory service remuneration and the achievement of long-term business sustainability. The joint analysis of the resource and market efficiency scores indicates that there is a trade-off between the corresponding business goals

Factors Affecting Efficiency Of Railways In Terms Of Safety At Railway Level Crossings

Road traffic accidents are in the top ten of all deaths, with around 1.4 million fatalities and 50 million injuries per year worldwide. Regarding railways, in the EU there were 1666 serious accidents registered in 2018 and 442 of them involved Level Crossings (LCs). The number of accidents on level crossings in the EU in 2018 was approximately 27% of the total number of accidents on railways, but these accidents can be predictable and preventable. The current study investigated the efficiency of railways in terms of accident risk at LCs in 24 countries of the EU. The Data Envelopment Analysis (DEA) method has been applied to evaluate the efficiency of selected railways in terms of safety at LCs. After extensive study of the subject, the comprehensive list of influencing factors has been identified and seven of them have been selected for further analysis. To investigate the relationship between selected factors and efficiency score of railways in terms of accident risk at LCs, the IBM SPSS software package has been deployed. The results show that GDP per capita and density of population in the selected countries have a strong influence on the efficiency of railways in terms of safety at LCs. The expected outcome of this research may contribute to a better understanding of the factors that influence the efficiency score of railways in term of accident risk at LCs and may help to develop preventative measures

Application of Multicriteria Decision-Making Methods in Railway Engineering: A Case Study of Train Control Information Systems (TCIS)

In order to improve its position in the transport market railway, as a complex system, it has to fulfill a number of objectives such as increased capacity and asset utilization, improved reliability and safety, higher customer service levels, better energy efficiency and fewer emissions, along with increased economic viability and profits. Some of these objectives call for the implementation of maximum values, while some of them require minimum values. Additionally, some can be expressed quantitatively, while some, for example, customer service, can be described qualitatively through a descriptive scale of points. The application of MCDM in railway engineering can play a significant role. Therefore, the major objective of this chapter is the review of the application of MCDM methods in railway engineering. As one of the means in achieving the objectives of railways and above all the utilization of capacity are Train Control Information Systems (TCIS). Based on that, the aim of this chapter is the evaluation of the efficiency of TCIS in the improvement of railway capacity utilization through defined technical-technological indicators. The non-radial Data Envelopment Analysis (DEA) model for the evaluation of TCIS efficiency in improvement of utilization of railway capacity using the selected indicators is proposed. The proposed non-radial DEA model for TCIS efficiency evaluation in using railway capacity could be applied to an overall network or for separate parts of railway lines

Input Usage, Ouput Mix and Industry Deregulation: An Analysis of the Australian Dairy Manufacturing Industry

In this paper we estimate a Translog output distance function for a balanced panel of state level data for the Australian dairy processing sector. We estimate a fixed effects specification employing Bayesian methods, with and without the imposition of monotonicity and curvature restrictions. Our results indicate that Tasmania and Victoria are the most technically efficient states with New South Wales being the least efficient. The imposition of theoretical restrictions marginally affects the results especially with respect to estimates of technical change and industry deregulation. Importantly, our bias estimates show changes in both input use and output mix that result from deregulation. Specifically, we find that deregulation has positively biased the production of butter, cheese and powders.deregulation, output distance function, Bayesian

Efficiency, Productivity and Risk Analysis in Turkish Banks: A Bootstrap DEA Approach

This paper, by using Data Envelopment Analysis (DEA) and Malmquist Productivity Index, addresses the impacts of 2007 global financial crisis on the efficiency and productivity of Turkish banks, during 2003-2010 periods. Moreover, a risk taking measure is introduced for each bank and two-stage regression is used to analyze the determinants of DEA efficiency scores. However, because of the existence of inherent dependency among DEA efficiency scores, the basic assumption of regression analysis, i.e., independence within the sample is violated. Hence, to overcome the dependency problem and to be able to make valid statistical inferences, bootstrapping method is applied. This paper attempts to extend the existing DEA literature by applying some of the remarkable methods suggested to improve DEA efficiency and productivity estimates altogether, for the case of Turkey to observe the impacts of recent 2007 global financial crisis.Data Envelopment Analysis, Malmquist Productivity Index, Efficiency and Productivity, Bootstrapping

Searching for Exoplanets Using Artificial Intelligence

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.Comment: Accepted, 16 Pages, 14 Figures, https://github.com/pearsonkyle/Exoplanet-Artificial-Intelligenc

COOPER-framework: A Unified Standard Process for Non-parametric Projects

Practitioners assess performance of entities in increasingly large and complicated datasets. If non-parametric models, such as Data Envelopment Analysis, were ever considered as simple push-button technologies, this is impossible when many variables are available or when data have to be compiled from several sources. This paper introduces by the ‘COOPER-framework’ a comprehensive model for carrying out non-parametric projects. The framework consists of six interrelated phases: Concepts and objectives, On structuring data, Operational models, Performance comparison model, Evaluation, and Result and deployment. Each of the phases describes some necessary steps a researcher should examine for a well defined and repeatable analysis. The COOPER-framework provides for the novice analyst guidance, structure and advice for a sound non-parametric analysis. The more experienced analyst benefits from a check list such that important issues are not forgotten. In addition, by the use of a standardized framework non-parametric assessments will be more reliable, more repeatable, more manageable, faster and less costly.DEA, non-parametric efficiency, unified standard process, COOPER-framework.

Initial synchronisation of wideband and UWB direct sequence systems: single- and multiple-antenna aided solutions

This survey guides the reader through the open literature on the principle of initial synchronisation in single-antenna-assisted single- and multi-carrier Code Division Multiple Access (CDMA) as well as Direct Sequence-Ultra WideBand (DS-UWB) systems, with special emphasis on the DownLink (DL). There is a paucity of up-to-date surveys and review articles on initial synchronization solutions for MIMO-aided and cooperative systems - even though there is a plethora of papers on both MIMOs and on cooperative systems, which assume perfect synchronization. Hence this paper aims to ?ll the related gap in the literature

Integrative Dynamic Reconfiguration in a Parallel Stream Processing Engine

Load balancing, operator instance collocations and horizontal scaling are critical issues in Parallel Stream Processing Engines to achieve low data processing latency, optimized cluster utilization and minimized communication cost respectively. In previous work, these issues are typically tackled separately and independently. We argue that these problems are tightly coupled in the sense that they all need to determine the allocations of workloads and migrate computational states at runtime. Optimizing them independently would result in suboptimal solutions. Therefore, in this paper, we investigate how these three issues can be modeled as one integrated optimization problem. In particular, we first consider jobs where workload allocations have little effect on the communication cost, and model the problem of load balance as a Mixed-Integer Linear Program. Afterwards, we present an extended solution called ALBIC, which support general jobs. We implement the proposed techniques on top of Apache Storm, an open-source Parallel Stream Processing Engine. The extensive experimental results over both synthetic and real datasets show that our techniques clearly outperform existing approaches