A quantitative analysis of the European Automakers’ voluntary commitment to reduce CO2 emissions from new passenger cars based on independent experimental data

The reduction of CO₂ emissions and fuel consumption from road transportation constitutes an important pillar of the European Union strategy for implementing the Kyoto Protocol. The commitment to reduce passenger car average CO₂ emissions at 140 g/km in 2008 signed by European car manufacturers and the European Commission is up to now the most important initiative towards limiting CO₂ emissions from road transportation and particularly from passenger cars. Until today, annual reports show the manufacturers’ efforts in limiting CO₂ emissions is within the intermediate target set by the commitment and these results are incorporated in emissions estimations and scientific studies. This paper analyses the origin of the progress achieved so far in CO₂ emissions and attempts an assessment of the commitment using independent experimental emission data. Additionally, the applicability of the commitment-monitoring data into policy and decision-making tools is being examined. The results indicate that a significant part of the reductions achieved so far is due to a market shift towards diesel vehicle sales and that no reduction factors should be applied yet in CO₂ emissions estimation models.     

A Bayesian nonparametric study of a dynamic nonlinear model

A Bayesian nonparametric approach to modeling a nonlinear dynamic model is presented. New techniques for sampling infinite mixture models are used. The inference procedure specifically in the case of the logistic model and when the nonparametric component is applied to the additive errors is demonstrated.     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

On the Generation of Time-Evolving Regional Data*

Benchmarking of spatio-temporal databases is an issue of growing importance. In case large real data sets are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations, shapes and sizes over time. Only a few innovative papers have recently addressed the topic of spatio-temporal data generators. However, all existing approaches do not consider several important aspects of continuously changing regional data. In this report, a new generator, called generator of time-evolving regional data (G-TERD), for this class of data is presented. The basic concepts that determine the function of G-TERD are the structure of complex 2-D regional objects, their color, maximum speed, zoom and rotation-angle per time slot, the influence of other moving or static objects on the speed and on the moving direction of an object, the position and movement of the scene-observer, the statistical distribution of each changing factor and finally, time. Apart from these concepts, the operation and basic algorithmic issues of G-TERD are presented. In the framework developed, the user can control the generator response by setting several parameters values. To demonstrate the use of G-TERD, the generation of a number of sample data sets is presented and commented. The source code and a visualization tool for using and testing the new generator are available on the Web.¹ Thus, it is easy for the user to manipulate the generator according to specific application requirements and at the same time to examine the reliability of the underlying generalized data model.     

A general form of perfectly matched layers for three-dimensional problems of acoustic scattering in lossless and lossy fluid media

The concept of perfectly matched layer (PML) has been proven very effective in absorbing electromagnetic waves in lossless media. An extension of this method and a complete three-dimensional (3-D) scheme properly suited for finite-difference, time-domain (FDTD) modeling of acoustic propagation and scattering in unbounded problems are presented in this paper. This generalized PML is constructed in such a way that it performs significant absorption of traveling waves in acoustics for both lossless and lossy media. Theoretically, no reflections occur when propagating waves encounter the lossy medium-PML interface, no matter what the angle of incidence is, introducing at the same time the possibility for further wave attenuation via the stretched coordinates idea. Numerical results support the suggested PML theory as well as reveal the proper modifications, which lead to the achievement of the optimum absorbing-boundary condition.     

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P (http://smartp2p.cs.ucy.ac.cy/) system deployed over our SmartLab (http://smartlab.cs.ucy.ac.cy/) testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95 %, with one order of magnitude less time and two orders of magnitude less energy than its competitors.     

Modeling users on the World Wide Web based on cognitive factors,navigation behavior and clustering techniques

This paper focuses on modeling users’ cognitive styles based on a set of Web usage mining techniques on user navigation patterns and clickstream data. Main aim is to investigate whether specific clustering techniques can group users of particular cognitive style using measures obtained from psychometric tests and content navigation behavior. Three navigation metrics are proposed and utilized to find identifiable groups of users that have similar navigation patterns in relation to their cognitive style. The proposed work has been evaluated with two user studies which entail a psychometric-based survey for extracting the users’ cognitive styles, combined with a real usage scenario of users navigating in a controlled Web 2.0 environment. A total of 106 participants of age between 17 and 25 participated in the study providing interesting insights with respect to cognitive styles and navigation behavior of users. Studies like the reported one can be useful for modeling users and assist adaptive Web 2.0 environments to organize and present information and functionalities in an adaptive format to diverse user groups.     

Analysing the operation of the basic pass transistor structure

Pass transistor logic has become important for the design of low‐power high‐performance digital circuits due to the smaller node capacitances and reduced transistors count it offers. However, the acceptance and application of this logic depends on the availability of supporting automation tools, e.g. timing simulators, that can accurately analyse the performance of large circuits at a speed, significantly faster than that of SPICE based tools. In this paper, a simple and robust modelling technique for the basic pass transistor structure is presented, which offers the possibility of fast timing analysis for circuits that employ pass transistors as controlled switches. The proposed methodology takes advantage of the physical mechanisms in the pass transistor operation. The obtained accuracy compared to SPICE simulation results is sufficient for a wide range of input and circuit parameters. Copyright © 2006 John Wiley & Sons, Ltd.     

Optimal trajectory tracking of dynamic multistage ROT cooling process with stochastic disturbances

Random disturbance input is bound to cause a state uncertainty. In particular, when the random disturbance input is time correlated, the impact on the state uncertainty becomes more severe. Time correlation, which could also be expressed in terms of an interstage correlation, implies that the value of a one time instant is correlated with the value of another time instant; it suggests that the random disturbance is nonwhite. A run-out-table cooling is a complex dynamic multistage process with intrinsic characteristics such as, long time delay and time-correlated disturbance, therefore it constitutes an exceptional case to investigate stochastic optimization methods, in order to optimally track the temperature trajectory along its stages. Stochastic optimization is applied on an augmented state model, which is a combination of a dynamic disturbance model and the system equation. The merit of the optimal solutions obtained by this approach is confirmed by simulation experiments. The results demonstrate the state propagation and robust tracking performance in the presence of stochastic time-correlated disturbance inputs.     

Developments in efficiently modelling eddy current testing of narrow cracks

Further developments in a Boundary Element Method model for eddy current inspection of ideal or narrow cracks are presented. First, the required Green’s function is rapidly computed in the spatial domain using the concept of discrete complex images. The approach is based on the analytical evaluation of the Sommerfeld type Integrals through the use of the Generalized Pencil of Function method. Computation times are recorded in order to show the remarkable reduction in the moment matrix fill time as well as in the total computational time. Second, the model is extended to arbitrary crack depth profiles. Theoretical results are compared to published benchmark measurements of the coil impedance change due to a semi-elliptic and an epicyclic crack and excellent agreement is achieved.