Relative hazard rate estimation for right censored and left truncated data

In medical research differences among treatment groups are a common focus of study. The concept of relative hazard rate is a tool for comparing two groups in terms of their difference in risk rates. A kernel estimator is proposed in the case where both samples are subject to left truncation and right censoring and an iid representation is obtained in this setup. The asymptotic distribution and the asymptotic mean squared error of the estimator are obtained. An application to the famous Channing House data set illustrates the theory. Research supported in part by MCyT Grants PB98-0182-C02-01 and BFM2002-00265 (ERDF support included) and XUGA Grant PGIDT00PX110501 PN for the first author and by the Ministry of the Flemish Community (Project BIL00/28, International Scientific and Technological Cooperation) and by the IAP research network nr P5/24 of the Belgian State (Federal Office for Scientific, Technical and Cultural Affairs) for the last two authors.     

Introduction to the Special Issue on Meta-Learning

Recent advances in meta-learning are providing the foundations to construct meta-learning assistants and task-adaptive learners. The goal of this special issue is to foster an interest in meta-learning by compiling representative work in the field. The contributions to this special issue provide strong insights into the construction of future meta-learning tools. In this introduction we present a common frame of reference to address work in meta-learning through the concept of meta-knowledge. We show how meta-learning can be simply defined as the process of exploiting knowledge about learning that enables us to understand and improve the performance of learning algorithms.     

Benchmarking System for Evaluating Management Practices in the Construction Industry

This paper presents the results obtained for the initial application of a management evaluation system whose objective is to provide a continuous improvement tool for construction companies through benchmarking management practices. The outlined system seeks to support a benchmarking system that has been recently established in the Chilean construction industry by incorporating qualitative management aspects in addition to performance indicators. Different analyses were made to determine trends in the sector and to establish correlations between qualitative aspects coming from surveys and quantitative aspects coming from performance indicators. Thirteen construction companies participated in the initial application of the benchmarking system. A correlation analysis found that safety performance was strongly related to companies having superior planning and control, quality management, cost control, and subcontractor management policies. A factor analysis found that central office priorities focus on strategic management policies having longer-term competitive impact, while site management emphasizes tactical management dimensions having short-term impact. There is scope to elevate the profile of continuous improvement initiatives to strategic significance at the central office level.     

Shot boundary detection using Zernike moments in multi-GPU multi-CPU architectures

This paper presents an analysis of a Multi-GPU Multi-CPU environment, along with the different possible hybrid combinations. The analysis has been performed for a shot boundary detection application, based on Zernike moments, although it is general enough to be applied to many different application areas. A deep study of the performance, bottlenecks and design challenges is carried out showing the validity of this approach and achieving very high frame per second rates. In this paper, Zernike calculations are carried out on GPUs, taking advantage of a packing strategy proposed to minimize host-device communication time.     

A methodology for screening of microalgae as a decision making tool for energy and green chemical process applications

The increasing interest for biotechnological use of microalgae demands a methodology for selection of species suitable to support the development of technologies based on the use of such non-conventional renewable raw material, i.e., green industrial applications. The vast and expanding collection of experimental data on both cell growth and biomass composition available in the literature can be used to reduce the cost of the experimental investigations required to support process engineering and optimization. Selecting the appropriate organism requires extracting useful information from such data, a cumbersome task since various multidisciplinary factors must be considered. This paper presents a computer-aided methodology for selecting appropriate algal species given an energy or green chemical process application employing microalgae as a renewable raw material. The approach is “system oriented”, based on biomass composition and chemical processing of the biomass downstream of the CO₂ biofixation and harvesting operations. Quantitative performance results are supported by professional process simulation. Besides comparison of a set of species performances, the proposed methodology also allows the discrimination among distinct algal compositions resulting from different growth conditions for a given species. Furthermore, three categories of screening metrics are proposed to be maximized by the decision making procedure in order to elicit the relevant information. To demonstrate the potential of the proposed methodology, a databank of both biochemical and elemental compositions of microalgal biomass was used in three green applications: Assessment of biomass heating value; production of syngas by gasification of the biomass; and production of Bio-H₂. Within the accuracy of the databank employed to illustrate the procedure, the methodology selected Botryococcus braunii and Isochrysis galbana as potential promising candidates, for the three examined applications.     

Distributed Approach to Control and Manage Context-based Multi-virtual Networks

Using the flexibility of Wireless Mesh Networks (WMNs) and network virtualization, we define a novel architecture that provides personalized access for users by splitting a WMN into several adaptable Virtual Networks (VNs). Each VN is configured to meet specific levels of user context demands (context can span from security, mobility or service requirements). Users are grouped according to similarity of their context, and associated to VNs matching their context. To break the traditional centralized solutions for WMN and virtualization control, this paper defines an overall framework to distribute the control responsibilities along the architectural entities, which can then cooperate to autonomously discover, select, create, extend or remove VNs on-demand according to user and WMN dynamics. We also propose an analytical model to evaluate several performance aspects of the distributed VN control when compared to centralized or decentralized solutions, validating the model against a simulation study.