Solving a real-life, large-scale energy management problem

This paper introduces a three-phase hybrid heuristic for a large-scale energy management and maintenance scheduling problem. The problem is to schedule maintenance periods and refueling amounts for nuclear power plants with a time horizon of up to five years, and handling a number of scenarios for future demand and prices. The goal is to minimize the expected total production cost. The first phase of the heuristic solves a constraint programming model of a simplified version of the problem, the second performs a local search, and the third handles overproduction in a greedy fashion. This work was initiated in the context of the ROADEF/EURO Challenge 2010. In the concluding phase of the competition, our team ranked second in the junior category and sixth overall. After correcting a small implementation bug in the program that was submitted for final evaluation, our solver ranks first in the overall results from the competition.     

The effect on accidents of technical inspections of heavy vehicles in Norway

This paper presents a study of the effects on accidents of technical inspections of heavy vehicles (trucks and buses) in Norway. Multiple regression analysis is applied in order to estimate the effects of technical inspections, controlling for annual trend in accident rate, the number of new drivers and annual economic growth. It is found that abolishing inspections may result in an increase of 5–10% in the number of heavy vehicles involved in injury accidents; increasing the number of inspections by 100% is associated with a similar reduction in the number of accidents. These results are not statistically significant and highly uncertain. The study clearly illustrates many of the difficulties often encountered in non-experimental accident research.     

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles)

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.     

Using mobile code to build a smart kitchen

Software infrastructures for handheld and ubiquitous computing systems help developers tackle many implementation problems. Sun Microsystems recently introduced an infrastructure called Jini. We report on our experiences of using Jini to build a smart kitchen. We found that the central Jini concept of code mobility proved very beneficial offering a simple mechanism for device interoperability and encouraging developers to better structure the software.     

Efficient algorithms for real-life instances of the variable size bin packing problem

In this paper we present a local search heuristic for real-life instances of the variable size bin packing problem, and an exact algorithm for small instances. One important issue our heuristic is able to satisfy is that solutions must be delivered within (milli)seconds and that the solution methods should be robust to last minute changes in the data. Furthermore we show that we are able to incorporate the concept of usable leftovers on a single bin, and the implementation of many additional constraints should be supported by the straightforward solution representation. The heuristic is compared to others from the literature, and comes out ahead on a large subset of the instances.     

Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data

A snow-cover mapping method accounting for forests (SnowFrac) is presented. SnowFrac uses spectral unmixing and endmember constraints to estimate the snow-cover fraction of a pixel. The unmixing is based on a linear spectral mixture model, which includes endmembers for snow, conifer, branches of leafless deciduous trees and snow-free ground. Model input consists of a land-cover fraction map and endmember spectra. The land-cover fraction map is applied in the unmixing procedure to identify the number and types of endmembers for every pixel, but also to set constraints on the area fractions of the forest endmembers. SnowFrac was applied on two Terra Moderate Resolution Imaging Spectroradiometer (MODIS) images with different snow conditions covering a forested area in southern Norway. Six experiments were carried out, each with different endmember constraints. Estimated snow-cover fractions were compared with snow-cover fraction reference maps derived from two Landsat Enhanced Thematic Mapper Plus (ETM+) images acquired the same days as the MODIS images. Results are presented for non-forested areas, deciduous forests, coniferous forests and mixed deciduous/coniferous forests. The snow-cover fraction estimates are enhanced by increasing constraints introduced to the unmixing procedure. The classification accuracy shows that 96% of the pixels are classified with less than 20% error (absolute units) on 7 May 2001 when all forested and non-forested areas are included. The corresponding figure for 4 May 2000 is 88%.     

Sense of coherence as a protective mechanism among targets of workplace bullying.

The present study investigated sense of coherence (SOC) as a protective factor among targets of workplace bullying. A hypothesis that strong SOC lessens the relationship between exposure to bullying and symptoms of posttraumatic stress was tested in a cross-sectional sample of 221 self-labeled targets of workplace bullying. The findings showed that SOC offers most protective benefits to targets exposed to low levels of bullying, whereas the benefits of SOC diminish as bullying becomes more severe. The results support previous findings that workplace bullying is a traumatic experience for those exposed to it, regardless of the target's available coping resources. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

State-set branching: Leveraging BDDs for heuristic search

In this article, we present a framework called state-set branching that combines symbolic search based on reduced ordered Binary Decision Diagrams (BDDs) with best-first search, such as A* and greedy best-first search. The framework relies on an extension of these algorithms from expanding a single state in each iteration to expanding a set of states. We prove that it is generally sound and optimal for two A* implementations and show how a new BDD technique called branching partitioning can be used to efficiently expand sets of states. The framework is general. It applies to any heuristic function, evaluation function, and transition cost function defined over a finite domain. Moreover, branching partitioning applies to both disjunctive and conjunctive transition relation partitioning. An extensive experimental evaluation of the two A* implementations proves state-set branching to be a powerful framework. The algorithms outperform the ordinary A* algorithm in almost all domains. In addition, they can improve the complexity of A* exponentially and often dominate both A* and blind BDD-based search by several orders of magnitude. Moreover, they have substantially better performance than BDDA*, the currently most efficient BDD-based implementation of A*.     

Addressing dependability by applying an approach for model-based risk assessment

This paper describes how an approach for model-based risk assessment (MBRA) can be applied for addressing different dependability factors in a critical application. Dependability factors, such as availability, reliability, safety and security, are important when assessing the dependability degree of total systems involving digital instrumentation and control (I&C) sub-systems. In order to identify risk sources their roles with regard to intentional system aspects such as system functions, component behaviours and intercommunications must be clarified. Traditional risk assessment is based on fault or risk models of the system. In contrast to this, MBRA utilizes success-oriented models describing all intended system aspects, including functional, operational and organizational aspects of the target. The EU-funded CORAS project developed a tool-supported methodology for the application of MBRA in security-critical systems. The methodology has been tried out within the telemedicine and e-commerce areas, and provided through a series of seven trials a sound basis for risk assessments. In this paper the results from the CORAS project are presented, and it is discussed how the approach for applying MBRA meets the needs of a risk-informed Man–Technology–Organization (MTO) model, and how methodology can be applied as a part of a trust case development.     

Parameterization and analysis of the optical absorption and scattering coefficients in a western Norwegian fjord: a case II water study

Based on statistical analyses of optical properties measured during a whole year of monthly cruises in a Norwegian fjord, we constructed a two-component model for the absorption and scattering coefficients for visible light. The input to the model is the concentrations of yellow substance and chlorophyll a. Because of the presence of a significant amount of nonalgal particles in coastal water, we assume that the absorption and scattering coefficients approach constant background values when the concentration of chlorophyll a approaches zero. The model works reasonably for a variety of optical conditions encountered throughout the year, with a possible exception during a bloom of the Emiliania huxleyi algae in June.