Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures

User Guide to MCA: Multiple Criteria Analysis of Discrete Alternatives with a Simple Preference Specification

This report provides detailed information about using the MCA, which is the Web-based application formultiple criteria analysis of discrete alternatives with a simple preference specification. MCA in its currently available version supports analysis of a set of discrete alternatves, defined either interactively or through a prepared file. Each alternative is characterized by several attributes (indicators) that serve for specification of criteria. MCA has been designed for problems having large numbers of alternatives and of criteria. However, it can also be used for problems with small numbers of criteria and/or alternatives. The report is composed of two main parts. The first one provides an overview of the MCA and summarize the methodology of multiple criteria analysis implemented in the MCA. The second part consists of the step-by-step detailed tutorial to the MCA organized into three stages corresponding to (1) basic funtions of the MCA, (2) specification of problems, instances, and analyses, and (3) interactive multiciteria analysis process

Requirement Analysis and Implementation of Multicriteria Analysis in the NEEDS Project

This report specifies the requirements for and implementation of the multicriteria analysis of future energy technologies performed by a large number of stakeholders within the EU-funded integrated projct NEEDS. The report is composed of two main parts and the appendix. The first part starts with a summary of the objectives of the analysis followed by a detailed specifiation of the analyzed problem, in particular the analysis context, discussion of the sets of criteria and alternatives, and the participation of the stakeholders. Next, the planned problem analysis process is first outlined, and then discussed in more detail. Finally, the requirements for the multicritria analysis are specified. The second part deals with the implementation of the dedicated Web-site developed for this analysis, and later extended to support analysis of any multicriteria choice between discrete alternatives. It starts with an overview of the problem analysis process and the corresponding basic assumptions. Te architecture of the application and its features are then presented. Lessons learned from the development and use of this application conclude this part of the report. The appendix contains a review of the state-of-the-art of applying multicriteria analysis to energy problems, as well as characteristics of three applications that exploit the multicriteria analysis methods for energy problems considered relevant to the analysis reported in this paper

Natural Environment Management and Applied Systems Analysis

This volume contains papers from the NEMASA Konan-IIASA Joint Workshop on Natural Environment Management and Applied Systems Analysis, which took place at IIASA September 6-8, 2000. The workshop was an activity of the research project "Modeling by Computational Intelligence and its Application to Natural Environment Management." The project is being supported by the Hirao Taro Foundation of the Konan University Association for Academic Research, Kobe, Japan. The management of the natural environment -- in particular, the use of advanced agricultural practices -- poses a major challenge to modern society, but perhaps applied systems analysis can help. The workshop set was about to: present new concepts and methodologies for managing the environment, and offer an open forum for the exchange of ideas among research disciplines, especially between agro-environmental and applied systems analysis research and between researchers and practitioners. The paper deal with a range of topics. The editors have arranged them into the following categories: (1) modeling methodologies, (2) data analysis, (3) land use, (4) water management, and (5) applications

Quantum Dynamics in a Time-dependent Hard-Wall Spherical Trap

Exact solution of the Schr\"{o}dinger equation is given for a particle inside a hard sphere whose wall is moving with a constant velocity. Numerical computations are presented for both contracting and expanding spheres. The propagator is constructed and compared with the propagator of a particle in an infinite square well with one wall in uniform motion.Comment: 6 pages, 4 figures, Accepted by Europhys. Let

On the interplay of temporal resolution power and spatial suppression in their prediction of psychometric intelligence.

As a measure of the brain's temporal fine-tuning capacity, temporal resolution power (TRP) explained repeatedly a substantial amount of variance in psychometric intelligence. Recently, spatial suppression, referred to as the increasing difficulty in quickly perceiving motion direction as the size of the moving stimulus increases, has attracted particular attention, when it was found to be positively related to psychometric intelligence. Due to the conceptual similarities of TRP and spatial suppression, the present study investigated their mutual interplay in the relation to psychometric intelligence in 273 young adults to better understand the reasons for these relationships. As in previous studies, psychometric intelligence was positively related to a latent variable representing TRP but, in contrast to previous reports, negatively to latent and manifest measures of spatial suppression. In a combined structural equation model, TRP still explained a substantial amount of variance in psychometric intelligence while the negative relation between spatial suppression and intelligence was completely explained by TRP. Thus, our findings confirmed TRP to be a robust predictor of psychometric intelligence but challenged the assumption of spatial suppression as a representation of general information processing efficiency as reflected in psychometric intelligence. Possible reasons for the contradictory findings on the relation between spatial suppression and psychometric intelligence are discussed

Mean-risk models using two risk measures: A multi-objective approach

This paper proposes a model for portfolio optimisation, in which distributions are characterised and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level. The problem is multi-objective and transformed into a single objective problem in which variance is minimised while constraints are imposed on the expected value and CVaR. In the case of discrete random variables, the problem is a quadratic program. The mean-variance (mean-CVaR) efficient solutions that are not dominated with respect to CVaR (variance) are particular efficient solutions of the proposed model. In addition, the model has efficient solutions that are discarded by both mean-variance and mean-CVaR models, although they may improve the return distribution. The model is tested on real data drawn from the FTSE 100 index. An analysis of the return distribution of the chosen portfolios is presented