Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

A self-structured adaptive decision feedback equalizer

In a decision feedback equalizer (DFE), the structural parameters, including the decision delay, the feedforward filter (FFF), and feedback filter (FBF) lengths, must be carefully chosen, as they greatly influence the performance. Although the FBF length can be set as the channel memory, there is no closed-form expression for the FFF length and decision delay. In this letter, first we analytically show that the two-dimensional search for the optimum FFF length and decision delay can be simplified to a one-dimensional search and then describe a new adaptive DFE where the optimum structural parameters can be self-adapted.

Host-gut microbiota metabolic interactions

The composition and activity of the gut microbiota codevelop with the host from birth and is subject to a complex interplay that depends on the host genome, nutrition, and life-style. The gut microbiota is involved in the regulation of multiple host metabolic pathways, giving rise to interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, muscle, and brain. A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to manipulate the gut microbiota to combat disease and improve health.

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

Performance as social practice

Starting point for these outputs is a large scale research project in collaboration with the Zurich University for the Arts and the Kunstmuseum Thun, looking at a redefinition of Social Sculpture (Joseph Beuys/ Bazon Brock, 1970) as a functional device re-deployed to expand the art discourse into a societal discourse. Although Beuys‘ version of a social sculpture involved notions of abstruse mysticism and reformulations of a national identity these were never-the less part of a social transformation that shifted and re-arranged power relations. Following Laclau and Mouffe in their contention that democray is a fundamentally antagonistic process and contesting Grant Kester’s understanding of a ethically based relational practice, this work is alignes itself with Hirschhorn’s claim to an aesthetic practice within communities, following the possibility to view a socially based practice from both ends of the ethics debate, whereby ethical aspects fuels the aethetic to “create situations that are beautiful because they are ethical and shocking because they are ethical, thus in turn aesthetic because they are ethical” (O’Donnell). This project sets out to engage in activities which interact with surrounding communities and evoce new imaginations of site, thereby understanding site as a catalysts for subjective emergences. Performance is tested as a site for social practice. Archival research into local audio/visual resources, such as the Swiss Radio Archive, the Swiss Military Film Archives and zoological film archives of the Basel Zoo, was instrumental to the navigation of this work, under theme of crisis, catastrophy, landscape, fallout, in order to create a visual language for an active performance site. Commissioned by the Kunstmuseum Thun in collaboration with the University for the Arts in Zurich as part of a year long exhibition programme, (other artists are Jeanne Van Heeswijk (NL) and San Keller (CH), ) this project brings together a series of different works in a new performace installation. The performance process includes a performance workshop with 30 school children from local Swiss schools and their teachers, which was conducted publicly in the museum spaces. It enabled the children to engage with an unexpected set of tribal and animalistic behaviours, looking at situations of flight and rescue, resulting in a large performance choreography orchestration without an apparent conductor, it includes a collaboration with renowned Swiss zoologist, Prof Klaus Zuberbühler(University of St Andrews) and the Colonal General Haldimann commander of the military base in Thun. The installation included 2 static video images, shot in an around spectacular local cave site (Beatus Caves) including 3 children. The project will culminate in an edited edition of the Oncurating Journal, (issue no, tbc, in 2012) including interviews and essays from project collaborators. (Army Commander General, Thun, Jörg Hess, performance script, Timothy Long, and others)

Fast identification algorithms for Gaussian process model

A class identification algorithms is introduced for Gaussian process(GP)models.The fundamental approach is to propose a new kernel function which leads to a covariance matrix with low rank,a property that is consequently exploited for computational efficiency for both model parameter estimation and model predictions.The objective of either maximizing the marginal likelihood or the Kullback–Leibler (K–L) divergence between the estimated output probability density function(pdf)and the true pdf has been used as respective cost functions.For each cost function,an efficient coordinate descent algorithm is proposed to estimate the kernel parameters using a one dimensional derivative free search, and noise variance using a fast gradient descent algorithm. Numerical examples are included to demonstrate the effectiveness of the new identification approaches.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.