Dominance Measuring Approach using Stochastic Weights

In this paper we propose an approach to obtain a ranking of alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker's preferences are represented by an additive multi-attribute utility function, in which weights are modeled by independent normal variables, the performance in each attribute for each alternative is an interval value and classes of utility functions are available for each attribute. The approach we propose is based on dominance measures, which are computed in a similar way that when the imprecision concerning weights is modeled by uniform distributions or by an ordinal relation. In this paper we will show how the approach can be applied when the imprecision concerning weights are represented by normal distributions. Extensions to other distributions, such as truncated normal or beta, can be feasible using Monte Carlo simulation techniques

Ranking Alternatives on the Basis of a Dominance Intensity Measure

The additive multi-attribute utility model is widely used within MultiAttribute Utility Theory (MAUT), demanding all the information describing the decision-making situation. However, these information requirements can obviously be far too strict in many practical situations. Consequently, incomplete information about input parameters has been incorporated into the decisionmaking process. We propose an approach based on a dominance intensity measure to deal with such situations. The approach is based on the dominance values between pairs of alternatives that can be computed by linear programming. These dominance values are transformed into dominance intensities from which a dominance intensity measure is derived. It is used to analyze the robustness of a ranking of technologies for the disposition of surplus weapons-grade plutonium by the Department of Energy in the USA, and compared with other dominance measuring methods

A new dominance intensity method to deal with ordinal information about a DM's preferences within MAVT

Dominance measuring methods are a new approach to deal with complex decision-making problems with imprecise information. These methods are based on the computation of pairwise dominance values and exploit the information in the dominance matrix in dirent ways to derive measures of dominance intensity and rank the alternatives under consideration. In this paper we propose a new dominance measuring method to deal with ordinal information about decision-maker preferences in both weights and component utilities. It takes advantage of the centroid of the polytope delimited by ordinal information and builds triangular fuzzy numbers whose distances to the crisp value 0 constitute the basis for the de?nition of a dominance intensity measure. Monte Carlo simulation techniques have been used to compare the performance of this method with other existing approaches

Preference intensity in MCDM when an additive utility function represents DM preferences

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation

Lessons learned from a performance analysis and optimization of a multiscale cellular simulation

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a locality-aware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field

Métodos de ordenación de alternativas basados en la intensidad de dominancia para problemas multiatributo impreciso

En MAUT uno de los modelos más usados para representar las preferencias de los decisores es el modelo de utilidad multiatributo aditivo. En muchos problemas reales la determinación de las funciones de utilidad y pesos de cada atributo no pueden ser obtenidos de una forma precisa. En este trabajo consideramos que la imprecisión en las funciones de utilidad queda representada al considerar clases de funciones de utilidad, y la de los pesos mediante una ordenación de los mismos, intervalos de pesos, distribuciones de probabilidad normales o números difusos. La metodología que proponemos para resolver los problemas multiatributo imprecisos está basada en la intensidad de dominancia entre pares de alternativas. Partiendo de la matriz de dominancia se calcula para cada alternativa el valor de dominancia total, en función de la intensidad con la que esta alternativa domina al resto y el resto la domina a ella, el cual nos permitirá obtener la ordenación de las alternativas

Prolonged survival of patients with angioimmunoblastic T-cell lymphoma after high-dose chemotherapy and autologous stem cell transplantation: the GELTAMO experience

Abstract OBJECTIVES: Angioimmunoblastic T-cell lymphoma (AIL) is a rare lymphoma with a poor prognosis and no standard treatment. Here, we report our experiences with 19 patients treated with high-dose chemotherapy and autologous stem cell transplantation (HDC/ASCT) within the GELTAMO co-operative group between 1992 and 2004. METHODS: The median age at transplantation was 46 yr. Fifteen patients underwent the procedure as front-line therapy and four patients as salvage therapy. Most patients received peripheral stem cells (90%) coupled with BEAM or BEAC as conditioning regimen (79%). RESULTS: A 79% of patients achieved complete response, 5% partial response and 16% failed the procedure. After a median follow-up of 25 months, eight patients died (seven of progressive disease and secondary neoplasia), while actuarial overall survival and progression-free survival at 3 yr was 60% and 55%. Prognostic factors associated with a poor outcome included bone marrow involvement, transplantation in refractory disease state, attributing more than one factor of the age-adjusted-International Prognostic Index, Pretransplant peripheral T-cell lymphoma (PTCL) Score or Prognostic Index for PTCL. CONCLUSIONS: More than half of the patients with AIL that display unfavourable prognostic factors at diagnosis or relapse would be expected to be alive and disease-free after 3 yr when treated with HDC/ASCT. Patients who are transplanted in a refractory disease state do not benefit from this procedure

Monitoring and Scoring Counter-Diffusion Protein Crystallization Experiments in Capillaries by in situ Dynamic Light Scattering

In this paper, we demonstrate the feasibility of using in situ Dynamic Light Scattering (DLS) to monitor counter-diffusion crystallization experiments in capillaries. Firstly, we have validated the quality of the DLS signal in thin capillaries, which is comparable to that obtained in standard quartz cuvettes. Then, we have carried out DLS measurements of a counter-diffusion crystallization experiment of glucose isomerase in capillaries of different diameters (0.1, 0.2 and 0.3 mm) in order to follow the temporal evolution of protein supersaturation. Finally, we have compared DLS data with optical recordings of the progression of the crystallization front and with a simulation model of counter-diffusion in 1D

Behavioral and Cytological Differences between Two Parkinson’s Disease Experimental Models

The knowledge about the biochemical and behavioral changes in humans with PD has allowed proposing animal models for its study; however, the results obtained so far have been heterogeneous. Recently, we established a novel PD model in rodents by manganese chloride (MnCl2) and manganese acetate (Mn (OAc)3) mixture inhalation. After inhaling, the rodents presented bilateral loss of SNc dopaminergic neurons. Later, we conclude that the alterations are of dopamine origin since L-DOPA reverted the alterations. After six months, SNc significantly reduced the number of cells, and striatal dopamine content decreased by 71%. The animals had postural instability, action tremor, and akinesia; these symptoms improved with L-DOPA, providing evidence that Mn mixture inhalation induces comparable alterations that those in PD patients. Thus, this study aimed to compare the alterations in two different PD experimental models: 6-OHDA unilateral lesion and Mn mixture inhalation through open field test, rotarod performance and the number of SNc dopaminergic neurons. The results show that the Mn-exposed animals have motor alterations and bilateral and progressive SNc neurons degeneration; in contrast, in the 6-OHDA model, the neuronal loss is unilateral and acute, demonstrating that the Mn exposure model better recreates the characteristics observed in PD patients