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In computational materials research, uncertainty analysis (more specifically, uncertainty propagation, UP) in the outcomes of model predictions is essential in order to establish confidence in the models as well as to validate them against the ground truth (experiments or higher fidelity simulations). Unfortunately, conventional UP models relying on exhaustive sampling from the distributions of input parameters may be impractical, particularly when the models are computationally expensive. In these cases, investigators must sacrifice accuracy in the propagated uncertainty by down-sampling the input distribution. Recently, a method was developed to correct for these inaccuracies by re-weighing the input distributions to create more statistically representative samples. In this work, the method is applied to computational models for the response of materials under high strain rates. The method is shown to effectively approximate converged output distributions at a lower cost than using conventional sampling approaches.

General multivariate distributions are notoriously expensive to sample from, particularly the high-dimensional posterior distributions in PDE-constrained inverse problems. This paper develops a sampler for arbitrary continuous multivariate distributions that is based on low-rank surrogates in the tensor train format, a methodology that has been exploited for many years for scalable, high-dimensional density function approximation in quantum physics and chemistry. We build upon recent developments of the cross approximation algorithms in linear algebra to construct a tensor train approximation to the target probability density function using a small number of function evaluations. For sufficiently smooth distributions, the storage required for accurate tensor train approximations is moderate, scaling linearly with dimension. In turn, the structure of the tensor train surrogate allows sampling by an efficient conditional distribution method since marginal distributions are computable with linear complexity in dimension. Expected values of non-smooth quantities of interest, with respect to the surrogate distribution, can be estimated using transformed independent uniformly-random seeds that provide Monte Carlo quadrature or transformed points from a quasi-Monte Carlo lattice to give more efficient quasi-Monte Carlo quadrature. Unbiased estimates may be calculated by correcting the transformed random seeds using a Metropolis–Hastings accept/reject step, while the quasi-Monte Carlo quadrature may be corrected either by a control-variate strategy or by importance weighting. We show that the error in the tensor train approximation propagates linearly into the Metropolis–Hastings rejection rate and the integrated autocorrelation time of the resulting Markov chain; thus, the integrated autocorrelation time may be made arbitrarily close to 1, implying that, asymptotic in sample size, the cost per effectively independent sample is one target density evaluation plus the cheap tensor train surrogate proposal that has linear cost with dimension. These methods are demonstrated in three computed examples: fitting failure time of shock absorbers; a PDE-constrained inverse diffusion problem; and sampling from the Rosenbrock distribution. The delayed rejection adaptive Metropolis (DRAM) algorithm is used as a benchmark. In all computed examples, the importance weight-corrected quasi-Monte Carlo quadrature performs best and is more efficient than DRAM by orders of magnitude across a wide range of approximation accuracies and sample sizes. Indeed, all the methods developed here significantly outperform DRAM in all computed examples. [ABSTRACT FROM AUTHOR]

One commonly used compromise standard‐setting method is the Beuk (1984) method. A key assumption of the Beuk method is that the emphasis given to the pass rate and the percent correct ratings should be proportional to the extent that the panelists agree on their ratings. However, whether the slope of Beuk line reflects the emphasis that panelists believe should be assigned to the pass rate and the percentage correct ratings has not be fully tested. In this article, I evaluate this critical assumption of the Beuk method by asking panelists to assign importance weights to their percentage correct and pass rate judgments. I show that in several cases that the emphasis suggested by the Beuk slope is noticeably different from what one would expect and is inconsistent with importance weight ratings. I also suggest two ways that the importance weights can be used to calculate alternate cut scores, and I show that one of the ways of calculating cut scores using the importance weights leads to larger potential differences in cut score estimates. I suggest that practitioners should consider collecting importance weights when the Beuk method is used for determining cut scores. [ABSTRACT FROM AUTHOR]

Introduction: Medical tourism is currently accepted as a developing industry. It describes the act of leaving home for treatment and care abroad. There are several factors that influence choice of destination for patients in medical tourism. Identification of these factors and prioritization according to importance weights could help international healthcare providers develop their marketing strategy.Methods: In the first stage of this research, an extensive review was done on recently published studies in medical tourism to identify influential factors on selecting a destination for medical tourism. The review yielded fourteen factors. In the second stage, 40 related papers were selected and the content analysis method was used to calculate frequency of repetition for each factor in the 40 papers. Furthermore, by using the Shannon entropy formula, marginal entropy and importance weight were analyzed for each factor.Results: Results indicated that the most influential factors on selection of a destination for medical tourism were “quality of care,” “cost” and “promotional programs,” results that indicate common sense. The least influential factor was “employer encouragement programs,” an indication that most medical tourists make a personal choice to embark on medical tourism and employer encouragement programs had lower priority in the decision making process.Conclusion: This study highlights the most important influential factors on choice of destination among patients, internationally. According to these findings, hospitals providing a service internationally must provide a high quality service at affordable cost. Consideration of travelers’ priorities can be applied to develop performance and promotional activities that will attract more patients and increase medical treatment revenue in a country.

Atanassov's intuitionistic fuzzy sets (AIFSs), characterized by a membership function, a nonmembership function, and a hesitancy function, is a generalization of a fuzzy set. Various aggregation operators are defined for AIFSs to deal with multicriteria decision-making problems in which there exists a prioritization of criteria. However, these existing intuitionistic fuzzy prioritized aggregation operators are not monotone with respect to the total order on Atanassov's intuitionistic fuzzy values (AIFVs), which is undesirable. We propose an intuitionistic fuzzy prioritized arithmetic mean based on the Łukasiewicz triangular norm, which is monotone with respect to the total order on AIFVs, and therefore is a true generalization of such operations. We give an example that a consumer selects a car to illustrate the validity and applicability of the proposed method aggregation operator. [ABSTRACT FROM AUTHOR]

In multicriteria decision making, the Choquet integral provides a commonly used method for the aggregation of the individual criteria satisfaction. Information about the criteria importance is expressed via a fuzzy measure on the set of criteria. One requirement in using the Choquet integral is to provide an ordering over the individual criteria satisfactions. When there exists some uncertainty regarding the criteria satisfactions, there typically exists some difficulty in providing this ordering. In an attempt to circumvent this difficulty, we suggest an approximation to the Choquet integral criteria aggregation that does not require an ordering. In this approximation, the criteria satisfactions are aggregated using as weights the Shapley index of the criteria. We provide an assessment of the merit of the approximation using the cardinality indices of the importance measure. [ABSTRACT FROM AUTHOR]

In this paper, we propose novel sub-band spectral centroid weighted wavelet packet cepstral coefficients (W-WPCC) for robust speech emotion recognition. Wavelet packet transform (WPT), as an effective tool for non-stationary signal analysis, is applied for speech analysis with a human auditory perception based WP filterbank structure. For each sub-band, the spectral centroid, which has been proved to be noise-robust, is calculated. On this basis, the W-WPCC feature is computed by combining the sub-band energies with sub-band spectral centroids via a weighting scheme to generate noise-robust acoustic features. The importance-weighted support vector machine (IW-SVM) is proposed to improve the robustness of classifier to the noises, while the important weight is utilized to compensate the covariate shift between test dataset and training dataset. Clean speech environments while demonstrates better noise-robustness in noisy environments and the IW-SVM improves the robustness to white Gaussian noise in speech emotion recognition compared with conventional classifiers. [ABSTRACT FROM AUTHOR]

Change is one of the major challenges that organizations face with in this competitive area. Organizations deal with changes in different aspects such as customer demand variations, technological innovations and instability of the environment. In order to adapt to this turbulent and volatile market, organizations should attempt to change their priorities, strategic visions and their traditional models. For that reason, in the past few decades, the manufacturing realm has taken a rather new orientation toward a manufacturing paradigm called agility. Agility as the 21stcentury’s paradigm is the organization’s ability to exploit the opportunities brought about by the occurring changes. Numerous researches have taken place in association withthis winner strategy, its evaluation and ways of making organizations agile. However, what has not taken much attention is weather getting to an agile state is vital and inevitable for organizations in the globaleconomic system. In this paper, environmental variables of Baspar Sazeh Company were identified as the agility drivers. Then, employing the fuzzy approach, the importance weights, and performance rate of drivers were aggregated; thereby an agility index was obtained enabling the agility-need-level of manufacturingcompanies to be evaluated.

En este trabajo se analizan problemas de optimización multi-objetivo en situaciones en que las preferencias del agente decisor pueden representarse por funciones aditivas y solo se dispone de información parcial sobre los pesos de importancia de los objetivos. Este análisis puede interpretarse también en términos de decisión en grupo, cuando distintos agentes proporcionan pesos distintos para los objetivos, y a partir de ahí hay que llegar a un consenso sobre la solución a elegir. En este contexto de información parcial se introduce una clase de soluciones que pueden considerarse un compromiso entre las soluciones del tipo maximin y las soluciones utilitarias. También se analizan algunas de sus principales propiedades. ABSTRACT We analyze multiple objective optimization problems in situations in which the preferences of the decision maker can be represented by additive functions and only partial information about the importance of the objectives is available. This analysis can also be interpreted in terms of group decision-making when different agents provide different weights for the objectives, and a consensus about the solution to choose has to be reached. In this context of partial information we introduce a class of solutions that can be considered as a compromise between the solutions based on a maxmin criterion and the utilitarian solutions. We also analyze some of their main properties.

Introduction: Medical tourism is currently accepted as a developing industry. It describes the act of leaving home for treatment and care abroad. There are several factors that influence choice of destination for patients in medical tourism. Identification of these factors and prioritization according to importance weights could help international healthcare providers develop their marketing strategy. Methods: In the first stage of this research, an extensive review was done on recently published studies in medical tourism to identify influential factors on selecting a destination for medical tourism. The review yielded fourteen factors. In the second stage, 40 related papers were selected and the content analysis method was used to calculate frequency of repetition for each factor in the 40 papers. Furthermore, by using the Shannon entropy formula, marginal entropy and importance weight were analyzed for each factor. Results: Results indicated that the most influential factors on selection of a destination for medical tourism were "quality of care," "cost" and "promotional programs," results that indicate common sense. The least influential factor was "employer encouragement programs," an indication that most medical tourists make a personal choice to embark on medical tourism and employer encouragement programs had lower priority in the decision making process. Conclusion: This study highlights the most important influential factors on choice of destination among patients, internationally. According to these findings, hospitals providing a service internationally must provide a high quality service at affordable cost. Consideration of travelers' priorities can be applied to develop performance and promotional activities that will attract more patients and increase medical treatment revenue in a country. [ABSTRACT FROM AUTHOR]

We introduce the OWA operator and note that it provides a parameterized class of aggregation operators. Here the parameterization is accomplished by the choice of the characterizing OWA weights, different characterizing weights results in different aggregation imperatives. We discuss various ways of providing these characterizing OWA weights. Most notable among these are the use of a vector containing the prescribed weights and the use of a function called the weight generating function from which the characterizing can be extracted. In many applications we are faced with situations in which the arguments being aggregated have different importances. This raises the issue of appropriately combining the individual argument weights with the characterizing weights of the operator to obtain operational weights to be used in the actual aggregation. Our goal here is looking at this issue under different methods of specification of the characterizing weights. [ABSTRACT FROM AUTHOR]

• A simplified form of existing approximated likelihood function is given. • A novel explicit expression of logarithm of likelihood function is proposed. • It deals directly with the distribution of the scaling factor. • A feasible weighting scheme is designed. In the random hypersurface model for extended target tracking problem, the scaling factor in the measurement equation brings difficulty for existing particle filter to calculate the likelihood in the weighting update stage. In this paper, we firstly simplify the existing approximate likelihood function where the distribution of the scaling factor is approximated by Gaussian one. Then, by directly dealing with the distribution of the scaling factor whose square has uniform distribution, we propose a novel explicit formula of the logarithm of likelihood. Based on this formula, a feasible weighting scheme is obtained and a novel particle filtering algorithm (NPFA) is proposed. Simulation shows that NPFA improves estimation accuracy compared with the existing unscented Kalman filter and particle filter for the tracking problem under discussion. [ABSTRACT FROM AUTHOR]

The importance sampling (IS) method lies at the core of many Monte Carlo‐based techniques. IS allows the approximation of a target probability distribution by drawing samples from a proposal (or importance) distribution, different from the target, and computing importance weights (IWs) that account for the discrepancy between these two distributions. The main drawback of IS schemes is the degeneracy of the IWs, which significantly reduces the efficiency of the method. It has been recently proposed to use transformed IWs (TIWs) to alleviate the degeneracy problem in the context of population Monte Carlo, which is an iterative version of IS. However, the effectiveness of this technique for standard IS is yet to be investigated. The performance of IS when using TIWs is numerically assessed, and showed that the method can attain robustness to weight degeneracy thanks to a bias/variance trade‐off. [ABSTRACT FROM AUTHOR]

A scale measuring quality of life (QOL) is important in adolescent research. Using the graded response model (GRM), this study evaluates the psychometric properties of the satisfaction ratings of the Quality of Life Profile Adolescent Version (QOLPAV). Data for 1,392 adolescents were used to check IRT assumptions such as unidimensionality and local item dependence (LID). The goodness of fit of the GRM to the data and the item characteristic curves were evaluated. The reliability and validity analyses included item/test information, Cronbach's α, and convergent and discriminant validity. Differential item functioning (DIF) procedures were also performed to detect item bias. The results provide evidence that the items sufficiently measured one single dimension. Few pairs of questions were flagged as LID due to content or wording similarity. Five items did not fit the GRM, and 4 were low in item discrimination. The findings also suggest that the assessment had appropriate reliability and validity. The DIF impact on the assessment score was considered minor. Because QOLPAV includes a respondent's perceived importance of various life aspects, a short form that only considers important life aspects in the overall QOL estimation for each respondent becomes feasible within the framework of IRT. Future studies focusing on the development of a QOL overall index using the items from QOLPAV are recommended. [ABSTRACT FROM AUTHOR]

Linear regression analysis is one of the most important tools in a researcher's toolbox for creating and testing predictive models. Although linear regression analysis indicates how strongly a set of predictor variables, taken together, will predict a relevant criterion (i.e., the multiple R), the analysis cannot indicate which predictors are the most important. Although there is no definitive or unambiguous method for establishing predictor variable importance, there are several accepted methods. This article reviews those methods for establishing predictor importance and provides a program (in Excel) for implementing them (available for direct download at . The program investigates all 2 - 1 submodels and produces several indices of predictor importance. This exploratory approach to linear regression, similar to other exploratory data analysis techniques, has the potential to yield both theoretical and practical benefits. [ABSTRACT FROM AUTHOR]

In this paper a model of soft aggregation of preferences in flexible database querying is proposed, based on the vector p-norm operator. The model allows aggregating conditions with distinct importance by modeling both veto and favour semantics of the conditions. We outline how the semantics of the compound query varies for increasing values of the parameter p, when the selection conditions are ANDed and ORed. [ABSTRACT FROM AUTHOR]

This article presents a methodology for implementing sensitivity analysis in the case of inverse problems and discusses its application to a real, complex, palaeoclimate problems. In palaeoclimatology and reconstruction of past climates, fossil data is modeled as functions of climate variables. However, in prediction of climate variables, indicating the inverse nature of the data is the problem. Technically, given a data set and a probability distribution, the problem is 'inverse' if, given a new observed y, prediction of the corresponding unknown x is required. On the other hand, the more usual forward problem considers prediction of y for given x. Sensitivity analysis in forward problems is about examining if the results are robust with respect to changes in the equation.

Five hundred Thai organizations (from both public and private sectors) were surveyed to learn how decision criteria were weighed when conducting job rotation. The organizations were categorized into three groups, namely, government agency (GA), private service company (PSC), and private manufacturing company (PMC). Three purposes of job rotation were considered: (1) restructuring of the organization, (2) periodic reshuffling of employee-job assignments, and (3) efficiency/productivity improvement. The findings from responded questionnaires (with a response rate of 41.40 per cent) show that the criterion weights are significantly dependent on both the organization type and the purpose of job rotation. It is found that organizations from both sectors considered 'knowledge, skills, and abilities' as a predominant decision criterion, irrespective of the purpose of job rotation. For the organizations that periodically reshuffled their current employee-job assignments, 'job seniority' was also considered as an important criterion. Regarding the other two job rotation purposes, organizations from the public sector consistently gave more attention to 'job seniority,' 'years of service,' and 'age' than did those from the private sector. [ABSTRACT FROM AUTHOR]

The present study evaluated subjective importance weighting using data collected with the Injection Drug User Quality of Life Scale (IDUQOL). Weighted and unweighted IDUQOL scores from 241 adults were correlated with convergent, discriminant, and criterion measures. Regression analysis was used to examine the contribution of importance ratings to scores on a global measure of life satisfaction and the corrected weighted IDUQOL total scores. Overall, the results showed that weighted scores did not perform better than unweighted scores in measuring quality of life. However, the mean satisfaction ratings for important domains correlated significantly higher with convergent measures than did the mean satisfaction ratings for unimportant domains. This finding suggests further attention needs to be paid to the meaning and measurement of subjective importance and how it may be incorporated more effectively into measures of quality of life. [ABSTRACT FROM AUTHOR]

We consider the problem of multicriteria decision making (MCDM) in the situation in which there exists a prioritization of criteria. A good example of prioritization among criteria occurs in the case of air travel, where concerns about passenger safety have a higher priority then economic concerns. Tradeoffs between saving on gasoline usage and jeopardizing passenger safety are unacceptable. We show how this prioritization of criteria can be modeled by using importance weights in which the weights associated with the lower priority criteria are related to the satisfaction of the higher priority criteria. We provide some models that allow for the formalization of these prioritized MCDM problems using both the Bellman-Zadeh paradigm for MCDM and the ordered weighted averaging (OWA) operator method. [ABSTRACT FROM AUTHOR]

In the assessment of health-related quality of life, nonpreference-based methods usually show only moderate correlations with utility-based measures. One cause may be that patients assign different weights to the various domains of health-related quality of life, for which nonpreference-based methods usually do not allow. Utilities reflect a weighted sum of these domains. The aim of this study is to assess whether the relationship between utility-based methods and nonpreference-based measures improves through the use of individual importance weights for the various domains of health-related quality of life. For this purpose, weights were obtained from 41 early-stage breast cancer patients, both before and during treatment, for seven pre-selected health status attributes representing important domains of health-related quality of life during chemotherapy. The importance weights were combined with the level of functioning on the attributes. These scores were regressed against patients' utilities for their actually experienced health state during chemotherapy, measured by means of a visual analog scale (VAS), a time trade-off (TTO), and a standard gamble (SG). Before weighting, the seven attribute scores were more strongly related to TTO and SG utilities than the nonpreference-based questionnaires. However, when they were combined with the importance weights, only the correlation with the SG utilities improved, and only so with the importance weights obtained before chemotherapy. In this study, assigning individually assessed preference weights to self-reported level of functioning did not result in stronger relationships with utilities. [ABSTRACT FROM AUTHOR]

Building on the notion that consumers are often uncertain about their tastes or weights, this research examines the proposition that consumers might infer their importance weights from their choices and the choice sets they evaluate. It is also hypothesized that the degree to which choice affects self-assessed weights depends on the consumer's familiarity with the product category and the available product information. The results of an experiment demonstrated that consumers' assessments of their importance weights can influenced by the choice set they previously evaluated. This effect, however, was not moderated by familiarity or provided product information. [ABSTRACT FROM AUTHOR]

A novel rotation and scale invariant texture classification methodology is proposed based on distribution matching in higher dimensional space. Feature extraction is performed by using uniform local binary patterns (uLBPs) in which the rotation and scale changes in an image cause shifts in the underlying uLBP histograms. To compensate for these shifts at the classification layer, the distributions of training and testing data using kernel methods are estimated and means of the two distributions in the transformed domain using importance weights are matched. These calculated importance weights are used in the standard support vector machines to compensate for the shift in the distributions. The proposed method is used for classifying the images in the Brodatz texture database demonstrating the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Background and Objective: The novel coronavirus disease 2019 (COVID-19) is considered a pandemic by the World Health Organization (WHO). As of April 3, 2020, there were 1,009,625 reported confirmed cases, and 51,737 reported deaths. Doctors have been faced with a myriad of patients who present with many different symptoms. This raises two important questions. What are the common symptoms, and what are their relative importance?Methods: A non-structured and incomplete COVID-19 dataset of 14,251 confirmed cases was preprocessed. This produced a complete and organized COVID-19 dataset of 738 confirmed cases. Six different feature selection algorithms were then applied to this new dataset. Five of these algorithms have been proposed earlier in the literature. The sixth is a novel algorithm being proposed by the authors, called Variance Based Feature Weighting (VBFW), which not only ranks the symptoms (based on their importance) but also assigns a quantitative importance measure to each symptom.Results: For our COVID-19 dataset, the five different feature selection algorithms provided different rankings for the most important top-five symptoms. They even selected different symptoms for inclusion within the top five. This is because each of the five algorithms ranks the symptoms based on different data characteristics. Each of these algorithms has advantages and disadvantages. However, when all these five rankings were aggregated (using two different aggregating methods) they produced two identical rankings of the five most important COVID-19 symptoms. Starting from the most important to least important, they were: Fever/Cough, Fatigue, Sore Throat, and Shortness of Breath. (Fever and cough were ranked equally in both aggregations.) Meanwhile, the sixth novel Variance Based Feature Weighting algorithm, chose the same top five symptoms, but ranked fever much higher than cough, based on its quantitative importance measures for each of those symptoms (Fever - 75 %, Cough - 39.8 %, Fatigue - 16.5 %, Sore Throat - 10.8 %, and Shortness of Breath - 6.6 %). Moreover, the proposed VBFW method achieved an accuracy of 92.1 % when used to build a one-class SVM model, and an NDCG@5 of 100 %.Conclusions: Based on the dataset, and the feature selection algorithms employed here, symptoms of Fever, Cough, Fatigue, Sore Throat and Shortness of Breath are important symptoms of COVID-19. The VBFW algorithm also indicates that Fever and Cough symptoms were especially indicative of COVID-19, for the confirmed cases that are documented in our database. [ABSTRACT FROM AUTHOR]