On the additivity of preference aggregation methods

The paper reviews some axioms of additivity concerning ranking methods used for generalized tournaments with possible missing values and multiple comparisons. It is shown that one of the most natural properties, called consistency, has strong links to independence of irrelevant comparisons, an axiom judged unfavourable when players have different opponents. Therefore some directions of weakening consistency are suggested, and several ranking methods, the score, generalized row sum and least squares as well as fair bets and its two variants (one of them entirely new) are analysed whether they satisfy the properties discussed. It turns out that least squares and generalized row sum with an appropriate parameter choice preserve the relative ranking of two objects if the ranking problems added have the same comparison structure.

A rating aggregation method for generating product reputations

Many websites offer the opportunity for customers to rate items and then use customers' ratings to generate items reputation, which can be used later by other users for decision making purposes. The aggregated value of the ratings per item represents the reputation of this item. The accuracy of the reputation scores is important as it is used to rank items. Most of the aggregation methods didn't consider the frequency of distinct ratings and they didn't test how accurate their reputation scores over different datasets with different sparsity. In this work we propose a new aggregation method which can be described as a weighted average, where weights are generated using the normal distribution. The evaluation result shows that the proposed method outperforms state-of-the-art methods over different sparsity datasets.

A normal-distribution based rating aggregation method for generating product reputations

With the extensive use of rating systems in the web, and their significance in decision making process by users, the need for more accurate aggregation methods has emerged. The Naïve aggregation method, using the simple mean, is not adequate anymore in providing accurate reputation scores for items [6 ], hence, several researches where conducted in order to provide more accurate alternative aggregation methods. Most of the current reputation models do not consider the distribution of ratings across the different possible ratings values. In this paper, we propose a novel reputation model, which generates more accurate reputation scores for items by deploying the normal distribution over ratings. Experiments show promising results for our proposed model over state-of-the-art ones on sparse and dense datasets.

Ranking Spatial and Temporal Hotspots for Road Safety Interventions Using a Nonlinear Aggregation Approach

In recent years, the concept of a composite performance index, brought from economic and business statistics, has gained popularity in the field of road safety. The construction of the Composite Safety Performance Index (CSPI) involves the following key steps: the selection of the most appropriate indicators to be aggregated and the method used to aggregate them.

Over the last decade, various aggregation methods for estimating the CSPI have been suggested in the literature. However, recent studies indicates that most of these methods suffer from many deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, as well as their high “degree of freedom” which enables one to readily manipulate them to produce desired outcomes.

The purpose of this study is to introduce an alternative aggregation method for the estimation of the CSPI, which is free from the aforementioned deficiencies. In contrast with the current aggregation methods, which generally use linear combinations of road safety indicators to estimate a CSPI, the approach advocated in this study is based on non-linear combinations of indicators and can be summarized into the following two main steps: the pairwise comparison of road safety indicators and the development of marginal and composite road safety performance functions. The introduced method has been successfully applied to identify and rank temporal and spatial hotspots for Northern Ireland, using road traffic collision data recorded in the UK STATs19 database. The obtained results highlight the promising features of the proposed approach including its stability and consistency, which enables significantly reduced deficiencies associated with the current aggregation methods. Progressively, the introduced method could evolve into an intelligent support system for road safety assessment.

Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification.

QDFA : query-dependent feature aggregation for medical image retrieval

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

Non-monotone averaging aggregation

We advance the theory of aggregation operators and introduce non-monotone aggregation methods based on minimization of a penalty for inputs disagreements. The application in mind is processing data sets which may contain noisy values. Our aim is to filter out noise while at the same time preserve signs of unusual values. We review various methods of robust estimators of location, and then introduce a new estimator based on penalty minimisation.

Quercetin inhibits collagen-stimulated platelet activation through inhibition of multiple components of the glycoprotein VI signaling pathway

Background: The regulation of platelet function by pharmacological agents that modulate platelet signaling haspharmacolo proven a successful approach to the prevention of thrombosis. A variety of molecules present in the diet have been shown to inhibit platelet activation, including the antioxidant quercetin. Objectives: In this report we investigate the molecular mechanisms through which quercetin inhibits collagen-stimulated platelet aggregation. Methods: The effect of quercetin on platelet aggregation, intracellular calcium release, whole cell tyrosine phosphorylation and intracellular signaling events including tyrosine phosphorylation and kinase activity of proteins involved in the collagen-stimulated glycoprotein (GP) signaling pathway were investigated. Results: We report that quercetin inhibits collagen-stimulated whole cell protein tyrosine phosphorylation and intracellular mobilization of calcium, in a concentration-dependent manner. Quercetin was also found to inhibit various events in signaling generated by the collagen receptor GPVI. This includes collagen-stimulated tyrosine phosphorylation of the Fc receptor gamma-chain, Syk, LAT and phospholipase Cgamma2. Inhibition of phosphorylation of the Fc receptor gamma-chain suggests that quercetin inhibits early signaling events following stimulation of platelets with collagen. The activity of the kinases that phosphorylate the Fc receptor gamma-chain, Fyn and Lyn, as well as the tyrosine kinase Syk and phosphoinositide 3-kinase was also inhibited by quercetin in a concentration-dependent manner, both in whole cells and in isolation. Conclusions: The present results provide a molecular basis for the inhibition by quercetin of collagen-stimulated platelet activation, through inhibition of multiple components of the GPVI signaling pathway, and may begin to explain the proposed health benefits of high quercetin intake.

Weakly monotone averaging functions

Averaging behaviour of aggregation functions depends on the fundamental property of monotonicity with respect to all arguments. Unfortunately this is a limiting property that ensures that many important averaging functions are excluded from the theoretical framework. We propose a definition for weakly monotone averaging functions to encompass the averaging aggregation functions in a framework with many commonly used non-monotonic means. Weakly monotonic averages are robust to outliers and noise, making them extremely important in practical applications. We show that several robust estimators of location are actually weakly monotone and we provide sufficient conditions for weak monotonicity of the Lehmer and Gini means and some mixture functions. In particular we show that mixture functions with Gaussian kernels, which arise frequently in image and signal processing applications, are actually weakly monotonic averages. Our concept of weak monotonicity provides a sound theoretical and practical basis for understanding both monotone and non-monotone averaging functions within the same framework. This allows us to effectively relate these previously disparate areas of research and gain a deeper understanding of averaging aggregation methods. © Springer International Publishing Switzerland 2014.

Profile and Prevalence of Aspirin Resistance in Indian Patients with Coronary Artery Disease

Background: Aspirin resistance is considered to be an enigma and the data available on aspirin resistance is scarce. This study was initiated to prospectively evaluate the prevalence of aspirin resistance in patients with stable coronary artery disease by using an established method of optical platelet aggregation. Methods and Results: We studied 50 patients who were on 150 mg of aspirin for the previous 7 days. Fasting blood samples were assessed using optical platelet aggregation (Chronolog Corp, USA). The mean platelet aggregation with 10 μm of adenosine diphosphate in our patient group was 49.42 ± 23.29% and with 0.5 mg/ ml of arachidonic acid it was 13.58 ± 21.40%. Aspirin resistance was defined as a mean aggregation of ≥70% with 10 μm of adenosine diphosphate and a mean aggregation of ≥ 20% with 0.5 mg/ml of arachidonic acid. Aspirin semi responders were defined as those meeting only one of the criteria. Based on these criteria, 2.08% patients were found to be aspirin-resistant, 39.58% were aspirin semi responders and 58.33% were aspirin responders. Females tended to be more aspirin semi responsive (p = 0.08). All other parameters tested, namely, age, smoking, diabetes mellitus, hypertension, obesity, lipids, hemoglobin, platelet count, ejection fraction and drug intake did not show any statistically significant difference among the groups. Thus, in our group 41.66% patients showed inadequate response to aspirin. Conclusions: This study shows that aspirin resistance and aspirin semi responsiveness do occur in the Indian patients and there are no reliable clinical predictors for this condition. The diagnosis therefore relies primarily on laboratory tests.

Estimación de la peligrosidad sísmica mediante protocolos de juicios de expertos

Este trabajo estudia la aportación que los métodos de agregación de juicios de expertos pueden realizar en el cálculo de la peligrosidad sísmica de emplazamientos. Se han realizado cálculos en dos emplazamientos de la Península Ibérica: Mugardos (La Coruña) y Cofrentes (Valencia) que están sometidos a regímenes tectónicos distintos y que, además, alojan instalaciones industriales de gran responsabilidad. Las zonas de estudio, de 320 Km de radio, son independientes. Se ha aplicado un planteamiento probabilista a la estimación de la tasa anual de superación de valores de la aceleración horizontal de pico y se ha utilizado el Método de Montecarlo para incorporar a los resultados la incertidumbre presente en los datos relativos a la definición de cada fuente sismogenética y de su sismicidad. Los cálculos se han operado mediante un programa de ordenador, desarrollado para este trabajo, que utiliza la metodología propuesta por el Senior Seismic Hazard Analysis Commitee (1997) para la NRC. La primera conclusión de los resultados ha sido que la Atenuación es la fuente principal de incertidumbre en las estimaciones de peligrosidad en ambos casos. Dada la dificultad de completar los datos históricos disponibles de esta variable se ha estudiado el comportamiento de cuatro métodos matemáticos de agregación de juicios de expertos a la hora de estimar una ley de atenuación en un emplazamiento. Los datos de partida se han obtenido del Catálogo de Isosistas del IGN. Los sismos utilizados como variables raíz se han elegido con el criterio de cubrir uniformemente la serie histórica disponible y los valores de magnitud observados. Se ha asignado un panel de expertos particular a cada uno de los dos emplazamientos y se han aplicado a sus juicios los métodos de Cooke, equipesos, Apostolakis_Mosleh y Morris. Sus propuestas se han comparado con los datos reales para juzgar su eficacia y su facilidad de operación. A partir de los resultados se ha concluido que el método de Cooke ha mostrado el comportamiento más eficiente y robusto para ambos emplazamientos. Este método, además, ha permitido identificar, razonadamente, a aquellos expertos que no deberían haberse introducido en un panel. The present work analyses the possible contribution of the mathematical methods of aggregation in the assessment of Seismic Hazzard. Two sites, in the Iberian Peninsula, have been considered: Mugardos ( La Coruña) and Cofrentes (Valencia).Both of them are subjected to different tectonic regimes an both accommodate high value industrial plants. Their areas of concern, with radius of 320 Km, are not overlapping. A probabilistic approach has been applied in the assessment the annual probability of exceedence of the horizontal peak acceleration. The Montecarlo Method has allowed to transfer the uncertainty in the models and parameters to the final results. A computer program has been developed for this purpose. The methodology proposed by the Senior Seismic Analysis Committee (1997) for the NRC has been considered. Attenuation in Ground motion has been proved to be the main source of uncertainty in seismic hazard for both sites. Taking into account the difficulties to complete existing historical data in this subject the performance of four mathematical methods of aggregation has been studied. Original data have been obtained from the catalogs of the Spanish National Institute of Geography. The seismic events considered were chosen to cover evenly the historical records and the observed values of magnitude. A panel of experts have been applied to each site and four aggregation methods have been developed : equal weights, Cooke, Apostolakis-Mosleh and Morris The four proposals have been compaired with the actual data to judge their performance and ease of application. The results have shown that the Method of Cooke have proved the most efficient and robust for both sites. This method, besides, allow the reasoned identification of those experts who should be rejected from the panel

Spillovers from foreign direct investment in Central and Eastern Europe:an index for measuring a country’s potential to benefit from technology spillovers

In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implications both the investment strategies of multinationals and government FDI policies.

Spillovers from Foreign Direct Investment in Central and Eastern Europe. An index for measuring a country’s potential to benefit from technology spillovers

In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implication both the investment strategies of multinationals and government FDI policies.

Measuring Benefits of Technology Spillovers from Foreign Direct Investment in Central and Eastern Europe

In this paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implications both for the investment strategies of multinationals and government FDI policies.