Search

This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster–Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster–Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system. [ABSTRACT FROM AUTHOR]

Cluster interpretation is an important step for a proper understanding of a set of classes, independently of whether they have been automatically discovered or expert-based. An understanding of classes is crucial for the further use of classes as the basis of a decision-making process. The abundant work on cluster validity found in the literature is mainly focused on the validation of clusters from the structural point of view. However, structural validation does not ensure that the clustering is useful, since meaningfulness is the key to guaranteeing that classes can support further decisions. In previous works, special significance tests taken from the field of multivariate analysis were introduced in an interpretation methodology for automatically assessing relevant variables in particular classes. In this paper, we present the interpretation of nested partitions and the relationships between both interpretations are studied. In particular, the inconsistencies produced in interpretation when a second partition refines the first one with a higher level of granularity are studied, diagnosed, and a modification of the original methodology is provided to guarantee consistency in these cases. The relevant characteristics detected in a parent class must also be inherited in subclasses, or at least in some of them. The proposal is evaluated using a real data set on baseline health conditions and dietary habits of a sample of the general population. [ABSTRACT FROM AUTHOR]

What explains cooperation in international environmental networks? What are the effects of skills and money on the decision by state agencies to collaborate across borders on environmental problems? This article answers these questions, showing that international cooperation provides environmental bureaucrats and their agencies with the opportunity to pool scarce resources, update critical skills, and attract funds from international donors. Theory and results offer novel findings on network homophily (the tendency of similar actors to work together), as international cooperation increases between environmental state agencies at similar levels of program development. Hypotheses are tested using social network analysis to measure cooperation on a dataset that includes all regional and global grants awarded over the past two decades by the Global Environment Facility (GEF). [ABSTRACT FROM AUTHOR]

Highlights: [•] Apply semantic analysis on internet data to identify weak signals. [•] Project further data into semantic subspace to identify weak signal time series. [•] Compare time series to given hypotheses. [•] Provide relevant weak signal developments to decision makers. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problem of cooperative spectrum sensing in the presence of the noise power uncertainty. We propose a new spectrum sensing method based on the fuzzy hypothesis test (FHT) that utilizes membership functions as hypotheses for the modeling and analyzing such uncertainty. In particular, we apply the Neyman–Pearson lemma on the FHT and propose a threshold-based local detector at each secondary user (SU) in which the threshold depends on the noise power uncertainty. In the proposed scheme, a centralized manner in the cooperative spectrum sensing is deployed in which each SU sends its one bit decision to a fusion center. The fusion center makes a final decision about the absence/presence of a primary user (PU). The performance of the PU''s signal detection is evaluated by the probability of signal detection for a specific signal to noise ratio when the probability of false alarm is set to a fixed value. The performance of the proposed algorithm is compared numerically with two classical threshold-based energy detectors. Simulation results show that the proposed algorithm considerably outperforms the methods with a bi-thresholds energy detector and a simple energy detector in the presence of the noise power uncertainty. [Copyright &y& Elsevier]

Background: Recent frameworks for understanding food rewards recognize the role of separable affective and motivational processes (liking and wanting) in driving human eating behavior. Separate assessments of liking and wanting may aid in understanding the complex eating-related behaviors seen in anorexia nervosa (AN). Objective: The aim of this study was to examine separately liking and wanting for foods of different energy densities in women at different stages of AN and in healthy volunteers at both an implicit and an explicit level. Design: Explicit liking and wanting responses to high- and low-calorie foods were derived from analog ratings, whereas an implicit "wanting" measure was identified by using reaction time in a forced-choice procedure. Explicit and implicit processes were compared across 3 groups of AN participants (current AN, weight-restored AN, and recovered AN) and healthy volunteers. Results: Currently underweight AN participants explicitly wanted high-calorie foods less than did the other groups. Both current and weight-restored AN groups demonstrated significantly less implicit "wanting" for high-calorie foods and more implicit "wanting" for low-calorie foods--an inverted pattern to never-ill participants. Conclusions: The aberrant responses to food that characterize AN may be driven more by altered motivational salience ("wanting") than by explicit liking responses. This pattern of aberrant food reward appears to be independent of weight status. Examining the processes that motivate approach or avoidance of low- and high-calorie foods in AN may aid the development of targeted strategies to augment existing interventions. [ABSTRACT FROM AUTHOR]

Abstract: The documentation of software architectural design decisions is important to help people understand the system and the rationale behind architectural solutions. In practice, the documentation of such decisions is regularly done after the fact, or skipped completely. To support software maintenance and evolution, the decisions have to be recovered and described. This is often hindered by the fact that the original architects are not available any more, or they do not completely remember the reasons for making the decisions. Additionally, the whole process is very expensive. In this paper, we hypothesize that architecture decision recovery can be more efficient by focusing on recovering decisions related to applying architecture patterns. To test this hypothesis, we designed a controlled experiment that was conducted to analyze the impact of architecture patterns on the quality and quantity of architecture decisions recovered after the fact. We are able to provide statistical evidence that a focus on patterns significantly increases the quality of decisions, while no conclusive evidence concerning the quantity of decisions was found. [Copyright &y& Elsevier]

Abstract: Process capability indices are widely used in industry to measure the ability of firms or their suppliers to meet quality specifications. The index , which is easy to use and analytically tractable, has been successfully developed and applied by competitive firms to dominate highly-profitable markets by improving quality and productivity. Hypothesis testing is very essential for practical decision-making. Generally, the underlying data are assumed to be precise numbers, but in general it is much more realistic to consider fuzzy values, which are imprecise numbers. In this case, the test statistic also yields an imprecise number, and decision rules based on the crisp-based approach are inappropriate. This study investigates the situation of uncertain or imprecise product quality measurements. A set of confidence intervals for sample mean and variance is used to produce triangular fuzzy numbers for estimating the index. Based on the -cuts of the fuzzy estimators, a decision testing rule and procedure are developed to evaluate process performance based on critical values and fuzzy p-values. An efficient computer program is also designed for calculating fuzzy p-values. Finally, an example is examined for demonstrating the application of the proposed approach. [Copyright &y& Elsevier]

Abstract: Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios. We illustrate the framework’s power with a meta-analysis of European wild boar (Sus scrofa) data. We test hypotheses about the effect of geographic region, hunting and mast years on wild boar population growth, to build models of wild boar dynamics for the UK. The framework quantifies the importance of hunting pressure as a driver of population growth, and confirms that reproductive success is greatly decreased in poor mast years, suggesting that the key to predicting wild boar dynamics is to ascertain local hunting pressure and to better understand changing food availability. Geography had no significant effect, indicating that it is not a good proxy for modelling the impact of change in climate or land-use on wild boar populations at the European scale. We use the framework to predict population abundance 9 years after an isolated population of wild boar established in the UK; in a comparison with the only field data and two independent modelling exercises, our framework provides the most robust and informative results. [Copyright &y& Elsevier]

ABSTRACT Objectives: Currently there is no tool available to adequately appraise the quality of the pediatric health economics literature. A comprehensive pediatric-specific instrument would be valuable in informing allocation decisions related to pediatric interventions and services. The goal of this study was to develop the Pediatric Quality Appraisal Questionnaire (PQAQ). Methods: A draft instrument was constructed from published checklists and questionnaires. New questions pertaining to the pediatric population were incorporated. An expert panel reviewed the draft instrument and the proposed scoring scheme for face and content validity. A revised version was pilot tested by three independent appraisers. After addressing discrepancies in scores, a final version was created and subjected to interrater and test–retest reliability assessment. Results: The 57 items in the final PQAQ were mapped onto 14 domains: economic evaluation, comparators, target population, time horizon, perspective, costs and resource use, outcomes, quality of life, analysis, discounting, incremental analysis, sensitivity analysis, conflict of interest, and conclusions. Among the 57 items, 46 have response options that are scored from 0 to 1. Interrater reliability was 0.75 (95% confidence interval [CI] 0.66–0.81) and test–retest reliability was 0.92 (95% CI 0.71–0.98). Conclusions: The PQAQ is a comprehensive instrument demonstrating face and content validity and strong interrater and test–retest reliability in the appraisal of pediatric economic evaluations. This tool will be valuable to health economists, methods researchers, and policy decision makers involved in allocation decisions for pediatric health care. [ABSTRACT FROM AUTHOR]

In statistical inference hypotheses related to different kinds of phenomena are formulated, and then data are collected and analyzed, which either confirm or falsify these hypotheses. Considering traditional statistics, in the underlying models hypotheses and sample data should be well defined. However, these models are often inadequate with regard to real-life problems, as theoretical specifications and observed information are frequently imprecise, vague, incomplete, qualitative, linguistic or noisy. To relax this rigidity, numerous researchers have proposed modifications and extensions of statistical inference approaches with the help of concepts of fuzzy statistics. In the meantime there are many papers on the topic of hypothesis testing in fuzzy environments, especially based on fuzzy hypotheses and/or by using fuzzy data. In order to structure this variety of contributions, proposals and applications, we give a comprehensive systematic review in this paper and offer a bibliography on fuzzy hypothesis testing. The paper seeks to consolidate the topic of fuzzy hypothesis testing with the purpose of supporting new researchers in this field and highlighting potential directions for future research. • Extensive systematic review, guided by nine key research questions, containing absolute essence on the topic of fuzzy hypothesis testing. • Discussion of various ways to improve decision making by employing methods of fuzzy statistics at numerous set screws of statistical hypothesis testing. • Comprehensive tables with detailed information on the approaches for a quick overview. • Comparison and critical discussion of the methods regarding their methodological applicability and practicability. • Identification of numerous potential directions for fruitful future research. [ABSTRACT FROM AUTHOR]

In any multi-criteria decision making (MCDM) process, the role of criteria weights is highly critical, as any inconsistency associated with their weights prompts an off-base assessment and leads to non-optimal selections. In this work, a novel subjective method for determining optimal criteria weights is presented, in which a newly proposed measure of inconsistency termed as 'Error Index' is used to formulate a non-linear constrained optimization model. The model was solved using Particle Swarm Optimization (PSO) technique, due to its faster convergence and simpler computation as compared to Genetic Algorithm, Ant Colony and, Firefly optimization methods. For the sake of simulation, a user-friendly solver named 'Criteria Weight Estimation using Non-Linear Programming (CWENLiP)' is also developed. Since the error index incorporates both the global and the local characteristics of the inconsistent matrix, it is more indicative of the overall inconsistency present in the comparison matrix, in contrast with the frequently employed consistency ratio index. Several numerical problems were solved to illustrate the procedure and to explore the potential applications of the proposed method. The robustness check, the statistical significance test and, the comparative analysis were also performed. The results revealed that the proposed method is highly robust and efficient. Hence, the Error Index based inconsistency quantification, combined with user-friendly CWENLiP solver, is a more reliable tool for criteria weight estimation which is crucial for accurate ranking and improved decision making, especially for problems encompassing large sets of criteria. • A new subjective method for finding highly consistent criteria weights, is proposed. • A nonlinear function defined as error function quantifies the overall inconsistency. • The error function was minimized using PSO to derive most consistent weights. • A MATLAB based solver was designed for simulation purpose. • Comparative studies validate the robustness of proposed method. [ABSTRACT FROM AUTHOR]

Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture of the Dirac and the Student's t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models and evaluated using probabilistic forecasting measures and significance tests. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 h of trading, but the approach allows for application to other continuous markets, especially in Europe. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility. They also indicate that the introduction of XBID reduced the market volatility. • An ensemble forecasting study is conducted in the German Intraday Continuous Market. • Realistic trajectories are generated that allow for efficient decision making. • The model utilizes a mixture of GAMLSS and logit-lasso estimation methods. • The model components are interpreted with respect to market behaviour. • The XBID system seems to reduce the intraday price volatility. [ABSTRACT FROM AUTHOR]