27,348 research outputs found

    Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment

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    Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteria decision analysis (MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This review paper presents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if 
 then 
” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches

    Color and texture associations in voice-induced synesthesia

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    Voice-induced synesthesia, a form of synesthesia in which synesthetic perceptions are induced by the sounds of people's voices, appears to be relatively rare and has not been systematically studied. In this study we investigated the synesthetic color and visual texture perceptions experienced in response to different types of “voice quality” (e.g., nasal, whisper, falsetto). Experiences of three different groups—self-reported voice synesthetes, phoneticians, and controls—were compared using both qualitative and quantitative analysis in a study conducted online. Whilst, in the qualitative analysis, synesthetes used more color and texture terms to describe voices than either phoneticians or controls, only weak differences, and many similarities, between groups were found in the quantitative analysis. Notable consistent results between groups were the matching of higher speech fundamental frequencies with lighter and redder colors, the matching of “whispery” voices with smoke-like textures, and the matching of “harsh” and “creaky” voices with textures resembling dry cracked soil. These data are discussed in the light of current thinking about definitions and categorizations of synesthesia, especially in cases where individuals apparently have a range of different synesthetic inducers

    Enhancing Big Data Feature Selection Using a Hybrid Correlation-Based Feature Selection

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    This study proposes an alternate data extraction method that combines three well-known feature selection methods for handling large and problematic datasets: the correlation-based feature selection (CFS), best first search (BFS), and dominance-based rough set approach (DRSA) methods. This study aims to enhance the classifier’s performance in decision analysis by eliminating uncorrelated and inconsistent data values. The proposed method, named CFS-DRSA, comprises several phases executed in sequence, with the main phases incorporating two crucial feature extraction tasks. Data reduction is first, which implements a CFS method with a BFS algorithm. Secondly, a data selection process applies a DRSA to generate the optimized dataset. Therefore, this study aims to solve the computational time complexity and increase the classification accuracy. Several datasets with various characteristics and volumes were used in the experimental process to evaluate the proposed method’s credibility. The method’s performance was validated using standard evaluation measures and benchmarked with other established methods such as deep learning (DL). Overall, the proposed work proved that it could assist the classifier in returning a significant result, with an accuracy rate of 82.1% for the neural network (NN) classifier, compared to the support vector machine (SVM), which returned 66.5% and 49.96% for DL. The one-way analysis of variance (ANOVA) statistical result indicates that the proposed method is an alternative extraction tool for those with difficulties acquiring expensive big data analysis tools and those who are new to the data analysis field.Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1)Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876SPEV project, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (ID: 2102–2021), “Smart Solutions in Ubiquitous Computing Environments

    Multi-criteria analysis: a manual

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    Health and Environmental Benefits of Reduced Pesticide Use in Uganda: An Experimental Economics Analysis

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    Two experimental procedures were employed to value both health and environmental benefits from reducing pesticide use in Uganda. The first experiment, an incentive compatible auction involved subjects with incomplete information placing bids to avoid consuming potentially contaminated groundnuts/water in a framed field experimental procedure. Three experimental treatments (information, proxy good, and group treatments) were used. Subjects were endowed with a monetary amount (starting capital) equivalent to half the country’s per capita daily income (in small denominations). Two hundred and fifty seven respondents were involved in a total of 35 experimental sessions in Kampala and Iganga districts. The Kampala sample consisted of urban (professional) residents while the Iganga sample consisted of rural (groundnut farmer) residents. Analyses with Tobit models indicated that subjects are willing to pay significant amounts to avoid ill health outcomes, although these values vary by region, by treatment and by socio-economic characteristics. Gender differences were important in explaining bid behavior, with male respondents in both study areas bidding higher to avoid ill health outcomes than females. Consistent with a priori expectation, rural population’s average willingness to pay to avoid ill health outcomes was lower (by 11.4 percent) than the urban population’s willingness to pay perhaps reflecting the poverty level/low incomes in the rural areas and how it translates into reduced regard for health and environmental improvements. Salaried respondents in Kampala were willing to pay more than those on hourly wages. Tests of hypotheses suggested: (i) providing brief information to subjects just prior to the valuation exercise does not influence bid behavior, (ii) subjects are indifferent to the source of contamination: willingness to pay to avoid health outcomes from potentially contaminated water versus groundnuts are not significantly different, and (iii) the classical tendency to free-ride in public goods provision was observed in both urban and rural areas, and this phenomenon was more pronounced in the urban than the rural area. The second experimental procedure, choice experiments, involved 132 urban respondents making repeated choices from a set of scenarios described by attributes of water quality, an environmental good. Water quality was represented by profiles of water safety levels at varying costs. Analysis using a conditional (fixed effects) logit model showed that urban subjects highly discount unsafe drinking water, and were willing to pay less for safe agricultural water, a result not unexpected considering that the urban population is not directly involved in agricultural activities and thus may not value agricultural water quality as much as drinking water quality. It was also found that subjects’ utility increased with the cost of a water sample (inconsistent with a downward sloping demand curve), suggesting perhaps that they perceived higher cost water to be associated with higher quality water. Advertisements for bottled water in Uganda would have consumers believe that higher cost bottled water is higher quality.Experimental auctions, Choice experiments, Crop Production/Industries, Environmental Economics and Policy, Health Economics and Policy,

    Uncertain Multi-Criteria Optimization Problems

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    Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

    Understanding the Novice Decision-Making Process in Forensic Footwear Examinations: Accuracy and Decision Rules

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    The reproducibility of experienced-based forensic pattern interpretation is founded on the notion that domain-specific knowledge can be successfully distributed and applied among experts within a group. This assumption persists, even when the examination is complicated by variations in case circumstances, such as impression clarity and totality, as well as media, substrate, collection mechanism and enhancement. While it is further theorized that many of these factors (as well as additional confounding factors) are at play during an examination, the manner and extent to which these sources of variability affect the examination of footwear evidence remain unclear. In order to explore this hypothesis, a data mining technique called dominance-based rough set approach (DRSA) was applied to characterize the novice examiners’ decision-making process, due to its ability to capture useful information from a set of hybrid data with latent preference orders and discover knowledge in the form of decision rules. Through this approach, two objectives were addressed: the identification of factors that affect footwear examination and conclusions within the novice group, and the evaluation of decision rule quality as a function of support, strength, certainty and lift factors. The results of the study showed that in general, novice examiners’ case assessments were found to be outside the acceptable conclusion range more than 50\% of the time, with general tendencies to assign ambiguous conclusions, such as ``limited association of class characteristics and ``lacks sufficient detail, rather than more definitive ones such as ``identification or ``exclusion. When assessments were further explored using DRSA, 23 decision rules were induced (13 \textit{certain} and 10 \textit{possible}). Of the 13 \textit{certain} rules, 75\% of the induced rules were dominated by the examiner’s background, rather than case attributes, and 50\% of the \textit{possible} rules indicated that media type was a prevalent factor in the examiners’ determination of similarity/dissimilarity, as they attempted to interpret media-substrate interaction and reconcile this interpretation with SWGTREAD conclusion guidelines. Only when examiner attributes were excluded from the analysis, forcing the induction of rules based on case attributes only, did case-based features become prominent, but only with very low rule-support. In the second phase of work related to this project, the nature and type of rules induced based on expert assessments will be examined and compared to those generated from this novice set in order to compare and interpret the manner in which domain-specific knowledge dominates induced rules

    Impact Assessment of Qualitative Policy Scenarios; A Comparative Case Study on Land Use in Sicily

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    The purpose of this paper is to offer a contribution to the study of integrated assessment procedures for evaluating the effectiveness of agri-environmental policy strategies. While in the past the studies in this context have typically concentrated on the contents of methods in isolation, there is a growing trend towards methodological perspectives that support the linking of such methods. The focus here is on the combination of discrete multicriteria approaches used for handling qualitative information in a sequence of steps: the regime method, the evamix method and rough-set analysis. The first two methods will be used to obtain a ranking of four alternative scenarios of agri-environmental policies in a selected area of study, in this case, Sicily. The results obtained are discussed and re-analysed by using the rough-set approach as a recent meta-analytical tool. Finally, the analysis findings are applied to an investigation into the potential effectiveness of agricultural policies in promoting sustainable rural development in Sicily. © 2003, MCB UP Limite
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