An Incentive Mechanism-Based Minimum Adjustment Consensus Model Under Dynamic Trust Relationship.

In traditional group decision making, the inconsistent experts are usually forced to make compromises toward the group opinion to increase the group consensus level. However, the strategy of reaching group consensus via an incentive mechanism encouraging adjustment of preferences is more effective than forcing, which is the aim of this article. Specifically, this article establishes a novel incentive mechanism to support group consensus under dynamic trust relationship.

CD-BFT: Canonical Decomposition-Based Belief Functions Transformation in Possibility Theory.

Based on subjective possibilistic semantics, an agent's subjective probability mass function is dominated by a qualitative Possibility Mass Function (PossMF), which can also be transformed into a unique consonant mass function. However, the existing transformation method cannot maintain the consistency of combination rules, i.e.

An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making.

In social network group decision making (SN-GDM) problem, subgroup weights are mostly unknown, many approaches have been proposed to determine the subgroup weights. However, most of these methods ignore the weight manipulation behavior of subgroups. Some studies indicated that weight manipulation behavior hinders consensus efficiency.

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection.

Selecting the optimal renewable energy source (RES) is a complex multi-criteria decision-making (MCDM) problem due to the association of diverse conflicting criteria with uncertain information. The utilization of Fermatean fuzzy numbers is successfully treated with the qualitative data and uncertain information that often occur in realistic MCDM problems. In this paper, an extended complex proportional assessment (COPRAS) approach is developed to treat the decision-making problems in a Fermatean fuzzy set (FFS) context.

Would they accept it? An interview study to identify barriers and facilitators to user acceptance of a prescribing advice service.

Objectives: Few studies have explored the factors influencing user uptake of interventions designed to enhance therapeutic drug monitoring (TDM). This study aimed to identify barriers and facilitators to acceptance of a pilot intervention, the TDM Advisory Service (the Service), that provided prescribing advice for the antibiotic, vancomycin at an Australian public hospital.

Methods: A sample of prescribers and pharmacists who had interacted with the Service (n = 10), and a sample who had not (n = 13), participated in semi-structured interviews.

A Model Procedure for Catalytic Conversion of Waste Cotton into Useful Chemicals.

Cotton is grown in about 90 countries and accounts for 24% of the fibers used in the global production of textiles. In 2018/2019, 25.8 Mt of cotton were produced around the world.

Evidence approach imprecise intervals: extensions and evaluation measures.

In a number of applications the data will be represented in an interval format. We consider here a nested representation of uncertain information in intervals using Dempster-Shafer evidence approaches. These representations can be used in variety of applications including spatial and temporal reasoning and economic cost valuations.

Drinking water quality in the glacial aquifer system, northern USA.

Groundwater supplies 50% of drinking water worldwide, but compromised water quality from anthropogenic and geogenic contaminants can limit usage of groundwater as a drinking water source. Groundwater quality in the glacial aquifer system, USA (GLAC), is presented in the context of a hydrogeologic framework that divides the study area into 17 hydrogeologic terranes. Results are reported at aquifer-system scale and regional (terrane) scale.

Geospatial Modeling Using Dempster-Shafer Theory.

Uncertainty in spatial geometrical issues is represented using Dempster-Shafer (D-S) theory. Interval approaches are used for D-S uncertainty of spatial locations and the associated arithmetic operations on such intervals described. Categories of uncertainty for points and lines are defined using interval formulations.

Evaluating Belief Structure Satisfaction to Uncertain Target Values.

We describe the basic properties of the Dempster-Shafer belief structure and introduce the associated measures of plausibility and belief. We look at the role of these structures for providing a model of imprecise probabilistic information. We next consider the problem of calculating the satisfaction of target values by a variable V whose value is expressed by a belief structure.

Simulation of the effects of seasonally varying pumping on intraborehole flow and the vulnerability of public-supply wells to contamination.

Public-supply wells with long screens in alluvial aquifers can produce waters of differing quality from different depths. Seasonal changes in quality are linked to seasonal changes in pumping rates that influence the distribution of flow into the well screens under pumping conditions and the magnitude and direction of intraborehole flow within the wells under ambient conditions. Groundwater flow and transport simulations with MODFLOW and MT3DMS were developed to quantify the effects of changes in average seasonal pumping rates on intraborehole flow and water quality at two long-screened, public-supply wells, in Albuquerque, New Mexico and Modesto, California, where widespread pumping has altered groundwater flow patterns.

On prioritized multiple-criteria aggregation.

We describe multicriteria aggregation and discuss its central role in many modern applications. The concept of aggregation imperative is introduced to indicate the description of how the individual criteria satisfactions should be combined to obtain the overall score. We focus on a particular type of aggregation imperative called prioritized aggregation that is characteristic of situations where lack of satisfaction to criteria denoted as higher priority cannot be compensated by increased satisfaction by those denoted as lower priority.

Intuitionistic fuzzy Bonferroni means.

The Bonferroni mean (BM) was originally introduced by Bonferroni and then more recently generalized by Yager. The desirable characteristic of the BM is its capability to capture the interrelationship between input arguments. Nevertheless, it seems that the existing literature only considers the BM for aggregating crisp numbers instead of any other types of arguments.

Full reinforcement operators in aggregation techniques.

We introduce the concept of upward reinforcement in aggregation as one in which a collection of high scores can reinforce or corroborate each other to give an even higher score than any of the individual arguments. The concept of downward reinforcement is also introduced as one in which low scores reinforce each other. Our concern is with full reinforcement aggregation operators, those exhibiting both upward and downward reinforcement.

Intelligent control of the hierarchical agglomerative clustering process.

The basic process of Hierarchical Agglomerative (HAG) clustering is described as a merging of clusters based on their proximity. The importance of the selected cluster distance measure in the determination of resulting clusters is pointed out. We note a fundamental distinction between the nearest neighbor cluster distance measure, Min, and the furthest neighbor measure, Max.

Induced ordered weighted averaging operators.

We briefly describe the Ordered Weighted Averaging (OWA) operator and discuss a methodology for learning the associated weighting vector from observational data. We then introduce a more general type of OWA operator called the Induced Ordered Weighted Averaging (IOWA) Operator. These operators take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are then aggregated.

Determining equivalent values for possibilistic variables.

We discuss the nature of possibilistic uncertainty. Two approaches to the determination of the equivalent value for a possibilistically uncertain variable are introduced. These approaches are then generalized to yield a parameterized class of functions for obtaining the equivalent value of a possibilistic variable.

Veristic variables.

We distinguish between variables having only one solution (possibilistic) and those allowing multiple solutions (veristic). A representation of information contained in statements involving veristic variables is presented. Using this representation we begin to develop a structure for the manipulation of knowledge involving variables that can have multiple solutions.

Fuzzy modeling for intelligent decision making under uncertainty.

We consider here the problem of decision making under uncertainty. We suggest an approach for the construction of decision functions which allow for the inclusion of probabilistic information as well as for the inclusion of information about the decision maker's attitude and preferences. Use is made of the fuzzy modeling technology to construct these functions from specifications provided by the decision maker.

Toward a language for specifying summarizing statistics.

We introduce the ordered weighted averaging (OWA) operator and discuss how it can provide a basis for generating summarizing statistics over large data sets. We further note how different forms of OWA operators can be induced using weight generating functions. We show how these weight generating functions can provide a vehicle with which a data analyst can express desired summarizing statistics.

Using fuzzy methods to model nearest neighbor rules.

The basic principle used in the construction of nearest-neighbor models is discussed. The induced ordered weighted averaging (IOWA) operators are shown to provide a useful formal structure for building nearest-neighbor models. A methodology for learning IOWA operator nearest-neighbor models is described.

Uncertainty representation using fuzzy measures.

We introduce the fuzzy measure and discuss its use as a unifying structure for modeling knowledge about an uncertain variable. We show that a large class of well-established types of uncertainty representations can be modeled within this framework. A view of the Dempster-Shafer (D-S) belief structure as an uncertainty representation corresponding to a set of possible fuzzy measures is discussed.

Modeling prioritized multicriteria decision making.

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.

K2[HCr2AsO10]: redetermination of phase II and the predicted structure of phase I.

Our prediction that phase II of dipotassium hydrogen chromatoarsenate, K(2)[HCr(2)AsO(10)], is ferroelectric, based on the analysis of the atomic coordinates by Averbuch-Pouchot, Durif & Guitel [Acta Cryst. (1978), B34, 3725-3727], led to an independent redetermination of the structure using two separate crystals. The resulting improved accuracy allows the inference that the H atom is located in the hydrogen bonds of length 2.