Preference Relations Research Articles

Aggregate gradation effect on the mechanical properties of asphalt mixtures

The issue of road deterioration in Mosul, Iraq, has become a matter of concern for local communities. The instability of road surfaces under the stress of vehicular traffic necessitates significant intervention to mitigate and manage this problem. This study has developed an asphalt mixture by following the Bailey method for aggregate gradation. The impact of aggregate gradation on the mechanical properties of the asphalt mixture was thoroughly analysed. Additionally, two separate mixtures were conducted using the conventional Marshall method to assess indirect tensile strength, Marshall properties, and susceptibility to moisture damage. The primary objective of this research is to design an aggregate structure that acts as a robust unit capable of withstanding heavy loads, repeated stresses, and high temperatures. The findings of the study indicate that the asphalt mixture created using the Bailey method exhibited superior stability with values of 13.6 kN, compared to the two traditional mixtures (11.8 kN, 9.2 kN), and. However, it is worth noting that the flow values also indicated a relative preference for the Bailey method, with values of and 3.1 mm, compared with 3.85 mm and 3.2 mm of the traditional mixtures. The results of Tensile strength ratio further corroborated the efficacy of Bailey’s technique, with outcomes of 85.1 %, thus enhancing the applicability of this method. This observation is attributed to the concept of granular interlocking, which forms the foundation of the Bailey method.

Patient preferences in diabetes care: a bibliometric analysis

Background: The purpose of the study was to explore patient preferences in diabetes care by analyzing network visualization, overlay visualization, and density visualization. Main body: The study used bibliometric analysis for assessing related topics and patient preferences in diabetes care based on the repetition terms received. The data sources were based on online searches via https://app.dimensions.ai/ collected on August 22, 2023. The literature search was restricted to publications years 2015-2023, focused on the fields of Health sciences, biomedical and clinical science, nursing, public health, psychology, human society, nutrition and diabetes. Data were analysed using VOSviewer, The type of analysis is selected to create a map based on text data, further reviewed by co-occurrence and co-authors. From the network visualization, it was identified that there were 172 terminology items which were divided into 8 clusters with 6247 links with a strength of 34258. From each cluster, patient preferences among patients with diabetes were identified. Through overlay visualization analysis, it identified that a new topic being researched by scholars in various countries was racism toward patients with diabetes. Furthermore, in density visualization, a topic that few researchers have explored related to fulfilling patient preferences in diabetes care by maximizing telemedicine technology. Conclusion: Patient preferences in diabetes care are necessary. If patient preferences are considered, patients will feel involved in every decision-making as a result, they will be more responsible for the behaviour changes they have to make in diabetes care.

Dust Properties around the Dust Cloud nearby White Dwarf LAWD 84 in IRIS and AKARI Data

An isolated dust cloud is discovered nearby the White Dwarf LAWD 84, located at (RA, DEC) (J2000): 21h19m36.53s, -55°50'4.47'', in the infrared data of Improved Reprocessingof the IRAS (IRIS) and AKARI infrared survey, within angular size 1.0° ×1.0°. The isolated region of dust cloud is found to be broken into the two parts both in IRIS and AKARI data, named as IRIS-UP, IRIS-LOW, AKARI-UP and AKARI-LOW. The isolated region of flux at 100 μm in IRIS and 140 μm in AKARI is also studied separately. The dust color temperature (Td) and dust mass (Md) are calculated from the infrared flux in both data. The average Td is found to be ∼24 K in IRIS data and ∼20 K in AKARI data in both parts. The ratio of dust mass in total region to isolated region is found ∼ 2.0 in IRIS-UP and AKARI-UP but the ratio is 5.06 in IRIS-LOW and 3.19 in AKARI-LOW clouds. The regression analysis between the infrared fluxes at two wavelength shows a linear relation between the infrared fluxes in both clouds, whereas there is no any preferred relation observed between the infrared flux (both wavelength) and dust color temperature but a direct linear relationship between infrared flux and dust color temperature, and inverse parabolic relationship obtained between the dust color temperature and dust mass in all clouds. The qualitative analysis of contour map of infrared flux, temperature and mass supports this result. Furthermore, the inclination angle of the each clouds, background point sources, Jeans Criteria and Gaussian distribution of the dust color temperature are studied within both clouds to explain the possible ongoing physical phenomenon within the clouds.

Group decision making based on consistency adjustment of distributed preference relations under asymmetric evaluation grades

Group decision making based on consistency adjustment of distributed preference relations under asymmetric evaluation grades

Effect of consumers’ materialistic values on consumption guilt as reflected in price preferences

ABSTRACT While simplistic arguments portend a possible relationship between consumers’ materialistic values and consumption guilt, empirical evidence in this regard, however, remains equivocal at best. In this paper, we address this issue by establishing the effect of materialism on consumption guilt – delimited by regulatory influence (promotion/prevention) – as reflected in consumers’ relative preferences for structurally distinct but financially equivalent price alternatives. Specifically, we conduct an experiment with consumers’ trait materialistic values, measured as a continuous variable, as an independent variable, with extraneous social influence as a two-level (promotion/prevention) manipulated moderator, consumption guilt as the mediating variable, and consumers’ relative price preference (as mentioned above) as the dependent variable of choice. Subsequently, we conduct a regression analysis, followed by a floodlight analysis, and, a series of mediation analyses, including a mediated moderation analysis, in establishing the effect of materialism on consumption guilt. Our results indicate that consumers higher/lower in materialism, both experienced consumption guilt. Subsequently, these consumers mitigated consumption guilt by preferring the price structure that best allowed them to do so in consummating the purchase of a new product – the mediating effect of consumption guilt. Our findings imply indirect price-discrimination opportunities for sellers, wherein demand for products increases as consumers (self) mitigate consumption guilt through price preferences, irrespective of their dominating materialistic tendencies. In summary, herein, we empirically establish the long-conjectured relationship between materialism and consumption guilt, unequivocally, both for consumers high, or low, in materialism.

Disentangling revealed preference from rationalization by a preference

The weak axiom of revealed preference (WARP) ensures that the revealed preference (i) is a preference relation (i.e., it is complete and transitive) and (ii) rationalizes the choices. However, when WARP fails, either one of these two properties is violated, but it is unclear which one it is. We provide an alternative characterization of WARP by showing that WARP is equivalent to the conjunction of two axioms each of which separately guarantees (i) and (ii).

Non-uniform allocation of information granularity to improve consistency and consensus in multi-criteria group decision-making: Application to building refurbishment

If a decision depends on the views of a group of people, it is crucial that these views are consistent, in particular if they are represented in terms of fuzzy preference relations. Moreover, before making the decision, the agreement achieved must be as high as possible. In most cases, to achieve it, the initial viewpoint of each group member must be modified during the decision-making process, which causes an information loss. To handle this issue, some conditions have been added to the modification process. Concretely, some approaches have turned to an allocation of information granularity to control it. However, these approaches either allocate the information granularity in a uniform way or not consider the issues of consistency and consensus in a scenario of multi-criteria group decision-making. To enhance these approaches, we elaborate a novel granular-based approach composed of two processes. Firstly, an automatic process based upon a non-uniform allocation of information granularity is developed to maximize both the consensus and the consistency in multi-criteria group decision-making where fuzzy preference relations are used to model the group members’ views. Based upon it, secondly, an interactive process requiring the implication of the group members is introduced, which also intends to maximize both the consensus and the consistency. Some experimental studies are completed to show the essence of this new approach and to demonstrate its performance and flexibility. A case study on building refurbishment is also conducted to validate its effectiveness and feasibility in practical real-world problem-solving.

Interactively iterative group decision-making method with interval-valued intuitionistic fuzzy preference relations based on a new additively consistent concept

Interval-valued intuitionistic fuzzy preference relation (IVIFPR) is a powerful instrument for describing uncertain judgement of expert in preference group decision making (PGDM). Nevertheless, the extant additive consistency definitions for IVIFPRs violate the invariance regarding arrangement of objectives. In addition, the modification for comparative judgments in the consensus reaching process (CRP) of existing PGDMs with IVIFPRs do not involve the interaction or communication with experts. Therefore, we propose an PGDM to avoid these situations. First, a new additive consistency definition for IVIFPRs is given satisfying the invariance regarding arrangement of objectives. Then, an interactively iterative algorithm considering minimum information loss is developed to enhance the consistency of IVIFPRs. Next, as to group decision making (GDM) with IVIFPRs, a bi-objective optimization model is constructed to determine the experts’ weights, and an interactively iterative algorithm considering minimum information loss is developed to enhance the consensus level among experts. Furthermore, a fuzzy optimization model is constructed to derive the interval priority weights of objectives from an IVIFPR. Finally, the feasibility and superiority of the proposed GDM method are verified by application examples and analyses. The proposed GDM method has obtained an average of 0.0432 for information deviations and 22/72 for modified judgments on two datasets, respectively, which is one of the best results compared to state-of-art.

Consistency and Consensus of Distributed Preference Relations Based on Stochastic Optimal Allocation in GDM Problems

Consistency and Consensus of Distributed Preference Relations Based on Stochastic Optimal Allocation in GDM Problems

Searching for quasar candidates with periodic variations from the Zwicky Transient Facility: results and implications

ABSTRACT We conduct a systematic search for quasars with periodic variations from the archival photometric data of the Zwicky Transient Facility by cross-matching with the quasar catalogues of the Sloan Digital Sky Survey and Véron-Cetty and Véron. We first select out 184 primitive periodic candidates using the generalized Lomb–Scargle periodogram and autocorrelation function and then estimate their statistical significance of periodicity based on two red-noise models, i.e. damped random walk (DRW) and single power-law (SPL) models. As such, we finally identify 106 (DRW) and 86 (SPL) candidates with the most significant periodic variations out of 143 700 quasars. We further compare DRW and SPL models using Bayes factors, which indicate a relative preference of the SPL model for our primitive sample. We thus adopt the candidates identified with SPL as the final sample and summarize its basic properties. We extend the light curves of the selected candidates by supplying other archival survey data to verify their periodicity. However, only three candidates (with 6–8 cycles of periods) meet the selection criteria. This result clearly implies that, instead of being strictly periodic, the variability must be quasi-periodic or caused by stochastic red-noise. This exerts a challenge to the existing search approaches and calls for developing new effective methods.

Leveraging mHealth to Mitigate the Impact of COVID-19 in Black American Communities: Qualitative Analysis.

COVID-19 remains an ongoing public health crisis. Black Americans remain underrepresented among those vaccinated and overrepresented in both COVID-19 morbidity and mortality. Medical misinformation, specifically related to COVID-19, has exacerbated the impact of the disease in Black American communities. Communication tools and strategies to build relationships and disseminate credible and trustworthy diagnostic and preventative health information are necessary to improve outcomes and equity for historically oppressed populations. As the initial phase of a larger mixed methods project to develop, pilot, and evaluate a mobile health (mHealth) intervention among a population at high risk for COVID-19 and cardiovascular comorbidities, this study sought to explore COVID-19 information behavior among Black Americans. Specifically, this study examined (1) preferences for COVID-19 education via mHealth, (2) barriers and facilitators to COVID-19 education and diagnostic testing and routine care for associated cardiovascular and respiratory comorbidities in the local community, and (3) key content for inclusion in a COVID-19 mHealth app. This qualitative study used principles of community-based participatory research and information systems research to conduct 7 focus groups across 3 sites. Focus groups were audio recorded and transcribed for thematic analysis using an abductive approach. The study sample included 54 individuals across sites with a mean age of 50.24 (SD 11.76; range 20-71) years. Participants were primarily female (n=42, 78%) and Black (n=54, 100%) with varied education levels. Over half (n=29, 54%) of the participants were employed full-time, and nearly three-fourths (n=40, 74%) had household incomes <US $65,000. Participants used both Android (n=23, 43%) and iOS devices (n=29, 54%) and were "very comfortable" (n=37, 69%) using their mobile devices. Participants reported using a variety of sources for health information. Content-related preferences reported focus on visual presentation, user-friendly design, and privacy and highlighted the importance of community relevance, access, and community-specific content. Key barriers identified included health literacy-limiting app use, access to technology and information, and lack of trust. Increasing community relevance through community-specific messaging and the inclusion of Black providers were noted as facilitators that may increase credibility and trust. Key content identified included user-specific information such as where to get vaccines and tests, updated local COVID-19 data, travel protocols, information about long COVID-19 (post COVID-19 condition), comorbidities, frequently asked questions, and testimonials or personal stories. Increasing transparency and building trust are 2 key strategies that may improve the impact of health information messaging in Black communities. Focusing on content over context fails in the provision of critical health information and perpetuates health inequities by reinforcing systemic and structural racism. COVID-19 messaging must consider contextual information, patient needs and preferences, and patient information-seeking and information-search behaviors to establish trust and credibility, positively impact patient health outcomes, and improve health equity.

Selection of technological and organizational solutions for construction works with the use of a fuzzy relation of preferences

Selection of technological and organizational solutions for construction works with the use of a fuzzy relation of preferences

Stochastic group preference acceptability analysis for interval-valued multiplicative preference relations based on TODIM method

Among many decision problems, interval-valued multiplicative preference relation (IMPR) is widely utilized due to its ability to express uncertain information. A new approach to solve group decision making (GDM) with IMPRs is proposed in this paper, named stochastic group preference acceptability analysis with TODIM (SGPAA-TODIM) method, by combining TODIM method (an acronym in Portuguese of Interactive and multi-criteria Decision Making) with stochastic multi-criteria acceptability analysis (SMAA-2). It effectively circumvents information loss and considers the weight and risk preferences of experts. Firstly, the stochastic multiplicative preference relation is defined through stochastic simulation employing a certain density function, and its priority weight vector is determined using the logarithmic least squares method (LLSM). Then, the priority preference comprehensive matrix is proposed by extracting information of priority vectors. Moreover, the novel SGPAA-TODIM method is developed to analyze the stochastic parameter spaces, with the optimal rank determined based on the analysis of acceptability degrees associated with dominance rank. Finally, to demonstrate the validity and applicability of the proposed method, numerical examples are given.

Self-truncated sampling produces more moderate covariation judgment and related decision than descriptive frequency information: The role of regressive frequency estimation.

Covariation judgment underlies a diversity of psychological theories and influences various everyday decisions. Information about covariation can be learned from either a summary description of frequencies (i.e., contingency tables) or trial-by-trial experience (i.e., sampling individual instances). Two studies were conducted to investigate the impact of information learning mode (i.e., description vs. self-truncated sampling) on covariation judgment and related decision. In each trial under the description condition, participants were presented with a contingency table with explicit cell frequencies, whereas in each trial under the self-truncated sampling condition, participants were allowed to determine when to stop sampling instances and thus the actual sample size. To eliminate sampling error, an other-yoked (i.e., between-subject) design was used in this research so that cell frequencies shown in a trial under the description condition were matched with those experienced in a trial under the self-truncated sampling condition. Experiment 1 showed that the self-truncated sampling condition led to more moderate covariation judgments than the description condition (i.e., a description-experience gap). Experiment 2 demonstrated further that the same gap extended to covariation-related decisions in terms of relative contingent preference (RCP). Regressive frequency estimation under self-truncated sampling appeared to underlie the consistent gaps found in the two studies, whereas the impact of regressive diagnosticity (i.e., the same sample of instances was viewed as less diagnostic under description than under self-truncated sampling) or simultaneous overestimation and underweighting of rare instances under experience was not supported by the observed data. Future research might examine alternative accounts of the observed gaps, such as the impacts of belief updating and stopping rule under self-truncated sampling.

Multiplicative Consistent q-Rung Orthopair Fuzzy Preference Relations with Application to Critical Factor Analysis in Crowdsourcing Task Recommendation

This paper presents a group decision-making (GDM) method based on q-rung orthopair fuzzy preference relations (q-ROFPRs). Firstly, the multiplicative consistent q-ROFPRs (MCq-ROFPRs) and the normalized q-rung orthopair fuzzy priority weight vectors (q-ROFPWVs) are introduced. Then, to obtain q-ROFPWVs, a goal programming model under q-ROFPRs is established to minimize their deviation from the MCq-ROFPRs and minimize the weight uncertainty. Further, a group goal programming model of ideal MCq-ROFPRs is constructed to obtain the expert weights using the compatibility measure between the ideal MCq-ROFPRs and the individual q-ROFPRs. Finally, a GDM method with unknown expert weights is solved by combining the group goal programming model and the simple q-rung orthopair fuzzy weighted geometric (Sq-ROFWG) operator. The effectiveness and practicality of the proposed GDM method are verified by solving the crucial factors in crowdsourcing task recommendation. The results show that the developed GDM method effectively considers the important measures of experts and identifies the crucial factors that are more reliable than two other methods.

Promoting a sustainable diet through carbon labeling of food: Insights from young consumers in the Americas

The agri-food system is a major contributor to global greenhouse gas emissions, and its contribution is projected to grow significantly in coming years. Consumers can drive system change and help mitigate the climate crisis through demand behaviour, with youth uniquely positioned as key actors. Carbon labelling offers a tool for informing consumers about the carbon footprint of food, and thereby promoting purchase of lower-carbon products. This cross-cultural study (n = 815) sought to investigate perceptions of carbon labels on food amongst youth (18–24 years) from two medium-high CO2-emitting countries in the Americas – Canada and Argentina. Using a mixed methods online survey approach that integrates a quantitative questionnaire with open response questions, we found knowledge deficits in familiarity with the concepts ‘carbon footprint’ and especially ‘carbon label’, and these varied with country, gender, and level of education. Further, exemplars of six food carbon labels, standardized for the carbon emissions information displayed, differed for scores of usefulness, clarity, understanding, confusion, and incomprehensibility. Labels also differed in their relative preference ratings, which varied with education level, with the ‘Traffic light horizontal scale’ the most preferred overall. Our findings inform carbon label design and emissions-related education initiatives, each of which should be optimized for different segments of the youth market in order to effectively promote consumption of lower-carbon foods.

A quality improvement project to improve the quality of medical emergency aide-mémoires.

Introduction Medical emergencies occur within the dental setting and we have a duty of care to manage them appropriately. Due to perceived anxiety in handling medical emergencies, aide-mémoires were developed to improve staff confidence in the management of these stressful situations.Methods A survey was carried out at The Royal London Dental Hospital to assess knowledge and experience in use of existing supportive materials for medical emergency management. New colour-coded flashcards were subsequently developed, providing guidance to identifying and managing medical emergencies. A questionnaire was produced assessing clinicians' awareness of existing prompts and preferences in relation to the new flashcards. Updated cards were incorporated into simulated medical emergency teaching and evaluated afterwards through a questionnaire.Results A total of 50 responses were gathered across departments to the questionnaire regarding preliminary flashcards: 95% favoured the flashcard approach and felt they would be more helpful managing medical emergencies. The new flashcards were used by 18 clinicians in medical emergency simulation and 17 (94%) felt that using the cards improved their confidence in the handling of medical emergencies.Discussion The flashcards have been perceived to be useful in the handling of medical emergencies within our dental hospital. A similar project may be beneficial to clinical teams working across dental settings.

Existence of Nash equilibria for generalized multiobjective games through the vector extension of Weierstrass and Berge maximum theorems

In this work, we deal with a vector extension of the generalized the Weierstrass and Berge maximum theorems, notably in mathematical economics. This is carried out by introducing the notions of transfer continuity and pseudo-continuity for those functions. Here the preference relation is given via a closed convex cone, having possibly empty interior. As a consequence, we present an existence of strong-Nash equilibria for generalized multiobjective games, and the Rosen model is revisited.

An Improved Similarity-based Fuzzy Group Decision Making Model through Preference Transformation and K-Means Clustering Algorithm

Group decision making plays a crucial role in organizational and community contexts, facilitating the exchange of expert opinions to arrive at effective decisions. The concept of preference, reflecting an individual's subjective evaluation of criteria or alternatives, forms a foundational element in this process. This study focuses on transforming non-fuzzy preferences, such as preference ordering and utility functions, into fuzzy preference relations (FPR) to address the uncertainty and uniformity inherent in expert preferences. To further enhance decision-making, we assess and visualize the similarity among the experts' uniform preferences. Integrating the K-means clustering algorithm into the fuzzy group decision making model allows for the predetermination of an appropriate number of groups based on the available alternatives. By aggregating individual preferences, we present a final ranking of alternatives. The enhanced methodology, as demonstrated through comparative analysis, showcases its ability to yield positive benefits when applied to decision-making applications.

Prospect theory-based large-scale group decision-making method with heterogeneous preference relations

Large-scale group decision-making (LSGDM) issues are increasingly prevalent in modern society across various domains. The preference information has emerged as a widely adopted approach to tackle LSGDM problems. However, a significant challenge lies in facilitating consensus among decision-makers (DMs) with diverse backgrounds while considering their hesitation and psychological behavior. Consequently, there is a pressing need to establish a novel model that enables DMs to evaluate alternatives with heterogeneous preference relations (HPRs). To this end, this research presents a new consensus-building method to address LSGDM problems with HPRs. First, a novel approach for solving collective priority weight is introduced based on cosine similarity and prospect theory. In particular, a new cosine similarity measure is defined for HPRs. Subsequently, a consensus index is provided to gauge the consensus level among DMs by considering their psychological behavior and risk attitudes. Further, a consensus-reaching model is developed to address LSGDM with HPRs. Finally, an instance of supplier selection is presented to demonstrate the practicality and efficacy of the proposed method.