Qualitative Preferences Research Articles

Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions

Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.

SAPEVO-PC: Integrating Multi-Criteria Decision-Making and Machine Learning to Evaluate Navy Ships

The selection of a navy ship is essential to guarantee a country’s sovereignty, deterrence capabilities, and national security, especially in the face of possible conflicts and diplomatic instability. This paper proposes the integration of concepts related to multi-criteria decision making (MCDM) methodology and machine learning, creating the Simple Aggregation of Preferences Expressed by Ordinal Vectors—Principal Components (SAPEVO-PC) method. The proposed method proposes an evolution of the SAPEVO family, allowing the inclusion of qualitative preferences, and adds concepts from Principal Component Analysis (PCA), aiming to simplify the decision-making process, maintaining precision and reliability. We carried out a case study analyzing 32 warships and ten quantitative criteria, demonstrating the practical application and effectiveness of the method. The generated rankings reflected both subjective perceptions and the quantitative performance data of each ship. This innovative integration of qualitative data with a quantitative machine learning algorithm ensures comprehensive and robust analyses, facilitating informed and strategic decisions. The results showed a high degree of consistency and reliability, with the top and bottom rankings remaining stable across different decision-makers’ perspectives. This study highlights the potential of SAPEVO-PC to improve decision-making efficiency in complex, multi-criteria environments, contributing to the field of marine science.

Argumentation with justified preferences

It is often necessary and reasonable to justify preferences before reasoning from them. Moreover, justifying a preference ordering is reduced to justifying the criterion that produces the ordering. This paper builds on the well-known ASPIC+ formalism to develop a model that integrates justifying qualitative preferences with reasoning from the justified preferences. We first introduce a notion of preference criterion in order to model the way in which preferences are justified by an argumentation framework. We also adapt the notion of argumentation theory to build a sequence of argumentation frameworks, in which an argumentation framework justifies preferences that are to underlie the next framework. That is, in our formalism, preferences become not only an input of an argumentation framework, but also an output of it. This kind of input-output process can be applied in the further steps of argumentation. We also explore some interesting properties of our formalism.

Heart failure patients' perspectives on treatment outcomes and unmet medical needs: A qualitative preference study.

Decision-makers still predominantly focus on the perspective of non-patient stakeholders, which may deviate from the unique perspective of heart failure (HF) patients. To enhance patient-centred decision-making, there is a need for more patient-based evidence derived directly from the patients themselves. Hence, this study aimed to understand (i) HF patients' unmet medical needs and preferred treatment outcomes; (ii) patients' risk tolerance; and (iii) their information needs, uncertainties and satisfaction towards HF treatment. This qualitative patient preference study consisted of a literature review with a systematic search strategy and semi-structured interviews with HF patients, analysed using the framework method. During the interviews, patients were asked to rank a predefined list of disease and treatment-related characteristics informed by the literature review and were able to spontaneously raise additional characteristics. The study included 14 Belgian HF patients (age range: 58-79, mean age: 72). (i) Regarding their unmet medical needs, HF patients reported that the most important unmet medical needs were shortness of breath and fatigue, as they negatively impact their quality of life (QoL) and independence. In the ranking exercise, patients prioritized improvements in QoL over improvements in life expectancy, whereby the following characteristics received the highest cumulative score: (1) independence, (2) shortness of breath, (3) impaired renal function, (4) survival, (5) fatigue, (6) risk of hospitalization and (7) communication with and between physicians. Patients most often spontaneously raise characteristics related to the general care process. Mechanism of action, route of administration, dose frequency and weight fluctuations scored among the least important characteristics. (ii) Regarding patients' risk tolerance towards HF treatment, some of the patients expressed zero tolerance for side effects, as they had not yet experienced any discomfort caused by the treatment or disease. (iii) Regarding their information needs, patients voiced their desire to receive practical and comprehensible advice orally from their physician because they highly value individualized treatment decision-making. Patients also expressed uncertainties regarding whether the experienced effects were due to their treatment, disease, ageing or other comorbidities. This study shows that, besides increasing life expectancy, HF patients prioritize improvements in symptoms and side effects reducing their QoL and independence, such as shortness of breath and fatigue. The patient-relevant characteristics identified in this study, from the perspective of HF patients themselves, may be useful to inform clinical trial endpoint selection and guide downstream drug development, evaluation and clinical decision-making towards addressing the unmet medical needs and treatment outcomes of importance to HF patients.

Concentration and compositional controls on degradation of permafrost‐derived dissolved organic matter on the Qinghai–Tibetan Plateau

AbstractUnderstanding the fate of permafrost‐derived dissolved organic matter (DOM) is critical for unraveling its role in carbon cycling. However, it remains unclear whether the high lability of permafrost‐derived DOM can be attributed to intrinsic chemical properties or elevated carbon concentrations. We investigated the dynamics of permafrost DOM from the Qinghai–Tibetan Plateau using both biodegradation and photodegradation experiments. Biodegradation and photodegradation of permafrost‐derived DOM exhibited distinct qualitative preferences for specific chemical groups (i.e., peptide‐like and aromatics, respectively). Notably, reducing the initial concentration of dissolved organic carbon (DOC) by half and a quarter resulted in shifts in biodegradable DOC content from 11.2% to 11.5% and 8.5%, respectively, accompanied by a corresponding decrease in the biodegradation rate from 0.11 to 0.06 and 0.03. This insight highlights the importance of recognizing the interplay between DOM quality and concentration and bears broader significance for our understanding of the fate of permafrost‐derived DOM in natural ecosystems.

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

Exploring the outcomes of research engagement using the observation method in an online setting

ObjectiveThe objective of this study was to explore the outcomes of research engagement (patient engagement, PE) in the context of qualitative research.DesignWe observed engagement in two groups comprised of patients,...

A general framework for preferences in answer set programming

We introduce a general, flexible, and extensible framework for quantitative and qualitative preferences among the stable models of logic programs. Since it is straightforward to capture propositional theories and constraint satisfaction problems with logic programs, our approach is also relevant to optimization in satisfiability testing and constraint processing. We show how complex preference relations can be specified through user-defined preference types and their arguments. We describe how preference specifications are handled internally by so-called preference programs, which are used for dominance testing. We also provide algorithms for computing one, or all, preferred stable models of a logic program, and study the complexity of these problems. We implemented our approach in the asprin system by means of multi-shot answer set solving technology. We demonstrate the generality and flexibility of our methodology by showing how easily existing preference languages can be implemented in asprin. Finally, we empirically evaluate our contributions and contrast them with dedicated implementations.

Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

PurposeThe purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.Design/methodology/approachThe paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.FindingsThe paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.Originality/valueThe paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.

A bi-direction performance evaluation model for water pollution treatment engineering under the intuitionistic multiplicative linguistic environment

As the increasingly serious water pollution problem affects the sustainable development of the ecological environment, the research of water pollution treatment engineering cannot be delayed. Among them, the performance evaluation of water pollution treatment engineering is a major focus. After reading the existing studies, it is found that most of the existing performance evaluation indicators of water pollution treatment engineering have qualitative indicators and there is an unbalanced preference representation. Intuitionistic multiplicative linguistic sets can be a good representation of the qualitative preference and non-preference of decision-makers in the context of decision-making containing unbalanced phenomena. Therefore, to better solve the problem of water pollution treatment engineering, this paper introduces intuitionistic multiplicative linguistic sets to the problem of water pollution treatment engineering and proposes an effective theory for it. First, considering the multiplicative nature of the intuitionistic multiplicative linguistic set, a new score function and accuracy function are defined, and on this basis, the priority rules of intuitionistic multiplicative linguistic set are given to prepare for the subsequent water pollution treatment engineering performance ranking. And the distance measure of intuitionistic multiplicative linguistic set is introduced and a CRITIC attribute weight determination model under intuitionistic multiplicative linguistic set is obtained on this basis. Secondly, the Choquet integral operator is applied to better represent the correlation between elements. However, the nature of membership degree and non-membership degree shows that it is not reasonable to aggregate the information of intuitionistic multiplicative linguistic sets with a single increasing and decreasing transformation. Therefore, in this paper, we propose the IMLS bi-direction exponent Choquet integral operator, which is inspired by the bi-direction Choquet integral. Lastly, we improve the original preference function of the classical PROMETHEE II method to obtain the bi-directional PROMETHEE II method in intuitionistic multiplicative linguistic information. Finally, a numerical case is also provided to illustrate the scientific and rational application of the bi-directional PROMETHEE II method in intuitionistic multiplicative linguistic information for the performance evaluation of water pollution treatment engineering.

Consensus improvement model in group decision making with hesitant fuzzy linguistic term sets or hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic term sets (HFLTSs) and hesitant fuzzy linguistic preference relations (HFLPRs) are widely used where decision makers are permitted to provide qualitative preferences with several linguistic terms over two alternatives. There are several issues deserving further investigation in the existing consensus models of HFLTSs and HFLPRs: the extension process of HFLTSs which is always performed in distance measure and aggregation of HFLTSs distorts the original information, the modified HFLTSs does not belong to standardized HFLTSs, and most of the existing models don’t discussed the number of optimal solutions. In order to solve the above problems, this paper introduces new distance and entropy measures for HFLTSs, by which information aggregation method and two consensus improvement models in group decision making (GDM) with HFLTSs and HFLPRs are proposed. The first model is a four-stage optimization model with four objectives, based on which an equivalent linear weighted optimization model is derived by assigning appropriate weights to four objectives with different priorities. According to either of these two models, the revised individual and collective opinions can be obtained. In addition, the prominent properties of the collective opinion obtained from optimization models are further investigated. As last, some examples are provided to demonstrate the applicability of the optimization models proposed, and the results demonstrate the models proposed can better deal with the abovementioned issues.

RISK-TAKING: CREATIVE EXPLORATIONS IN A STUDENT ENVIRONMENT

This study analyzes the relationship between the level of risk-taking and creativity among a group of students in Abidjan. Using a Likert questionnaire to assess their propensity for risk, we discovered a significant and positive correlation between risk-taking and creativity, highlighting a qualitative preference for aspects such as flexibility and originality. By exploring the role of emotions and the psychotism trait, this research suggests that risk-taking promotes cognitive disinhibition, thus paving the way for innovative idea associations. These findings also encourage rethinking creativity from the perspective of mental courage and emotional management.

FACTOR ANALYSIS OF NON-FINANCIAL MOTIVATORS FOR SUCCESSFUL MANAGEMENT AND PROMOTION OF THE COMPANY

Factor analysis of non-financial motivators will be presented through a statistical model whose goal is to group factors. Composing a statistical model implies grouping the factors that determine the contributions of qualitative preference to business efficiency of the whole company. Empirical material is obtained by surveying the target group, and the response pattern to clarify the management problem is factor analysis. The purpose of factor analysis is to rationalize the number of dependent variables of business efficiency in relation to qualitative and quantitative requirements within the surveyed target group. Considering the modern lifestyle, it is reasonable to assume that general dissatisfaction level is increasing every day. This includes professional orientation, working conditions, leisure time management and all levels of interpersonal relationships such as collegial, friendly, partner and family relationships. It is reasonable to assume that within business systems there is a space for recognizing this dimension and its careful analysis that respects the non-economic dimension of employees’ (dis) satisfaction and manages business efficiency in terms of non-economic factors. This paper aims to present thorough analysis of grouped factors that are basically non-financial; they represent a group of spiritual factors that contribute to a better internal climate of the company. All this leads to a prosperous company management that can represent and promote the company in public well.

Handling qualitative preferences in SPARQL over virtual ontology-based data access

With the increase of data volume in heterogeneous datasets that are being published following Open Data initiatives, new operators are necessary to help users to find the subset of data that best satisfies their preference criteria. Quantitative approaches such as top-k queries may not be the most appropriate approaches as they require the user to assign weights that may not be known beforehand to a scoring function. Unlike the quantitative approach, under the qualitative approach, which includes the well-known skyline, preference criteria are more intuitive in certain cases and can be expressed more naturally. In this paper, we address the problem of evaluating SPARQL qualitative preference queries over an Ontology-Based Data Access (OBDA) approach, which provides uniform access over multiple and heterogeneous data sources. Our main contribution is Morph-Skyline++, a framework for processing SPARQL qualitative preferences by directly querying relational databases. Our framework implements a technique that translates SPARQL qualitative preference queries directly into queries that can be evaluated by a relational database management system. We evaluate our approach over different scenarios, reporting the effects of data distribution, data size, and query complexity on the performance of our proposed technique in comparison with state-of-the-art techniques. Obtained results suggest that the execution time can be reduced by up to two orders of magnitude in comparison to current techniques scaling up to larger datasets while identifying precisely the result set.

A Fuzzy Query Method Oriented to Knowledge Graph

With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the fuzzy query of the knowledge graph, this paper introduces the unclear expansion syntax of aggregate function based on summarizing and analyzing the existing SPARQL fuzzy expansion. Supplements the qualitative preference analysis method then proposes and SPARQL1. 1 Corresponding fuzzy SPARQL1.1 (f-SPARQL1.1) simultaneously provides strategies and techniques for rewriting ambiguous queries into classic SPARQL queries. In addition, this article also provides a visual interface for defining the membership functions of fuzzy terms and provides three interactive methods for setting fuzzy membership functions. Compared with the previous fuzzy query methods for RDF models, the method proposed in this paper has more vital fuzzy query ability and more flexible universality. The experimental results show the usability of the method proposed in this paper.

A mixed methods end-user assessment to determine the ideal supraglottic airway device for inclusion into the medic's aid bag.

Many advancements in supraglottic airway technology have occurred since the start of the Global War on Terrorism. While the Tactical Combat Casualty Care guidelines previously recommend the i-gel device, this is based on little data and minimal end-user input. We sought to use a mixed methods approach to investigate the properties of an ideal device for inclusion into the medic's aid bag. We performed prospective, serial qualitative studies to uncover and articulate themes relative to airway device usability with 68W-combat medics. 68W are trained roughly to the level of a civilian advanced emergency medical technician with a heavier focus on trauma care. Physicians with airway expertise demonstrated the use of each device and provided formal training on all the presented devices. We then administered performed focus groups to solicit end-user feedback along with survey data. We enrolled 250 medics during the study. The preponderance of medics were of the rank E4 (28%) and E5 (44%). Only 35% reported ever placing a supraglottic airway in a real human. When reporting on usability, the i-gel had the highest median score, ease of manipulation, grip comfort and ease of insertion while also scoring the best in regard to requiring minimal training. The other compared devices had no clear highest score. Qualitative data saturated around a strong preference for the BaskaMask and/or the i-gel airway device, with the least favorite being the AirQ and the LMA Fastrach airway devises. There was a strong qualitative alignment in how both the BaskaMask and i-gel provided ease of use and simplicity of training. There were strong qualitative preferences for two specific airway devices: Baska Mask and i-gel. However, many medics commented on their previous experience with the i-gel compared with the other devices, which may have biased them toward the i-gel. The overall data suggest that medics would prefer a device engineered with features from several devices. Therapeutic/care management; Level V.

Advance Care Planning Among Elderly Acquired Immunodeficiency Syndrome Patients: A Qualitative Preference Study.

Complications arising from human immunodeficiency virus may affect the ability of elderly AIDS patients to communicate and make decisions about future medical care. It is important for elderly AIDS patients to be able to express effectively their preferences for future treatments and care. This study explored preferences and influencing factors of advance care planning among elderly AIDS patients. A semistructured interview outline was developed based on the theory of planned behavior. A qualitative preference interview was conducted among 16 eligible elderly AIDS patients. Using thematic analysis, the data were analyzed and categorized into 3 themes and 9 subthemes that were classified under the theory of planned behavior framework. The 3 themes were as follows: a positive attitude toward advance care planning, a strong desire for family and social support, and some obstacles to discussing advance care planning in the current environment. These themes provided valuable insights to advance care planning educators and practitioners from different work environments and units to aid them in constructing future ACP intervention models for elderly AIDS patients.

Hesitant Fuzzy 2-Dimension Linguistic Programming Technique for Multidimensional Analysis of Preference for Multicriteria Group Decision Making

The hesitant fuzzy 2-dimension linguistic element (HF2DLE) allows decision makers to express the importance or reliability of each term included in a hesitant fuzzy linguistic element as a linguistic term. This paper investigates a programming technique for multidimensional analysis of preference for hesitant fuzzy 2-dimension linguistic multicriteria group decision making. Considering the flexibility of HF2DLEs in expressing hesitant qualitative preference information, we first adopt HF2DLEs to depict both the evaluation values of alternatives and the truth degrees of alternative comparisons. To calculate the relative closeness degrees (RCDs) of alternatives, the Euclidean distances between HF2DLEs are defined. Based on RCDs and preference relations on alternatives, the group consistency and inconsistency indices are constructed, and a bi-objective hesitant fuzzy 2-dimension linguistic programming model is established to derive the criteria weights and positive and negative ideal solutions. Since the objective functions and partial constraint coefficients of the established model are HF2DLEs, an effective solution is developed, through which the RCDs can be calculated to obtain the individual rankings of alternatives. Furthermore, a single-objective assignment model is constructed to determine the best alternative. Finally, a case study is conducted to illustrate the feasibility of the proposed method, and its effectiveness is demonstrated by comparison analyses.

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function.

Sequential Learning-based IaaS Composition

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.