Qualitative Judgments Research Articles

Fermatean fuzzy Heronian mean operators and MEREC‐based additive ratio assessment method: An application to food waste treatment technology selection

Uncertainty is often occurred in real-life decision-making problems due to the lack of complete information, imprecise data, and the vagueness of decision making experts in qualitative judgment, thus, the crisp values of criteria may be insufficient to handle such types of complex real situations. As the extension of fuzzy set, intuitionistic fuzzy set and Pythagorean fuzzy set, the Fermatean Fuzzy Set (FFS) has been demonstrated as a powerful tool to handle the uncertainty arisen in practical decision-making problems. Thus, this study aims to introduce an integrated Fermatean fuzzy information-based decision-making method by combining method based on the removal effects of criteria (MEREC) and additive ratio assessment (ARAS) methods with the application in a food waste treatment technology selection problem. By using Fermatean fuzzy numbers, the suggested approach successfully handle the qualitative data and uncertain information that often occur in practical situations. This study consists of four phases. First, entropy measure is developed for FFS and further utilized for determining the experts’ weights. Second, some Fermatean fuzzy Heronian mean operators and their properties are introduced to aggregate the Fermatean fuzzy information. These operators can provide us a valuable means to handle practical multicriteria decision-making problems on FFSs context. Third, an extended MEREC technique is originated to assess objective criteria weights within FFS context. Fourth, an integrated ARAS method is introduced with the combination of proposed entropy measure, generalized weighted Fermatean fuzzy Heronian mean operator and MEREC technique to evaluate and rank the alternatives. To confirm the reasonableness and practicality of the proposed methodology, an empirical case study of food waste treatment technology selection is discussed on FFSs settings. Further, a comparison with extant models and a sensitivity investigation are performed to confirm the validity and robustness of the obtained outcomes.

Methodology for phenomenological code assessment with integral test data

The use of codes in the licensing process requires a rigorous validation process that can be accomplished by means of qualitative and quantitative assessment. In thermal hydraulics, this validation has to be performed at different levels, from separate effects to the integral response of a plant design. Even though the quantitative assessment is preferred, for complex phenomenology involving the behaviour of the whole plant system this approach is difficult and the assessment is usually performed through qualitative expert judgement. In the present article, a methodology is proposed that combines the use of qualitative and quantitative adequacy assessment for the simulation of experiments at integral test facilities. The method makes use of statistical quantification by means of Best Estimate Plus Uncertainty calculations.

Rater-task interaction effects on testing EFL learners’ written performances

This study examines the interaction effect between rater groups and tasks on evaluating EFL learners’ written performances. Fifty raters took part in this study. The experienced rater group (n=25) and the novice rater group (n=25) judged sixty essays (30 narratives and 30 argumentative writing modes) written by third-year English students. Raters’ decision-making behaviours, in terms of scores assignment and written comments, were diagnosed based on different quantitative and qualitative tools. Scores were analysed based on FACETS to examine the effects of rater-task interaction on raters’ severity and internal consistency and the analytic scale’s functionality. Qualitative data were also analysed to diagnose which aspects of writing were deemed more important than others across rater groups and task types. The analysis revealed that both raters and tasks were substantially influential factors. The majority of expert raters displayed more severity in assessing narrative essays than argumentative essays. Different qualitative judgments are also detected across raters and tasks due to rating experience and task requirements. The findings of this study reflected implications not only for testing learners’ writing proficiency but also for test validation research in the task-based writing performance assessment field.

Inter-rater agreement of rotator cuff tendon and muscle magnetic resonance imaging parameters evaluated preoperatively and during the first postoperative year following rotator cuff repair.

Magnetic resonance imaging (MRI) is standard of care for rotator cuff evaluation, with clinical interpretation usually limited to qualitative judgments. The reliability of MRI-based measurements and scoring systems has been evaluated only preoperatively or ≥6 months following rotator cuff repair, when repairs are in the later stages of healing. This study describes the MRI assessments and inter-rater agreement of various rotator cuff tendon and muscle parameters evaluated preoperatively and 4 times during the first postoperative year. Two musculoskeletal radiologists independently assessed MRI scans of 42 patients preoperatively and 3, 12, 26, and 52 weeks after rotator cuff repair. Using standardized reading rules, readers assessed tendon integrity (5-point Sugaya classification), tear dimensions, muscle fat (5-point Goutallier classification) and atrophy (4-point Warner classification), muscle cross-sectional areas, and myotendinous junction distance. Raw exact agreement proportions, κ statistics, and correlation coefficients were used to quantify inter-rater agreement. Readers showed moderate to substantial above-chance agreement in scoring rotator cuff tendon integrity and supraspinatus muscle atrophy and good to excellent agreement on tear dimensions and muscle cross-sectional areas but only fair to moderate agreement for fatty infiltration and myotendinous junction distance. Only fatty infiltration grades evidenced observer bias. Inter-rater agreement did not appear time dependent. By use of defined reading rules in a research setting, MRI evaluations of rotator cuff tendon integrity, tear dimensions, muscle atrophy, and cross-sectional areas have reasonable reliability at all time points in the first postoperative year. However, the presence of clinically significant disagreements, even in such favorable circumstances, indicates the need for improved imaging tools for precise rotator cuff evaluation.

THE EFFECTS OF RATING EXPERIENCE AND DISCOURSE MODES ON ASSESSING EFL LEARNERS� WRITING PERFORMANCES

This study aims at investigating the effects of discourse modes on assessing EFL learners written performances. A total of fifty raters judged sixty essays (30 narratives and 30 argumentative writing modes) written by third-year English students from the Faculty of Letters and Humanities. Raters not only scored the compositions but also justified their scores assignments based on written explanations. Raters rating behaviors were diagnosed based on a variety of quantitative and qualitative tools. Essay scores were analyzed based on the statistical model FACETS to measure raters severity and internal consistency, task difficulty, and the scale functioning across writing modes. Qualitative data (gathered from interviews and report forms) were also analyzed in order to examine which aspects of writing were deemed more important than others across task types. The analysis revealed that the discourse mode was substantially an influential factor. The narrative task was more difficult than the argumentative one. Narrative essays were judged harsher than argumentative essays. Less consistent ratings could be detected from the narrative mode, compared to the argumentative one. Qualitative findings showed that the two writing modes were different in their qualitative judgments due to their different genre requirements and norms.

Research on the Evaluation and Optimization Method of the Impact of Chorus Education on University Culture Based on Coevolution Model in the Background of Artificial Intelligence

The development of artificial intelligence technology is a field where all walks of life need to carry out in-depth research in the future, and the introduction of artificial intelligence technology in the field of university evaluation has become an inevitable trend. Through the collection and collation of the literature at home and abroad, the influence of chorus education on college culture in China has long remained in qualitative and experiential judgment and the significance and value of chorus education to colleges and universities are relatively single. Therefore, it is of great innovative value and practical significance to establish a scientific, systematic, and comprehensive evaluation mechanism for the impact of chorus education on university culture and to scientifically analyze key issues, establish evaluation criteria, and inject new research perspectives into the promotion of chorus education in colleges and universities in China, combining with the mature coevolution theoretical model of management science. It is of great innovative value and significance to combine the DEMATEL research method with the current practice of promoting chorus education in China’s colleges and universities and to systematically and comprehensively construct the evaluation system and research paradigm in line with chorus education by using the qualitative and quantitative methods.

A Bayesian approach to age estimation of marine terraces and implications for the history of the great Kanto earthquakes, central Japan

We quantitatively evaluated the emergence ages of the tectonically uplifted marine terraces, called the Numa terraces, in the southernmost part of the Boso Peninsula, central Japan. The combination of the complete dataset of the geological dating survey and the model inversion method is newly proposed. Along the Sagami Trough, the subduction boundary of the Philippine Sea Plate, M8 class interplate earthquakes are known to have repeatedly occurred and caused huge crustal deformation in adjacent regions, leaving tectonic landforms behind, such as the Numa terraces. Although many geological studies have investigated the Numa terraces, the estimation of their emergence ages contains ambiguity due to employed qualitative judgments and separate evaluations at different survey points. In this study, we first construct a unified dataset for dating throughout the southern Boso area by compiling the existing data and our new data. We also examin the lateral continuity of the terraces using a new technique to amplify subtle topographic changes in digital elevation data. Second, we propose a new method to estimate the emergence ages of terraces using the temporal distribution of the sample ages via a model of the sedimentation process and Bayesian statistic. The emergence ages of the three levels of the Numa terraces, uplifted in the prehistorical era, are determined as 5855–5455 yBP, 3345–3025 yBP, and 2125–1415 yBP, in descending order. This result robustly shows that the recurrence intervals of terrace-forming great Kanto earthquakes can vary by more than a factor of two. The robustness of the result is supported by the quantitative evaluations of the formation ages and the errors in the sedimentation process, made possible by our method for the first time. This result will provide inevitable information for further subduction zone researches and future hazard assessments.

Approaches to the application of the analytic hierarchy process in order to make a decision by the head of the border control unit

The head of the border control unit faces the need to make the decision on the protection of the state border at the border crossing point every day. Decision-making on the protection of the state border is a complex multifactorial process with the variability of possible final values. The article discusses the possibility of using the hierarchy analysis method for the decision-making process by the head of the border control unit. In order to make an optimal decision, using the method of analyzing hierarchies, it is necessary to decompose the known factors into indicators that directly affect the protection of the state border and the situation at the border crossing point. Decomposition of factors into indicators was carried out by the method of expert assessment, the disadvantage of which is the possible subjectivity of expert judgments. To obtain a sufficient number of qualitative judgments when choosing indicators of factors that directly affect the protection of the state border, the authors propose a method for determining the required number of experts when conducting expert assessments. Based on the results obtained, the allowable size of the expert group was 18 specialists involved in assessing the research problem. The research indicates that the involvement of experts in excess of the specified number leads to the presentation by them of insignificant for decision-making or repeated judgments. In order to achieve the best results, substantiate the proposal of all possible optimal indicators of factors that directly affect the protection of the state border, a categorical selection of experts in the direction of protecting the state border at the border crossing point has been established.

Two characterization methods of ripple errors for the large square aperture.

The specification and characterization of mid-spatial-frequency (MSF) ripples for the large-square-aperture optical elements, typically used in high-power laser systems, have received considerable critical attention. It is necessary to resort to a simple and robust way to characterize error surfaces for facilitating prediction of performance degradation and guiding the fabrication and tolerance settings. In this paper, we characterize residual periodic surface undulations called ripple errors for the large square aperture generated from modern subapertures and deterministic optical fabrication techniques through two methods, taking a step from qualitative judgment to quantitative analysis. The cross artifact reduction technology, instead of traditional windowed preprocessing, is introduced into power spectral density to suppress spectrum leakage while retaining the information about the part. An efficient algorithm to generate Legendre moments for two-dimensional Legendre polynomials is proposed to quantify ripple errors. This work contributes to understanding the optical degradation caused by MSF errors and associating the design and performance index with surface parametric description.

A New Semi-Quantitative Process Safety Assessment Method and Its Application for Fluorochemical Industry

Traditional hazard and operability analysis (HAZOP) is one of the most widely applied methods for process safety management in process enterprises. Due to its principles based on the conservative and qualitative judgment, it often leads to too conservative risk identification results for the fluorine chemical industry usually with high-risk processes to keep the continuity of production. Most of improved quantitative and semi-quantitative methods are based on the layer of protection analysis (LOPA) to resolve the over-conservative problem of traditional HAZOP with the database of LOPA. However, the improved model, taking LOPA as the main line and HAZOP only as the provider of scenarios and influencing factors, is limited to the fact that LOPA can only analyze complete and independent protection layers (IPLs). Therefore, in order to realize the quantitative or semi-quantitative analysis of disaster causes and consequences, a new semi-quantitative HAZOP method takes HAZOP as the main line to integrate LOPA, F&EI (fire and explosion index) for quantitatively calculating the reduction factors, probability on failure demand (PFD) of general protection layers (GPLs) and PFD of IPLs. With the case comparison of fluorine chemical industry, it is proved that this new method can effectively improve the problem that traditional HAZOP are too conservative in complex scenarios.

Identifying critical causal criteria of green supplier evaluation using heterogeneous judgements: An integrated approach based on cloud model and DEMATEL

With the increasing awareness of environmental protection, green supplier selection as an indispensable part of green supply chain management has received extensive attention. Effective and reliable green supplier evaluation criteria are crucial to the success of green supplier selection. Therefore, identifying the critical criteria and determining the causality of these criteria is an important requirement of stakeholders. However, the existing approaches on identifying critical causal criteria suffer at least two weaknesses: firstly it assumes the same cognitive levels between different decision makers by using a pre-determined and uniformed formation to characterize evaluation judgements, which may cause potential decision bias; secondly there is a lack of effective methods to analyse and identify critical causal criteria in the face of heterogeneous judgements, potentially causing the duplication consideration of the impacts of some criteria in green supplier selection. To address these issues, this study proposes a new approach integrating cloud model and DEMATEL (decision making trial and evaluation laboratory) to determine critical causal criteria for green supplier evaluation with qualitative heterogeneous judgements. The contribution of this study is threefold. First, to address the difficulty in processing uncertain and heterogeneous judgements, the cloud model theory is utilized and further developed to convert heterogeneous qualitative judgements into homogeneous quantitative data with the form of interval integrated clouds, which realizes the flow of uncertainty from qualitative judgements to quantitative data. Second, to enable the identification of critical causal criteria, the DEMATEL method is extended to accommodate the cloud model environment to solve the identification problem. Third, a case study, followed by a comparison analysis is provided to illustrate the applicability and advantages of the proposed approach. The results indicate that the proposed approach can handle heterogeneous judgements effectively as well as that staff environmental training, green production innovation, green marketing and green corporate culture are the critical causal criteria for the given application.

Urban Pluvial Flood Management Part 1: Implementing an AHP-TOPSIS Multi-Criteria Decision Analysis Method for Stakeholder Integration in Urban Climate and Stormwater Adaptation

Cities are facing increasing pressures to enact adaptation measures due to climate change. While blue-green infrastructure has emerged as a focal adaptation technique for stormwater management, in order to craft adaptation policies cities must consider a multitude of emerging, complex, and competing stakeholder interests around multiple adaptation alternatives. However, accounting for these different interests, analyzing their diverse priorities, and maintaining a transparent decision-making process is not easily achieved within the existing policy frameworks. Here we define and present a combined multi-criteria decision analysis (MCDA) of the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) methods that easily integrates and quantifies stakeholder priorities while remaining accessible for non-experts engaged in the policy-making process. We demonstrate the method’s effectiveness through analyzing opinions about stormwater adaptation in New York City across several stakeholder groups. The method succeeds in integrating quantitative and qualitative judgements, indicating stakeholder preferential differences and allowing for more inclusive policy to be crafted. It can be extended beyond stormwater to many urban climate adaptation decisions facing multi-criteria considerations.

Quantified Assessment of Internal Short-Circuit State for 18 650 Batteries Using an Extreme Learning Machine-Based Pseudo-Distributed Model

To facilitate the diagnosis of battery internal short circuit (ISC) using thermal behaviors, this work integrates several thermal effects, including the commonly ignored heat conduction hysteresis and radiation, to elaborate a lumped thermal model. Then, a pseudo-distributed model structure is built up to approximate the characteristics of real batteries by synthesizing multiple isomorphic electrical/thermal submodels with the extreme learning machine network. Besides, three kinds of configurable destructions are conducted to incur ISC consequences. From thermal and electrical model residuals, four ISC features are extracted and the multiclass relevance vector machine is utilized to assess ISC intensity, in which not only qualitative judgments are given but also quantitative confidences can be derived according to the posterior probabilities. Finally, experiments on 18650 Li-ion cells verify the reliability of the synthesized models and suggest that the diagnosis scheme can recognize ISC faults effectively with low grade and state misjudgment rates (14.59% and 3.13%, respectively).

Group decision making based on consistency and consensus analysis of dual multiplicative linguistic preference relations

This paper proposes a new group decision making (GDM) method based on the consistency and the consensus analysis of dual multiplicative linguistic preference relations (DMLPRs). A new type of linguistic variables , called dual multiplicative linguistic variables (DMLVs), is presented, which is defined on the multiplicative linguistic scale. DMLVs are used to represent asymmetrical qualitative hesitancy judgments of decision makers (DMs). A maximum-consistency-based interactive algorithm to derive multiplicative linguistic intuitionistic preference relations (MLIPRs) is presented, by which the consistency concept for DMLPRs is obtained. Then, we define the concept of inconsistent DMLPRs and propose an optimal-model-based method for deriving consistent DMLPRs. Furthermore, incomplete DMLPRs also can be dealt with by the proposed maximum-consistency-based interactive algorithm. For GDM, the weights of DMs are determined by the cosine-based correlation coefficient between individual DMLPRs. Moreover, we propose a consensus measure to calculate the agreement degree of DMLPRs and build an optimal model to increase the consensus level of individual DMLPRs. Finally, a new GDM method (call Algorithm III ) is offered and an application example is used to illustrate the proposed GDM method. The proposed GDM method outperforms the former GDM methods for GDM in the environments of DMLPRs.

Evaluation Model for Influences of Driving Forces for Electronic Procurement Systems Application in Ghanaian Construction Projects

Recent recognition for increased digitization of construction processes presents an important need to accelerate electronic procurement systems (EPS) application on projects. The EPS uptake rate in construction is relatively low, and the decision process required to increase the uptake is complex due to the influences of several competing driving forces (DFs). Previous studies identified individual DFs based on experts’ qualitative judgements and knowledge, which are subjective and lack the inherent clustering influences of DFs on EPS application. This study developed a fuzzy-based evaluation model for assessing DFs influences on EPS application in construction projects. The model transforms qualitative judgments into quantitative and more-usable assessments using fuzzy sets for effective long-term planning and consistent implementation decisions. A data set of 121 expert responses, resulting in 6 clusters of DFs, was applied to fuzzy logic to develop the DFs influence evaluation model. The integrity and environment-related forces and process optimization–related forces were the top two DFs clusters influencing EPS uptake. The remaining four clusters had significant influences, resulting in an overall high influence threshold. This implies that significant influence levels of the DFs clusters are required to accelerate EPS uptake on projects. This study contributes to knowledge by developing an influence model integrating subjective expressions to indicate EPS uptake within a project’s environment. The model’s flexibility helps to assess influences based on a decision maker’s situation.

26.4: A Quantitative Method for L0 Light Leakage Sensitivity of TFT‐LCD Based on JND Energy Ratio

In this paper, a quantitative evaluation method of L0 light leakage sensitivity based on JND energy ratio is established by studying the optical characterization and physical significance of L0 light leakage phenomenon. This method uses continuous quantitative indicators instead of human eye evaluation, which fully improves the accuracy and repeatability, and it can also predict the L0 light leakage level after the test module is assembled into the system set. An optical test system was built to simulate the influence of the system set. The liquid crystal module samples with different glass thickness and different sealant were tested. The test results were consistent with the qualitative judgment results of the human eye after the test module is assembled into tablet PC, and the similar level that the human eye can't be distinguished can be quantitatively distinguished. The repeatability error of the method was only 0.89%.

MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks

This study proposes a prototype for the smart rendering of urban master plans via artificial intelligence algorithms, a process which is time-consuming and relies on professionals’ experience. With the help of crowdsourced data and generative adversarial networks (GAN), a generation model was trained to provide colorful rendering of master plans similar to those produced by experienced urban designers. Approximately 5000 master plans from Pinterest were processed and CycleGAN was applied as the core algorithm to build this model, the so-called MasterplanGAN. Using the uncolored input design files in an AutoCAD format, the MasterplanGAN can provide master plan renderings within a few seconds. The validation of the generated results was achieved using quantitative and qualitative judgments. The achievements of this study contribute to the development of automatic generation of previously subjective and experience-oriented processes, which can serve as a useful tool for urban designers and planners to save time in real projects. It also contributes to push the methodological boundaries of urban design by addressing urban design requirements with new urban data and new techniques. This initial exploration indicates that a large but clear picture of computational urban design can be presented, integrating scientific thinking, design, and computer techniques.

Feasibility, quality and validity of narrative multisource feedback in postgraduate training: a mixed-method study

ObjectivesTo examine a narrative multisource feedback (MSF) instrument concerning feasibility, quality of narrative comments, perceptions of users (face validity), consequential validity, discriminating capacity and number of assessors needed.DesignQualitative text analysis...

Algorithmically Flexible Style Composition Through Multi-Objective Fitness Functions

Creating a musical fitness function is largely subjective and can be critically affected by the designer's biases. Previous attempts to create such functions for use in genetic algorithms lack scope or are prejudiced to a certain genre of music. They also are limited to producing music strictly in the style determined by the programmer. We show in this paper that musical feature extractors, which avoid the challenges of qualitative judgment, enable creation of a multi-objective function for direct music production. The main result is that the multi-objective fitness function enables creation of music with varying identifiable styles. To demonstrate this, we use three different multi-objective fitness functions to create three distinct sets of musical melodies. We then evaluate the distinctness of these sets using three different approaches: a set of traditional computational clustering metrics; a survey of non-musicians; and analysis by three trained musicians.

Evaluation on consequences prediction of fire accident in emergency processes for oil-gas storage and transportation by scenario deduction

This paper takes the safety in emergency processes as the starting point, from the perspective of scenario deduction, to study the consequences of fire accidents for oil-gas storage and transportation. Through the statistical analysis of actual accident cases, 19 frequently occurring basic scenarios in emergency processes are summarized. The scenario evolution paths of fire accidents for oil-gas storage and transportation are given by analyzing the evolution law of the accident development. Fuzzy numbers are introduced to express experts' qualitative judgment on accident scenarios. The empirical probabilities of scenario nodes are obtained by defuzzification calculation, and the state probability of each scenario node is calculated by using the dynamic Bayesian network joint probability formula. Under the comprehensive consideration about the probability statistics of actual accident cases, the critical scenario nodes on the evolution path and their final scenario probabilities are jointly determined to realize the optimization of the scenario evolution path. By constructing the correlation between the optimized scenario evolution path and the accident consequences, an accident consequence prediction model is established. The occurrence probability of accident consequences is calculated by the defuzzification method and dynamic Bayesian network. The accuracy of the consequence prediction model is verified by the July 16 Dalian's Xingang Harbor oil pipeline explosion accident. The research results provide scientific basis for helping decision makers to make the effective emergency measures that are most conducive to the rapid elimination of accidents and reducing the severity of accident consequences.