Qualitative Judgments Research Articles

Hear no evil, see no evil: Why the United States gets net assessment wrong

ABSTRACT Whether it’s the attack on Pearl Harbor, the outbreak of the Korean War, the start of the Gulf War, or the 9–11 terrorist attacks, the United States has suffered no shortage of strategic intelligence failures in its history. More recently, however, the United States has suffered from a different brand of mishap – a failure of net assessment, where the United States under- or overestimated how our allies would fare in a conflict against known adversaries. This article lays out two competing theories for why these net assessment failures happen – the missing variable hypothesis versus self-delusion. It then tests these theories against three cases of net assessment failures – Iraq in 2014, Afghanistan in 2021 and Ukraine in 2022. This article argues that contrary to popular belief, American net assessment failures are not primarily due a failure to collect the right information or an inability to assess the ‘will to fight’. Rather, American net assessment failures often stem from self-delusion. Because these assessments are often done at the upper rungs of government and include more qualitative judgements about friendly and enemy willpower and proficiency, senior government officials often shape their assessments to best fit their policy preferences. In other words, they fall victim to the confirmation trap. Consequently, if the United States aims to produce better net assessments in the future, then it needs to guard against this bias by professionalizing net assessment.

Site Selection of Large Shopping Malls in Wanbailin District of Taiyuan City

The construction and site selection of shopping malls are directly related to their commercial operation effectiveness and market competitiveness. Some scholars have conducted extensive research in this field, particularly achieving notable results in terms of influencing factors, methodologies, and technological applications for site selection, yet there are still some deficiencies. Traditional site selection methods primarily rely on qualitative analysis and empirical judgment. However, with technological advancements, Geographic Information System technology has gradually been applied to shopping mall site selection. GIS technology enables the integration of multiple data sources, spatial analysis, and visualization, providing a scientific basis for site selection decisions. This article utilizes ArcGIS software to construct an application model for shopping mall site selection in Wanbailin District. Through spatial analysis, eligible areas are identified, achieving a scientific and visual approach to site selection. By considering the main factors affecting the location of large shopping malls in Wanbailin District and using ArcGIS as the basic platform, an application model is built to find the optimal location for building shopping malls. The results show that introducing GIS into mall location planning improves efficiency and accuracy while providing a reliable basis for scientific location.

Quantitative Process of Retrieving Documents on Specific Technology Using an Academic Database and the Sentence Transformer

ABSTRACTAs competition in technologies such as semiconductors and AI intensifies, the importance of technology analysis in establishing R&D strategies is increasing. Although frequently used, technological document searches by domain experts often suffer from limitations in qualitative judgment and inconsistency. This study proposes a process for quantitatively assembling a collection of documents on specific technologies using an academic database and a language model. Using approximately 92 million records from the Web of Science, we identified related keywords from author keywords and used scientometric approaches to determine a core set of technology‐related literature. Then, a deep learning‐based Sentence Transformer model was employed to extract technology literature with high similarity to the documents, ultimately forming a set of documents for technology analysis. This study aims to overcome cognitive limitations and introduce quantitative criteria for technology analysis, which is particularly significant for data‐driven science and technology policy.

The MAMCABM framework for the evaluation of mobility decision‐making problems: theory and practice

AbstractIn the constantly evolving transportation and mobility industry, objective and reliable decision‐support systems (DSS) are crucial for addressing complex issues such as transit planning, mode selection, and policy formulation. This paper presents multiactor multicriteria analysis plus agent‐based modeling (MAMCABM), a novel framework that combines multiactor multicriteria analysis (MAMCA) and agent‐based modeling (ABM) to provide a comprehensive DSS. MAMCA excels in facilitating stakeholder‐centric evaluations, while ABM, enhanced by data analytics, adeptly models intricate, interactive systems. The combination of MAMCA and ABM enhances adaptability and precision in decision making. This integration utilizes data analytics and optimization algorithms to provide solutions that consider multifaceted criteria and diverse stakeholder perspectives in dynamic and uncertain contexts. The study outlines the mathematical underpinnings of MAMCABM and offers a practical guide for its implementation. The framework's efficacy is demonstrated through an empirical investigation that addresses mobility challenges in the Brussels Capital Region of Belgium. Compared to the previous study, this approach leverages simulated quantitative data alongside qualitative judgments from stakeholders. The integration of a consensus‐reaching algorithm further enhances the robustness of outcomes and effectively addresses uncertainties.

Reliable Stenosis Detection Based on Thrill Waveform Analysis Using Non-Contact Arteriovenous Fistula Imaging.

Hemodialysis therapy is an extracorporeal circulation treatment that serves as a substitute for renal function. In Japan, patients receive this efficient four-hour treatment, three times per week, allowing them to maintain a social life nearly equivalent to that of healthy individuals. Before the treatment, two punctures are performed to establish extracorporeal circulation, and a high blood flow rate is essential to ensure efficient therapy. Specialized blood vessels created through arteriovenous fistula (AVF) surgery are utilized to achieve high blood flow rates. Although the AVF allows safe and efficient dialysis treatment, AVF stenosis leads to a serious problem in dialysis. To early detect this abnormal blood flow, auscultation and palpation methods are widely used in hospitals. However, these methods can only provide qualitative judgment of the AVF condition, so the results cannot be shared among other doctors and staff. Additionally, since the conventional methods require contact with the skin, some issues require consideration regarding infection and low reproducibility. In our previous study, we proposed an alternative method for auscultation using non-contact optical imaging technology. This study aims to construct a reliable AVF stenosis detection method using Thrill waveform analysis based on the developed non-contact device to solve the problem with the contact palpation method. This paper demonstrates the performance validation of the non-contact imaging in the normal AVF group (206 total data, 75 patients, mean age: 69.1 years) and in the treatable stenosis group (107 total data, 17 patients, mean age: 70.1 years). The experimental results of the Mann-Whitney U test showed a significant difference (p=0.0002) between the normal and abnormal groups, which indicated the effectiveness of the proposed method as a new possible alternative to palpation.

Improving Quantitative Analysis with Cross Instrument-Sparse Bayesian Learning (CI-SBL) Raman Spectroscopy Analysis Algorithm.

Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on unknown samples. However, current Raman spectroscopy analysis algorithms still have many drawbacks. They struggled to handle quantitative analysis between different instruments. Their prediction accuracy for concentration is generally low, with poor robustness. Therefore, this study addresses these deficiencies by designing the cross instrument-sparse Bayesian learning (CI-SBL) Raman spectroscopy analysis algorithm. CI-SBL can facilitate spectroscopic analysis between different instruments through the cross instrument module. CI-SBL converts data from portable instruments into data from scientific instruments, with high similarity between the converted spectrum and the spectrum from the scientific instruments reaching 98.6%. The similarity between the raw portable instrument spectrum and the scientific instrument spectrum is often lower than 90%. The cross instrument effect of the CI-SBL is remarkable. Moreover, CI-SBL employs sparse Bayesian learning (SBL) as the core module for analysis. Through multiple iterations, the SBL algorithm effectively identified various components within mixtures. In experiments, CI-SBL can achieve a qualitative accuracy of 100% for the majority of binary and multicomponent mixtures. On the other hand, the previous Raman spectroscopy analysis algorithms predominantly yield a qualitative accuracy below 80% for the same data. Additionally, CI-SBL incorporates a quantitative module to calculate the concentration of each component within the mixed samples. In the experiment, the quantification error for all substances was below 3%, with the majority of the substances exhibiting an error of approximately 1%. These experimental results illustrate that CI-SBL significantly enhances the accuracy of qualitative judgment of mixture spectra and the prediction of mixture concentrations compared with previous Raman spectroscopy analysis algorithms. Furthermore, the cross instrument module of CI-SBL allows for a flexible handling of data acquired from different instruments.

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

Integrating energy simulations and analytical hierarchy process procedure in multi-criteria evaluation of heating systems for industrial buildings

Industrial buildings in Italy are generally aged and widely outdated from an architectural, technological, and environmental perspective. Despite the current limitations affecting both envelope and system installations, enhancement strategies are not frequently debated in the literature or addressed recurring to not adequately comprehensive approaches. This research examines space heating options for integrated retrofit interventions of manufacturing facilities using a novel methodology that combines multi-criteria decision analysis based on the Analytic Hierarchy Process (AHP) with building energy simulations. Interviews with representatives from medium-sized companies in Tuscany provide insights into the exigencies of industrial buildings, while on-site surveys identify common features of industrial buildings in the region leading to the identification of a representative case study building. This facility is examined producing several energy models to account for the different heating system alternatives to be evaluated according to criteria including economic, environmental, comfort, space, and reliability. Both qualitative judgments and quantitative outputs from energy simulations are considered to rank the technological alternatives encompassed in the analysis. In addition, sensitivity analyses are carried out to highlight the impact of boundary conditions on optimal configuration selection. Economic considerations weigh heavily for industrial owners, often outweighing environmental concerns due to higher initial investment requirements for low-impact solutions. Consequently, gas boiler generators and hot-water fan heaters emerge as preferred options for their flexibility, low initial cost, and ease of installation.

Research on sustainable renewal of historic blocks based on urban catalyst theory

Currently, cities in China have entered the development stage of stock renewal. Urban renewal has become the focus of urban development, and optimization of stock space has become the main aspect of urban design. At the same time, applying the concept of micro-renewal of historic blocks in recent years has also put forward new requirements for the renewal mode of old blocks. This paper discusses the possibility of micro-renewal and transformation of historic Chinese blocks in the new era from the perspective of catalyst theory. Through streetscape recognition technology and crawling of Point of Information (POI) data, the existing problems of blocks and the potential vitality points of blocks are determined, which changes the traditional research that often lacks quantitative judgment and only uses qualitative judgment. Through the intervention of quantitative technology, the problems existing in the perspective rate of commercial streets and the business model of historical blocks were found. Analyzing from the perspective of space, function, and culture, and then, selecting catalyst elements, which promotes the transformation of the historic blocks from a top-down transformation pattern to a bottom-up transformation pattern.

A refined and concise model of indices for quantitatively measuring lexical richness of Chinese university students’ EFL writing

In the existing literature, scholars have proposed various indices to measure the lexical richness (LR) of English as a foreign language (EFL) writing. However, there are currently issues of redundant indices and inconsistent usage. Attempting to address the research question of which indices are the most sensitive and effective ones to distinguish between different grade levels of Chinese university students’ EFL writing, this study aims to put forward a refined and concise model of indices that can truthfully reflect LR in EFL writing. A total of 180 compositions were selected from a Chinese EFL learner corpus: <i>Spoken and written English corpus of Chinese learners</i>. Scores of 28 LR indices of these compositions were computed using the software <i>Lexical Complexity Analyzer</i>,<i> MATTR</i>,<i> </i>and<i> Coh-Metrix</i>. One-way ANOVA or Welch’s ANOVA, depending on the variable’s homogeneity of variances, was conducted for each index. Two criteria were applied to determine which index of a measure should be included in the refined model: whether the difference of an index is significant among different grade levels and the effect size of ANOVA. Based on the quantitative results of ANOVAs and qualitative human judgment based on literature, six indices of the six LR measures were included in the refined model: lexical density, lexical sophistication-I, verb sophistication-II, number of different words-expected sequence 50, corrected TTR, and squared verb variation-I. This refined model addresses the issues of redundancy and inconsistency in previous studies, providing a more accurate and efficient tool for assessing LR in EFL writing.

Interactive inverse design of periodic non-uniform/inhomogeneous rod structures based on q-learning method

The existing researches on controlling the longitudinal waves through periodic rods with frequency bands mainly focus on the analysis of band structures together with their influences by various geometrical/material parameters and on the corresponding forward design via passive/active modulations. This paper originally proposes the interactive inverse design method by virtue of the Q-Learning algorithm for catering required objectives with introducing the cross-sectional nonuniformity and material inhomogeneity to the constituent components in periodic rods. Firstly, theoretically analyzing the frequency bands of periodic non-uniform/inhomogeneous rods with linear variations to the cross-sectional area and to the Young’s modulus and material density, is presented using transfer matrix method (TMM). After verifying the analysis method by comparing it with the finite element method (FEM) for obtaining band structures in three kinds of exemplified periodic rods with non-uniform/inhomogeneous components, the effects of non-uniformity and inhomogeneity of components on the frequency bands are discussed. These effects provide qualitative judgment criteria to the results of inverse design. Secondly, the inverse design method by the Q-learning algorithm for periodic non-uniform/inhomogeneous rods is proposed as the optimization objective is the maximum of the first bandgap width. The optimization results of the periodic rods with only non-uniformity, with only inhomogeneity or with both non-uniformity and inhomogeneity are provided to validate the accuracy and efficiency of the proposed Q-Learning algorithm that has the advantages of obtaining the optimal result from any initial states and giving the evolutionary path along the gradient to the objective. It should be pointed out that our proposed inverse design method can actually be extended for other optimization objectives and to other kinds of periodic structures for controlling diversified elastic waves.

The forced battle between peer-review and scientometric research assessment: Why the CoARA initiative is unsound

Abstract Endorsed by the European Research Area, a Coalition for Advancing Research Assessment (CoARA), primarily composed of research institutions and funders, was established in 2022. Its mission is to reform research assessment, emphasizing a shift towards qualitative judgment. This study examines the CoARA initiative from theoretical, practical, and applicative perspectives, exploring its implications. The manuscript questions a standardized way to evaluate research suitable in every context. Through empirical evidence and internal logical arguments, it challenges the supremacy of qualitative over quantitative methods in terms of robustness, accuracy, validity, functionality, and time and cost convenience in STEMM research assessment. The aim is to clear up common misunderstandings and offer insights to policymakers and decision-makers in charge of defining how research should be evaluated.

Research on the Energy Consumption Influence Mechanism and Prediction for the Early Design Stage of University Public Teaching Buildings in Beijing

The public teaching buildings of universities have a large flow of people, high lighting requirements, and large energy consumption, which present significant potential for energy saving. The greatest opportunity for integrating “green” architectural design strategies lies in the design phase, especially the early stage of architectural design. However, current designers often rely on experience or qualitative judgment for decision-making. Thus, there is a pressing need for rational and quantitative green architectural design theories and techniques to guide and support decision-making for the design parameters of teaching buildings. This study, based on field surveys of 40 teaching buildings, constructs building archetypes regarding energy consumption including 28 typical values. Based on the “Rectangle”, “L”, “U”, and “Courtyard” archetypes, through batch energy consumption simulation and multiple regression methods, the influence mechanisms of nine energy consumption influencing factors on four types of building energy consumptions were explored, and energy consumption prediction models were derived. The findings of this research can serve as factor evaluation and selection in the early stage of architectural design for public teaching buildings at universities, and the prediction model can assist in the early estimation of energy consumption. This aims to enrich and supplement green architectural design methods by supporting the design of green public teaching buildings and providing reference and application for relevant engineering practices.

Analyzing Primary Sector Selection for Economic Activity in Romania: An Interval-Valued Fuzzy Multi-Criteria Approach

This study presents an in-depth analysis of the selection process for primary sectors impacting the economic activity in Romania, employing an interval-valued fuzzy (IVF) approach combined with multi-criteria decision-making (MCDM) methodologies. This research aims to identify eight key criteria influencing the selection of Romanian primary sectors, including technology adaptation, infrastructure development and investment, gross domestic product (GDP), sustainability, employment generation, market demand, risk management and government policies. The current analysis evaluates eight primary sector performances against these eight criteria through the application of three MCDM methods, namely, Simple Additive Weighting (SAW), Weighted Product Model (WPM), and Weighted Aggregated Sum Product Assessment (WASPAS). Ten economic experts comprising a committee have been invited to provide their views on the criteria’s importance and the alternatives’ performance. Based on the decision-maker’s qualitative judgement, GDP acquires the highest weightage, followed by environmental impact and sustainability, thus indicating the most critical factors among the group. The IVF-MCDM hybrid model indicates the energy sector as Romanian primary sector with the most potential, followed by the agriculture and forestry sector among the list of eight alternatives. It also explores the robustness of results by considering sensitivity analysis and the potential impacts of political and international factors, such as pandemics or armed conflicts, on sector selection. The findings indicate consistency in sector rankings across the different methodologies employed, underscoring the importance of methodological choice and criteria weighting. Additionally, this study sheds light on the potential influence of political and international dynamics on sector prioritization, emphasizing the need for comprehensive decision-making frameworks in economic planning processes.

A quantitative assessment of the status of reef fish communities from a large-scale probability survey in southern Florida

Numerous reef fish monitoring programs worldwide produce the data necessary to describe the status and trends of coral reefs; however, quantitative description of status at ecosystem scales has been challenging. Our goal was to use southern Florida's coral reefs as the template to complete a holistic, ecosystem-scale evaluation of reef fish community status using large-scale diver surveys that sampled across a spatial gradient of human urbanization, exploitation, and fishery protection. Key aspects of the analysis were: (1) identification of a low human impact reference area as the basis for quantifying resource condition; (2) selection of indicator variables that helped discriminate two classes of impacts: habitat quality and fishing; (3) application of estimation methods that facilitated distinguishing anthropogenic impacts from inherent productivity of different habitats; and (4) use of a sustainability benchmark to gauge the resource condition of the reference area. The reference-centering analysis approach reduced reliance on qualitative judgements by an expert panel and produced results on a scale that was informative and could be easily interpreted by a variety of audiences. Our findings identified habitat quality issues in the most urbanized region, southeast Florida, and pervasive fishing issues throughout the ecosystem, including the remote Dry Tortugas region.

Unveiling the Art and Science of Investment and Financing Decision Making

Purpose: This study explores the intricate dynamics of investment and financing decision-making, emphasizing the integration of qualitative judgment and quantitative analysis. The research investigates the foundational theories, empirical findings, and practical implications that shape these decisions, highlighting their significance in financial management and economic activities. Research Design and Methodology: The research employs a comprehensive literature review methodology, systematically analyzing existing scholarly works to identify themes, patterns, and relationships within the literature. This approach involves identifying relevant sources through extensive search strategies, critically evaluating the quality and relevance of the selected literature and synthesizing key findings and theoretical insights. Findings and Discussion: The study reveals that a complex interplay of theoretical frameworks and empirical evidence drives investment and financing decisions. The Modigliani-Miller theorem and the efficient market hypothesis provide foundational insights, while behavioral finance highlights cognitive biases that influence decision-making. Empirical findings emphasize the role of firm-specific characteristics and market conditions in shaping capital structure choices and investment behavior. Practical considerations, including managerial overconfidence and asymmetric information, further complicate these decisions. Implications: The insights from this research are valuable for practitioners, policymakers, and researchers. Understanding the multifaceted nature of investment and financing decisions can enhance financial management practices, inform regulatory frameworks, and guide future research directions. Integrating theoretical insights with empirical evidence and practical considerations enables stakeholders to navigate the complexities of financial markets, fostering informed decision-making and promoting sustainable economic growth.

Strategic outsourcing in reverse logistics: Neutrosophic integrated approach with a hierarchical and interactive quality function deployment

The efficiency of strategic outsourcing in reverse logistics (RL) depends on stakeholder requirements (SRs) directing organization development and experts’ knowledge decomposing and reconstructing strategic decision, which urges the adoption of hierarchical and interactive quality function deployment (QFD). Considering the uncertainty within a QFD, single valued neutrosophic number (SVNN) is introduced to capture both indeterminacy and inconsistency hidden in quantitive votes and qualitative judgments of stakeholders and experts. With unknown weight assignments, two maximum deviation models are constructed to obtain the priority of SRs and Shapley weights of experts. An SVNN-enabled grey relational analysis is extended to identify the interdependence priority of engineer characterizers (ECs). Due to the interactions among components such as experts in a group or ECs in QFD, two information aggregation tools under neutrosophic environments are presented based on fuzzy integral delineating the positive or negative effect. Built on these, we propose an integrated neutrosophic approach composed of a transformation module, weight module, integration module and QFD analysis module to support the outsourcing decision. Finally, an illustration example is used to confirm the practicality of the proposed approach. Sensitivity analysis and comparative analysis with multiple views have been conducted to show the flexibility and superiority of the given decision.

Eco-performance evaluation of product designs – an integrated fuzzy complex proportional assessment with life cycle assessment

ABSTRACT The growing public awareness has driven enterprises to develop environmentally friendly designs to fulfill customer satisfaction. Some eco-design tools have been introduced to designers to support greener product design and development qualitatively. However, most require qualitative judgement during the evaluation process. Although life cycle assessment (LCA) helps quantify a product system’s environmental loads, it requires much time and resources for data collection. Design selections are not preferred during the early product development stage, especially when the product development schedule is rather tight nowadays. This paper proposes a practical approach integrating a fast-track LCA, fuzzy-set, with COmplex PRoportional ASsessment (COPRAS). This paper proposes an approach by incorporating the fuzzy-set, COPRAS, and LCA for decision-makers to evaluate design alternatives at the early product development stage. The originality of the paper is to provide an efficient approach to evaluating various design alternatives by quantifying their corresponding environmental burdens using LCA and fuzzy-set. Three distinct features can be found in the proposed generic approach. This study brings two contributions to the related field. For the area related to environmental impact assessment, this study illustrates the applicability of LCA in conjunction with COPRAS and fuzzy-set for the design alternative selection. Decision-makers can apply the proposed method that in-depth knowledge of environmental impact assessment is not essential, and the qualitative input required is minimal compared to a comprehensive LCA study.

Determination of multi-UAVs formation shape: Using a requirement satisfaction and spherical fuzzy ANP based TOPSIS approach

Multiple unmanned aerial vehicles (multi-UAVs) formation shape refers to the geometric shape when multi-UAVs fly in formation and describes their relative positions. It plays a necessary role in multi-UAVs collaboration to improve performance, avoid collision, and provide reference for control. This study aims to determine the most appropriate multi-UAVs formation shape in a specific mission to meet different and even conflicting requirements. The proposed approach introduces requirement satisfaction and spherical fuzzy analytic network process (SFANP) to improve the technique for order preference by similarity to ideal solution (TOPSIS). First, multi-UAVs capability criteria and their evaluation models are constructed. Next, performance data are transformed into requirement satisfaction of capability and unified into a same scale. Qualitative judgments are made and quantified based on spherical fuzzy sets and nonlinear transformation functions are developed for benefit, cost, and interval metrics. Then, SFANP is used to handle interrelationships among criteria and determine their global weights, which takes decision vagueness and hesitancy into account and extends decision-makers’ preference domain onto a spherical surface. Finally, alternative formation shapes are ranked by their distances to the positive and negative ideal solution according to the TOPSIS. Furthermore, a case study of 9 UAVs performing a search-attack mission is set up to illustrate the proposed approach, and a comparative analysis is conducted to verify the applicability and credibility.

Managing incomplete general hesitant linguistic preference relations and their application

<p>Hesitant linguistic preference relations (HLPRs) are useful tools for decision makers (DMs) to express their qualitative judgements. However, the traditional HLPRs have one prominent drawback, which is to sort the linguistic values in a hesitant linguistic set. This will distort the DMs' initial judgements. In the present paper, a revised definition of HLPR, called general HLPR (GHLPR), was proposed. A characterization was explored for LPRs. Then, the characterization was extended to GHLPRs. Based on the characterization, the estimation of unknown entries in incomplete GHLPRs were carried out by two algorithms. The group decision-making problems with incomplete GHLPRs were settled by another algorithm. Finally, a case study was illustrated, and comparisons showed that our methods were more reasonable than the existent methods.</p>