Decision Procedure Research Articles

Digitalization of Legal Transformation on Judicial Review in the Constitutional Court

Efficient, affordable, and uncomplicated justice poses a complex dilemma within the Indonesian judicial system. However, applying technological advancements to assessing legal regulations can resolve this issue. This research aims to examine the necessity and implementation of digitalization in the Constitutional Court's judicial review process. This research utilized a normative legal approach, characterized by its prescriptive and applied nature, to examine the development and implementation of digitalization in the justice systems of the European Union, the United States, and the United Kingdom through comparative studies. The research results show that: first, technology in the Constitutional Court's judicial review process can most effectively promote openness and clarity in the decision-making procedure. However, there remain several areas in which the Constitutional Court must further refine itself to attain digital status, as inconsistencies persist in its practical rulings. Consequently, the Constitutional Court must increase the rate of digital transformation in its judicial review operations. Second, numerous other nations, including the European Union, the United Kingdom, and the United States, have observed the implementation of technological advancements, leading to enhanced transparency and accessibility in their justice systems. Thirdly, to overcome the challenges above, we must apply the fundamental principles of good judicial governance, procedural law at the Constitutional Court, the paperless principle, and the contactless service principle when developing digital transformation for future judicial review at the Court. This will enable us to achieve transparency and accessibility in the Constitutional Court's judicial system.

Decision process for industry 5.0

ABSTRACT Industry 5.0 is an emerging paradigm that enhances the established Industry 4.0. Automation, digitalisation, big data, and artificial intelligence are becoming complemented by human-centricity, active cooperation between people and machines, and decision-making based on human expertise. This research introduces a generic decision-making process for Industry 5.0. It defines the underlying principles to incorporate multi-criteria decision models, decision procedures, and decision support technology into business and production processes for Industry 5.0. We justify the motivation to aggregate holistic objectivedecisions, which are inferred automatically from big data, with subjective judgements and knowledge of human decision-makers to guarantee unbiased actions. We draw parallels with the aggregation-disaggregation paradigm and constructive learning to address the human-centric specification of preferences in cooperation with technology-driven entities. We assess the relevance and efficiency of decision support for Industry 5.0 with a SWOT analysis. Finally, we apply the introduced generic process to the use case of the flexible electricity supply market.

The practicalities of a partial lottery to allocate research funding

Abstract This article explores the practical dimension of organizing a partial lottery as an alternative funding allocation mechanism in academic institutions. Following an assessment of the recent literature that presents criticism of traditional ranking-based system derived from academic peer assessment and the need for improved decision-making procedures in research funding, we highlight four key arguments for organizing a partial lottery: improved decision quality (more fair, transparent, and divers), an alternative to ranking-based selection, prevention of questionable research practices, and increased efficiency. Based on our observations from a lottery-based ‘seed money call’ at Tilburg University, we provide insights into the organization and implementation of a lottery procedure, including the criteria for proposal eligibility, the administrative process, and the weighting mechanism based on budget categories. We furthermore share six lessons we learned that might be useful for future implementation in other institutional contexts, emphasizing the need for transparency, external supervision, and pre-registration of the lottery procedure to ensure trust and minimize gaming. As such, we not only contribute to the ongoing conceptual discussion about improving funding allocation mechanisms in academia. Our article also offers an account of how partial lotteries could be put into practice.

Rational cross‐sections, bounded generation, and orders on groups

AbstractWe provide new examples of groups without rational cross‐sections (also called regular normal forms), using connections with bounded generation and rational orders on groups. Our examples contain a finitely presented HNN‐extension of the first Grigorchuk group. This last group is the first example of finitely presented group with solvable word problem and without rational cross‐sections. It is also not autostackable, and has no left‐regular complete rewriting system.

The HDI + ROPE decision rule is logically incoherent but we can fix it.

The Bayesian highest-density interval plus region of practical equivalence (HDI + ROPE) decision rule is an increasingly common approach to testing null parameter values. The decision procedure involves a comparison between a posterior highest-density interval (HDI) and a prespecified region of practical equivalence. One then accepts or rejects the null parameter value depending on the overlap (or lack thereof) between these intervals. Here, we demonstrate, both theoretically and through examples, that this procedure is logically incoherent. Because the HDI is not transformation invariant, the ultimate inferential decision depends on statistically arbitrary and scientifically irrelevant properties of the statistical model. The incoherence arises from a common confusion between probability density and probability proper. The HDI + ROPE procedure relies on characterizing posterior densities as opposed to being based directly on probability. We conclude with recommendations for alternative Bayesian testing procedures that do not exhibit this pathology and provide a "quick fix" in the form of quantile intervals. This article is the work of the authors and is reformatted from the original, which was published under a CC-BY Attribution 4.0 International license and is available at https://psyarxiv.com/5p2qt/. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information.

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

Sustainable and smart electric bus charging station deployment via hybrid spherical fuzzy BWM and MULTIMOORA framework

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain.

Foundations of reasoning with uncertainty via real-valued logics

Interest in logics with some notion of real-valued truths has existed since at least Boole and has been increasing in AI due to the emergence of neuro-symbolic approaches, though often their logical inference capabilities are characterized only qualitatively. We provide foundations for establishing the correctness and power of such systems. We introduce a rich class of multidimensional sentences, with a sound and complete axiomatization that can be parameterized to cover many real-valued logics, including all the common fuzzy logics, and extend these to weighted versions, and to the case where the truth values are probabilities. Our multidimensional sentences form a very rich class. Each of our multidimensional sentences describes a set of possible truth values for a collection of formulas of the real-valued logic, including which combinations of truth values are possible. Our completeness result is strong, in the sense that it allows us to derive exactly what information can be inferred about the combinations of truth values of a collection of formulas given information about the combinations of truth values of a finite number of other collections of formulas. We give a decision procedure based on linear programming for deciding, for certain real-valued logics and under certain natural assumptions, whether a set of our sentences logically implies another of our sentences. The generality of this work, compared to many previous works on special cases, may provide insights for both existing and new real-valued logics whose inference properties have never been characterized. This work may also provide insights into the reasoning capabilities of deep learning models.

Finite-dimensional perturbation of the Dirichlet boundary value problem for the biharmonic equation

Abstract The biharmonic equation is one of the important equations of mathematical physics, describing the behaviour of harmonic functions in higher-dimensional spaces. The main purpose of this study was to construct a finite-dimensional perturbation for the Dirichlet boundary value problem associated with the biharmonic equation. The methodological basis for this study was an integrated approach that includes mathematical analysis, algebraic methods, operator theory, and the theorem on the existence and uniqueness of a solution for a boundary value. The main tool is a finite-dimensional perturbation, which allows for examining the properties and behaviour of boundary value problems in as much detail as possible. In the study, descriptions of correctly solvable internal boundary value problems for a biharmonic equation in non-simply connected domains were considered in detail. The study is also devoted to the search for solutions and the analytical representation of resolvents of boundary value problems for a biharmonic equation in multi-connected domains. Within the framework of the study, theorems and their consequences were proved, and a finite-dimensional perturbation was constructed for the Dirichlet boundary value problem. Analytical representations of resolvents of boundary value problems for a biharmonic equation in multi-connected domains were also obtained. The examination of a finite-dimensional perturbation of the Dirichlet boundary value problem for a biharmonic equation has expanded the understanding of the properties of this equation in various contexts.

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

AI Transforms Ecommerce Management and Regulation in Uzbekistan

This study investigates the integration of artificial intelligence (AI) into Uzbekistan's e-commerce sector, aiming to enhance business management, develop new models, and increase governmental regulatory effectiveness. By employing mathematical models based on a knowledge base and product logic principles, the research predicts and facilitates alternative management decisions, improving market monitoring and administrative procedures. The findings reveal that AI significantly enhances decision-making quality and regulatory efficiency, suggesting its potential as a cornerstone for modern e-commerce management strategies. This contributes to the advancement of the digital economy and the improvement of business and consumer conditions in Uzbekistan. Highlights: AI Integration: Enhances decision-making and administrative procedures in Uzbekistan's e-commerce sector. Mathematical Models: Uses knowledge bases and product logic to improve market monitoring and management decisions. Economic Impact: Contributes to the development of the digital economy, benefiting businesses and consumers. Keywords: Public Administration, E-commerce, Digital Economy, Artificial Intelligence, Regulatory Efficiency

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers’ perspectives

BackgroundThe integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers’ perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.MethodsA structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.ResultsThree hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.ConclusionsThis study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

CONSUMER BEHAVIOUR AND DECISION MAKING IN HEALTHCARE PURCHASES

In the complicated healthcare environment of today, obtaining health insurance is essential to guaranteeing both financial stability and access to high-quality medical care. However, there are still many unknowns when it comes to consumer behaviors while choosing health insurance, which affects market dynamics and decision-making procedures. By combining knowledge from the fields of economics, psychology, marketing, and public policy with a thorough investigation of consumer behaviors in the healthcare insurance market, this study seeks to close these gaps. The research attempts to elucidate the elements influencing decision-making, the complexities of choosing insurance, and the implications for market dynamics, consumer welfare, and policy development through theoretical investigation, empirical analysis, and practical implications. Important conclusions highlight the need for focused marketing and governmental interventions by revealing the intricate interactions between social, marketing, and personal aspects in decision-making. The study emphasizes how crucial decision support tools and consumer education are to enhancing consumer comprehension and decision-making. Furthermore, it highlights how important it is to have regulatory frameworks that support competition, consumer welfare, and market efficiency that are based on a deeper understanding of consumer behaviour. Through the utilisation of these insights, interested parties can endeavor to establish a healthcare insurance market that is reasonably priced, easily accessible, and adaptable to the various requirements of people and communities, guaranteeing access to top-notch medical care while upholding financial stability. Key Words: health insurance, consumer behaviours, decision-making, market structure, policy formulation, safety of consumers, regulatory frameworks, affordability, access, quality care.

Impact of Sound Decision-Making on Small and Medium Businesses in Pakistan

This research aims to investigate the significance of sound decision-making in the success and sustainability of Small and Medium Businesses (SMBs). The study will concentrate on key constructs such as risk management, financial stability, innovation and adoption, and customer relationship management. The study uses a quantitative approach, surveying a sample of SMB owners and managers in Pakistan's different industries to gather data. In order to find out how respondents feel about decision-making processes and how they affect SMB performance, a structured questionnaire is used. Regression analysis is one of the statistical techniques used to examine the data and evaluate the hypotheses. The results demonstrate how important sound decision-making is to the resilience and success of SMBs in Pakistan. Financial stability stands out as a crucial component that impacts the performance of small and medium-sized businesses (SMBs). This is followed by efficient risk management, innovation and adoption, and customer relationship management. These results highlight how crucial strategic decision-making is for promoting SMB sustainability and growth in a fast-paced business climate. The results provide useful information to managers, owners, and regulators of SMBs who want to improve SMB decision-making procedures. Small and medium-sized businesses (SMBs) can enhance their competitive standing, boost operational effectiveness, and promote long-term growth and profitability by placing a high priority on financial stability, proactive risk management, innovation, and customer-centric methods

Spreading in graphs

Several concepts that model processes of spreading (of information, disease, objects, etc.) in graphs or networks have been studied. In many contexts, we assume that some vertices of a graph G are contaminated initially, before the process starts. By the q-forcing rule, a contaminated vertex having at most q uncontaminated neighbors enforces all the neighbors to become contaminated, while by the p-percolation rule, an uncontaminated vertex becomes contaminated if at least p of its neighbors are contaminated. If given a set S of initially contaminated vertices all vertices eventually become contaminated when continuously applying the q-forcing rule (respectively the p-percolation rule), S is called a q-forcing set (respectively, a p-percolating set) in G. In this paper, we consider sets S that are at the same time q-forcing sets and p-percolating sets, and call them (p,q)-spreading sets. Given positive integers p and q, the minimum cardinality of a (p,q)-spreading set in G is a (p,q)-spreading number, σ(p,q)(G), of G. While q-forcing sets have been studied in a dozen of papers, the decision version of the corresponding graph invariant has not been considered earlier, and we fill the gap by proving its NP-completeness. This, in turn, enables us to prove the NP-completeness of the decision version of the (p,q)-spreading number in graphs for an arbitrary choice of p and q. Again, for every p∈N and q∈N∪{∞}, we find a linear-time algorithm for determining the (p,q)-spreading number of a tree, where in the case p≥2 we apply Riedl’s algorithm from [Largest and smallest minimal percolating sets in trees, Electron. J. Combin. 19 (2012) Paper 64] on p-percolation in trees. In addition, we present a lower and an upper bound on the (p,q)-spreading number of a tree and characterize extremal families of trees. In the case of square grids, we combine some results of Bollobás from [The Art of Mathematics: Coffee Time in Memphis. Cambridge Univ. Press, New York, 2006], and the AIM Minimum Rank-Special Graphs Work Group from [Zero forcing sets and the minimum rank of graphs, Linear algebra Appl. 428 2008 1628–1648], and new results on (2,1)-spreading and (4,q)-spreading to obtain σ(p,q)(Pm□Pn) for all (p,q)∈(N∖{3})×(N∪{∞}) and all m,n∈N.

Enhancing Transparency and Trust in Turbofan Engine Classification: Leveraging Explainable AI

Implementing predictive maintenance is paramount in guaranteeing the dependability and efficiency of intricate industrial systems. Classification approaches have been extensively utilized to detect and anticipate potential malfunctions in acknowledged systems. Numerous machine learning algorithms exhibit effectiveness, yet they encounter interpretability issues, posing a challenge in comprehending the fundamental factors contributing to their predictions. This study investigates the utilization of machine learning algorithms for classification in the domain of predictive maintenance. This research focuses on applying Random Forest, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) algorithms for Turbofan Engine classification into multiple categories. We use Explainable Artificial Intelligence (XAI) methodologies to mitigate the interpretability challenges commonly associated with black-box models. We aim to utilize XAI algorithms on the most effective models to elucidate the essential characteristics and decision-making procedures underlying the classification outcomes. The findings of our experiment indicate that the Random Forest algorithm based on ensemble learning and the XGBoost algorithm based on gradient boosting exhibit superior classification accuracy. Additionally, the utilization of Explainable Artificial Intelligence (XAI) algorithms amplifies the transparency and interpretability of said models, thereby facilitating a more profound comprehension of the determinants that impact the classification results. The results of this study make a valuable contribution to predictive maintenance by demonstrating the efficacy of machine learning algorithms in multiclass classification and emphasizing the significance of explainable artificial intelligence (XAI) in furnishing practical insights for decision-making.

Was Bolsonaro’s 2018 electoral victory an institutional accident?

Presidential Elections are critical moments for polyarchical systems, particularly in contexts of high social tension. In this regard, the 2018 presidential election in Brazil, which used a two-round system, is a significant case study. Intriguingly, the most divisive candidates went to the second round. Was this an institutional accident? Pairwise and positional voting procedures embody different generalizations of a majoritarian credo that underpins such elections. The paper mobilizes both perspectives and, using representative survey data, reconstructs the top four preferences of the Brazilian electorate a week before the election. The analysis reveals that the electoral winner, Jair Messias Bolsonaro – despite being a divisive candidate – was a Condorcet Winner and a Borda Winner. Conversely, the second-round loser, Fernando Haddad, was a Condorcet Loser. Thus, Bolsonaro’s victory in 2018 was not an institutional accident. The paper also explores possible alternative scenarios under different feasible sets of candidates through simulations, contributing to a deeper understanding of the role of decision procedures in critical junctures.

Analysis of the Application and Potential of Renewable Energy in Landscape Architecture

The field of landscape architecture is constantly evolving to address sustainability and climate change. There is a rising chance to use these technology into landscape design as renewable energy sources become more prevalent. An effective technique for evaluating the possibility of incorporating renewable energy management into landscape architecture is currently required. As a result, decision-making procedures are now manual and subjective, requiring greater precision and consistency. Deep learning algorithms can be used to examine the possibilities for renewable energy management in landscape architecture, which would help to solve this problem. Deep learning is a branch of artificial intelligence that automatically extracts complicated relationships and patterns from data using multi-layer neural networks. With inputs like topography, solar radiation, and climate, the algorithm can determine where in a particular landscape renewable energy installations would be most effective.

Shared Decision-making in Breast Cancer Reconstructive Surgery: Experience in a Leading Hospital.

The paradigm of healthcare has evolved toward patient-centered approaches, where shared decision-making (SDM) plays a pivotal role. This study aimed to explore the implementation of SDM during breast cancer reconstruction consultations and assess its impact on patient satisfaction and the decision-making process as a whole. A total of 102 female patients undergoing breast reconstruction were included in a multidisciplinary breast pathology unit. A streamlined SDM model involving choice introduction, option description, and preference exploration was implemented. A validated Spanish version of the nine-item Shared Decision Making Questionnaire was used alongside a complementary questionnaire. Data analysis was carried out using electronic data capture software. The nine-item Shared Decision Making Questionnaire results indicate strong agreement in presenting various options and explaining their advantages and disadvantages. Patients were less confident about their participation in decision-making. The Complementary Shared Decision Making Questionnaire highlighted high satisfaction with interview times and language clarity but areas for improvement in consultation space and therapeutic choice participation. Integrating SDM into breast reconstruction consultations empowers patients in the decision-making process and enhances satisfaction. Decision aids prove effective in this context, facilitating patients' comprehension and reducing decisional conflict. There are areas for improvement within the SDM strategy, and they are detectable through scales. Although challenges in information transmission and patient involvement persist, adopting an SDM model has potential benefits that warrant further investigation.

Development and Validation of the Coronary Heart Disease Scale Among the System of Quality of Life Instruments for Chronic Diseases QLICD-CHD (V2.0) Based on Classical Test Theory and Generalizability Theory.

Coronary heart disease (CHD) is a common and frequent disease with a long and incurable course, and the quality of life of patients is severely reduced. This study was to develop and validate a quality of life scale for patients with CHD based on the Chinese context. The scale QLICD-CHD (V2.0) was developed based on the QLICD-CHD (V1.0), using a programmed decision procedures. Based on the data measuring QoL 3 times before and after treatments from 189 patients with CHD, the psychometric properties of the scale were evaluated with respect to validity, reliability and responsiveness employing correlation analysis, multi-trait scaling analysis, structural equation modeling, t-test and also G-study and D-study of generalizability theory analysis. The SF-36 scale was used as the criterion to evaluate the criterion-related validity. Paired t tests were conducted to evaluate the responsiveness on each domain/facet as well as the total of the scale, with Standardized Response Mean (SRM) being calculated. The QLICD-CHD (V2.0) has been developed with 42 items in 4 domains. The Cronbach's α of the general module, the specific module and the total scale were 0.91, 0.92 and 0.91 respectively. The overall score and the test-retest reliability coefficients in all domains are higher than 0.60, except for the specific module. Correlation and factor analysis confirmed good construct validity and criterion-related validity. After treatments, the overall score and score of all domains have statistically significant changes (P<0.01). The SRM of domain-level score ranges from 0.27 to 0.50. Generalizability Theory further confirm the reliability of the scale through more accurate variance component studies. The QLICD-CHD (V2.0) could be used as a useful instrument in assessing QoL for patients with CHD, with good psychometric properties.