Decision-making Process Research Articles

Pharmacological and ethical comparisons of lung cancer medicine accessibility in Australia and New Zealand

Gaps in funded cancer medicines between New Zealand and Australia can have significant implications for patients and their families. Pharmac, the New Zealand pharmaceutical funding agency, has been criticised for...

Clinical cases referring to the 2023 EACTS/STS guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ.

Clinical cases referring to the EACTS/STS Guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ aim to assist physicians in selecting the best management strategies for individual patients with a given condition. These expert opinions consider the impact on patient outcomes as well as the risk-benefit ratio of different diagnostic or therapeutic methods. These cases serve as a vital tool to aid physicians in making decisions in their daily practice. However, in essence, although these recommendations serve as a valuable resource to guide clinical practice, their application should be tailored to the needs of the individual patient. Each patient's case is unique, presenting its own set of variables and circumstances. This editorial is a tool designed to support, but not supersede, the decision-making process of physicians, based on their knowledge, expertise and understanding of their patients' individual situations. Furthermore, these clinical cases are based on the EACTS/STS Guidelines for diagnosing and treating acute and chronic syndromes of the aortic organ but should not be interpreted as legally binding documents. The legal responsibilities of healthcare professionals remain firmly grounded in applicable laws and regulations, and the guidelines and the clinical cases presented in this document do not alter these obligations.

Decision as ritual: How power structure affects collective tourism destination decision-making

The optimal destination decision made by a group is the one that perfectly balances group utility maximization with individual satisfaction. The power structure of the group and personal interactions underpinning group decision-making will directly determine individuals’ evaluations of and emotions generated by these decisions. However, research has not sufficiently revealed this pattern, which limits our understanding of how optimal group decisions can be made. By integrating the power perspective with the theory of interactive ritual chains, this study applies the experiential focus group observation method to track the decision-making process. The analysis produces six models of that process, which show how different power structures and leadership influence the joint focus of attention, shared mood and behavioral co-presence in reaching a decision. In summary, leader democracy and effective equality can create a rewarding situation, influencer dominance creates a paradoxical situation, while, multiple leadership, ineffective equality and leader dominance lead to a ritualistic punishment situation.

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method's detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

Inclusive Innovation Governance for Just Transitions: Insights from the Bean Agri-Food System in the Brunca Region of Costa Rica

This study explores how an inclusive governance of “niche” innovations with family farmers enables or limits the realization of just transitions in family farming at different scales, from the local to the national levels. The research focuses on a case study in the Brunca region of Costa Rica, which has pioneered innovations for bean research with family farmers. The primary source of information for the study was in-depth interviews with relevant stakeholders. Secondary information comes from a desk review of the relevant policies and strategic documents from the relevant authorities. Lastly, a semi-scoping review was conducted to explore gender and intergenerational gaps in plant breeding, as well as potential conflicts between plant breeding and on-farm agro-diversity. The inclusive governance of bean research contributes to the realization of just-transition dimensions of justice, such as recognitional, procedural, and distributional justice, as a result of farmers’ engagement in participatory plant-breeding processes, and due to the coordination between different institutions at the local and national levels. Progress has been made in terms of the recognition and participation of farmers in research and in decision-making locally. The findings show interactions between the local and national scales, in terms of the commercialization of farmers’ seeds. However, the Costa Rican program for bean research struggles to engage family farmers in national decision-making processes and falls short of addressing equality and intergenerational issues. It also faces challenges in reaching a balance between short-term income generation and long-term conservation of agro-biodiversity on the farm. Fostering just transitions in family farming would require a greater inclusion of women and youth by the Costa Rican bean system. This implies allocating resources to increase their capacities and participation in bean research, as well as in decision-making at all levels. This is imperative and could be achieved if the government and relevant stakeholders align their strategic objectives and priorities to shorten gender and intergenerational gaps within the bean production system.

Exploring dietary supplement consumption behaviors and determinants among the urban Thai elderly

The consumption of dietary supplements among the elderly is on the rise. Despite the potential benefits, a comprehensive understanding of the decision-making processes leading to the consumption is lacking. This study explores the conditions influencing the decision-making and behavioral patterns of older adults related to dietary supplement consumption. Using a qualitative approach, in-depth interviews were conducted with 21 elderly participants from a seniors’ club in Bangkok, Thailand, who had consistently consumed dietary supplements for at least one year. The behavior was classified into five primary categories: enduring use of identical dietary supplements, insufficient regard for health compatibility, replacing medications with supplements, not verifying before consumption, and opting for supplements over medical treatments. These patterns are aligned with the core constructs of the Theory of Planned Behavior (attitude, subjective norm, and perceived behavioral control). Many individuals perceive supplements as pivotal health investments, while others view them as a direct route to robust health. Trusted advice from friends and television significantly influence their choices, with a prevailing sentiment that dietary supplements are generally safe. The high price tag on supplements is often associated with superior quality. The findings highlight the multifaceted nature of dietary supplement consumption decisions among the Thai elderly, suggesting the need for interventions to promote safer and more informed choices.

Utility-Driven End-to-End Network Slicing for Diverse IoT Users in MEC: A Multi-Agent Deep Reinforcement Learning Approach

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor–critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C.

Influence of Big Data Analytics on Decision-Making Processes in Financial Firms in South Africa

Purpose: The aim of the study was to assess the influence of big data analytics on decision-making processes in financial firms in South Africa. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: By harnessing vast volumes of structured and unstructured data, these firms can derive valuable insights into market trends, customer behavior, and risk assessment with unprecedented accuracy and speed. This capability enables more informed and data-driven decision-making across various facets of financial operations, including investment strategies, risk management, and customer relationship management. Moreover, big data analytics facilitates the identification of previously hidden patterns and correlations within financial data, enhancing predictive modeling and forecasting capabilities. This proactive approach allows firms to anticipate market shifts, optimize portfolio performance, and mitigate potential risks effectively. Additionally, the adoption of advanced analytics techniques, such as machine learning and artificial intelligence, further enhances decision-making by automating processes and refining predictive accuracy. Implications to Theory, Practice and Policy: Diffusion of innovation theory, technology acceptance model and resource-based view may be used to anchor future studies on assessing influence of big data analytics on decision-making processes in financial firms in South Africa. In practice, financial firms should establish robust data governance frameworks to ensure data accuracy, privacy, and security. Policymakers should develop and enforce regulatory frameworks that support the ethical and effective use of BDA in financial firms.

Agent-based power management in apartment buildings: Tenant preferences and distributed energy resources

Apartment buildings (APBs) consist of multiple residential spaces and common facilities, unlike single-family buildings, and have correspondingly diverse energy demands; however, household preferences and behaviors have received little attention so far. Therefore, bidirectional flows between households and the APB must be considered to manage the APB energy demands by considering dynamic relationships built by households with preferences and behaviors. This study presents a demand-side management system called the unified home energy management system (U-HEMS), which considers distributed energy resources (DERs) for the APB. The U-HEMS was developed by bidirectional flows of management and decision-making processes for households and the APB. In the management process from the household to the APB, the APB power demands were optimized using a two-stage system, whereas in the decision-making process from the APB to the households, the next actions expressed by weighting factors of the household side were determined for the management process based on an agent-based model. The functionality and performance of the U-HEMS were compared with those of conventional approaches for a 100-unit APB, and five case studies were conducted to analyze their power demands considering different aspects: decision-making process, combinations of households’ preferences, reward system, penetration of electric vehicles, and DER capacities. The results reveal that the U-HEMS effectively reduces the overall costs and total peak power demand because a bidirectional model considers the dynamic relationships between households in the APB and DER operations subject to the time-varying pricing policy. Further studies are needed to assess seasonal effects on APB demands.

Optimization of BIPV Utilization with Parametric Approach: Case Study on Nearly Zero Carbon Building Studio Design in Medan, Indonesia

Buildings significantly consume fossil energy, leading to substantial carbon emissions contributing to global warming and rising sea levels. These environmental impacts pose serious challenges, necessitating a shift towards sustainable building designs. By integrating clean energy solutions into building architecture, we can reduce the environmental burden of emissions. This approach mitigates adverse effects on living organisms and ecosystems and promotes long-term sustainability and resilience in urban development. Building-Integrated Photovoltaic (BIPV) systems represent one such approach for substituting fossil energy. However, implementing BIPV effectively necessitates comprehensive analysis considering numerous parameters and variables. This paper introduces building design strategies employing the BIPV approach. The decision-making process in designing alternatives utilizes parametric methods, recognized for their ability to yield optimization results for various variables through the development of design algorithms. The study focuses on the design of a studio building at the University of North Sumatra. Comparative analysis of the results obtained through parametric design aims to identify the most favorable design outcomes. The research includes developing a parameterized design algorithm, workflow, and assessment results for selecting optimal design alternatives. The results indicate that the placement of PV panels on the side area (PVOS) has more potential than placement on the top area (PVOT). Despite the PVOT area being observed to be 0.57% smaller than PVOS, PVOS shows a significant increase in total Incident Radiation (IR), average IR, and total Energy Generated by 137.14%, 128.40%, and 132.97%, respectively.

A priory allocation of ecosystem services to forest stands in a forest management context considering scientific suitability, stakeholder engagement and sustainability concept with multi-criteria decision analysis (MCDA) technique: A case study in Turkey

Various research endeavours are designed to identify ecosystem services, assess their spatial distribution, and prioritize them in a given forest landscape. The Turkish State Forest Organization has introduced an ecosystem-based multiple-use forest management philosophy since 2008, which emphasizes the need for identifying and allocating ecosystem services to each forest planning unit. This paper aims to investigate the use of Multiple Criteria Decision Analysis (MCDA) techniques and explores their effectiveness and suitability in identifying and allocating ecosystem services to forest units, considering scientific suitability, stakeholder engagement and the sustainability concept in the context of ecosystem-based forest management decision-making processes in a case study area of Turkey. We propose a framework that entails an iterative process comprising various stages, starting from identifying ecosystem services (ES) to allocating them to forest stands with a participatory approach. We employed the Analytical Hierarchy Process (AHP) and the Delphi method to determine stakeholder preferences and allocate ecosystem services to forest stands. This was achieved through an equation newly developed using scientific suitability, stakeholder preferences, and the sustainability concept. The landscape percentage allocated primarily to ES was as follows: water regulation (55.44%), soil protection (16.47%), biodiversity conservation (14.03%), wood production (13.08%), and aesthetic-recreation (0.84%). Notably, no allocations were made for national defence and climate regulation services. In conclusion, the stratification of Posof forests into zones was efficiently achieved a priori, considering both scientific-technical and socio-cultural criteria through MCDA techniques based on stakeholder preferences. This study streamlines the decision-making process involved in spatially allocating ecosystem services and provides crucial information instrumental in determining management objectives and optimal forest activities.

Deep Learning-Based System Combining Chest X-Ray and Computerized Tomography Images for COVID-19 Diagnosis.

Aims/Background: The coronavirus disease 2019 (COVID-19) pandemic has highlighted the need for accurate and efficient diagnostic methods. This study aims to improve COVID-19 detection by integrating chest X-ray (CXR) and computerized tomography (CT) images using deep learning techniques, further improving diagnostic accuracy by using a combined imaging approach. Methods: The study used two publicly accessible databases, COVID-19 Questionnaires for Understanding the Exposure (COVID-QU-Ex) and Integrated Clinical and Translational Cancer Foundation (iCTCF), containing CXR and CT images, respectively. The proposed system employed convolutional neural networks (CNNs) for classification, specifically EfficientNet and ResNet architectures. The data underwent preprocessing steps, including image resizing, Gaussian noise addition, and data augmentation. The dataset was divided into training, validation, and test sets. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for model interpretability. Results: The EfficientNet-based models outperformed the ResNet-based models across all metrics. The highest accuracy achieved was 99.44% for CXR images and 99.81% for CT images with EfficientNetB5. The models also demonstrated high precision, recall, and F1 scores. For statistical significance, the p-values were less than 0.05, indicating that the results are significant. Conclusion: Integrating CXR and CT images using deep learning significantly improves the accuracy of COVID-19 diagnosis. The EfficientNet-based models, with their superior feature extraction capabilities, show better performance than ResNet models. Grad-CAM Visualizations provide insights into the model's decision-making process, potentially reducing diagnostic errors and accelerating diagnosis processes. This approach can improve patient care and support healthcare systems in managing the pandemic more effectively.

Advanced Decision- Making Framework for Sustainable Energy Retrofit of Existing Commercial Office Buildings

In the background of rising global initiatives to combat climate change and promote better energy management, retrofitting of current structures has become a major strategy. This abstract and literature review aims at presenting a full review of a decision-making approach that can be used in choosing sustainable options for energy retrofitting. This particular framework is designed to help guide retrofit decision-making due to the many issues that surround the process by providing an economic as well as an environmental and social context. Energy retrofitting means improving the existing structure or complex to decrease its energy intensity and have a negative impact on the environment. As stated earlier, it is a critical process for attaining sustainability goals and advancing the performance of buildings. But the decision on which retrofit measures should be implemented can be rather difficult because of the availability of a vast number of technologies and because more factors must be considered, such as cost and energy savings. The decision-making framework provided in this article aims at categorizing retrofit selection into this list of features and views the current article as a way of making the process more efficient. It encompasses the life-cycle cost analysis (LCCA), the economic valuation, and the multi-criteria decision analysis tools and criteria. These tools facilitate the evaluation of financial viability, the advantages of climatically retrofitting, and the environmental effects of distinct retrofitting choices. The above-mentioned framework is exemplified with a case study of an office building retrofit project. The following case includes an example of the application of the mentioned framework and illustrates the explanation of how it helps to make decisions. Due to the consideration of costs, energy, and environmental performance, the framework enables the stakeholders to choose appropriate retrofit measures. Other than the case study, the framework also focuses on the need to establish a sustainable energy retrofit DSS. Thus, the DSS supplements collected data on building performance, retrofit technologies, and economic aspects, which can be accessed by the stakeholders in real-time and with built-in facilities for scenario analysis. This system improves decision-making since retrofit results can be monitored and evaluated frequently. Both tools and strategies for the decision-making of sustainable energy retrofits bring a logical and systematic approach aimed at neutralizing the decision-making challenges related to retrofit selection. By applying economic, environmental, and social factors in the decision-making process, the framework assists the stakeholders in reaching energy efficiency and sustainability goals. The development of another strong decision-support system also improves the working of the framework and checks that retrofit projects are done efficiently and are strategic to the general goal of sustainable development.

The Effect of Public Safety Policies on Crime Reduction in France

Purpose: To aim of the study was to analyze the effect of public safety policies on crime reduction in France. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Leadership in Indian organizations plays a pivotal role in enhancing organizational learning and knowledge management by promoting knowledge sharing, continuous learning, and innovation. Effective leaders facilitate the retention of critical knowledge and improve decision-making processes, thereby driving competitive advantage and organizational success. This leadership approach fosters a culture of continuous improvement and adaptability, essential for thriving in dynamic business environments. Unique Contribution to Theory, Practice and Policy: Routine activity theory, social disorganization theory & broken windows theory may be used to anchor future studies on the effect of public safety policies on crime reduction in France. Enhance training programs for police officers to include comprehensive modules on community engagement, cultural competency, and communication skills. Encourage the adoption of diverse public safety policies tailored to the unique needs of different communities. Policymakers should prioritize flexibility and adaptability in policy design, allowing for the integration of multiple strategies based on local crime dynamics and community feedback.

Impact of Data Governance on Data Quality in Healthcare Institutions

Purpose: The aim of the study was to assess the impact of data governance on data quality in healthcare institutions. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that institutions with robust data governance protocols experience fewer instances of data inconsistency and errors. This, in turn, improves the reliability of clinical decision-making processes and patient outcomes. Moreover, compliant data governance practices contribute to better data integration across various healthcare systems and departments, facilitating comprehensive patient care and longitudinal health monitoring. By promoting transparency and accountability in data management, healthcare institutions can mitigate risks associated with data breaches and ensure compliance with regulatory standards, thereby fostering trust among patients and stakeholders alike. Implications to Theory, Practice and Policy: Control theory, information theory and agency theory may be used to anchor future studies on assessing the impact of data governance on data quality in healthcare institutions. Healthcare institutions should prioritize the adoption of comprehensive data governance frameworks that integrate policy development, stakeholder engagement, and technological infrastructure. Policymakers should align regulatory frameworks with evolving data governance standards to promote interoperability and data exchange across healthcare networks.

Enhancing agricultural land valuation in land consolidation projects through cooperative game theory and genetic algorithm optimization

The purpose of land valuation in land consolidation projects is to determine the relative value between lands of different quality, location and etc. This principle is crucial for ensuring fairness and justice among participants. Efficient land utilization and rural progress are hindered by the fragmentation of agricultural lands, necessitating land consolidation. However, fair valuation of parcels remains a challenge, impeding the success of consolidation efforts. This study delves into the intricate issues related to land consolidation and complex dynamics of decision-making in agricultural land valuation. This study aims to address the challenge of fair valuation in land consolidation projects by introducing a novel methodology that integrates cooperative game theory with metaheuristic algorithms. The goal is to increase the likelihood of stakeholders accepting land consolidation schemes by utilizing the interactive components of the decision-making process. This approach takes into account individual preferences of land owners and utilizes game theory to model competition, interaction, and cooperation among them. Central to this concept are genetic algorithm, which plays a key role in producing diverse solutions taking into account various landowners' priorities. A case study in Fartan village, Esfarayen city, Iran, is conducted to evaluate the effectiveness of the method. Two scenarios are examined: one combining unanimity fallback bargaining technique with Nash bargaining, and another incorporating fallback bargaining with impasse technique with Nash bargaining. Upon comparative analysis, the second scenario proves to be better in ensuring equity among stakeholders. It encourages landowners to negotiate decisively, using a process that integrates fallback bargaining with impasse and Nash bargaining, taking into account the varying levels of each owner's negotiation power in both steps. This ensures a negotiated outcome that respects the diverse priorities of each landowner. This scenario considers variations in individual negotiating power not only in Nash bargaining step but also in fallback bargaining and ensures a comprehensive and fair decision-making process during land valuation. This research introduces a novel strategy to enhance group decision-making in agricultural land valuation, fostering stakeholders' agreement and facilitating successful land consolidation projects. The proposed method provides a land valuation approach that can receive high degree of farmers' agreement and provides a good base to enter the next stages of the land consolidation.

Selective State DOT Lane Width Standards and Guidelines to Reduce Speeds and Improve Safety

This research investigates the lane width standards and guidelines implemented by various State Departments of Transportation (DOTs) to reduce vehicle speeds and enhance road safety. Lane width reduction is often perceived as a strategy to mitigate speed and improve safety. Still, its effectiveness and implications vary across different contexts, including regions, urban/rural settings, or other geometric design features. Drawing from interviews with five State DOTs and a review of their road design manuals, this study aims to identify suggested lane widths depending on the contexts, design exception process when narrowing or widening lane widths, and introduce representative before/after studies. The findings indicate that State DOTs tend to have lower recommended lane widths in urban areas than in rural areas. Moreover, lane width standards among these states vary due to several factors, including the geographical location of roadways (urban or rural areas), design or posted speeds, traffic volume, road classification, and geometric road design features. Design exceptions are required if the existing or proposed design element is incompatible with both AASHTO and department governing criteria. In conclusion, the findings will provide valuable insights and recommendations for policymakers, transportation planners, and road engineers to inform optimal lane width and decision-making processes.

Innovating the bank-firm relationship: a spherical fuzzy approach to SME funding

PurposeInnovation in financing processes, enabled by the advent of new technologies, has supported the development of alternative finance funding tools. In this context, the study analyses the growing importance of alternative finance instruments (such as equity crowdfunding, peer-to-peer (P2P) lending, venture capital, and others) in addressing the small and medioum enterprises' (SMEs) financing needs beyond traditional bank and market-based funding channels. By providing more flexible terms and faster approval times, these instruments are gradually reshaping the traditional bank-firm relationship.Design/methodology/approachTo comprehensively understand this innovation shift in funding processes, the study employs a novel approach that merges three MCDA methods: Spherical Fuzzy Entropy, ARAS and TOPSIS. These methodologies allow for handling ambiguity and subjectivity in financial decision-making processes, examining the effects of multiple criteria, including interest rate, flexibility, accessibility, support, riskiness, and approval time, on the appeal of various financial alternatives.FindingsThe study’s results have significant theoretical and practical implications, supporting SMEs in carefully evaluate financing alternatives and enables banks to better identify the main “competitors” according to the “financial need” of the firm. Moreover, the rise of alternative finance, notably P2P lending, indicates a shift towards more efficient capital access, suggesting banks must innovate their funding channels to remain competitive, especially in offering flexible solutions for restructuring and high-risk scenarios.Practical implicationsThe study advises top management that SMEs prefer traditional loans for their reliability and accessibility, necessitating banks to enhance transparency, innovate, and adopt digital solutions to meet evolving financing needs and improve customer satisfaction.Originality/valueThe study introduces a novel integration of Spherical Fuzzy TOPSIS, Entropy, and ARAS methodologies to face the complexities of financial decision-making for SME financing, addressing ambiguity and multiple criteria like interest rates, flexibility, and riskiness. It emphasizes the importance of traditional loans, the rising significance of alternative financing such as P2P lending, and the necessity for banks to innovate, thereby enriching the literature on bank-firm relationships and SME funding strategies.

Reasonableness of court decisions in the paradigm of psychological, logical and doctrinal analysis

The article is devoted to the study of the concept of reasoning of court decisions through psychological, logical and doctrinal aspects. Reasonableness of court decisions is a multifaceted concept, which is influenced by the interaction of these three key aspects. Studying the characteristics of a court decision as a special type of legal document through the prism of interdisciplinary dialogue improves understanding of the processes underlying judicial thinking and the formalization of its outcome. Cognitive biases, emotional state and social pressure influence the decision-making process. The article analyzes certain psychological theories and concepts that emphasize the influence of psychological factors on the motivation of judicial decisions. The author emphasizes the importance of logical consistency in legal reasoning. The author examines the common logical techniques, used in rendering court decisions and their reasoning, and also identifies the importance of consistent and rational judgments in law enforcement. In addition, the author examines how established legal doctrines affect the characteristics of a court decision, including its motivation, and how deviations from these doctrines may affect public perception of court decisions. Understanding and taking into account psychological, logical and doctrinal aspects in the process of making a court decision is important for increasing the level of motivation of court decisions, and as a result, ensuring the right to a fair trial and building trust in the legal system in general

Knowledge representation to support EMDAP implementation in advanced reactor licensing applications

The Data, Information, Knowledge, and Wisdom (DIKW) pyramid depicts the general principle of a decision-making process. Various levels of abstraction are required to formalize the available data/information/knowledge with respect to the decision being supported. The Evaluation Model Development and Assessment Process (EMDAP) was developed by the United States Nuclear Regulatory Commission (US NRC) to guide the development and assessment of Evaluation Models (EMs) for transient and accident analysis of nuclear power plants under design basis accidents. It concerns the decision of adequacy of an EM with respect to the purpose of analysis (NPP application). However, lack of data, scaling issues, modeling limitations, computation, and experimental overhead are some of the key factors that challenge the EM adequacy decision in EMDAP. Phenomena Identification and Ranking Table (PIRT) which is used for complexity resolution can also introduce noise due to bias and variance in expert opinion and judgment. The uncertainty in the decision due to these issues and challenges can be minimized by enhancing the EMDAP from the DIKW perspective. In this work, we formulate a systematic scheme for knowledge representation, and classification and characterization of evidence to enhance clarity and transparency in the EMDAP implementation. The proposed scheme is governed by the concept of the value of information, where we contextualize, classify, characterize, and evaluate the evidence (relevant data/knowledge/information related to EM development, verification, validation, and uncertainty quantification) with respect to the relevant decision attributes or elements of EMDAP. We make use of the Predictive Capability Maturity Model (PCMM), Argumentation technique, and process quality assurance for knowledge representation to support EMDAP implementation in advanced reactor licensing applications. The main objective of the proposed scheme is to support decision-making by making it more transparent, traceable, and accountable. The illustration of the proposed scheme is provided using a system simulation code (SAS4A/SASSYS-1) as the primary EM and a generic sodium fast reactor under loss of flow accident as the target reactor application.EMDAP involves different elements that require expert input and insights. However, the process of expert elicitation may require significant resources and time. To address these issues, we identify generative artificial intelligence models as potential tool which (after systematic testing and capabilities analysis) can be used for domain specific expert elicitation and information extraction for efficient implementation of EMDAP.