Expert Decision Research Articles

Prediction of Mung Bean Production Using Machine Learning Algorithms

Agriculture is the backbone of the Ethiopian economy. It plays a significant role in the growth of the national economy. Among these, mung bean production takes the highest level of income for most smallholder farmers in all regions of Ethiopia who cultivate mung bean crops. Accordingly, this study aims to predict mung bean production using a machine learning algorithm. For this study, the datasets were collected from the Central Statistical Agency of Ethiopia database. A total of 10273 instances were used for the experiment. Python machine learning tool was used to conduct the experiment and build an optimal model. To achieve the objective of this study, different experiments were conducted using Random Forest, Gradient Boosting, and Xgboosting algorithms. In addition, the predictive performances of the classifiers are evaluated and compared using accuracy, precision, recall, F1-score, and confusion matrix. Experimental result shows that the Xgboosting classifiers algorithm achieves the best performance with 98.65% test accuracy and 99.8% train accuracy. As a result, the Xgboosting classifier was selected for implementing the model to predict mung bean production. The findings of this study show that the main determinant factors for mung bean production include Meher season, use of extension program, fertilizer used, and fertilizer type. Therefore, the outcome of this research is essential to support the decision-making of experts engaged in attention and take corrective measures on the factors affecting mung bean production.

The role of generative AI in navigating trade-offs in policy research design: balancing validity, rigour, and innovation

ABSTRACT This study systematically examines how generative AI can assist in policy research design by analysing methodological trade-offs. Using Claude Pro, we evaluated research designs in 164 articles from leading policy journals across six dimensions: internal, external, construct, and predictive validity, methodological rigour, and conceptual novelty. The analysis demonstrates that AI can effectively identify methodological trade-offs and suggest design improvements within resource constraints, though its utility varies by research type. The findings advance our understanding of AI’s role as a complementary tool in research design while highlighting the critical importance of human expertise in methodological decisions.

Assessing the factors of blockchain technology-enabled hospitals using an integrated interval-valued q-rung orthopair fuzzy decision-making model

In the current era, blockchain technology (BT) has emerged as a novel technique to maintain the operations of healthcare management systems. Assessment of blockchain technology (BT)-enabled hospitals can be considered as a multi-criteria decision making (MCDM) problem because of the existence of several criteria. The aim of this study is to develop a hybrid MCDM method for evaluating the factors of multi-criteria BT-enabled hospital selection problem under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). For this purpose, a weighted aggregated sum product assessment (WASPAS) model is presented with the combination of IVq-ROF interaction aggregation operators, the standard deviation (SD)-based model and pivot pairwise relative criteria importance assessment (PIPRECIA) tool called IV-q-ROF-SD-PIPRECIA-WASPAS model within the context of IVq-ROFSs. For this purpose, some new IVq-ROF interaction aggregation operators are developed with their desirable characteristics. Next, the standard deviation-based model and PIPRECIA model on IVq-ROFSs are proposed to obtain the final weight of criteria, whereas the rank-based formula is presented to determine the decision experts’ weights with IVq-ROF information. The presented IV-q-ROF-SD-PIPRECIA-WASPAS model is applied on a case study of BT-enabled hospitals assessment, which confirms its applicability and usefulness. Sensitivity analysis and comparative discussion have been performed to reveal the consistency, robustness and efficiency of the presented model. The BT-enabled hospital-II with highest UD (0.4453) has emerged as the best choice among a set of BT-enabled hospitals. The factor "flexibilty" with highest weight (0.0898) value followed that the scalability (0.0809), transaction speed and accountability with same weight (0.0779) value, and network availability with weight (0.0771) for BT-enabled hospitals assessment. The final results conclude that the developed methodology can provide more accurate decisions while considering multiple indicators and input uncertainties.

Fermatean fuzzy score function and distance measure based group decision making framework for household waste recycling plant location selection

The household waste (HW) disposal and recycling have become a significant challenge due to increasing quantities of generated household wastes and increased levels of urbanization. Selecting locations/sites for building new HW recycling plant comprises numerous sustainability dimensions, thus, this work aims to develop new decision-making model for evaluating and prioritizing the HW recycling plant locations. This paper is categorized into three phases. First, we propose new improved score function to compare the Fermatean fuzzy numbers. Moreover, an example is presented to validate the effectiveness of proposed score function over the extant ones. Second, we introduce new distance measure to estimate the discrimination degree between Fermatean fuzzy sets (FFSs) and further discuss its advantages over the prior developed Fermatean fuzzy distance measures. Third, we introduce an integrated methodology by combining the method with the removal effects of criteria (MEREC), the stepwise weight assessment ratio analysis (SWARA) and the measurement alternatives and the ranking according to compromise solution (MARCOS) approaches with Fermatean fuzzy (FF) information, and named as the “FF-MEREC-SWARA-MARCOS” framework. In this method, the FF-distance measure is used to find the weights of involved decision-making experts. Moreover, an integrated criteria weighting method is presented with the combination of MEREC and SWARA models under the context of FFSs, while the combined FF-MEREC-SWARA-MARCOS model is applied to evaluate and prioritize the locations for HW recycling plant development, which illustrates its feasibility of the developed framework. Comparative study and sensitivity assessment are conducted to validate the obtained outcomes. This work provides a hybrid decision analysis approach, which marks a significant impact to the HW recycling plant location selection process with uncertain information.

New therapies for hormone-positive HER2-negative metastatic breast cancer with AKT signaling alterations: the Expert Panel Decision

On May 20, 2024, the Expert Panel discussed key issues of the diagnosis of alterations of the AKT signaling pathway, the effectiveness and safety of targeted therapy for hormone-positive (HR+) HER2-negative metastatic breast cancer (BC) concerning the emergence of a new selective AKT inhibitor capivasertib. The results of the CAPItello-291 study are presented. The experts concluded that a new effective treatment line has become available for patients with hormone-resistant advanced hormone-positive HER2-negative BC with genetic alterations in the AKT signaling pathway and relapse on previous adjuvant endocrine therapy with aromatase inhibitors or within the first year after the therapy or with progression during previous therapy with aromatase inhibitors for metastatic BC. Modern genetic testing methods for the alterations in the AKT signaling pathway can confirm the presence of this key biological target in the tumor for tailoring effective targeted therapy.

Fermatean fuzzy distance and Sugeno–Weber operators-based SPC-MARCOS approach for sustainable supplier evaluation in the healthcare supply chain

The present work proposes a new decision support tool for assessing the sustainable suppliers in the healthcare supply chain. For this purpose, the classical Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model is integrated with the Sugeno–Weber weighted averaging operators, modified symmetry point of criterion (SPC) model, rank sum (RS) tool and Fermatean fuzzy sets (FFSs), and named as the ‘FF-SPC-RS-MARCOS’ framework. The developed model firstly determines the decision experts’ weights through RS model. Second, novel Sugeno–Weber weighted operators are introduced to combine the experts’ opinions. Third, a unified weighting procedure is presented based on the combination of modified SPC approach for objective weight and RS method for subjective weight of attributes. To this aim, a novel distance measure is introduced for FFSs and further applied to compute the distance between aggregated Fermatean fuzzy numbers and symmetry point value of an attribute in the modified SPC approach. Further, a hybrid FF-SPC-RS-MARCOS approach is proposed to tackle the decision-making problems on FFSs setting. To elucidate the efficacy of the developed method, it is applied to a case study of sustainable supplier selection problem in the healthcare supply chain. The paper further conducts sensitivity investigation and comparison with existent approaches to test the stability and robustness of the ranking outcomes. This study shows how the proposed MARCOS method in combination with SPC and RS models can be used to prioritize the alternative suppliers in the healthcare supply chain. The introduced work provides a new methodology, which can help the practitioners and academics to evaluate suppliers with uncertain information and can also be employed to other areas facing similar types of decision-making problems.

Enhancing Expert Decision-Making for Wastewater Treatment Plants with Seidel Laplacian Energy and Cosine Similarity Measure in Intuitionistic Fuzzy Graphs

Wastewater treatment facilities’ main goal is to protect the public and environment from the hazardous and poisonous materials found in wastewater. Water treatment facilities were developed to speed up the natural process of cleansing water. A novel cosine similarity measure across intuitionistic fuzzy graphs has been proven to be more effective than certain present ones in group decision-making issues using example verification. This paper provides a unique approach for calculating expert-certified, well-known scores by finding the ambiguous information of intuitionistic fuzzy preference relations as well as the regular cosine similarity grades from one separable intuitionistic fuzzy preference relation to another. The new technique considers both "objective" and "subjective" information provided by experts. Using intuitionistic fuzzy preference relations, we provide workable techniques for judging experts’ eligible reputational ratings. This can be used to raise or decrease the relevance of the stated criteria in an evaluation that takes into account several competing elements. We give a solution to a decisional problem by using two effective methods: the newly constructed cosine similarity measure and the Seidel Laplacian energy (SLe+) of an intuitionistic fuzzy graph. Finally, two working procedures and circumstances are offered to show the effectiveness and superiority of the proposed techniques.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

Enhancing gas drainage and ventilation efficiency in underground coal mines: A hybrid expert decision approach for booster fan prioritization

Expanding mining operations in goaf zones heightens gas production potential, posing challenges in maintaining adequate ventilation within development panels, consequently impacting coal production. Various strategies have been explored to enhance mine ventilation and gas drainage effectiveness. However, deficiencies persist in the proposed ventilation system for the Okaba underground coal mine, prompting this study’s necessity. Addressing these concerns, the study evaluates the feasibility of employing booster fans to mitigate the identified drawbacks. Prioritizing booster fans for airflow distribution in underground mines is a complex decision-making process, requiring an advanced expert system approach. To address this, the study proposes an intuitionistic-based fuzzy TOPSIS (IFT) method for booster fan prioritization in the Okaba mine. Results indicate that booster fan 4 (BF4) ranks highest, followed by booster fan 3 (BF3), consistent with fuzzy TOPSIS findings. Sensitivity analysis supports the predicted importance order, affirming the efficacy of the hybrid expert decision method in selecting a booster fan capable of enhancing the overall efficiency of gas drainage and ventilation systems in underground mines. This study introduces a Hybrid Expert Decision Approach that integrates IFT and traditional fuzzy TOPSIS methodologies. This hybrid approach is particularly novel because it combines the strengths of both methods to prioritize booster fans in underground coal mines.

Expert opinion aggregation-based decision support for human-robot collaboration digital twin maturity assessment

Human-centered smart manufacturing is an essential direction for the future development of manufacturing. Safe and reliable smart human-robot collaboration is the foundation for realizing human-centered smart manufacturing. Digital twin-based human-robot collaboration has been proposed as a new manufacturing paradigm to devise collaborative strategies, simulate collaborative processes, and ensure worker safety. Establishing a maturity model is essential to accurately assess the capabilities of the constructed human-robot collaboration digital twin. This paper aims to contribute to the formalization and standardization of the human-robot collaboration digital twin. It constructs a novel assessment framework for the overall maturity measurement of existing digital twin-based human-robot collaboration projects. The developed human-robot collaboration digital twin maturity model includes 5 evaluation dimensions and 24 evaluation factors. Additionally, 5 maturity levels and their definitions are defined for each evaluation factor for maturity scoring. The expert opinion aggregation approach is proposed to quantify the evaluation factor metrics and ultimately to obtain a maturity level for the human-robot collaboration digital twin. The effectiveness and feasibility of the proposed method are verified through a collaborative assembly case study. This paper provides a generic method for assessing the competency level of human-robot collaboration digital twins, which can provide insights into the maturity of digital twins for practitioners in the human-robot collaboration field to develop targeted strategies for optimizing and upgrading human-robot collaboration digital twins.

An applied cognitive task analysis investigation of professional judgement and decision-making in a small group of high-level, UK-based caving instructors

ABSTRACT The professional judgement and decision-making processes of adventure sports coaches’ leadership practice is a growing area of research. However, the PJDM and underpinning epistemology of caving instructors has recived limited research focus. The decision-making practice of professionals working in complex and constrained environments has significant transferability to domains such as rescue and emergency services. Consequently, this paper investigates the leadership practice of a purposive sample of 3 UK based caving instructors, utilising an Applied Cognitive Task Analysis to access expert cognition. The study aimed specifically to examine the decision-making processes of this sub-group of outdoor professionals who work in difficult, multifaceted and demanding environments. Results supported and built upon previous work in adventure sports, specifically in confirming the complexity of judgement and decision-making underground. In particular, decision-making creates high cognitive loads because of environmental factors inherent in extended caving journeys. Loads are assuaged by the application of heuristics and planning, but naturalistic decision-making processes remain numerous and multifaceted. Findings have implications for National Governing Body instructor training programme design, with particular reference to the development of decision-making expertise in complex environments.

RANKING ALTERNATIVE ROUTES FOR PEDESTRIAN WALKWAYS FOR A FACTORY BY APPLYING ANALYTICAL HIERARCHY PROCESS

The Analytical Hierarchy Process (AHP) is one of the Multi Criteria Decision-making (MCDM) methods developed by Thomas Lorie Saaty. It can be defined as the theory of measurement through pairwise comparisons, relying on expert judgments of priority scales. This study aims to compare the alternative pedestrian routes within the production area of a manufacturing company located in Manisa, Türkiye, utilizing AHP. AHP is selected as a suitable tool for this case study, since it allows inclusion of tangible and intangible criteria and consideration of sub-criteria as part of the decision-making process. Three alternative pedestrian routes were evaluated by applying the AHP method to define the best route enabling access for pedestrians to the targeted area in the manufacturing building. The decision criteria were defined by expert Decision Makers of the company and are in line with criteria, such as safety, cost effectiveness and time, utilized broadly for layout selection related studies in manufacturing as per the conducted literature review. The best alternative route was determined as Route 1, and the main criteria priorities were ranked as Criteria 1- “Operational Health and Safety (OHS) Risk Assessment,” Criteria 4 “Restricted access to the material warehouse,” Criteria 2 “Investment Feasibility” and Criteria 3 “Walking Time.” As a conclusion, AHP is effectively applied to prioritize the tangible and intangible criteria and select most suitable pedestrian routes within a manufacturing facility.

Multi-agent long-distance end-to-end indoor navigation: using imitation learning pre-training and global map

Abstract To address the navigation challenges of mobile robot swarm coordination in complex environments, this paper presents a distributed navigation approach for mobile robot swarm. The study initially formulates the problem of robot swarm navigation as a partially observable Markov decision process, employing a distributed proximal policy optimization algorithm to train decision models mapping observations to actions directly. Furthermore, it adopts imitation learning pre-training to maximize expert decisions in the initial training phase, thereby reducing exploration space. Lastly, an improved path point generation algorithm along with a spatiotemporal reachability calculation method is proposed to guide the Deep Reinforcement Learning policy in accomplishing long-distance indoor navigation. Comprehensive performance evaluations are conducted in simulated environments to compare the proposed method with existing approaches. Results demonstrate that the proposed approach effectively enhances both convergence speed and model performance, while significantly improving the navigation capabilities of robot swarms in complex environments.

FROM DISCRETION TO EXPERT JUDGEMENT: RECASTING SEDIMENTED CONCEPTS IN ADMINISTRATIVE LAW

Abstract This article re-examines the traditional account of administrative decision-making under wide conferrals of statutory power. The received wisdom in such cases is that public officials exercise “discretion”, usually defined as freedom of choice. Based on a doctrinal study of the English planning system and related case law, this paper contends that the notion of discretion as choice obscures one of the defining characteristics of modern government. That is, the making of public decisions tackling practical problems with intelligent and expert judgement under legal standards set out in legislation and further developed by the courts. More widely, the paper discusses the foundational role of tacit knowledge and decision-making expertise in public administration.

Evaluating Large Language Models for Material Selection

Abstract Material selection is a crucial step in conceptual design due to its significant impact on the functionality, aesthetics, manufacturability, and sustainability impact of the final product. This study investigates the use of Large Language Models (LLMs) for material selection in the product design process and compares the performance of LLMs against expert choices for various design scenarios. By collecting a dataset of expert material preferences, the study provides a basis for evaluating how well LLMs can align with expert recommendations through prompt engineering and hyperparameter tuning. The divergence between LLM and expert recommendations is measured across different model configurations, prompt strategies, and temperature settings. This approach allows for a detailed analysis of factors influencing the LLMs' effectiveness in recommending materials. The results from this study highlight two failure modes, and identify parallel prompting as a useful prompt-engineering method when using LLMs for material selection. The findings further suggest that, while LLMs can provide valuable assistance, their recommendations often vary significantly from those of human experts. This discrepancy underscores the need for further research into how LLMs can be better tailored to replicate expert decision-making in material selection. This work contributes to the growing body of knowledge on how LLMs can be integrated into the design process, offering insights into their current limitations and potential for future improvements.

Financial Sustainability of Social Insurance: The Case of Korean Workers’ Compensation Insurance and Possibility of Triple Moral Hazard

Abstract This study examines the economic stability of Korean Workers’ Compensation Insurance (WCI), highlighting the system’s vulnerability to triple moral hazard. Korean WCI features more generous worker compensation, lower employer premiums, and more active government involvement in accident prevention in addition to a higher industrial accident rate, compared to the system in Germany and Japan. The negotiation process of the WCI among workers, employers, and insurance managers often prioritizes short-term benefits, undermining long-term financial health. Workers seek greater compensation, employers push for lower premiums, and insurance managers may prioritize collective interests, exacerbating the system’s economic instability. Over recent years, applications for occupational disease recognition have surged, and premium rates have decreased, indicating increasing strain. To mitigate these issues, the study proposes enhancing transparency and rationality, involving non-political experts in decision-making, and clearly defining the government’s role in managing WCI. These measures aim to balance stakeholder interests and ensure the WCI system’s sustainability for future generations.

From Bench to Bedside With Large Language Models: AJR Expert Panel Narrative Review.

Large language models (LLMs) hold immense potential to revolutionize radiology. However, their integration into practice requires careful consideration. Artificial intelligence (AI) chatbots and general-purpose LLMs have potential pitfalls related to privacy, transparency, and accuracy, limiting their current clinical readiness. Thus, LLM-based tools must be optimized for radiology practice to overcome these limitations. Although research and validation for radiology applications remain in their infancy, commercial products incorporating LLMs are becoming available alongside promises of transforming practice. To help radiologists navigate this landscape, this AJR Expert Panel Narrative Review provides a multidimensional perspective on LLMs, encompassing considerations from bench (development and optimization) to bedside (use in practice). At present, LLMs are not autonomous entities that can replace expert decision-making, and radiologists remain responsible for the content of their reports. Patient-facing tools, particularly medical AI chatbots, require additional guardrails to ensure safety and prevent misuse. Still, if responsibly implemented, LLMs are well-positioned to transform efficiency and quality in radiology. Radiologists must be well-informed and proactively involved in guiding the implementation of LLMs in practice to mitigate risks and maximize benefits to patient care.

Development of an Expert System for Diagnosing Musculoskeletal Disease

Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.

Analýza judikatury Nejvyššího správního soudu týkající se požadavků na odůvodnění opatření podle tzv. pandemického zákona

The article reflects the requirements for the justification of extraordinary measures adopted pursuant to Act No. 94/2021 Sb. (the so-called Pandemic Act), which result from the case law of the Supreme Administrative Court of the Czech Republic. In addition to the requirements for the justification of extraordinary measures, a detailed analysis of all decisions of the Supreme Administrative Court concerning the relevant topic identifies the most important specific errors in the justification of extraordinary measures, as well as the limits of the justification of extraordinary measures, i.e. exceptions to the obligation to justify a specific legal regulation. The identified requirements and limits of the justification of extraordinary measures point to a balanced approach of the Supreme Administrative Court, which does not intend to substitute its decisions for the expert decisions of the primary decision-maker in emergency situations, but at the same time does not give up its role as a body reviewing general measures, even in a situation of uncertainty and changing conditions that accompanied the COVID-19 pandemic. On the basis of the requirements, limitations and specific errors identified in the justification of extraordinary measures adopted under the Pandemic Act, the article draws more general conclusions concerning the justification of acts of a public authority adopted during states of emergency, even outside the legislative framework of the Pandemic Act.

Enhanced EDAS methodology for multiple-criteria group decision analysis utilizing linguistic q-rung orthopair fuzzy hamacher aggregation operators

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