Decision Uncertainty Research Articles

Application of Fuzzy Discount Factors in Behavioural Decision-Making for Financial Market Modelling

This paper presents an innovative approach to financial market modelling by integrating fuzzy discount factors into the decision-making process, thereby reflecting the complexities of human behaviour. Traditional financial models often fail to account for market dynamics’ psychological factors. The proposed method utilizes fuzzy logic to encapsulate the uncertainty and subjective judgment inherent in financial decisions. By representing financial variables as fuzzy numbers, the model better simulates the way humans assess information and make decisions under uncertainty. The incorporation of fuzzy discount factors marks a significant shift from deterministic to a more realistic representation of financial markets, suitable for practical application. This methodology offers a nuanced investment strategy that balances theoretical rigour with real-world applicability, appealing to a broad spectrum of investors. The aim of the following paper is to introduce an alternative to price modelling with the use of fuzzy return rates, which results in some errors in the mathematical model. The solution has the form of introducing fuzzy discount factors (FDFs) that retain the advantages of the fuzzy approach (e.g., encompassing subjectivity and imprecision) while preserving the shape of the fuzzy number modelling a price.

Effective connectivity predicts distributed neural coding of perceptual decision confidence, uncertainty, and speed

Abstract Decision making is often accompanied by a level of confidence regarding the accuracy of one’s decision. Previous studies have indicated neural activity associated with perceptual decision confidence during sensory stimulus presentation. Choice-based reaction time (RT) has been suggested as an indirect but more objective measure of decision confidence—generally faster RT for higher confidence. However, it is unclear whether choice confidence and RT are mediated by distinct neural pathways, and whether their neural correlates are encoded nonlinearly. Within a perceptual decision-making task, we applied functional magnetic resonance imaging-informed electroencephalography-based effective connectivity analysis via dynamic causal modelling (DCM) on event-related potentials and found the frontoparietal network for fast-vs-slow RT condition to be different from that of high-vs-low confidence rating condition. Furthermore, trial-by-trial DCM analysis predicted cortical layer-based, distributed, and nonlinear coding of RT, confidence or uncertainty. Collectively, our study suggests that decision confidence and speed are instantiated by different dynamical networks distributed across cortical layers.

Identification of off-site construction disputes: technical, managerial and external dispute causes

PurposeWhile off-site construction (OSC) offers a promising solution to many problems plaguing traditional construction (e.g. low productivity, waste and safety risks), a lack of standards and knowledge about OSC, especially regarding disputes, hinders its wider adoption. This study aims to address this gap by identifying and analyzing the importance levels of technical, managerial and external disputes specific to OSC projects.Design/methodology/approachThree steps methodology was employed in the study. Focus group discussion (FGD) technique was used to identification and finalize dispute causes found from literature and collect data for the next step. In the study, two multi-criteria decision-making MCDM (methods) [a hybrid approach-Pythagorean fuzzy analytic hierarchy process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)] were employed. While Pythagorean fuzzy AHP was used to calculate the weights of criteria, fuzzy TOPSIS analysis was used to calculate the weights of main and sub-dispute causes. Instead of using the classical AHP method, the Pythagorean fuzzy AHP method was employed due to its superiority in capturing the inherent uncertainty and ambiguity of decision-makers, giving flexibility to decision-makers with linguistic variables instead of expecting exact evaluation scores and flexibility in the integration with other methods. During the analysis of the weights of the main and sub-dispute causes, the fuzzy TOPSIS method was preferred. The fuzzy TOPSIS method involves a quicker and more straightforward decision-making process. Also, the fuzzy TOPSIS method allows the consideration of numerous alternatives and evaluation criteria and uncertainty in the decision-making process.FindingsThe analysis reveals that technical disputes pose the biggest challenge in off-site construction compared to managerial or external disputes. Specifically, “late completion, delivery and installation of components” emerged as the most significant technical dispute. Within managerial disputes, “poor planning and management of the project” ranked highest, while “the complexity of legal expressions” was the most prominent external dispute factor.Originality/valueIn the literature, three studies offer some insight on OSC disputes by analyzing the litigation cases. Nonetheless, the results can be misleading, because some disputes can be resolved before the litigation process. Therefore, the study findings can aid in foreseeing technical, managerial and external factors and in generating robust OSC contracts by considering these issues.

A meta-heuristic approach to estimate and explain classifier uncertainty

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model’s recommendations. However, existing approaches for quantifying decision-making uncertainty are not model-agnostic, or they rely on complex mathematical derivations that are not easily understood by laypersons or end-users, making them less useful for explaining the model’s decision-making process. This work proposes a set of class-independent meta-heuristics that can characterise the complexity of an instance in terms of factors that are mutually relevant to both human and ML decision-making. The measures are integrated into a meta-learning framework that estimates the risk of misclassification. The proposed framework outperformed predicted probabilities and entropy-based methods of identifying instances at risk of being misclassified. Furthermore, the proposed approach resulted in uncertainty estimates that proves more independent of model accuracy and calibration than existing approaches. The proposed measures and framework demonstrate promise for improving model development for more complex instances and provides a new means of model abstention and explanation.

Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set

In the financial industry, financial fraud is an ever-evolving risk with extreme consequences. Data mining has been instrumental in the recognition of credit card fraud (CCF) during online transactions. CCF recognition, which is a data mining problem, become a challenge owing to its two main reasons - firstly, the profiles of fraudulent and normal behaviors modify continually and then, CCF dataset is extremely lopsided. The implementation of fraud recognition in credit card transactions is tremendously influenced by the sampling methodology on data, detection approach and variable selection utilized. The conception of the neutrosophic hypersoft set (NHSS) is a parameterized family that handles the sub-attributes of the parameter and is an appropriate extension of the NHSS to correctly evaluate the uncertainty, deficiencies, and anxiety in decision-making. In comparison to previous research, NHSS can accommodate additional uncertainty, which is the crucial approach to describe fuzzy datasets in the decision-making algorithm. This study introduces an Automated Credit Card Risk Assessment using Fuzzy Parameterized Neutrosophic Hypersoft Expert Set (ACCRA-FPNHES) technique. In the ACCRA-FPNHES technique, a three-step process is involved. As a primary step, the ACCRA-FPNHES technique designs sparrow search algorithm (SSA) for choosing features. In the second step, the detection of CCF takes place using FPNHES technique. Finally, in the third step, the parameters related to the FPNHES technique can be adjusted by arithmetic optimization algorithm (AOA). The simulation validation of the ACCRA-FPNHES technique can be studied on credit card dataset. The obtained values indicate that the ACCRA-FPNHES technique showcases better performance

Pedestrian decision-making uncertainty in urgent scenarios modulates multi-level, neural hierarchies

Pedestrian decision-making uncertainty in urgent scenarios modulates multi-level, neural hierarchies

Enhancing technical proposal evaluation in consultant selection in Department of Water Resources and Irrigation, Nepal: a fuzzy analytic hierarchy process and fuzzy TOPSIS

In today's dynamic construction landscape, where challenges like complexity, costs, and uncertainties loom large, the selection of consultants emerges as a pivotal factor for project triumph. Among the array of methods available, Quality and Cost Based Selection (QCBS) stands out for its holistic approach, considering both technical prowess and financial considerations. Yet, amidst its structured framework, the evaluation of technical proposals within QCBS often grapples with subjectivity, despite the presence of well-defined criteria. This study boldly tackles this challenge head-on by delineating common criteria and sub-criteria for consultant selection within the esteemed Department of Water Resources and Irrigation (DWRI). Leveraging the innovative Fuzzy Analytic Hierarchy Process (AHP), our endeavor is to ascertain the relative significance of these criteria and sub-criteria. Through the fusion of Fuzzy AHP and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS), our study aspires to revolutionize consultant ranking methodologies. The ingenious application of Fuzzy AHP empowers us to derive independent weights for criteria and sub-criteria, thereby embracing the inherent ambiguity and nuances of qualitative factors. Subsequently, employing Fuzzy TOPSIS, we aim to systematically rank consultant alternatives based on their Closeness Coefficients, heralding a paradigm shift in evaluation paradigms. Through this groundbreaking hybrid approach, our study endeavors to elevate the consultant selection process, ensuring an impeccable alignment of chosen consultants with project requisites and benchmarks. By dispelling the shadows of subjective decision-making uncertainties, our research holds the promise of ushering in a new era of excellence in the realm of construction projects under the aegis of the Department of Water Resources and Irrigation.

Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent.

Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized. We compare iSTM-produced estimates of decision uncertainty and VOI to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. We use numerical simulations to illustrate our findings and explore the impact of relaxing normality assumptions or having >2 decision alternatives. iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias approaches 0 as number of microsimulated individuals approaches infinity). Decision uncertainty depends on 1 tail of the INMB distribution (e.g., P[INMB <0]), which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated since this increases INMB variance. iSTMs are useful for probabilistic economic evaluations. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results. Individual-level state-transition microsimulation models (iSTMs) produce biased but consistent estimates of the probability that interventions are cost-effective.iSTMs also produce biased but consistent estimates of the expected value of perfect information.The biases in these decision uncertainty and value-of-information measures are not reduced by more parameter sets being sampled from their population-level uncertainty distribution but rather by more individuals being microsimulated for each parameter set sampled.Analysts using iSTMs to quantify decision uncertainty and value of information should account for these biases when allocating their computational budgets and, at minimum, characterize such bias in their reported results.

Perbandingan Akurasi Metode Fuzzy Mamdani dan Fuzzy Sugeno dalam Memprediksi Kebutuhan Darah PMI Kota Medan

Blood demand prediction is important to avoid the instability of blood stocks or supplies. Too much and abundant blood stock can cause losses, because the blood will be wasted when it passes the storage expiration date. Meanwhile, too little stock can risk a shortage of blood supply. To anticipate this, it is necessary to predict the amount of blood demand that must be received in the future in order to achieve optimum blood availability. Fuzzy Mamdani and Fuzzy Sugeno methods that use the concept of fuzzy logic are considered capable of handling complexity and uncertainty in decision making, so they can be used to predict the amount of blood demand by considering the varying and uncertain amounts of blood demand and supply. The calculation results show that the Fuzzy Mamdani method has better accuracy than the Fuzzy Sugeno method with MAPE values of 16.707% and 17.987% respectively. The Fuzzy Mamdani method has an accuracy level in the good category according to the MAPE method and can be used to predict blood demand at PMI Medan.

Understanding Uncertainty in Market-Mediated Responses to US Oilseed Biodiesel Demand: Sensitivity of ILUC Emission Estimates to GLOBIOM Parametric Uncertainty.

The life cycle greenhouse gas (GHG) emissions of biofuels depend on uncertain estimates of induced land use change (ILUC) and subsequent emissions from carbon stock changes. Demand for oilseed-based biofuels is associated with particularly complex market and supply chain dynamics, which must be considered. Using the global partial equilibrium model GLOBIOM, this study explores the uncertainty in market-mediated impacts and ILUC-related emissions from increasing demand for soybean biodiesel in the United States in the period 2020-2050. A one-at-a-time (OAT) analysis and a Monte Carlo (MC) analysis are performed to assess the sensitivity of modeled ILUC-GHG emissions intensities (gCO2e/MJ) to varying key economic and biophysical model parameters. Additionally, the influence of the approach on the simulation of future ILUC effects is explored using two alternative ILUC-GHG metrics: a comparative-static approach for 2030 and a recursive-dynamic approach using model outputs through 2050. We find that projected ILUC-GHG values largely vary based on which vegetable oils replace diverted soybean oil, market responses to coproducts, and the carbon content of land converted for agricultural use. These are all, in turn, subject to decision uncertainty through the choice of the modeling approach and the time horizon considered for each ILUC-GHG metric. Given the longer simulation period, ILUC-GHG emission uncertainty ranges increase under the recursive-dynamic approach (42.4 ± 25.9 gCO2e/MJ) compared to the comparative-static approach (40.8 ± 20.5 gCO2e/MJ). The combination of MC analysis with other techniques such as Bayesian Additive Regression Trees (BART) is powerful for understanding model behavior and clarifying the sensitivity of market responses, ILUC, and associated GHG emissions to specific model parameters when simulated with global economic models. The BART reveals that biophysical parameters generate more linear ILUC-GHG responses to changes in assumed parameter values while changes in economic parameters lead to more nonlinear ILUC-GHG results as multiple effects at the interplay of food, feed, and fuel uses overlap. The choice of the recursive-dynamic metric allows capturing the longer-term evolution of ILUC while generating additional uncertainties derived from the baseline definition.

Decision-making in feminizing genital gender-affirming surgery – the effect of the introduction of the GenderAid decision-aid

Introduction Transgender individuals considering feminizing genital gender-affirming surgery (gGAS) face multiple options and potential decisional conflict (DC). Shared decision-making (SDM) between patients and healthcare professionals (HCPs) can support personalized care and improve outcomes. To aid trans women during the decision-making process for gGAS, a decision aid, the GenderAid (GA), was developed. Methods This study aimed to investigate the effect of the GA on SDM and DC levels during the decision-making process for feminizing gGAS. An observational cross-sectional mixed methods study was conducted at the departments of Psychology and Plastic Surgery at the Center of Expertise on Gender Dysphoria of the Amsterdam UMC between 2020 and 2023. Questionnaires administered before and after implementing the GA included the validated translations of the SDM-Q-doc and SDM-Q-9 and the Decisional Conflict Scale. Qualitative data from interviews explored trans women’s perceptions of the concepts of SDM and DC when deciding on feminizing gGAS. Results Significantly higher levels of SDM experienced by trans women were observed after the introduction of the GA, while no such difference was observed for HCPs. The GA facilitated more thorough discussions with HCPs, and trans women felt well prepared for consultations by the decision-aid. DC was low before and after introduction of the GA; however, both quantitative as qualitative data revealed trans women experienced uncertainties during the process, due to new information on (non-)surgical options for gGAS. Discussion This study suggests that the GA enhanced SDM in individuals seeking feminizing gGAS and had limited impact on DC by offering extensive information on all (non-)surgical options. However, the improved information overview regarding surgical options also resulted in increased decision uncertainty among trans women. Future controlled research should further explore optimizing information provision throughout the care process, managing uncertainties, and implementing the use of decision-aids by HCPs.

Streamflow simulation at different temporal scales under rating curve uncertainty conditions using machine learning models

ABSTRACT Reducing uncertainty in streamflow simulation is vital for effective water resource management. The impact of uncertainty in model calibration data (discharge), commonly derived from the rating curve, is often overlooked. This study applies the Monte Carlo simulation technique (MCST) to assess uncertainty in the rating curve. Advanced machine learning (ML) models, bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent units (BiGRUs) were used comparatively to evaluate the propagation of this uncertainty onto streamflow simulation on both daily and monthly temporal scales. Different sets of streamflow data, derived from the fitted curve and its lower and upper uncertainty bands, were utilized to train ML models independently. The results show the substantial impact of rating curve uncertainty in streamflow simulations, with the BiGRU model surpassing the BiLSTM model on both scales. As a result, the uncertainty in the rating curve results in an uncertainty of the streamflow of up to 30 and 25% on daily and monthly simulations, respectively. These findings underscore the importance of considering rating curve uncertainty in streamflow simulation to ensure accurate and reliable results. Therefore, streamflow should be treated as an uncertain variable and managed by incorporating rating curve uncertainty in decision-making.

An online driving decision aid for older drivers reduces ambivalence and regret about driving decisions: Randomized trial.

Decisions about driving cessation can be stressful for older adults. We tested effects of a driving decision aid (DDA) on psychosocial outcomes among older drivers during two-year follow-up. Multisite randomized controlled trial of licensed drivers ages ≥70 with at least one diagnosis associated with increased likelihood of driving cessation, without significant cognitive impairment. The intervention was the online Healthwise® DDA, addressing "Is it time to stop driving?"; controls received National Institute on Aging web-based information for older drivers. Outcomes were assessed at baseline, 6, 12, 18, and 24 months. Primary outcomes were the Decision Regret Scale, Decisional Conflict Scale (assessing decisional ambivalence or uncertainty), and PROMIS Depression (4a) Scale. Self-reported Life-Space Assessment (assessing community mobility), crashes and driving outcomes were also assessed. Using intention-to-treat analyses, we tested whether DDA (vs. control) effects on each outcome differed during follow-up using a study group by time interaction. Longitudinal outcomes were modeled using generalized linear mixed models, accounting for repeated measures, age, site, and baseline visit before vs. during COVID. We enrolled 301 participants (age at enrollment: mean 77.1 (range 70-92) years; 51% identifying as female). During follow-up, the DDA group had less decisional conflict (pinteraction = 0.010) and decision regret (pinteraction = 0.012). The DDA had its greatest effect on decisional conflict immediately post-intervention (adjusted mean ratio [aMR] = 0.87; 95%CI: 0.79, 0.97) and on decision regret at 12-month follow-up (aMR = 0.45; 95%CI: 0.27, 0.72). Odds of depression were similar between groups during follow-up (pinteraction = 0.237). The intervention did not negatively affect life space, crashes, or other driving outcomes. In older drivers, the Healthwise® DDA reduced uncertainty and regret about driving decisions during longitudinal follow-up, without adversely affecting community mobility or crash risk. Use of DDAs in clinical and other settings may reduce the distress older adults often experience when making decisions about driving cessation.

Early Warning Panel

Artificial intelligence methods are normally used to make predictions, classify objectives, group and optimize human work and minimize uncertainty for decision making, but despite the advantages they offer, they are not entirely friendly or accessible to the majority of people. That is why we use an artificial intelligence model, the LSTM model, it is used to predict time series and can be extended to any endemic disease for which there is enough data for deep learning of an artificial intelligence model, which which will allow us to draw up plans to combat and confront this disease in our country.Paraguay is a country that epidemiologically is considered endemic for some arboviruses, we can mention: Dengue with cases since 2009, Chikungunya since 2013 and Zika since 2015.The epidemiological indicators provide us with information on the epidemiological situation of Dengue in Paragua, the indicators that we collect through a bibliographic review and that found in our Early Warning Panel were identified through a bibliographic review, the search engine used was Google Scholar, the inclusion criteria were: articles that mention epidemiological indicators and the way in which they are calculated. Finally, the epidemiological indicators used were: Incidence, bitting rate, probability of infection from mosquito to human, probability of infection from human to mosquito.For the implementation of the prediction in our Early Warning Panel, the LSTM model was defined, pre-training. It is one of the most advanced and successful deep learning architectures for time series prediction. For the evaluation of our Panel, a survey was developed for potential users in order to evaluate the usability, relevance and performance of the system. Experts in epidemiology and control of infectious diseases, with experience in managing dengue cases, were selected. A representative sample was sought that included epidemiologists, doctors specializing in tropical diseases, data analysts, and public health officials. With the potenticial users was evaluated: Usability, Relevance and Performance.The results of this evaluation revealed good acceptance by the experts, who highlighted the clarity and usefulness of the information presented. These findings confirm the effectiveness and relevance of the epidemiological panel as a useful tool for the surveillance and control of dengue.The panel is available at: https://alertatemprana.vercel.app

PD144 Uncertainty In Precision Medicine: The Value Of Research To Support The Value Of Personalized Intervention Policies

IntroductionOne-size-fits-all policies are not always optimal. Stratified decision-making is only possible when the characteristics used to define subgroups can be identified for stratum-specific predictions of outcomes. Conditioning decisions on characteristics that are not readily known may require information (diagnostic, prognostic, predictive) to be derived, the value of which needs to be assessed to support personalized strategies.MethodsA general framework was developed to show how personalized policies can be accountably informed by characterizing uncertainty, heterogeneity, and bias in evidence. In the framework, observed heterogeneity was disentangled from random variability by conditioning the value of the model input parameters on a set of prognostic or predictive variables, while unobserved heterogeneity was quantified as the systematic variability that cannot be explained given current information. Value of information analysis was used to quantify the value of additional information for resolving decision uncertainty in model input parameters and to identify individual- or subgroup-level attributes that contribute to the degree of heterogeneity.ResultsDecision-making based on average cost effectiveness fails to account for the role that sources of outcome variability play in guiding nuanced decision-making. Conditioning on a set of known covariates to reflect observable heterogeneity may be extended to conditioning on the latent random variable for unobservable covariates to quantify unobservable heterogeneity. Quantifying the potential value of research to inform subgroup- or individual-level attributes may be used to direct further research toward the attributes expected to be of most interest because they drive the value of individualized decisions—the expected value of sample information for attributes.ConclusionsTwo distinct, but interrelated concepts for assessing the value of stratified decision-making are important: (i) the value of heterogeneity; and (ii) the value of further research to inform both heterogeneous factors and to reduce decision uncertainty in precision medicine. Assessing the value of unexplained heterogeneity and bias can be central to supporting the value of personalized intervention strategies in health technology assessment.

A literature review of artificial intelligence (AI) for medical image segmentation: from AI and explainable AI to trustworthy AI.

Medical image segmentation is a vital aspect of medical image processing, allowing healthcare professionals to conduct precise and comprehensive lesion analyses. Traditional segmentation methods are often labor intensive and influenced by the subjectivity of individual physicians. The advent of artificial intelligence (AI) has transformed this field by reducing the workload of physicians, and improving the accuracy and efficiency of disease diagnosis. However, conventional AI techniques are not without challenges. Issues such as inexplicability, uncontrollable decision-making processes, and unpredictability can lead to confusion and uncertainty in clinical decision-making. This review explores the evolution of AI in medical image segmentation, focusing on the development and impact of explainable AI (XAI) and trustworthy AI (TAI). This review synthesizes existing literature on traditional segmentation methods, AI-based approaches, and the transition from conventional AI to XAI and TAI. The review highlights the key principles and advancements in XAI that aim to address the shortcomings of conventional AI by enhancing transparency and interpretability. It further examines how TAI builds on XAI to improve the reliability, safety, and accountability of AI systems in medical image segmentation. XAI has emerged as a solution to the limitations of conventional AI by providing greater transparency and interpretability, allowing healthcare professionals to better understand and trust AI-driven decisions. However, XAI itself faces challenges, including those related to safety, robustness, and value alignment. TAI has been developed to overcome these challenges, offering a more reliable framework for AI applications in medical image segmentation. By integrating the principles of XAI with enhanced safety and dependability, TAI addresses the critical need for TAI systems in clinical settings. TAI presents a promising future for medical image segmentation, combining the benefits of AI with improved reliability and safety. Thus, TAI is a more viable and dependable option for healthcare applications, and could ultimately lead to better clinical outcomes for patients, and advance the field of medical image processing.

EE507 Deriving Decision Uncertainty Associated With Calibration Target Data: A Value of Information Approach

EE507 Deriving Decision Uncertainty Associated With Calibration Target Data: A Value of Information Approach

Fuzzy Quadripartitioned Neutrosophic Matrices in Strategy Finding

Researchers have primarily proposed fuzzy oriented matrix theories have to model uncertainty for real-world decision- making (DM). Fuzzy neutrosophic soft matrix (FNSM) theory has been developed recently by combining the fuzzy neutrosophic set and soft set to handle the indeterminate information parametrically. The concept of fuzzy Quadripartitioned neutrosophic soft matrices are discussed. Some properties of trace, Determinant of a Fuzzy Quadripartitioned Neutrosophic Soft Matrix with some examples were discussed. An application of FQNSMS for Dimensionality reduction in data mining were discussed with an algorithm.

An integrated outranking technique with spherical fuzzy rough numbers for the treatment of cadmium-contaminated water problem in China

The Chinese economy is one of the largest and most dynamic economies in the world. Over the past few decades, China has experienced rapid economic growth from agrarian to industrial powerhouse fueled by manufacturing, exports, and services. However, this rapid growth has also brought about challenges, including environmental issues like water contamination. The indulgence of cadmium metal in regular used water can cause serious health issues, including kidney damage and cancer. Many strategies have been implemented for treatment of water contamination. The main focus of this research is to introduce a novel methodology for treatment of cadmium contaminated water problem in China. This study seeks to demonstrate the multi-criteria group decision-making ability based on the outranking relations within the confines of a contemporary, well-organized and extremely flexible model of spherical fuzzy rough numbers. Spherical fuzzy rough numbers, amalgamation of rough numbers with traditional spherical fuzzy numbers, make the use of membership, non-membership and neutral membership degrees along with the manipulation of the subjectivity and reliance on objective uncertainties. The combination of spherical fuzzy rough numbers with an outranking multi-criteria group decision making technique, Elimination and Choice Expressing Reality, integrates spherical fuzzy logic to handle uncertainty and imprecision in multi-criteria decision-making. This approach captures degrees of uncertainty and hesitancy with spherical fuzzy numbers, improving the handling of imprecise information. The working mechanism involves generation of outranking relations among alternatives by comparing predominant and subdominant options, calculating score degrees, concordance and discordance sets, and incorporating subjective spherical fuzzy rough criteria weights. Unlike traditional methods that use crisp or conventional fuzzy numbers, this technique provides a more reliable and flexible evaluation by integrating rough set theory for better handling of imprecision and uncertainty. Finally, an outranking graph is drawn that points from the supreme option to inferior one. The legitimacy of the proposed technique is, then, testified by making its comparison with other existing techniques.

Health Economic Evaluation of Antimicrobial Stewardship, Procalcitonin Testing, and Rapid Blood Culture Identification in Sepsis Care: A 90-Day Model-Based, Cost-Utility Analysis.

We evaluated the cost-effectiveness of a bundled intervention including an antimicrobial stewardship program (ASP), procalcitonin (PCT) testing, and rapid blood culture identification (BCID), compared with pre-implementation standard care in critically ill adult patients with sepsis. We conducted a decision tree model-based cost-effectiveness analysis alongside a previously published pre- and post-implementation quality improvement study. We adopted a public Canadian healthcare payer's perspective. Two intensive care units in Alberta with 727 adult critically ill patients were included. Our bundled intervention was compared with pre-implementation standard care. We collected healthcare resource use and estimated unit costs in 2022 Canadian dollars (CAD) over a time horizon from study entry to hospital discharge or death. We calculated the incremental net monetary benefit (iNMB) of the intervention group compared with the pre-intervention group. The primary outcome was cost per sepsis case. Secondary outcomes included readmission rates, Clostridioides difficile infections, mortality, and lengths of stay. Uncertainty was investigated using cost-effectiveness acceptability curves, cost-effectiveness plane scatterplots, and sensitivity analyses. Mean (standard deviation [SD]) cost per index hospital admission was CAD $83,251 ($107,926) for patients in the intervention group and CAD $87,044 ($104,406) for the pre-intervention group, though the difference ($3,793 [$7,897]) was not statistically significant. Costs were higher in the pre-intervention group for antibiotics, readmissions, and C. difficile infections. The intervention group had a lower mean expected cost; $110,580 ($108,917) compared with pre-intervention ($125,745 [$113,210]), with a difference of $15,165 ($8278). There were no statistically significant differences in quality adjusted life years (QALYs) between groups. The iNMB of the intervention group compared with pre-intervention was greater than $15,000 for willingness-to-pay (WTP) per QALY values of between $0 and $100,000. In our sensitivity analysis, the intervention was most likely to be cost-effective in roughly 56% of simulations at all WTP thresholds. Our bundled intervention of ASP, PCT, and BCID among adult critically ill patients with sepsis was potentially cost-effective, but with substantial decision uncertainty.