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Journal of Analytical Uncertainty
Published by Yayasan Winaya Inspirasi Nusantara
ISSN : 31246818     EISSN : 31246818     DOI : https://doi.org/10.63924/jau
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The Journal of Analytical Uncertainty (JAU) is an international, peer-reviewed, multidisciplinary journal devoted to advancing theoretical, computational, and applied research applied research on randomness and uncertainty in decision-making. The journal provides a platform for researchers, academicians, and practitioners working in the diverse domains of mathematics, statistics, and fuzzy sciences to exchange innovative ideas and findings addressing the complexities of uncertain, imprecise, and vague information. The JAU aims to promote the integration of analytical modeling, statistical inference, and uncertainty quantification for enhanced decision-making across various fields including economics, finance, engineering, healthcare, environmental studies, and social sciences. The journal welcomes contributions that develop new methods, propose models, or present applications in which uncertainty plays a critical role in decision processes.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 12 Documents
 1   2 
AI Integrated Neutrosophic MCDM Framework for Promoting Carbon Neutrality through Li-Ion Battery Selection for Electric Vehicles Nivetha Martin; Rajkumar S; Broumi Said
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.296

Abstract

vehicles in the coming decades to promote carbon neutrality. The compatibility and robustness of Li-ion based batteries make them more preferable in BEVs. A recent study has identified five different groups of Li- ion based batteries used in BEVs and applied a very simple multi–criteria decision making (MCDM) method of Weighted sum with equal criterion weights and linguistic matrix to rank the batteries. The present research work considers the same decision making problem and applies different MCDM methods to determine the criterion weights and ranking of the batteries with Triangular Neutrosophic matrix. The methodology proposed in this work works in five phases. The consistency of the neutrosophic ranking results is validated using Random forest technique, Feature importance analysis and SHAP explainability analysis. The comprehensive MCDM framework presented in this work will enable the decision makers of manufacturing company to make ideal selection of the electric vehicle batteries on comparing the ranking scores of the batteries using different methods with different criterion weights.
Bayesian Modeling of Monthly Upper Record Precipitation Using the Exponentiated Log Logistic Distribution: A Case Study from the Upper Indus Basin Tahir Mehmood
Journal of Analytical Uncertainty Vol. 1 No. 2 (2026): JAU: June 2026
Publisher : Winaya Inspirasi Nusantara Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63924/jau.v1i2.297

Abstract

Record-breaking precipitation events represent a stochastic process that differs fundamentally from annual maxima and threshold exceedances. This study examines monthly upper record precipitation in the Upper Indus Basin (UIB) using a Bayesian framework based on the Exponentiated Log Logistic distribution (ELLD). Upper records defined as observations exceeding all previously observed values were extracted from observed monthly precipitation series spanning 1980–2020. Bayesian inference via Markov Chain Monte Carlo was employed to estimate model parameters, quantify uncertainty, and derive predictive distributions for the magnitude of the next potential upper record. Model performance and adequacy were evaluated using a combination of likelihood-based measures, posterior summaries, convergence diagnostics, and posterior predictive simulations to assess the ability of the ELLD to represent the observed record magnitudes under sparse data conditions. Exceedance probabilities for selected precipitation thresholds were derived from the posterior predictive distribution to provide an interpretable probabilistic characterization of extreme record magnitudes. Rather than relying on return periods or return levels, which are not applicable to record-based processes, the analysis adopts a predictive probabilistic framework focused on uncertainty quantification. The study is intended as a methodological illustration of Bayesian inference for record-breaking precipitation under severe data scarcity and does not attempt trend detection, attribution, or climate-change assessment.

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