About the Journal
Aim & Scope
Reliability: Theory & Applications (ISSN 1932-2321) is an international peer-reviewed journal dedicated to advancing both the theory and practice of risk analysis and reliability engineering. Its primary aim is to publish high-quality research that contributes to the safe, secure, and reliable design and operation of engineering systems across all industrial sectors, bridging foundational knowledge with practical application.
The journal's scope encompasses a broad spectrum of topics, including systems reliability assessment, risk analysis methods, accelerated testing, operations research, and uncertainty analysis. It welcomes theoretical, methodological, and applied contributions, with a distinct preference for clear, practical relevance. The publication covers original research, reviews, and case studies, maintaining rigorous ethical standards as a fee-free, open-access journal.
Serves to the world community of experts in the topic to spread important current information about International meetings, Conferences,, Financial support for young researchers and students, etc. It also publishes selected volumes of previously prepared articles on talks, presented at suche Internationl events.
Open Access Policy
Proceedings on Engineering Sciences is an open access journal which means that the content is freely available without charge to the user or his/her institution. All papers are licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0) license, which allows users to distribute, remix, adapt, and build upon the material in any medium or format for non-commercial purposes only, and only so long as attribution is given to the creator.
Copyright Agreement
Authors transfer the copyright and the associated rights along with it to the Reliability: Theory & Applications after receiving the acceptance decision from the Editor. This enable us (Reliability: Theory & Applications) to have the rights to the paper in order to ensure copyright protection against infringerment, and to disseminate your article, and our journal, as wide as possible.
Article Processing Charge (APC)
Reliability: Theory & Applications does not use any Article Processing Charges (APC) or any other author charges. There are no fees for rejected articles, no submission charges, and no surcharges based on the length of an article, figures or supplementary data.
Introduction Requirements & Desk Reject Criteria
1. Mandatory Structure of the Introduction Section
The Introduction section of the submitted manuscript must explicitly contain the following logically interconnected elements. The absence or unclear formulation of any of these is considered a significant flaw and constitutes grounds for rejection or a request for major revision prior to the peer review stage.
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Research Motivation. A clear justification of the relevance and the practical or theoretical significance of the problem being addressed. The author must answer the question: "Why is this research important and necessary?"
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Critical Literature Review and Statement of Novelty. A concise analysis of key works directly related to the article's topic, highlighting unresolved questions or limitations of existing approaches. Against this background, the scientific novelty of the proposed method, model, approach, or experiment must be unambiguously defined.
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Clear Statement of Contribution. A specific and verifiable description of the main results of the work. The contribution must be stated explicitly (e.g., "This paper proposes a new mathematical model..."; "We demonstrate the correct application of model X for analyzing a new class of systems Y..."; "It is experimentally established that..."). Statements regarding the contribution must directly correspond to the claimed novelty.
2. Desk Reject Policy
The Editor-in-Chief reserves the right to reject a manuscript at the initial stage, without sending it for peer review or assigning an Associate Editor, in the following cases:
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Out of Scope: The article's topic is clearly misaligned with the journal's aims, scope, and subject area, or is irrelevant/outdated.
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Insufficient Novelty or Quality: The work fails to meet basic standards of scientific novelty, significance, or presentation quality (including structure, clarity, and adherence to the requirements for the Introduction section outlined above).
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Publication Ethics Violations: Detected plagiarism (including self-plagiarism, text recycling without proper citation), duplicate publication, or simultaneous submission to multiple venues.
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Formal Violations: The manuscript is incomplete, significantly exceeds the stipulated length limit, or does not comply with the basic formatting requirements set in the "Author Guidelines".
Note: If a decision is made to request a Major Revision after the peer-review stage, only one round of major revision is permitted. A manuscript resubmitted after revision that still fails to address the key concerns of the reviewers and editors will be rejected.
Current Issue
Vol. 21 No. 3 (2026): Reliability: Theory & Applications
From Control to Catastrophe: A Common Thread in Modern Applied Statistics
This issue of Reliability: Theory & Applications presents a rare collection of studies that, at first glance, belong to incommensurable subject domains — microgrid power engineering, reliability theory, actuarial modeling, and organizational risk assessment at explosive facilities. Yet a careful reading reveals a common methodological axis running through all five papers: the problem of justified model selection under uncertainty and the price of that choice.
The opening paper by Belhouchet et al. sets the tone for the entire collection. Comparing three microgrid control strategies — classical droop control, model predictive control (MPC), and fuzzy logic control (FLC) — the authors move beyond a simple comparison of technical indicators. The application of two-way analysis of variance (ANOVA) and a regression model for predicting energy cost transforms an engineering experiment into a full-fledged statistical study. The key finding — that MPC achieves the best absolute performance while FLC demonstrates practically comparable robustness at substantially lower computational cost — reads as an invitation to discuss where the boundary between optimality and expediency lies. It is precisely this question that becomes the leitmotif of the issue. The papers on distribution theory — the new extended exponential Rayleigh (NEER) distribution by
Kumar et al., the exponentiated Gumbel power function (EGuPF) distribution by Uwadi et al., the induced Xgamma distribution by Ruidas et al., and the resilience distribution based on the Marshall–Olkin framework (RD) by Shameera and Bindu — form a second line of meaning. All four works are devoted to extending classical distributions in order to enhance their flexibility: capturing J-shaped, bathtub-shaped, and non-monotonic hazard functions, modeling right-skewed data, and accounting for the "resilience" effects of a system. What they share is meticulous attention to estimation procedures: maximum likelihood, least squares, maximum product of spacings, and their comparison by mean squared error in simulation studies. Notably, the paper by Ruidas et al. includes a dedicated section proving model identifiability — an issue that often remains behind the scenes in applied literature, yet without which any parameter estimate loses its meaning.
The paper by Gvozdev and Ovsyanik transports us to an entirely different plane — organizational risks at explosion- and fire-hazardous enterprises. The "conditional bit" measure proposed by the authors represents an original attempt to quantify the contribution of qualitatively described organizational causes to a realized hazardous event. The use of an additive weighted model and an interval scale, as well as Kendall's coefficient of concordance for assessing expert agreement, demonstrates how a rigorous statistical apparatus can be adapted to weakly structured subject domains. This work is an important reminder that the reliability of technical systems is not reducible to equipment failures; it is to a significant extent determined by the quality of management and the human factor.
The issue closes with a short but conceptually important note by Schabe, in which the Akaike information criterion is applied to compare parametric models with artificial intelligence models. The author's conclusion is disheartening for adherents of "black boxes": as sample size grows, the information loss of AI models increases as O(n log n), whereas a parametric model, if correct, retains a linear dependence. This does not mean that AI is useless — rather, it points to its proper place in the research process: as a tool for preliminary exploration, not for final inference.
The common thread uniting all the papers can be formulated as follows: every model is a compromise between accuracy and complexity, and statistics is the discipline that makes this compromise explicit. Whether it is the choice between MPC and FLC in a microgrid, between MLE and MPS in parameter estimation, or between a parametric and a non-parametric approach — everywhere it is a matter of the same thing: the need for a justified, verifiable, and reproducible choice.
The editorial board hopes that the materials presented here will resonate both with reliability theory specialists and with researchers working at the intersection of statistics, engineering, and risk management. Interdisciplinarity is not a fashionable slogan but a necessary condition for solving the problems that the modern technosphere poses to us.
Editorial Board
Published:
2026-09-23
