Engineering reports : open access最新文献

{"title":"Machine Learning Approaches for Predicting Employee Turnover: A Systematic Review","authors":"Hojat Talebi,&nbsp;Amid Khatibi Bardsiri,&nbsp;Vahid Khatibi Bardsiri","doi":"10.1002/eng2.70298","DOIUrl":"https://doi.org/10.1002/eng2.70298","url":null,"abstract":"<p>Employee turnover prediction remains a critical issue for organizations aiming to improve talent retention and minimize recruitment costs. The ability to predict when and why employees are likely to leave enables companies to take proactive measures to reduce turnover rates. This paper presents a systematic review of 58 studies focused on applying machine learning (ML) algorithms to predict employee turnover. We analyze various ML techniques, including Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Neural Networks, highlighting their effectiveness in predicting turnover based on employee data. The review reveals that Random Forest emerged as the most widely used technique, achieving high predictive accuracy across multiple studies. Among the features, Job Satisfaction was identified as the most critical factor in turnover prediction, appearing in a majority of studies. Additionally, large datasets (more than 10,000 samples) were predominantly used, suggesting that more comprehensive data improve model performance. This review emphasizes the potential of ML in HR analytics and provides valuable insights into the strengths and limitations of each method.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Two-Dimensional Mathematical Model of Chlorine Residual Transport in a Water Distribution Pipe","authors":"John Tulirinya,&nbsp;Mathew Kinyanjui,&nbsp;Samuel Mutua,&nbsp;Asaph Muhumuza","doi":"10.1002/eng2.70325","DOIUrl":"https://doi.org/10.1002/eng2.70325","url":null,"abstract":"<p>This study introduces a two-dimensional (2D) axisymmetric mathematical model for simulating chlorine residual transport in pressurized water distribution pipes. Unlike existing one-dimensional or simplified decay models, the proposed model integrates laminar flow dynamics, advection-diffusion processes, and first-order reaction kinetics with temperature-dependent decay, based on the Navier–Stokes framework. The governing equations are solved using the Finite Element Method (FEM) in COMSOL Multiphysics (6.2), allowing for a more realistic and spatially resolved analysis of chlorine concentration profiles. The model also advances understanding of how Peclet and Reynolds numbers affect chlorine decay and distribution. Results reveal that chlorine concentration decreases progressively along the pipe, with decay rates significantly influenced by the Peclet and Reynolds numbers. Higher Peclet numbers result in advection-dominated transport with steeper concentration gradients, while higher Reynolds numbers enhance mixing and promote more uniform chlorine distribution. The velocity profile exhibits a parabolic shape characteristic of laminar flow, and pressure consistently declines along the pipe due to frictional resistance. Additionally, higher temperatures accelerate chlorine decay, underscoring the importance of thermal effects in disinfection dynamics. These insights enable water utility operators to optimize disinfection processes, ensure compliance with safety standards, and enhance the efficiency of water treatment systems.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70325","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Flexible Approach to Quantile Regression Modeling With Unit Burr-XII-Poisson and Its Applications to Cancer, Chemotherapy, and Energy Data","authors":"Mustapha Muhammad,&nbsp;Gaber Sallam Salem Abdalla,&nbsp;Abdoulie Faal,&nbsp;Ehab M. Almetwally,&nbsp;Mohammed Elgarhy","doi":"10.1002/eng2.70312","DOIUrl":"https://doi.org/10.1002/eng2.70312","url":null,"abstract":"<p>This article introduces the unit-Burr XII-Poisson (UBXIIP) distribution, a flexible model for bounded data in the unit interval. Unlike many existing alternatives, the UBXIIP offers enhanced versatility in modeling unit-domain phenomena. We further develop a quantile-based regression framework by reparameterizing the UBXIIP, enabling the direct interpretation of its parameters as quantiles. The regression coefficients are linked to the median of the response variable, providing intuitive and meaningful inference. Our approach provides a robust and interpretable method for analyzing relationships between predictors and bounded responses. The key statistical properties of the model are examined, including explicit closed-form expressions for the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>r</mi>\u0000 <mtext>th</mtext>\u0000 </mrow>\u0000 <annotation>$$ rmathrm{th} $$</annotation>\u0000 </semantics></math> moments, the quantile function, and the Shannon entropy. Parameter estimation for the UBXIIP distribution is performed using the maximum likelihood estimation (MLE) method. The efficiency of this estimation approach is assessed through Monte Carlo simulation studies by observing the behavior of the mean square error of the estimates. Furthermore, MLEs of the UBXIIP-quantile regression model is observed by comprehensive simulation studies through residual analysis. Three real-world applications are illustrated: modeling cell recovery rates post-chemotherapy, fitting remission times of bladder cancer patients, and assessing wind energy data. These case studies highlight the versatility and robustness of the UBXIIP distribution and its quantile regression counterpart, emphasizing their potential for diverse applications in medical research and renewable energy analysis. Likewise, they demonstrate their superior performance over the standard unit Burr XII and other competing distributions in terms of fit and predictive accuracy.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Simulation Analysis of Filling and Decommissioning Oil and Gas Pipelines","authors":"Jiesong Liu,&nbsp;Yuguo Wu,&nbsp;Xiaoling Li","doi":"10.1002/eng2.70331","DOIUrl":"https://doi.org/10.1002/eng2.70331","url":null,"abstract":"<p>Oil pipeline transportation is the primary mode of transporting petroleum products, and its safety and environmental concerns have garnered significant attention. The decommissioning of oil and gas pipelines plays a crucial role in the life cycle management of these pipelines. Worldwide, addressing the aging issue of oil and gas pipelines has become an urgent matter that requires immediate resolution. In order to effectively abandon underground oil and gas pipelines while ensuring sustainable development, this study conducts numerical simulation analysis on Controlled Low Strength Material (CLSM) used for filling and decommissioning purposes. In this paper, CLSM slurry, a new material with low strength, high fluidity, and low bleeding rate, is regarded as Bingham fluid. The changes of initial yield stress and plastic viscosity of the slurry with time are studied by rheological equations, and the rheological equations of CLSM slurry are proposed. In this paper, the flow dynamics characteristics of CLSM slurry in a straight pipe are studied by numerical simulation, and the flow velocity field changes of slurry in a straight pipe at different time points, different flows, and different diameters are analyzed. Additionally, a numerical calculation model is established to simulate the flow dynamic characteristics of CLSM slurry pipelines, aiming to provide reliable theoretical and technical support for developing a comprehensive and efficient CLSM filling technique within the context of decommissioned oil and gas pipeline systems.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classifying Power Quality Issues in Railway Electrification Systems Using a Nonsubsampled Contourlet Transform Approach","authors":"Pampa Sinha,&nbsp;Kaushik Paul,&nbsp;I. M. Elzein,&nbsp;Mohamed Metwally Mahmoud,&nbsp;Ali M. El-Rifaie,&nbsp;Wulfran Fendzi Mbasso,&nbsp;Ahmed M. Ewais","doi":"10.1002/eng2.70301","DOIUrl":"https://doi.org/10.1002/eng2.70301","url":null,"abstract":"<p>Railway electrification systems (ESs) pose significant challenges due to highly variable power demands and dynamic train operations. Effective power quality (PQ) monitoring for high-speed trains (HSTs) is crucial for maintaining stable and uninterrupted system performance. Rapid changes in load profiles can result in voltage sags, swells, frequency deviations, and harmonic distortion. Traditional metering systems often face limitations in accuracy and communication reliability under these conditions. This study proposes a robust signal decomposition and classification framework combining nonsubsampled contourlet transform (NSCT) with morphological component analysis (MCA) to accurately identify PQ disturbances. NSCT's shift-invariant, multiscale, and multidirectional capabilities allow for precise separation of oscillatory and transient components, while the split augmented Lagrangian shrinkage algorithm enhances decomposition efficiency. Features such as signal energy, entropy, and trend energy were extracted and visualized in a 3D feature space, demonstrating clear clustering for different PQ events. The system was tested using synthetic and Kaggle-derived datasets, achieving a classification accuracy of 100%, precision of 99.6%, recall of 99.3%, and F1-score of 99.4% across five event classes: Normal, Sag, Swell, Harmonic, and Noise. The results validate the NSCT-MCA framework's capability to reliably detect and distinguish PQ disturbances under noisy and fluctuating railway conditions, reinforcing its suitability for real-time deployment in modern electrification infrastructures.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fraud Detection in Privacy Preserving Health Insurance System Using Blockchain Technology","authors":"Md. Mazharul Islam,&nbsp;Mubasshir Ahmed,&nbsp;Rajesh Palit,&nbsp;Mohammad Shahriar Rahman,&nbsp;Salekul Islam","doi":"10.1002/eng2.70315","DOIUrl":"https://doi.org/10.1002/eng2.70315","url":null,"abstract":"<p>In developed countries, around 90% of the population is covered by health insurance through public or private providers. However, fraudulent activities account for an estimated 3%–10% of total healthcare expenditures, resulting in financial losses exceeding $300 billion annually. These fraudulent practices erode trust among patients, healthcare providers, and insurers, further complicating the insurance claim process. Additionally, claim rejection rates due to fraudulent activities are estimated to range between 25% and 35%, which impacts its efficiency and trustworthiness and weakens the industry's reliability. The digitization of healthcare and the health insurance industry has amplified the need for robust and trustworthy systems that ensure data security and optimize the insurance claim process. To address these issues, this paper proposes a system that ensures patient anonymity through secure credentials and advanced fraud detection mechanisms. Privacy is preserved using cryptographic techniques such as secure hashing and anonymous credentials, which ensure that sensitive patient information remains confidential throughout the claim process. Smart contract algorithms are utilized in two scenarios: patient-submitted claims and healthcare provider-submitted claims, ensuring accurate processing and validation while detecting fraudulent activities such as duplicate claims, inflated medical bills, billing for unprovided services, falsifying patient records, and submitting claims for nonexistent treatments. The proposed system has been implemented and tested on a blockchain platform, demonstrating its effectiveness in preserving privacy and detecting fraud. Performance evaluations reveal its scalability and efficiency in managing increased user loads, offering a robust solution to modern health insurance challenges while fostering trust and operational efficiency among participants.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Teaching Plans on Prostate Cancer Generated by Various Large Language Models and a Human Expert","authors":"Rong Wang,&nbsp;Yue Ding,&nbsp;Yajun Shen,&nbsp;Haiyong Liu,&nbsp;Ping Wang,&nbsp;Zhixiang Gao","doi":"10.1002/eng2.70303","DOIUrl":"https://doi.org/10.1002/eng2.70303","url":null,"abstract":"<p>Prostate cancer remains one of the most common malignancies affecting men globally, characterized by high morbidity and mortality rates. The complexity and variability of the disease necessitate diverse treatment strategies, ranging from active surveillance to more aggressive interventions such as radical prostatectomy, radiation therapy, and androgen deprivation therapy. This study investigates the potential of large language models (LLMs) in generating educational content for prostate cancer, focusing on the creation of teaching plans in both Chinese and English. Four LLMs—GPT-4 (OpenAI), Gemini 1.5 Pro (Google), Kimi AI (Microsoft), and Douban (ByteDance)—were evaluated against teaching plans developed by an experienced urology professor. A double-blind assessment by 25 urology faculty members using a standardized 10-point scale was employed to compare the quality of curriculum content, learning objectives, and outcomes. The results revealed that GPT-4 and Gemini 1.5 Pro outperformed Kimi AI and Douban, yet still lagged behind human-generated plans, particularly in Chinese. Statistical analyses indicated significant differences in the quality scores among the LLMs and the human experts, underscoring the necessity of integrating domain-specific knowledge into AI-generated content. This research highlights the promise and limitations of LLMs in medical education, suggesting that future developments should focus on hybrid models that combine artificial intelligence with human expertise to enhance educational efficacy.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attn-DeCGAN: A Diversity-Enhanced CycleGAN With Attention for High-Fidelity Medical Image Translation","authors":"Matthew Cobbinah,&nbsp;Henry Nunoo-Mensah,&nbsp;Prince Ebenezer Adjei,&nbsp;Francisca Adoma Acheampong,&nbsp;Isaac Acquah,&nbsp;Eric Tutu Tchao,&nbsp;Andrew Selasi Agbemenu,&nbsp;Emmanuel Abaidoo,&nbsp;Ike Asamoah-Ansah,&nbsp;Obed Kojo Otoo,&nbsp;Amina Salifu,&nbsp;Albert Dede,&nbsp;Julius Adinkrah,&nbsp;Jerry John Kponyo","doi":"10.1002/eng2.70320","DOIUrl":"https://doi.org/10.1002/eng2.70320","url":null,"abstract":"<p>Unpaired image-to-image translation has emerged as a transformative paradigm in medical imaging, enabling unpaired image translation without the need for aligned datasets. While cycle-consistent generative adversarial networks (CycleGANs) have shown considerable promise in this domain, they remain inherently constrained by the locality of convolutional operations, resulting in global structural inconsistencies, and by mode collapse, which restricts generative diversity. To overcome these limitations, we propose Attn-DeCGAN, a novel attention-augmented, diversity-aware CycleGAN framework designed to enhance both structural fidelity and perceptual diversity in CT-MRI translation tasks. Attn-DeCGAN replaces conventional ResNet-based generators with Hybrid Perception Blocks (HPBs), which synergise depthwise convolutions for spatially efficient local feature extraction with a Dual-Pruned Self-Attention (DPSA) mechanism that enables sparse, content-adaptive modeling of long-range dependencies at linear complexity. This architectural innovation facilitates the modeling of anatomically distant relationships while maintaining inference efficiency. The model is trained using a composite loss function incorporating adversarial, cycle-consistency, identity, and VGG19-based structural consistency losses to preserve both realism and anatomical detail. Extensive empirical evaluations demonstrate that Attn-DeCGAN achieves superior performance across key metrics, including the lowest FID scores (60, 58), highest PSNR (27, 33), and statistically significant improvements in perceptual diversity (LPIPS, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>&lt;</mo>\u0000 <mn>0</mn>\u0000 <mo>.</mo>\u0000 <mn>05</mn>\u0000 </mrow>\u0000 <annotation>$$ p&lt;0.05 $$</annotation>\u0000 </semantics></math>) compared to state-of-the-art baselines. Ablation studies underscore the critical role of spectral normalization in stabilizing adversarial training and enhancing attention effectiveness. Expert radiologist assessments confirmed the clinical superiority of Attn-DeCGAN over the next best baseline, DeCGAN, with 100% real classifications and higher confidence scores in CT synthesis, and more anatomically convincing outputs in MRI translation. This has particular utility in low-resource clinical environments where MRI is scarce, supporting synthetic MRI generation for diagnosis, radiotherapy planning, and medical image dataset augmentation. Despite increased training complexity, Attn-DeCGAN retains efficient inference, positioning it as a technically robust and clinically deployable solution for high-fidelity unpaired medical image translation.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 8","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Entropy-Transformed Inverse Weibull Distribution: Development, Properties, and Application in Diverse Data Modeling","authors":"Tabassum Naz Sindhu,&nbsp;Anum Shafiq,&nbsp;Qasem M. Al-Mdallal,&nbsp;Tahani A. Abushal,&nbsp;Muhammad Aslam","doi":"10.1002/eng2.70171","DOIUrl":"https://doi.org/10.1002/eng2.70171","url":null,"abstract":"<p>Standard distributions must be improved to enhance their capacity for data modeling because they do not inherently suit all sorts of data sets in an acceptable manner. Due to this lack of previous ones, we developed a novel model employing the entropy-transformed function. We utilized the inverse Weibull model to function as the reference model to assess the applicability of the entropy transformation. The distribution, referred to as the “Entropy-Transformed Inverse Weibull Distribution” (ETIWL), is derived by applying the entropy transformation to the inverse Weibull model. The proposed distribution's core characteristics have been taken into account. The maximum-likelihood approach is used to estimate the parameters of the given distribution. Four real data sets are used in this study with the thorough simulation analysis to see whether the proposed distribution is superior.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70171","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis and Reliability Prediction of Multi-State Service Systems With Multiple Failure Modes of Unreliable Server: An Engineering Perspective","authors":"Shreekant Varshney,&nbsp;Mohit Bajaj,&nbsp;Kapil Kumar Choudhary,&nbsp;Mukesh Pushkarna,&nbsp;Ievgen Zaitsev","doi":"10.1002/eng2.70268","DOIUrl":"https://doi.org/10.1002/eng2.70268","url":null,"abstract":"<p>The long-term reliability of a machining system is essential for ensuring the uninterrupted operation of an automated manufacturing process. For maintaining optimal system performance, it is essential to model and analyze the system's reliability and availability. The current study introduces a unique framework established through evolutionary advancements of the fundamental models, incorporating impatience characteristics of individuals, the notion of unreliable servers, and enhancing its practical significance by considering the working breakdown of the server/repairman. A comparative investigation of the developed model with conventional models is carried out to analyze the overall impact on the reliability and availability characteristics of the machining system through a queueing-theoretic approach. Further, the differential-difference equations are implemented to construct the mathematical model, and the Laplace transformation is applied to demonstrate the state probability distribution. Several critical system performance measures, such as mean-time-to-failure (MTTF), machining systems' reliability, and steady-state availability, are thoroughly investigated to evaluate machining system stability and efficiency. The results demonstrate that the developed model significantly increases system reliability and availability, presenting a notable increase in reliability compared to conventional models under certain service interruption circumstances. To validate the model's applicability in real-world scenarios, multiple combinations of system parameters are taken into consideration. For straightforward visualization of the impacts of multiple parameters on system reliability and availability, the results are provided, consisting of extensive tables and graphical representations. The proposed research contributes to the queueing literature by addressing the research gaps between theoretical modeling and real-life applications, highlighting the insights that are essential for system designers, decision-makers, and researchers aiming to optimize the reliability and availability of complex machining systems.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70268","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}