Field Of Engineering Research Articles

Digital Archives for Academic Heritage: Tools and Processing Environments for the Museum Metaverse and Scientific Dissemination

Abstract. In recent years the digital visualisation techniques, including 3D digital models of indoor spaces, saw an exponential increase in their applications, both in the fields of Architecture, Engineering and Construction (AEC) and of Cultural Heritage (CH). This growth is mainly due to the increasing affordability of tools for the acquisition and elaboration of three-dimensional digital survey/data management and to their increasing ‘user friendliness’.The research proposes a further step on research started some years ago by our group about the exploration of indoor mapping for the creation of the 3D virtual museum of our Institution, starting with the realisation of a virtual model of the university’s spaces (departments and halls). At first, the virtual museum will host the Curioni Digital Collection, which consist of circa 140 wooden models of building details, bridges, tunnels, and vaults designed by prof. G. Curioni during the second half of the XIX century as teaching aids for the Construction and Structural Design classes. A comparison between two main tools to support management and diffusion of geometrical, alphanumerical, and topological data will be given (Matterport and Unity). The parallel approach considering two tools is to provide a digital twin of a real collection placed in a real space, and, on the other hand, to use a real historical university space, virtualized, to host a virtual collection.

An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization

Metaheuristic algorithms (MHAs) are widely used in engineering applications in view of their global optimization capability. Researchers continuously develop new MHAs trying to improve the computational efficiency of optimization search. However, most of the newly proposed algorithms rapidly lost their attractiveness right after their release. In the present study, two classical and powerful MHAs, namely the grey wolf optimizer (GWO) and the JAYA algorithm, which still attract the attention of optimization experts, were combined into a new hybrid algorithm called FHGWJA (Fast Hybrid Grey Wolf JAYA). FHGWJA utilized elitist strategies and repairing schemes to generate high-quality new trial solutions that may always improve the current best record or at least the old population. The proposed FHGWJA algorithm was successfully tested in seven engineering optimization problems formulated in the fields of robotics, hydraulics, and mechanical and civil engineering. Design examples included up to 29 optimization variables and 1200 nonlinear constraints. The optimization results proved that FHGWJA always was superior or very competitive with the other state-of-the-art MHAs including other GWO and JAYA variants. In fact, FHGWJA always converged to the global optimum and very often achieved 0 or nearly 0 standard deviation, with all optimization runs practically converging to the target design. Furthermore, FHGWJA always ranked 1st or 2nd in terms of average computational speed, and its fastest optimization runs were better or highly competitive with those of the best MHA taken for comparison.

Research on the Collaborative Education Mechanism among In-School Mentors, Enterprise Mentors, and Counselors from the Perspective of Sanquan Education

This study addresses the issues existing in the current cultivation process of outstanding graduate students and explores the collaborative education mechanism among in-school mentors, enterprise mentors, and counselors in the cultivation of outstanding engineers in the mechanical field based on the perspective of Sanquan education (i.e., holistic, full-course, and all-round education). Through exploring this mechanism, we attempt to construct a more scientific and efficient education model to enhance students’ comprehensive quality and practical skills, thereby meeting society’s demand for high-quality talents in the mechanical field.

Statistical analysis for progressive stress accelerated life test with transmutation Weibull distribution

The Transmutation Weibull distribution is derived from the quadratic rank transmutation map of the Weibull distribution. This distribution integrates the high adaptability of the Weibull distribution in engineering and materials fields, and has great potential utility for modeling lifetime data. Therefore, the article explores the progressive stress accelerated life test model for components under the Transmutation Weibull distribution, conducts statistical analysis using the maximum likelihood estimation (MLE), Lindley’s approximation, and Markov Chain Monte Carlo (MCMC) algorithm to provide approximate estimates for the model. Finally, by comparing the results of numerical simulations, it is confirmed that the Bayesian estimation is more accurate and effective.

Relationship between MO/(SiO2+Al2O3) of materials—amorphous gel structure—properties of the one-part FA-Slag-based AAMs

The study investigates the impact of different amorphous gel structure by MO (CaO+Na2O)/(SiO2+Al2O3) ratios (specifically 0.66, 0.69, 0.73, 0.76, 0.84, and 0.90) on the fresh and hardened properties of one-part alkali-activated materials (OP-AAMs) derived from fly ash-slag. Unlike traditional two-part AAMs, the "just-add-water" preparation process of OP-AAMs facilitates the transition of geopolymer products from laboratories to the engineering field. Higher (CaO+Na2O)/(SiO2+Al2O3) ratios in fresh pastes promote the dissolution of active aluminosilicate in the alkali-activators, resulting in the formation of Si-rich gel with low-Al amorphous gel. This leads to reduced setting time, increased fluidity, and the occurrence of a viscoelastic phenomenon in the fresh properties. Throughout the geopolymerization process, the main product with the shortest mean chain length and higher Si/Al ratios is the C-(N)-A-S-H and N-A-S-H gel in the (CaO+Na2O)/(SiO2+Al2O3)=0.76 composition, which exhibits the highest 28-day compressive strength of 46 MPa. The findings highlight the potential of OP-AAMs and provide insights into the adjustment mechanism of MO/(SiO2+Al2O3) ratios to optimize the properties of OP-AAMs for engineering applications.

Analysis of Entropy Optimization in MHD Flow of Non-Newtonian Nanofluids with Chemical Reaction and Thermal Energies

Investigation of nanofluid flows under various conditions is becoming fascinating research due to its numerous applications in many science and engineering fields such as solar energy, geothermal energy, water purification, glass blowing, enhanced oil recovery, petroleum production and food processing. For the present requirements in thermal energy applications, this mathematical model is considered to explore the diffusion and heat source effects on the flow of conductive Casson and Maxwell nanofluids in a porous medium on an elongating sheet with a chemical reaction. Further, the effects of viscous dissipation and thermal radiation are also examined. Moreover, the entropy generation is analyzed since this approach is employed in solar energy exchangers. The governing equations and the corresponding boundary conditions are constructed. This system of partial differential equations (PDEs) is changed into a system of nonlinear ordinary differential equations (ODEs) with the help of a suitable transformation and then solved numerically by the bvp5c MATLAB package. The influences of the physical parameters on the flow phenomena are discussed and presented through figures and tables. This analysis explains that the Casson fluid flow has a higher velocity field than the Maxwell fluid flow. The Nusselt number becomes higher as the Dufour numbers increase. It has also been noticed that increasing the Brinkmann number improves the entropy and, an increase in radiation enriches the temperature profile. The concentration of the fluid is decreased for the increasing values of the higher-order chemical reaction parameter. Furthermore, comparisons of the current results were conducted for limiting examples of the problem and found to be in good agreement with existing results in the literature.

Encoding human activities using multimodal wearable sensory data

Human Activity Recognition (HAR) is the task to automatically analyze and recognize human body gestures or actions. HAR using time-series multi-modal sensory data is a challenging and important task in the field of machine learning and feature engineering due to its increasing demands in numerous real-world applications such as healthcare, sports and surveillance. Numerous daily wearable devices e.g., smartphones, smartwatches, and smart glasses can be used to collect and analyze the human activities on an unprecedented scale. This paper presents a generic framework to recognize the different human activities using continuous time-series multimodal sensory data of these smart gadgets. The proposed framework follows the channel of Bag-of-Features which consists of four steps: (i) Data acquisition and pre-processing, (ii) codebook computation, (iii) feature encoding, and (iv) classification. Each step in the framework plays a significant role to generate an appropriate feature representation of raw sensory data for efficient activity recognition. In the first step, we employed a simple overlapped-window sampling approach to segment the continuous time-series sensory data to make it suitable for activity recognition. Secondly, we build a codebook using k-means clustering algorithm to group the similar sub-sequences. The center of each group is known as codeword and we assume that it represents a specific movement in the activity sequence. The third step consists of feature encoding which transform the raw sensory data of activity sequence into its respective high-level representation for the classification. Specifically, we presented three reconstruction-based encoding techniques to encode sensory data, namely: Sparse Coding, Local Coordinate Coding, and Locality-constrained Linear Coding. The segmented activity sub-sequences are transformed to high-level representation using these techniques and earlier computed codebook. Finally, the encoded features are classified using a simple Random Forest classifier. The proposed HAR framework using three different encoding techniques is evaluated on three large benchmark datasets: UniMiB-SHAR dataset, MHEALTH dataset and WISDM dataset and their results are compared with most recent state-of-the-art techniques. It outperforms the existing techniques and achieves 98.39%, 99.5%, and 99.4% recognition scores on UniMiB-SHAR dataset, MHEALTH dataset, and WISDM dataset respectively. The excellent recognition results and computational analysis confirms the effectiveness and efficiency of the proposed framework.

A Specialized Pipeline for Efficient and Reliable 3D Semantic Model Reconstruction of Buildings from Indoor Point Clouds.

Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline's efficiency and accuracy offer valuable contributions to future advancements in point cloud processing.

Improving strength-conductivity synergy of Al-Cu-(Sn/Er) alloy with nanoscale substructure

As a conductive material for power transmission, Al-Cu alloy is widely used in power engineering, microelectronics and other fields. In view of the contradiction between the conductivity and strength of aluminum alloy, Sn and Er elements were selected to micro-alloy Al-Cu alloy. Al-Cu, Al-Cu-Sn and Al-Cu-Sn-Er alloys were prepared by near-liquidus casting. After solution treatment, it was subjected to three-stage alternating process of rolling and aging. The effects of micro-alloying and alternating process on the microstructure and properties of Al-Cu alloy were studied. The results show that the precipitated phase Al2Cu of Al-1.12 Cu alloy is rod-shaped after the first-stage process, and the average size is about 278±87 nm. The precipitated phases in the Al-Cu-Sn alloy are Al2Cu (average size of about 206±79 nm) and nano-sized β-Sn particles. The fine β-Sn nanoparticles in the Al-Cu-Sn-Er alloy heterogeneously nucleated on Al8Cu4Er (average size of about 167±45 nm), making Al8Cu4Er grow into particles. After secondary processing, stacking faults (SFs) appeared in the substructures of the three alloys, and the precipitated phases were further refined. The size of most precipitated phases in Al-1.13Cu-0.12Sn and Al-1.01Cu-0.15Sn-0.22Er alloys is reduced to nanometer level. After the three-stage process, some of the Al2Cu precipitates in the Al-Cu alloy are also refined to the nanometer level, and the precipitates in the other two alloys are all refined to the nanometer level. Nanotwins also appear in Al-1.01Cu-0.15Sn-0.22Er alloy. The tensile strength of Al-1.13Cu-0.12Sn and Al-1.01Cu-0.15Sn-0.22Er alloys after three-stage alternating process is 303 MPa and 327 MPa, respectively. The elongations are 21.6 % and 23.3 %, respectively. The relative conductivity was 61.7 %IACS and 59.8 %IACS, respectively. The synergistic effect of microalloying of Sn and Er elements and three-stage alternating processing technology not only reduces the size of precipitates and twins in the alloy to the nanometer level, but also adjusts the distribution of dislocation tangles and stacking faults (SFs), improves the substructure configuration, and improves the mechanical and electrical properties of conductive aluminum alloys.

The degeneration mechanism of lubricating oil in the ammonia fuel engine

As a hydrogen-rich carbon-free fuel, ammonia has attracted great attention in the engine field. However, there are few studies on the lubricating oil of the ammonia engine, which hinders the development of the ammonia engine. To study the impact of ammonia on the physicochemical properties and the lubricating performances of the lubricating oil, water-lubricating oil, and lubricating oil-water-ammonia were prepared. By investigating dispersion stability, viscosity, thermal stability, and film-forming ability of different lubricating oils, the effect of ammonia on the physicochemical properties and lubricating properties of lubricating oil was analyzed. The effect mechanism of ammonia on the lubricating oil was also explored. This work can be a foundational contribution to the lubricating oil for the ammonia engine.

Spatially defined microenvironment for engineering organoids

In the intricately defined spatial microenvironment, a single fertilized egg remarkably develops into a conserved and well-organized multicellular organism. This observation leads us to hypothesize that stem cells or other seed cell types have the potential to construct fully structured and functional tissues or organs, provided the spatial cues are appropriately configured. Current organoid technology, however, largely depends on spontaneous growth and self-organization, lacking systematic guided intervention. As a result, the structures replicated in vitro often emerge in a disordered and sparse manner during growth phases. Although existing organoids have made significant contributions in many aspects, such as advancing our understanding of development and pathogenesis, aiding personalized drug selection, as well as expediting drug development, their potential in creating large-scale implantable tissue or organ constructs, and constructing multicomponent microphysiological systems, together with functioning at metabolic levels remains underutilized. Recent discoveries have demonstrated that the spatial definition of growth factors not only induces directional growth and migration of organoids but also leads to the formation of assembloids with multiple regional identities. This opens new avenues for the innovative engineering of higher-order organoids. Concurrently, the spatial organization of other microenvironmental cues, such as physical stresses, mechanical loads, and material composition, has been minimally explored. This review delves into the burgeoning field of organoid engineering with a focus on potential spatial microenvironmental control. It offers insight into the molecular principles, expected outcomes, and potential applications, envisioning a future perspective in this domain.

Unveiling exotic multi-scale microstructure transformation and crack formation mechanisms in eutectic ceramic composite by laser powder bed fusion

Laser powder bed fusion (LPBF) represents an advanced and versatile technology. Exploiting its distinctive advantages for direct and rapid fabrication of complex-structured melt-grown oxide eutectic ceramic composite represents a pioneering yet challenging endeavor. In this work, LPBF is creatively employed to fabricate turbine blade-shaped, in-situ ternary eutectic ceramic composite. Through the integration of experimental procedures, Finite element method (FEM) simulations, and numerical analyses, an innovative design and manufacturing of oxide eutectic ceramic composites have been successfully established. Comprehensive FEM simulations, with detailed interface characteristic analysis, have revealed macro-scale cracks induced by intense maximum principal stress, and micro-cracks stemming from significant interfacial energy disparities among the three constituent phases. The applications of rapid solidification and nucleation theories have facilitated profound insights into the formation mechanisms of multi-scale exotic microstructures, including top-layer coarse dendrites, rosette-like spherical internally grown eutectic colony within layers, and columnar eutectic colonies with ultra-fine lamellar eutectic structures. Micro-mechanical property testing reveals enhanced performance in the inter-layer ultra-fine lamellar eutectic structure, which is attributed to a refined eutectic spacing of approximately 61 nm, coupled with distinct and robust bonding interfaces. These groundbreaking achievements, focusing on the processing-microstructure-property relationship in the fabrication of gas turbine blade-shaped solidified eutectic ceramic composite using LPBF, provide invaluable theoretical insights and data. This knowledge is crucial for the LPBF production of high-temperature structural materials, highlighting its significant potential applications in fields of aerospace and mechanical engineering.

3D-BIOPRINTING OF BONE TISSUE IN VITRO И IN SITU

Advanced techniques in bone tissue regeneration include innovative achievements in the field of tissue engineering, among which 3D bioprinting technology holds a special place. This method demonstrates significant progress, driven by its numerous advantages, confirmed through extensive scientific research. 3D bioprinting offers the capability of precise printing of bone transplants with specified geometry, incorporating necessary biomaterials such as cells and growth factors, which facilitates effective osteoinduction, osteoconduction, and vascularization. This review article analyzes various modern methods of bone tissue restoration, including the use of autotransplants, allotransplants, and synthetic bone transplants. Particular attention is paid to the latest innovations in 3D bioprinting, opening prospects for creating individualized bone tissues characterized by a high degree of biocompatibility and minimizing the need for invasive procedures. Additionally, the article presents the concept of in situ bioprinting, developed by Weiss and his colleagues in 2007, as a method of direct printing of bio-inks directly in the area of damage in a living organism. This progressive approach provides high precision in the restoration of tissue defects, regardless of their complexity and geometric features. Over the last decade, several successful clinical cases of using printed structures have been registered, demonstrating the significance of 3D bioprinting in treating life-threatening diseases, as well as in accelerating the regeneration process and creating tissue transplants for subsequent implantation.

Study on the Prediction of Motion Response of Offshore Platforms Based on ResCNN-LSTM

In the random sea environment, offshore platforms are influenced by factors such as wind, waves, and currents, as well as their interactions, leading to complex motion phenomena that affect the safety of offshore platform operations. Consequently, accurately predicting the motion response of offshore platforms has long been a key focus in the fields of naval architecture and ocean engineering. This paper utilizes STAR-CCM+ to simulate time-history data of offshore platform motion responses under both regular and irregular waves. Furthermore, a predictive model combining residual convolutional neural networks and long short-term memory neural networks using neural network technology is also studied. This model utilizes an autoregressive approach to predict the motion responses of offshore platforms, with its predictive accuracy validated through comprehensive evaluations. Under regular wave conditions, the coefficient of determination (R2) for the platform’s heave and pitch responses consistently exceeds 0.99. Meanwhile, under irregular wave conditions, the R2 values remain generally above 0.4. Additionally, the model exhibits commendable performance in terms of Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) metrics. The aim of this study is to present a novel approach to predicting offshore platform motion responses, while providing a more scientific basis for decision-making in offshore platform operations.

A novel tetra hybrid bio-nanofluid model with stenosed artery

Abstract For treating and diagnosing cardiovascular diseases, the field of biomedical engineering is significant because it develops new ways and techniques. Stenosis is the narrowing of an artery, and it leads reduction in the flow rate of blood. This study investigates the blood flow mechanism in an artery using a mathematical model of Carreau nanofluid with four distinct nanoparticles. Tetra nanofluid model produces significant advancement in the simulation of blood flow through the stenosed arteries. The model is capable of predicting the pressure drop and velocity distribution for diagnosing and treating stenosis. The spectral relaxation approach is used to present the model's efficiency and effectiveness, which makes it a suitable method for solving the governing equations of this study. The findings of this study have important implications for the development of new treatments and diagnostic techniques for stenosis and other cardiovascular diseases.

The entropy-transformed Gompertz distribution: Distributional insights and cross-disciplinary utilizations

A novel two-parameter continuous model titled the entropy-transformed Gompertz (ETGPZ) distribution has been developed via the entropy transform. A new framework has been investigated and found to meet the criteria of the probability function. By significantly improving the functional shape and having the ability to model the most likely form of the hazard rate function, this new modification has increased the adaptability of the typical distribution. Some of its core characteristics, such as its statistical and computational features, are clearly presented. A thorough simulation analysis has been done to examine the final behavior of maximum likelihood estimators while estimating model parameters. We assess the performance and practical applicability of the ETGPZ distribution using eight real datasets from engineering and biomedical fields. The results demonstrate that the ETGPZ outperforms the baseline Gompertz (GPZ) distribution, highlighting its superiority and broader potential for various applications.

Unlocking Intracellular Protein Delivery by Harnessing Polymersomes Synthesized at Microliter Volumes using Photo-PISA.

Efficient delivery of therapeutic proteins and vaccine antigens to intracellular targets is challenging due to generally poor cell membrane permeation and endolysosomal entrapment causing degradation. Herein, these challenges are addressed by developing an oxygen-tolerant photoinitiated polymerization-induced self-assembly (Photo-PISA) process, allowing for the microliter-scale (10µL) synthesis of protein-loaded polymersomes directly in 1536-well plates. High-resolution techniques capable of analysis at a single particle level are employed to analyze protein encapsulation and release mechanisms. Using confocal microscopy and super-resolution stochastic optical reconstruction microscopy (STORM) imaging, their ability to deliver proteins into the cytosol following endosomal escape is subsequently visualized. Lastly, the adaptability of these polymersomes is exploited to encapsulate and deliver a prototype vaccine antigen, demonstrating its ability to activate antigen-presenting cells and support antigen cross-presentation for applications in subunit vaccines and cancer immunotherapy. This combination of ultralow volume synthesis and efficient intracellular delivery holds significant promise for unlocking the high throughput screening of a broad range of otherwise cost-prohibitive or early-stage therapeutic protein and vaccine antigen candidates that can be difficult to obtain in large quantities. The versatility of this platform for rapid screening of intracellular protein delivery can result in significant advancements across the fields of nanomedicine and biomedical engineering.

A Decade of the Database Systems Research with the Lens of PODS, SIGMOD, TODS, and TKDE: A Bibliometric Perspective

Database management systems are an essential research field in data engineering, data mining, and computer science. The development and research in this field have evolved from manual bookkeeping to automatic data storage, rapidly growing with time. In this bibliometric study, the articles from four premier venues of publication in the field of database systems have been collected for analysis, including ACM Special Interest Group on Management of Data SIGMOD, a well-reputed symposium PODS (Principles of Database Systems) and two journals of the domain namely: Transactions on Database Systems (TODS) by ACM, and Transactions on Knowledge and Data Engineering (TKDE). This study includes the articles published between 2010 and 2019 in the selected venues to perform bibliometric analysis. Both TKDE and TODS are highly reputed journals in the field of database systems but publish quite different research. The SIGMOD conference began in 1975 and specializes in the research area of databases and data management problems, whereas PODS is the research conference that specializes in research related to database theory. This study presents the analysis of the collected articles from three major aspects: 1) metadata analysis, 2) content-based analysis, and 3) citation-based analysis. Furthermore, it also looked at the leading institutes, countries, and authorities based on article citation and publication count. As a whole, this study proposes a methodology for performing the extensive bibliometric analysis for the database management system and conducts a bibliometric analysis for the aforementioned top publication venues in this area of research.

Strain and External Electric Field Engineering of S-Terminated MXene on Selective and Sensitive Detection of N-Containing Compound Gases: A Computational Study.

Gas sensors are used for monitoring hazardous gases and vapors. With the emergence of S-terminated MXene synthesis, herein, we explore the sensing ability of Ti3C2S2 toward N-containing gases, including NH3, NO, NO2, trimethylamine (TMA), and nicotine (NT), using first-principles calculations and a statistical thermodynamic model. We find that Ti3C2S2 exhibits high selectivity to TMA, NO, and NT with moderate adsorption energies of -0.610, -0.490, and -0.476 eV, respectively, minimizing environmental noise from ambient gases. The electronic structure of Ti3C2S2 subtly alters upon adsorption of TMA, NO, and NT, facilitating detectable signals in the sensing device. However, the adsorption of NO2 and NH3 is less pronounced due to weak physisorption (<0.3 eV). Employing engineering strategies including biaxial strain and an external electric field greatly enhances the selectivity and sensitivity of NO2 (NH3) detection by boosting adsorption strength up to -0.351 eV with ε = 5% (-0.370 eV with |E⃗| = 0.6 V/ Å). In addition, the moderate adsorption energies of the gases result in a suitable recovery time in the range of milliseconds, leading to high reusability of the sensing device. The estimated adsorption densities suggest potential coverage of these N-containing molecules even at low concentrations and room temperature. Computational analysis of the sensing capability of Ti3C2S2 using the nonequilibrium Green's function method indicates that it is a promising gas-sensing material. In addition, mechanical modifications, electric field adjustments, and gate voltage alterations could be used to obtain effective sensing materials for N-containing gas detection.

Optimizing System Efficiency and Reliability: Integrating Semi-Markov Processes and Regenerative Point Techniques for Maintenance Strategies in Plate Manufacturing

This paper offers a modeling methodology to evaluate similar systems’ performance and optimize system effectiveness with customized maintenance plans. This strategy, which combines regeneration point techniques with semi-Markov processes, is used to assess a plate manufacturing company’s system. Key insights are obtained using a thorough cost-benefit analysis that includes numerical analysis, graphical interpretations, and system efficacy indicators. The study clarifies the dynamics of the system’s reliability under different repair rates by showing an inverse relationship between Mean Time to System Failures (MTSF) and failure rate. Additionally, the analysis clarifies how profit is affected by failure rate, highlighting the necessity of efficient maintenance plans from an economic standpoint. This research highlights the growing significance of availability and dependability in technology-driven sectors and emphasizes the need for dependable systems. This paper makes a substantial contribution to the knowledge already available in the field of reliability engineering by providing a thorough framework that combines regeneration point approaches and semi-Markov processes. The metrics that are obtained offer a comprehensive understanding of the behavior of the system, which in turn helps industry practitioners make well-informed decisions.