Engineering Research Articles

CTNet: Improving the non-stationary predictive ability of remaining useful life of aero-engine under multiple time-varying operating conditions

Remaining useful life (RUL) estimation has been widely studied in prognostics and health management. Multiple time-varying operating conditions explicitly influence the measured performance parameters of the aero-engine, which results in non-stationarity and impedes the predictive ability of deep learning models. Previous data-driven studies primarily ignore the causality between operating condition descriptors and measured performance parameters, which is inconsistent with practical aero-engine engineering applications. To address this issue, we propose a causality Transformer network (CTNet), which improves the prediction stability of non-stationary series under multiple operating conditions. First, a novel graph structure is used to capture the connectivity relationship between operating condition descriptors and measured sensor nodes. Then the causality attention is proposed to inject additional causality information into the self-attention module without affecting the similarity computations of query and key values within the window. The causality attention can generate distinguishable attention for operating conditions, which properly release the predictive potential for non-stationary series. Extensive experiments are performed on public datasets (CMAPSS, N-CMAPSS), showing that CTNet outperforms existing state-of-the-art methods. We have also conducted experiments on the real-world quick access recorder (QAR) data, which validates the performance of the proposed model in perceiving time-varying operating conditions.

Advanced genetic algorithm-based signal processing for multi-degradation detection in steam turbines

This research contributes to the field of reliability engineering and system safety by introducing aninnovative diagnostic method to enhance the reliability and safety of complex technological systems. Steam turbines are specifically referred to. This study focuses on the integration of advanced signal processing techniques and engineering dynamics in addressing critical issues in the monitoring and maintenance of mechanical systems. By utilizing genetic algorithms, we improve the capability to detect, localize, and ascertain the causes of both singular and intricate degradations, including three-fold and four-fold faults, within steam turbine operations. We can detect degradation with accuracies of 72.6% for three-fold faults and 62.2% for four-fold faults. This significant advancement emphasizes the potential for improved machine and structural health monitoring, especially where non-stationary and random vibrations are common, such as in powertrain and drivetrain systems. This methodology is vital for the maintenance and operational strategies of critical infrastructures like nuclear power plants, chemical plants, and manufacturing facilities where steam turbines play a crucial role. The novelty of this approach lies in the use of genetic algorithms for thermal-flow diagnostics of steam turbines, which had been unaddressed in literature. Moreover, the merger of theoretical and experimental aspects in this study underscores its relevance to practical applications, thereby demonstrating an original contribution to engineering knowledge and showcasing significant advancements over established methods. The research underscores the method’s potential as a universal tool for diagnosing complex systems, representing an advance in reliability engineering practices. By applying genetic algorithms, a noticeable link to improving the safety and reliability of technological systems is established, offering valuable insights into the design, maintenance, and extension of the lifespan of critical infrastructure.

Lesson Planning: Bridging Conceptual to Practical Engineering Design Classroom Activity

ABSTRACT Understanding the tools that mediate the transfer of learning from professional learning experiences (PLE) to classroom practice is critical for maximizing educational and financial investment in professional teacher learning. This inquiry sought to illuminate the mediating tools facilitating the transfer of teachers’ learning from an engineering design professional learning experience to the secondary science classrooms. Data were collected from two cohorts of secondary science teachers over 2 years engaged in a week-long engineering design camp. A design-based qualitative inquiry approach was used to investigate the engineering design classroom activity associated with the development of engineering design lessons and other tools. Contextual relevance, NGSS pedagogical understanding, experiential learning, and collaborative reflexivity were all perceived by the participants as tools that mediated practice. The engineering design classroom activity for secondary science teachers includes context- and content-specific factors such as motivation, goals, and tools that mediate practice. The identification of these factors may assist professional learning developers in the development and delivery of design-based learning experiences for secondary science teachers, with the intent of increasing the implementation of engineering design in science classrooms.

Study on the length of smoke overflow from tunnel fire under the combination of longitudinal ventilation and centralized smoke exhaust mode

By combining longitudinal ventilation and centralized smoke exhaust, the smoke exhaust mode can simultaneously control the length of upstream smoke back-layering and the length of downstream smoke overflow from the exhaust outlet to facilitate two-way rescue and evacuation of tunnel fires, but the current research focuses on the upstream smoke back-layering length, and lacks the research on the downstream overflow length of the exhaust outlet. In this paper, the longitudinal velocity and single-point centralized smoke exhaust mode smoke overflow length of the exhaust outlet are investigated, and through theoretical analysis, a prediction model for smoke overflow length is proposed by considering the longitudinal velocity, the heat release rate of the fire source, the exhaust velocity, and the distance of the fire source from the exhaust outlet, etc. The results show that the smoke exhaust velocity and the fire source distance from the smoke vent decreases can effectively increase the smoke exhaust efficiency, but will reduce the inhibition effect on the length of smoke overflow, and the increase in longitudinal velocity will lead to a reduction in the smoke exhaust efficiency and an increase in the length of smoke overflow. When the longitudinal velocity is below 0.2 m/s, the ratio of confinement velocity to total confinement velocity is approximately 0.65, and the critical confinement smoke overflow length is around 4H. Both of these values remain largely unaffected by the heat release rate of the fire source. When the longitudinal velocity is greater than 0.2 m/s, with the longitudinal velocity increases the ratio of the confinement velocity to the total confinement velocity and the critical confinement smoke overflow length increases, and when the longitudinal velocity increases to 0.4 m/s, the critical confinemen smoke overflow length increases to 6H. The laws and values obtained from the experiments can provide reference for practical engineering. The results of the prediction model were compared and analyzed with the existing models and experimental results to verify the reliability of the model, which can be applied to a wide range of smoke vent layouts with different combinations of longitudinal ventilation and smoke exhaust velocity, and has wide applicability.

Accelerating phase-field simulation of multi-component alloy solidification by shallow artificial neural network

Low computing efficiency is a significant barrier in the phase-field modeling of multi-component alloys coupled with the CALPHAD (CALculation of PHAse Diagram) method. The fundamental issue is that the quasi-equilibrium thermodynamic data (QETD) required for calculating the chemical driving force must be acquired by repeatedly solving a large number of nonlinear equations. In this work, a novel method is developed to predict the QETD by a shallow neural network in a straightforward manner, so circumventing the repetitive numerical calculation of nonlinear quasi-equilibrium thermodynamic conditions. The numerical evaluation of a Ni-Cr-Al ternary alloy demonstrates that the proposed scheme can decrease the computing consuming to about 1/80 of that required by the conventional phase-field method when the computational domain size reaches the millimeter scale, while accurately reproducing the equiaxed dendritic growth and solute segregation kinetics during polycrystalline solidification. The method presented in this work will provide an effective tool for modeling the microstructure evolution of complex materials involved in practical engineering applications.

Social Engineering Attack in the Era of Generative AI

A significant cybersecurity risk arises when sophisticated artificial intelligence (AI) is combined with social engineering. The aim of this study was to assess how much the sophistication and skills of social engineering are enhanced through advanced AI techniques. The study found that generative AI has revolutionized social engineering in six key areas: multi-channel attack vectors, automated infrastructure for social engineering campaigns, advanced personalization, the development of evasion and obfuscation tactics, and the creation of hyper-realistic content. The study also proposed a framework to recognize the evolving threat landscape, termed the Generative AI Social Engineering Framework. It highlights how structured and organized AI can be leveraged in deceptive social engineering tactics by integrating elements from machine learning, cybersecurity, and human-computer interaction. Besides showing up the vulnerabilities, the research also lays out recommendations to safeguard organizations and individuals in order to attain digital resilience.

Assessment of the challenges of integrating cyber-physical systems in Nigerian construction

PurposeThe internet’s revolutionary impact on communication, information access and business transactions exemplifies how technology can reshape human behaviour. Similarly, cyber-physical systems (CPSs) hold immense potential to transform our interaction and control of the physical environment. This study investigates the challenges hindering the adoption of CPSs in Nigeria’s construction industry, aiming to pave the way for a more sustainable future in construction.Design/methodology/approachThis study examined challenges related to CPSs in Nigerian sustainable construction projects. Data were gathered through a literature review and a questionnaire survey of Lagos-based construction professionals (architects, quantity surveyors, engineers and builders). Statistical methods were used to analyse the data. Mean and standard deviation determined respondents’ opinions’ central tendency and variability. The data’s suitability for factor analysis was confirmed through Kaiser–Meyer–Olkin (KMO) and Bartlett’s sphericity tests, indicating strong correlations between variables. Factor analysis subsequently identified key challenges hindering CPSs implementation in Nigerian construction.FindingsThe mean and standard deviation analysis findings indicated that the most significant challenges of CPSs for sustainable construction projects in Nigeria are lack of awareness, inadequate public–private partnership, professional complacencies, data overload and lack of capital. In addition, the analysis was also conducted using factor analysis, which includes five groupings: financial inhibition, management barrier, geopolitical inhibition, national security and lack of awareness.Originality/valueThis study recommended that the challenges listed in this study should be considered and worked on through financial, political and professional support of CPSs for sustainable construction projects, such as Sustainable Development Goals (SDGs) programmes and national awareness programmes.

Transforming enterprise systems with performance engineering excellence

This article explores the transformation of enterprise systems through advanced performance engineering methodologies and strategies. It examines how Fortune 500 enterprises have revolutionized their system performance by implementing sophisticated performance engineering practices across various domains. The article investigates the challenges faced by modern enterprise applications in distributed environments, including latency issues, resource allocation inefficiencies, and workload unpredictability. Through comprehensive analysis of real-world implementations, the article demonstrates how organizations have achieved significant improvements in operational efficiency, customer satisfaction, and system reliability through strategic performance engineering initiatives. The article covers various aspects including cloud-native architectures, shift-left testing practices, and the establishment of performance-oriented organizational cultures, providing insights into best practices and measurable business impacts of performance engineering excellence.

Integration of Engineering Practices into Primary Science Classrooms

Integration of Engineering Practices into Primary Science Classrooms

Evaluating the effectiveness of rhythmic visual guidance technology for mitigating driving risks in highway tunnel groups: A simulation study.

Driving in highway tunnel groups necessitates frequent adaptation to drastic changes in the traffic environment, thereby increasing driving difficulty and risk. This study integrates drivers' preferences for rhythmic information with the inherent rhythmic characteristics of tunnel group structures to propose a new and adaptive method to mitigate driving risks using rhythmic visual guidance (RVG) technology. Unlike traditional visual guidance systems, which often rely on static signals, RVG utilizes dynamic, rhythmically varying cues to capture drivers' attention and improve situational awareness more effectively. By employing principles of fuzzy mathematics, the study quantifies the applicability of various rhythmic forms in visual guidance technology and establishes priority application principles for undulating and staggered rhythms. After verifying the accuracy of the simulation model, the effectiveness of RVG technology in mitigating driving risks in highway tunnel groups was analyzed using lateral offset, driving speed, and vehicle acceleration as evaluation metrics. The findings reveal that RVG technology significantly reduces vehicle lateral offset and enhances drivers' perception and control of tunnel sidewalls and driving trajectories. This effect is particularly pronounced under limited lighting conditions or in large tunnel groups with extended driving distances. Regardless of whether the lighting level is set at 0% or 100% of the standard brightness, the implementation of RVG results in reduced vehicle driving speeds. The variation in the 25th to 75th percentile distribution of driving speeds was insignificant, demonstrating that RVG technology effectively regulates driving speed and is not significantly affected by lighting conditions. Furthermore, when the lighting level is set at 100% of the standard brightness, the 25th to 75th percentile distribution interval of driving speeds is [89.576, 102.416], indicating the highest and least stable driving speeds suggests that blindly increasing tunnel lighting levels not only raises operating costs but may also adversely affect driving safety. This study provides novel insights into applying dynamic visual cues for highway tunnel groups' traffic operation and safety management. It has significant practical engineering value for guiding the low-carbon design of tunnel groups.

Factors influencing student interest in STEM careers: motivational, cognitive, and socioeconomic status

There is a disparity between the number of graduates and the demand for professionals in science, technology, engineering, and mathematics (STEM)-related fields globally. This gap underscores the importance of understanding and addressing the factors that drive student interest in the STEM career. Consequently, the education sector around the world is increasingly focused on identifying and improving these influencing factors to better align educational outcomes with the needs of the STEM industry. Thus, this study examined the cognitive (mathematics knowledge, science knowledge, and academic achievement), motivational (self-efficacy and outcome expectation), and socioeconomic status (parents’ education and family income) factors involved in predicting student interest in pursuing STEM careers. The data were conducted from tests, questionnaires, and documents from grade 10 and 11 students (n = 738) in Indonesia. In addition, two theoretical models (i.e., Models 1 and 2) were developed and were tested using structural equation modeling. The results showed that both models met the required standards for good fit, but Model 2 fit the data better overall, while Model 1 was only slightly below the ideal range for one measure (RMSEA). We found that motivational and cognitive factors were crucial predictors in shaping student interest in general STEM and STEM discipline–specific fields. A strong indirect effect was found in the relationship between self-efficacy and career interest through the outcome expectation factor, and the indirect effect of mathematics and science knowledge on interest in STEM careers through academic achievement is an important concern. Similar and different factors are discussed in terms of student interest in general STEM-related fields and STEM discipline–specific careers.

Quantum Software Engineering: Roadmap and Challenges Ahead

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective —key qualities of any industry-grade software— mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering.

Research on the stress concentration effects and fracture mechanisms of DC04 sheet steel with holes

DC04 sheet steel is widely used in automotive engineering because of its light weight and high ductility. The sheet steel with holes will emerge stress concentration under loading, then the peripore crack appeared around holes until the fracture occurred. The conventional analysis started around the macroscopic experiment and theoretical analysis, but the study on the evolution law and fracture mechanism of peripore cracks caused by stress concentration effect was not systematic. So, the hole stress of DC04 sheet steel with circular, oval, rectangular and diamond holes were firstly analyzed in this paper. At the same time, 15 groups of tensile tests were verified the influence of cavity shape on stress concentration. Then, taking the sheet steel with circular hole and oval hole as the research object, the evolution of aperture and stress change law were analyzed, and the corresponding empirical formula of stress concentration coefficient about net section was obtained. Finally, the microscopic crack development was observed by electron microscopy, and the crack evolution law and the fracture mechanism caused by stress concentration around the hole were analyzed. These studies will provide theoretical support for the practical engineering design.

Stimulating Engineering Students’ Potential for Sustainable Development

Engineers can play a critical role in achieving sustainable development (SD). Despite this, there is a lack of specialized courses delivered in the undergraduate engineering curricula in Lebanon. The purpose of this study is to assess the outcomes of a newly developed core course on sustainability for engineering students integrated into the undergraduate engineering curriculum at one university in Lebanon. The study makes an original contribution as no similar course, in its current structure, has been found in the existing literature. A quantitative methodology was employed through a survey administered to the students both before and after course delivery. The survey aimed to assess the effectiveness of the course in changing students’ awareness, perceptions, practices, and views on the engineering profession and how it relates to sustainability. The findings indicate that the course has significantly enhanced the understanding and awareness of the engineering students regarding sustainable development. This enhancement impacted positively on their perceptions, practices, and views regarding the importance of sustainable development in engineering education. This study presents an interdisciplinary course integrated into the engineering curriculum and augmented the students’ awareness and knowledge on how to incorporate sustainable development into their design processes.

UV-Resistant Nanostructured Anti-reflective Film for Achieving Efficiency Enhancement of Perovskite Solar Cells and Potential of Fabricating Large-Scale Cu(In, Ga)Se2 Solar Cells.

Sticker-type transparent antireflective film (STAF) is applied to perovskite solar cells (PSCs) to reduce the reflection and improve the light-trapping ability of PSCs. However, the development of STAF is hindered by many factors, such as expensive materials, low actual service life, unsatisfactory antireflective effect, and a lack of research on stability. This work proposes an ultraviolet (UV)-resistant enhanced sticker-type nanostructure acrylic resin antireflective film (SNAAF), which is applied to the incident surface of PSCs. SNAAF is prepared by using a cleverly designed two-step peeling transfer process. The average reflectance of the related device is reduced by 4.06% through the entire visible light spectrum, which also helps achieve the champion performance of the PSCs with STAF. The excellent antireflection performance increases power conversion efficiency (PCE) from 20.77% to 22.1% owing to the significantly enhanced short-circuit current density by 5.5% with the SNAAF. Additionally, the target device maintains nearly 80% of its initial PCE after 480 h of irradiation with UV light (365 nm), far exceeding the exposure levels in IEC 61215. Moreover, the designed SNAAF is applicable to large-area Cu(In, Ga)Se2 (CIGS) solar cells (area: 225 cm2), which develops a practical external engineering strategy for optimizing device performance for different types of commercial solar cells.

Barriers and facilitators to sustaining a lupus patient decision aid in regular rheumatology outpatient care in the US.

This study investigates the barriers and facilitators to sustaining a decision aid (DA) tool for patients with systemic lupus erythematosus (SLE) in routine rheumatology outpatient care across the U.S. The DA was initially developed for assisting Patients with SLE in making informed medication choices by providing personalized information on their treatment process. We conducted semi-structured interviews with clinicians, administrators, and information technology (IT) professionals from 15 geographically diverse rheumatology clinics that participated in a large-scale DA implementation trial. Interviews were transcribed and analyzed using NVivo, with thematic coding to identify key factors influencing DA sustainability. Analysis identified several facilitators, including the DA's user-friendly design and its effectiveness in simplifying complex medical information, aiding informed decision-making for Patients with SLE. Challenges included inconsistent staff knowledge about the DA, difficulties integrating the DA into clinical workflows, a lack of executive and physician champions, and a lack of structured training and communication strategies. Additionally, concerns were raised about the availability of staff support and the technological capabilities required to maintain the DA's use. To sustain the DA in clinical practice, there is a need for enhanced training programs, better workflow integration, in-clinic physician champions, and effective use of electronic medical records for DA dissemination. Addressing these areas can improve the DA's impact on patient education and decision-making, ensuring its continued use in routine care.

Harmonic analysis and optimization for closed-loop superconducting shim coils of 7 T MRI magnet.

High-resolution brain imaging is crucial in clinical diagnosis and neuroscience, with ultra-high field strength MRI systems ( ) offering significant advantages for imaging neuronal microstructures. However, achieving magnetic field homogeneity is challenging due to engineering faults during the installation of superconducting strip windings and the primary magnet. This study aims to design and optimize active superconducting shim coils for a 7 T animal MRI system, focusing on the impact of safety margin, size, and adjustability of the second-order shim coils on the MRI system's optimization. The study employs a nonlinear optimization method to determine the parameters of the shim coils, considering the size of the coil, the level of undesired harmonics, and the whole number approximation of the turns in each coil. The study also conducts a thorough robustness analysis, examining the effects of coil winding accuracy, former processing accuracy, and assembly angle accuracy on the harmonic intensity of each coil. The optimization design results for the 7 T MRI system's shim coils show that the magnetic field changes are less than 0.5 %. After second-order shimming and the harmonic coupling an, the low-order harmonics are minimized, resulting in an improved magnetic field peak-to-peak uniformity from 254.47 to 8.970ppm. The study successfully demonstrates the creation of a set of second-order shim coils for a 7 T animal MRI system through numerical optimization. The design outputs provide essential technological support for the development of a human whole-body 7 T MRI system, ensuring high-quality imaging at the neuronal level. The project also highlights the importance of considering manufacturing and assembly flaws in the shim coil design process to achieve effective shimming in practical engineering scenarios.

A Methodology Simplifying River Habitat Quality Assessment through Regression Analysis: Application in Slovakia

In the past, river restoration mainly focused on flood protection and somewhat ignored the degradation of river ecosystems, e.g., the habitats of river organisms. In recent decades, however, there has been an increased effort to carefully assess the quality of aquatic habitats as well. The authors have collected data from mountain and sub-mountain streams, including the habitat preferences of fish, topographic surveys, hydrometric measurements, and hydraulic modelling. These data were used to model the quality of stream habitats using the System of Environmental Flow Analysis (SEFA). SEFA, however, may be perceived as impractical for routine usage since the data it requires are extensive and time-consuming to obtain. For simplification, a regression analysis was conducted using only part of SEFA’s input data to balance reliability and data requirements of the computations. The general contribution of the paper is a demonstration of working with small datasets, addressing the challenges of analyzing the quality of river habitat through techniques such as boosting and regularization in regression analyses. The study confirmed a satisfactory agreement between the SEFA model’s results and the proposed regression methods, especially when using the boosting machine learning algorithm for the regression analysis (with a correlation of 0.9). The regression method significantly reduced the input data necessary to evaluate the quality of a habitat compared to the SEFA model. This permits an assessment of the ecological state of streams not only in a scientific context, but also in standard engineering practice.

Theoretical Investigation of C4F7N–CO2 Mixture Decomposition Characteristics Under Extreme Conditions

Due to their low greenhouse effect and exceptional insulating properties, C4F7N-CO2 gas mixtures have garnered significant attention. In particular, understanding the decomposition characteristics of C4F7N-CO2 is crucial for their practical use as an eco-friendly dielectric medium. At elevated temperatures, the pyrolysis of C4F7N produces high concentrations of CFN, CF3, and C2F2, along with lower levels of C3F5, C4F6N, C2F, and CN. A further increase in temperature may lead to the decomposition of CO2 into CO and additional components such as C2, C2F3, C3F4, C4F7 and C3F6, CF, CO, C3F7, C3F2, C3F, C3F3, C3F3N, C3, CF2, and CF2N. Under electrical discharge conditions, the decomposition of CO2 becomes more pronounced, forming products like CO, C2O, O2, C2O2, and C2O4, with up to 25 decomposition components observed. These include products originated from both C4F7N and CO2 and their combinations. In ultra-high electric field intensities, only small molecules such as O2, C2, C3, and N2 are detected among the decomposition products. This study aims to provide theoretical insights and valuable data to advance research into the decomposition behavior and practical engineering applications of C4F7N-CO2 gas mixtures under extreme conditions.

Insurance-Service-Oriented Maintenance Strategy of Rural Highway Slopes under Typhoon Disasters

Rural highway slopes are characterized by poor resistance to natural disasters and low insurance coverage, which poses a significant challenge for insurance companies. This paper aims to construct an insurance-oriented rural highway slope maintenance decision-making framework to optimize the slope maintenance strategy and effectively reduce the insurance company’s claim costs. Based on the expectation theory, a set of flexible calculation methods for slope maintenance cost and post-disaster reconstruction cost is proposed. Combined with the probability of slope disaster occurrence, the corresponding maintenance decision-making scheme is formulated. Through the analysis of practical engineering cases in China, the effectiveness of the proposed method is verified. The results show that, after calculating the maintenance strategy of 184 slopes, it is found that nine of the slopes are maintained before the typhoon disaster, and it is expected that claims costs can be saved of up to 44.5 million Chinese yuan. This study not only provides a new way to improve the income of insurance companies, but also provides theoretical support and practical guidance for disaster risk management of rural highway slopes.