Engineering Domain Research Articles

Impact of artificial intelligence in transforming and replacing traditional learning and development of employees

Purpose The purpose of this study was to examine the impact of artificial intelligence interventions in transforming learning and development of organizations and to investigate whether artificial intelligence can replace traditional learning methods. Design/methodology/approach Mixed method approach was adopted to conduct the study. The sample size for quantitative study was 300 employees belonging to domains of technology, finance, health care and manufacturing. Structural equation modeling was done to arrive at the results. The qualitative study was done on 25 employees by conducting in-depth interviews followed by thematic analysis. Findings The findings of quantitative study revealed that perceived usefulness, perceived experience and learning effectiveness significantly contributed to transformation of traditional learning. The same was validated by qualitative study, and it also indicated that respondents preferred blended learning and artificial intelligence cannot replace traditional learning. Originality/value The study contributes to research by highlighting the impact of artificial intelligence in transformation of traditional learning and development based on departmental and job-specific roles as well as sectors that require physical training in addition to knowledge-based training.

Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework

Abstract The electromagnetic-gravity optimization (EMGO) framework is a novel optimization technique that integrates the fine-structure constant and leverages electromagnetism and gravity principles to achieve efficient and robust optimization solutions. Through comprehensive performance evaluation and comparative analyses against state-of-the-art optimization techniques, EMGO demonstrates superior convergence speed and solution quality. Its unique balance between exploration and exploitation, enabled by the interplay of electromagnetic and gravity forces, makes it a powerful tool for finding optimal or near-optimal solutions in complex problem landscapes. The research contributes by introducing EMGO as a promising optimization approach with diverse applications in engineering, decision support systems, machine learning, data mining, and financial optimization. EMGO’s potential to revolutionize optimization methodologies, handle real-world problems effectively, and balance global exploration and local exploitation establishes its significance. Future research opportunities include exploring adaptive mechanisms, hybrid approaches, handling high-dimensional problems, and integrating machine learning techniques to enhance its capabilities further. EMGO gives a novel approach to optimization, and its efficacy, advantages, and potential for extensive adoption open new paths for advancing optimization in many scientific, engineering, and real-world domains.

Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction

Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.

Enhancing Thermal Conductivity of Water-Ethylene Glycol Mixtures: A Study on TiO2-Al2O3 Hybrid Nanofluids with Surfactants

This analysis examines the thermal behaviour of 40% ethylene glycol-based TiO2-Al2O3 hybrid nanofluids by synthesizing them and assessing their thermal conductivity as a function of volume concentration and temperature. These hybrid nanofluids, emerging as a promising new class of advanced operating fluids, offer remarkable potential for enhancing heat transfer performance in various thermal engineering applications. Using a two-stage synthesis method, TiO2-Al2O3/40% ethylene glycol nanofluids were prepared across five distinct volume focuses (varying from 0.02% to 0.1%), incorporating Polyvinylpyrrolidone (PVP) as a stabilizing emulsifier. Precise thermal conductivity measurements were conducted over 30 °C to 80 °C, with incremental steps of 10 °C, ensuring comprehensive data collection. The investigation yielded significant findings, notably a substantial maximum thermal conductivity enhancement of 37.44%, observed at 80 °C with a 0.1% volume concentration, demonstrating pronounced sensitivity to elevated temperatures and concentrations. The strategic addition of PVP surfactant resulted in a remarkable 125% improvement in the nanofluid's stability period, although a maximum 5.33% reduction in thermal conductivity. Considering the inadequacy of existing predictive models to capture the observed data accurately, this study proposed developing a high-precision predictive model, achieving an impressive maximum deviation of less than 3%. The research concludes that these hybrid nanofluids, characterized by their exceptional stability and significantly enhanced thermal conductivity, hold immense potential to revolutionize heat transfer applications across various domains of practical thermal engineering. This breakthrough paves the way for developing more efficient, high-performance heat transfer systems, potentially catalysing energy efficiency and thermal management advancements across diverse industrial sectors.

Conceptual Model of Innovation Decision Process and Hesitation to Adopt Augmented Reality in Tourism Industry

Augmented reality (AR) is gaining popularity in tourism by improving immersive experiences. Therefore, understanding the factors influencing perceptions and barriers is critical. This study examines visitors’ attitudes and decision making hesitation. The innovation decision process is a conceptual model for comprehending user decisions and analyzing AR features and visitor characteristics. The early majority of AR users completed 401 online questionnaires. The causal relationships were investigated using PLS-SEM and IPMA. PLS-SEM results discovered that visual appeal and environmental embedding influenced AR attitudes. This study found that low tourist memorability affected visitor resistance and AR attitudes. The dimensions of visitor perception show that novelty and enjoyment significantly influence AR attitudes. Additionally, we discovered that AR attitudes significantly affected hesitation to reuse AR, revisit and engage in EWOM. IPMA results indicate that the AR attitude is significantly effective and crucial in reducing tourism AR hesitation. Additionally, we present a novel framework that integrates the domains of policy, people and technology.

The new era of Technology Mysticism: Generative Artificial Intelligence and its effects

Generative Artificial Intelligence (GenAI) started to disrupt many application areas in the domain of information technology and is developing at a rapid pace. GenAI exhibits different systemic characteristics, it is a trained technology as opposed to the engineered technologies that have been developed in the IT domain in the past. As trained technologies are heavily depending on huge amounts of training data, their behaviour is not deterministic but of a stochastic nature, leading to a limited understanding of those systems. their behaviour is emergent. Assumptions and beliefs in the abilities of technologies by their users create a new age of mysticism that can be compared with our past and the relation of people in medieval times to the age of Mysticism with respect to their understanding of nature and their surroundings. We are facing AI today like our ancestors faced incomprehensible natural phenomena. This article is discussing the resulting effects from a technical but also from a philosophical perspective.

Exploring software engineering knowledge domains

Software engineering, as the primary value-based process, is influenced by the culture a given company establishes, formal and informal procedures, and technological improvements introduced during the implementation. Having this in mind it could be articulated further how these innovative methods could result in best practices, communication flows, strong-bonded teams, and successful projects. By adding an organisational domain to the model, the relationships between management's organizational aspects and technological development approaches are discussed.This paper aims to explore and classify the software engineering domains in order to facilitate the process of managing knowledge within ICT companies. Based on a literature overview, a model is proposed which combines the multiple perspectives for adopting knowledge management practices more efficiently.

Hierarchical Bayesian modeling for Inverse Uncertainty Quantification of system thermal-hydraulics code using critical flow experimental data

The best estimate plus uncertainty methodology in nuclear system thermal-hydraulic studies necessitates a comprehensive understanding of uncertainties in system code predictions. The forward uncertainty quantification (UQ) process involves the propagation of input uncertainties through the computational models to obtain uncertainties in the outputs. To this end, achieving an accurate estimation of input uncertainties is important, which is the focus of inverse UQ (IUQ). Traditionally, research in Bayesian IUQ within the nuclear engineering domain has largely relied on single-level Bayesian inference. While being effective for relatively small datasets, this approach encounters limitations for cases with large datasets. The use of a single-level model may prove inefficient, as the resultant posterior distributions can significantly differ when distinct subsets of data are employed. To address this issue, we employ an hierarchical Bayesian model for IUQ. This approach involves organizing observations into different groups based on the test conditions, thereby accommodating varying calibration parameters across these distinct groups. In this study, we developed and implemented a hierarchical Bayesian IUQ method to consider the grouping effect of critical flow measurement data from various geometries. Comparing the outcomes of IUQ under different selections of test data using hierarchical Bayesian IUQ against those obtained from single-level Bayesian IUQ, the forward propagation of hierarchical Bayesian IUQ results demonstrates a notably improved agreement with the experimental data.

Self-adaptive weighted physics-informed neural networks for inferring bubble motion in two-phase flow

Modeling complex fluid flow using machine learning is increasingly recognized as a valuable approach for revealing multiphase fluid phenomena. Bubble dynamics represent a classical two-phase flow problem that plays a crucial role in various engineering domains. In this paper, physics-informed neural networks (PINNs) are applied to facilitate incompressible two-phase bubble motion modeling by integrating governing equations and interface evolution equations. The loss function of PINNs consists of multiple loss terms, including initial and boundary conditions constraints, partial differential equations residuals, and volume fraction constraints. The performance of PINNs is influenced by the competing effects of these loss terms. Therefore, we introduce a heuristic adaptive weights approach to automatically adjust loss weights for each training point, avoiding manual tuning and improving the accuracy of PINNs. We investigate typical bubble motion cases, specifically focusing on bubble rising and breakup, to showcase the capabilities of the proposed method. We explore the impact of weights and present the results in comparison to the baselines. Through the bubble breakup case, we illustrate that our model shows superior performance even with more complex scenarios. Then we further discuss the generalization and robustness of our model, showing their indispensability over traditional solvers in gas–liquid two-phase systems. Specifically, we accelerate computation speed in transfer learning without the need to modify the original model. We also show that our method effectively solves ill-posed problems, such as those without initial data or with incomplete or noisy boundary conditions.

AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness

This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . Traditionally, assessing fracture toughness relies heavily on destructive testing methods, specifically using edge-notch disc bend specimens subjected to three-point bending tests. These established methods are not only expensive and time-consuming but also frequently limited by the availability of data. To address these challenges, this study introduces a data fusion source modeling approach. This approach integrates primary fracture toughness test data with secondary predictive data derived from the maximum tangential stress criterion and the local strain energy density criterion. By employing adaptive boosting and general regression neural network algorithms, the models developed in this research demonstrate a marked improvement in predictive performance compared to traditional primary source models. Additionally, a feature importance analysis using Shapley Additive exPlanations values reveals that the mode mixity parameter, specimen thickness, and radius are critical factors influencing fracture toughness. The study highlights that while mode mixity emerges as the most significant factor, a reduction in specimen thickness generally leads to decreased fracture toughness, whereas an increase in radius has a more complex, often negative, effect. The results of this study indicate that AI-powered models using data fusion can overcome limitations related to data scarcity, enabling more accurate predictions in fracture mechanics. Furthermore, this approach provides a pathway for utilizing AI in other engineering domains where data sets are limited.

Simulation and non-similar analysis of magnetized SWCNT-MWCNT hybrid nanofluid flow in porous media using Darcy-Forchheimer-Brinkman model

The field of hybrid nanofluid transport through porous media holds immense promise for optimizing thermal processing and various thermodynamic applications. This study investigates the flow dynamics of a hybrid nanofluid, comprising water and a synergistic combination of single-walled and multi-walled carbon nanotubes (SWCNT-MWCNT), as it traverses a vertically stretched porous surface. The mathematical modeling of this flow scenario considers the influential factors of magnetohydrodynamics (MHD), viscous dissipation, heat sources, and ohmic heating. The Darcy-Forchheimer-Brinkman model is employed to capture the transport of fluid through the porous medium. Through the application of Local Non-Similarity (LNS) technique, the governing equations are converted into a dimensionless system and solved numerically using the robust bvp4c function in MATLAB. Interestingly, higher values of heat source parameter leads to a rising trend in the temperature profile, highlighting the intricate interplay between the thermal and fluid dynamic aspects of the system. This work provides valuable insights into the tailored design of hybrid nanofluids and porous media configurations to harness their enhanced thermal transport capabilities, with potential applications in diverse fields such as energy storage systems, heat exchangers, and thermal management devices. The findings contribute to the broader understanding of hybrid nanofluid transport in porous media and pave the way for the development of innovative thermal management solutions in a wide range of industrial and technological domains.

Unified Machine Learning Techniques for High-Dimensional Survival Data Analysis

Survival analysis is a statistical method used to analyze time-to-event data. The existence of states where event results become invisible after a given time is one of the primary difficulties with this regulation. This is considered censorship. This kind of data may be found in a variety of applications, such as mechanical system failure rates, patient mortality rates during clinical trials, and the length of unemployment in a community. Assessing the so-called survival and hazard functions is one of the primary goals of survival analysis. Many machine-learning algorithms have also been developed to tackle censored data and other difficult problems with real-world data. Machine-learning techniques enhance survival analysis by incorporating flexible modeling approaches, handling high-dimensional data, capturing nonlinear relationships, and accounting for censorship. They provide powerful tools to extract meaningful insights and make accurate predictions in time-to-event analysis across various healthcare, finance, and engineering domains. The standard statistical approaches and machine learning techniques advanced for survival analysis are structured in this unified framework, as is the implementation of some machine learning techniques applying to the GBSG2 survival analysis dataset.

Breaking Down the Metrics: A Comparative Analysis of LLM Benchmarks

Due to the fast advancement in large language models (LLMs), the natural language processing (NLP) technology domain has witnessed a massive change. It has brought ground breaking developments in how machines understand and generate human language. However, even with these advancements, it remains hard to objectively compare and evaluate LLMs' performance because various benchmarks and metrics are used in the assessment. Such a method requires this study's writers to conduct a comparative analysis of various benchmark LLMs to explain the viability of different evaluation metrics. Analyzing the recognized standards, including GLUE, SuperGLUE, and SQuAD, reveals weaknesses and potential in the present evaluating systems. It embraces quantitative assessments of model performances and benchmarking and critical evaluations of benchmark designs and their scope. The paper discusses methodological issues in benchmarking research and explains that current measures are insufficient and lack comprehensive evaluation patterns. The conclusions of this research underline the significance of creating fully integrated and efficient reference sets that match the rate of innovation in LLM ability and inform other studies and the overall subsequent advancement of more sophisticated and flexible language models.

An Improved Parallel Heterogeneous Long Short-Term Memory Model with Bayesian Optimization for Time Series Prediction

Currently, Deep Learning (DL) with the Recurrent Neural Networks (RNN) variants is being applied successfully in many domains of Engineering for prediction. In view of the demand for precise forecasting and the aid of Artificial Intelligence Tools, time series prediction reveals a vital task in decision-making and risk assessment. However, the application of novel Recurrent DL models for obtaining an accurate prediction of time series is yet to be explored. Recent trends reveal that Hybrid Neural Networks and DL models are appropriate for time series forecasts. At the same time, the model's selection and the hyperparameter's tuning can greatly impact its performance. To address this problem, a parallel long-term memory (PLSTM) model integrated with Bayesian hyperparameter optimization (PLSTM-BO) is proposed for time series prediction. The model is tuned in terms of key parameters, including the number of neurons, dropout, learning rate, and optimization technique. The model's performance is assessed using the SARS-COVID-19 cumulative cases, deaths, recovery cases, and NIFTY 50 stock closing price time series dataset. The obtained results convey that the current model exhibits remarkable performance compared to existing models.

Key Competencies among Engineering Leaders in the Malaysian Construction Industry

In the rapidly evolving field of engineering, effective leadership is essential for the successful completion of complex projects. However, engineers often hesitate to take on leadership roles and are underrepresented in leadership positions (Jamieson & Donald, 2020). While there is a growing body of literature on engineering leadership, much of it lacks empirical rigor and tends to focus on managing engineers rather than leadership competencies (Schell et al., 2021). Moreover, research specific to engineering leadership, particularly in Malaysia, is limited. This study addresses these gaps by exploring the key competencies required for engineering leaders in Malaysia’s construction industry. Using a qualitative research design, data was collected through in-depth interviews with 17 engineering leaders. Purposive sampling was employed to select participants based on specific criteria. Constant comparative analysis revealed three primary competencies: (1) the ability to establish and work toward a set vision, (2) the ability to frame and address engineering project issues, and (3) the ability to manage resources effectively. This study contributes valuable insights into the competencies that define effective engineering leaders, offering practical implications for leadership development and engineering management practices. By cultivating these competencies, organizations can enhance leadership capabilities and improve project outcomes. The findings suggest that focusing on strategic vision, problem-solving, and resource management can help foster better leadership in engineering projects. Ultimately, this research lays the foundation for further exploration into engineering leadership, particularly in non-Western contexts, and provides a model for leadership development tailored to the engineering domain.

The impact of supply chain integration practices on competitive advantage: A study of food and beverage manufacturing firms in Kenya

Purpose: This study aims to establish the relationship between supply chain integration practices and competitive advantage in Kenyan food and beverage manufacturing firms. Methodology: A cross-sectional survey design was employed. Data was collected from 146 respondents across 73 firms using structured and unstructured questionnaires. Linear regression analysis was conducted using SPSS version 28. Results: The study found positive significant correlations between functional integration, supplier integration, customer integration, and technology integration with competitive advantage of the sampled firms. Theoretical contribution: This research extends the application of systems theory, resource-based view, social exchange theory, and relational exchange theory to supply chain integration in an emerging market context. Practical implications: Food and beverage manufacturers in Kenya can enhance their competitive advantage by strategically implementing supply chain integration practices across functional, supplier, customer, and technological domains. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production; SDG 17: Partnerships for the Goals

Reducing the entry barrier for users and developers of peridynamic high performance computing

AbstractOver the past two decades, Peridynamics (PD) research has exhibited remarkable diversity, with contributions spanning more than 180 journals and involving over 1000 researchers. Despite its broad applicability, a persistent challenge remains—how to foster widespread adoption. In the engineering domain, classical continuum mechanics, predominantly facilitated by the finite element method, enjoys extensive utilization, supported by a plethora of commercial and open‐source software tools. PD simulation tools often find themselves competing with these well‐established counterparts. In research, custom codes tailored for specific applications are frequently created and applied, with little consideration for their future reuse or third‐party utilization. The complexity is further exacerbated in High‐performance computing (HPC) applications, where a deep understanding of solvers, parallel computing, and PD is imperative for a successful software development. Given the constraints of a typical doctoral thesis, large‐scale problems are often left unexplored, and research tends to be confined to simplistic geometries. This paper aims to elucidate various pathways for the seamless integration and utilization of a PD framework. We will showcase examples that illustrate how the barriers for both users and developers can be significantly lowered, ultimately propelling PD simulation tools to a higher standard of maturity in the medium term.

Larsen & Toubro: constructing competencies and building the nation

Learning outcomes The following are the objectives of the case study: building and leveraging core competence, realizing the strategic advantage of incumbency and contribution to nation building as a business potential. Case overview/synopsis Larsen and Toubro (L&T) started as a trading company in 1938. By 2023 L&T was a mammoth infrastructure company with a market cap INR 4,750bn operating 800–1,000 projects in engineering and construction at any point in time. It also worked in the domain of hydrocarbons, power and heavy engineering including defence engineering, financial services and development projects supported by technology. The company went through several phases of environmental disruptions in the form of the Second World War and India’s independence, which brought several opportunities for growth. L&T built competencies to leverage these opportunities, which also contributed to the nation building efforts in India. In 2023, several changes were occurring in the ecosystem in the form of energy changes, sustainability becoming a way of life and digitalization impacting every aspect of business. The managing director and chief executive officer Mr S.N. Subrahmanyan reiterated the need to focus on performance to make L&T a global leader in futuristic tech-driven engineering and solutions. He faced three major challenges – reducing exposure in non-core businesses, adopting technology to strengthen traditional business and leveraging the competence built over 85 years to improve the performance of L&T, while contributing to nation building. Complexity academic level This case study is suitable for MBA and executive programmes. Supplementary materials Teaching notes are available for educators only. Video of protagonist in conversation with Anant Maheshwari, President Microsoft India discussing the future plans of L&T for adopting new age technology. The video is available at the following YouTube link https://www.youtube.com/watch?v=OKb-_z_ch4E Subject code CSS 11: Strategy.

GDTE-based crack diagnosis for planetary gear: Mechanism, validation, and advantages compared to vibration-based technology

Effective health monitoring and fault diagnosis technologies are crucial to timely grasping the operation status of the planetary gear train (PGT). In this study, the mechanism for global dynamic transmission error (GDTE)-based crack diagnosis of PGT is revealed from dynamic responses and validated through experiments, and the advantages of this new technique are compared with the commonly used vibration-based technology. Initially, a dynamic model of the PGT considering various crack degrees and transfer path effects is established, and the effectiveness of the model response is verified through experiment. Subsequently, GDTE and vibration signals under different crack conditions are statistically analyzed in time domain and frequency domain to evaluate the differences in the ability to reveal gear failure information. Ultimately, the reasons for the different distribution of fault characterization information in the two signals are explained from a dynamic perspective. The results show that GDTE-based fault diagnosis exhibits greater sensitivity to gear crack degree compared to vibration-based monitoring technology, with both time and frequency domain responses containing richer fault characteristic information, particularly in low-frequency regions. The fault characteristics concentrated in higher-frequency regions in the vibration signal are mainly caused by the modulation between gear crack shock and the gear single and double tooth alternating meshing shock. Conversely, fault frequencies in GDTE signals are mainly found in low-frequency regions, as fault shocks in GDTE signals directly correlate with the rotational frequency of the cracked gear. Modulation effects and attenuation on the transfer path are identified as the main reasons for the weaker representation of gear fault information in vibration signals compared to GDTE signals.

Meta-Review of Wearable Devices for Healthcare in the Metaverse

In recent years, there has been a growing interest in leveraging the metaverse to enhance community engagement and healthcare. This paper provides a comprehensive examination of wearable devices and sensors utilized within immersive environments to improve well-being and healthcare outcomes. We categorize the healthcare application domains that employ wearable devices and identify commonly used devices and sensors based on a thorough review of the literature. Our study offers a detailed summary of these applications, highlighting their potential to enhance overall quality of life through remote monitoring, rehabilitation, and chronic disease management. Furthermore, we address existing research gaps and challenges in this field, offering insights for future research directions. This meta-review emphasizes the need for further exploration in the rapidly evolving domain of wearable healthcare technologies within the metaverse, presenting an overview of the current state of wearable devices in healthcare and underscoring their significance in advancing healthcare delivery and outcomes.