Engineering Domain Research Articles

Advances in EV wireless charging technology – A systematic review and future trends

Current advancements in wireless power transfer systems have improved the viability of enhancing the scope of the driving journey of an Electric Vehicle. This paper addresses the prime aspects of wireless charging infrastructure using a systematic approach, such as compensation topologies, power converter circuit design, and power transfer methods. The exclusive wireless charging track on the road minimizes the size of the battery device and the charging duration of energy storage during driving. The ability to transmit high power through a coil placed on the road to the Electric Vehicle requires an appropriate design for the complete wireless power transmission module. This paper also presents advancements in the domain of wireless technologies and recommends upcoming opportunities and future research trends.

Superapps: At the crossroads of enhanced customer experience and innovation management theories

This study focuses on one of the most notable digital innovations in recent times - the SuperApps, which synergically combine customer service, mobile usability, and integrated service offerings. Utilizing a comprehensive review of existing literature, this research explores the concept of the SuperApp, evaluates the feasibility of establishing a ranking system for SuperApps, and constructs a definitive top ten list of leading applications. Following this, the research advances to a primary investigative task, which entails a thorough analysis of academic sources to assemble and define the essential attributes that define a SuperApp, specifically revealing what essential features distinguish a mobile application as a SuperApp. To achieve these objectives, the study rigorously processes scientific articles available in the Scopus and Web of Science database, aiming to enhance the academic understanding and categorization of SuperApps within the digital technology domain. Using a bibliometric mapping methodology, which involves analysing keyword occurrences through VOSViewer, the conducted research identifies a notable academic gap in Scopus- and Web of Science-indexed documents. The analysis indicates that despite the prevalence of recent articles and academic texts, the defining characteristics of a SuperApp (commonly referred to as 'Super-App' in Scopus-indexed scholarly literature) remain inadequately explored and understood. This gap underscores the need for further empirical research to clarify the features and implications of SuperApps. Such research could enrich the body of knowledge in the above-described documents, contributing to a more comprehensive understanding in the field. Based on experiences with Scopus and Web of Science by selecting some of the most cited articles from Google Scholar, this study has compiled a six-element list that encapsulates the most crucial characteristics of SuperApps. These fundamental features are then briefly analysed and presented, linked to some of the most impactful innovation management theories linked to SuperApp concepts also presented. This offers a comprehensive understanding of their significance and functionality within the SuperApp framework.

Semantic-Electromagnetic Inversion With Pretrained Multimodal Generative Model.

Across diverse domains of science and technology, electromagnetic (EM) inversion problems benefit from the ability to account for multimodal prior information to regularize their inherent ill-posedness. Indeed, besides priors that are formulated mathematically or learned from quantitative data, valuable prior information may be available in the form of text or images. Besides handling semantic multimodality, it is furthermore important to minimize the cost of adapting to a new physical measurement operator and to limit the requirements for costly labeled data. Here, these challenges are tackled with a frugal and multimodal semantic-EM inversion technique. The key ingredient is a multimodal generator of reconstruction results that can be pretrained, being agnostic to the physical measurement operator. The generator is fed by a multimodal foundation model encoding the multimodal semantic prior and a physical adapter encoding the measured data. For a new physical setting, only the lightweight physical adapter is retrained. The authors' architecture also enables a flexible iterative step-by-step solution to the inverse problem where each step can be semantically controlled. The feasibility and benefits of this methodology are demonstrated for three EM inverse problems: a canonical two-dimensional inverse-scattering problem in numerics, as well as three-dimensional and four-dimensional compressive microwave meta-imaging experiments.

Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy

The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains.

From tradition to technological advancement: embracing blockchain technology in family businesses

PurposeDespite the rapid advancement of blockchain technology across various sectors, scholarly research on its application within family businesses remains significantly underdeveloped. This study aims to address this gap by examining the application of blockchain technology within family businesses to identify key application domains, benefits and implementation challenges.Design/methodology/approachThe study employs a conceptual approach, drawing on existing literature on family businesses and blockchain technology. This review aimed to identify the unique characteristics of family businesses, their challenges and the distinctive features of blockchain technology that can potentially be mapped to each other. Based on the literature review, we develop a conceptual framework exploring blockchain technology applications in family businesses. Real-world case studies of family businesses that have implemented blockchain technology were identified to provide practical insights and implementation challenges.FindingsBlockchain technology possesses transformative potential for family businesses across several critical domains. It includes enhancing trust and transparency in operations, improving governance and decision-making and facilitating succession planning and intergenerational wealth management. Case study evidence illustrates the tangible benefits of blockchain, including enhanced supply chain transparency, optimized business processes, increased customer trust and resultant business sustainability. Blockchain technology implementation challenges include data privacy concerns, integration with legacy systems, regulatory uncertainty and change management issues.Research limitations/implicationsThis study is limited by its reliance on existing literature and case studies. It may not capture the full spectrum of challenges and opportunities associated with blockchain applications in family businesses. Future research should focus on longitudinal and empirical research to provide a deeper understanding of the impact of blockchain technology application in family businesses.Originality/valueThis study contributes to the literature by exploring the intersection of family businesses and blockchain technology, an area that has received limited academic attention. It identifies potential application domains of blockchain technology in family businesses and develops a conceptual framework based on existing literature. Through case studies, the research provides practical insights and valuable lessons for family businesses considering blockchain implementation. It also addresses key considerations and challenges, providing a clear roadmap for blockchain technology integration in family businesses. The study lays the groundwork for further research and exploration in blockchain technology and family businesses.

When inclusive growth is not enough: advances and limitations of development policies in Uruguay 2005–2019

ABSTRACT Along most of the 2005–2019 period, Uruguay experienced an impressive economic growth. The country extended its public policies across industrial, technological, and social domains. However, there was a dearth of significant structural changes, constraining Uruguay’s ability to embark on a sustained developmental trajectory. Our findings reveal that the majority of implemented policies were designed to enhance competitiveness and build capabilities horizontally, rather than promoting structural changes. This allows us to discuss the dynamic nature of developmental processes marked by increasing policy challenges. In doing so, we stress the relevance of policy support and coordination to sustain inclusive growth processes.

Love wave propagation in a smart composite structure of linear and exponential functionally graded porous piezoelectric material

Functionally graded materials (FGMs) have emerged as a promising avenue for enhancing the performance and longevity of surface acoustic wave (SAW) devices. This importance is gained due to gradual variation in functional properties of FGMs. In this paper, a mathematical model of a piezoelectric layer overlying a functionally graded porous piezoelectric (FGPP) layer resting on the elastic substrate is presented for the study of Love waves. The material parameters of the FGPP layer are considered to vary linearly and exponentially with the thickness of the layer. The solutions of governing equations corresponding to the FGPP layer are obtained by the Wentzel Kramers Brillouin (WKB) method. Dispersion equations are derived for both electrically open and shorted boundaries, linear and exponential gradation in both mechanical and electrical parameters, and for gradation in mechanical parameters alone as well. After numerical computation, dispersion curves are plotted to investigate the influence of wavenumber, type of gradation, extent of gradation, and type of boundaries on the phase and group velocity. The phase velocity decreases with wavenumber and is found more for linear gradation in comparison to exponential gradation. The electromechanical coupling factor is analyzed for different propagating modes of Love waves, in the case when gradation in mechanical properties is considered, the electromechanical coupling factor is greater than that when variation in both mechanical and electrical properties is considered. It is also observed that the tailoring of gradation can help to get the suitable value of the electromechanical coupling factor. The insights obtained from these findings can offer valuable contributions toward advancing the functionality and efficiency of SAW devices, there by bolstering their applicability in various technological domains.

Development of data-driven models to predict seismic drift response of RC wall structures: An application of deep neural networks

This research aimed to develop data-driven models using deep neural networks (DNNs) that can rapidly predict the seismic drift responses of reinforced concrete (RC) wall structures in building frame systems, aiming to overcome the challenges of costly seismic performance evaluation of building structures. A total of 46 RC wall structures ranging from four to 40 stories were analyzed and subjected to 1,000 ground motions including far-field, near-field-pulse, and near-field-no-pulse types. Two input sets were investigated initially. The first input set comprises peak ground acceleration (PGA), spectral accelerations at 1 s–6 s with 1-s intervals, and the first five natural periods of the wall structure. The second input set contains PGA and spectral accelerations at the first five natural periods of the wall structure. The DNN model developed based on the first input set demonstrated superior accuracy achieving an R2 value of 0.880, and slightly outperformed other reputable machine learning models. To enhance the applicability of the developed model, the ground motion type was incorporated as an additional variable to the first input set, which led to an R2 value of 0.869. Ultimately, two DNN models, one based on the first input set and the other considering the ground motion type, were proposed in this study. Finally, the two models were applied to quickly predict seismic drift responses of a new 24-story RC wall structure, and the predicted results were then utilized to efficiently construct the fragility function. The findings highlight the potential of DNNs as an efficient solution to address complex and costly challenges in the earthquake engineering domain.

Dynamics and control of two-dimensional discrete-time biological model incorporating weak Allee's effect.

Incorporating a weak Allee effect in a two-dimensional biological model in ℜ2, the study delves into the application of bifurcation theory, including center manifold and Ljapunov-Schmidt reduction, normal form theory, and universal unfolding, to analyze nonlinear stability issues across various engineering domains. The focus lies on the qualitative dynamics of a discrete-time system describing the interaction between prey and predator. Unlike its continuous counterpart, the discrete-time model exhibits heightened chaotic behavior. By exploring a biological Mmdel with linear functional prey response, the research elucidates the local asymptotic properties of equilibria. Additionally, employing bifurcation theory and the center manifold theorem, the analysis reveals that, for all α1 (i.e., intrinsic growth rate of prey), ð1˙ (i.e., parameter that scales the terms yn), and m (i.e., Allee effect constant), the model exhibits boundary fixed points A1 and A2, along with the unique positive fixed point A∗, given that the all parameters are positive. Additionally, stability theory is employed to explore the local dynamic characteristics, along with topological classifications, for the fixed points A1, A2, and A∗, considering the impact of the weak Allee effect on prey dynamics. A flip bifurcation is identified for the boundary fixed point A2, and a Neimark-Sacker bifurcation is observed in a small parameter neighborhood around the unique positive fixed point A∗=(mð1˙-1,α1-1-α1mð1˙-1). Furthermore, it implements two chaos control strategies, namely, state feedback and a hybrid approach. The effectiveness of these methods is demonstrated through numerical simulations, providing concrete illustrations of the theoretical findings. The model incorporates essential elements of population dynamics, considering interactions such as predation, competition, and environmental factors, along with a weak Allee effect influencing the prey population.

Soliton-based modeling of nano-ionic currents in transmission line

Many nonlinear evolution equations, such as the nano-ionic currents (NIC) equation, are used extensively in many scientific and technological domains particularly in nanoelectronics and bioelectronics. The mathematical modeling of NIC phenomena is vital for understanding their behavior and optimizing device performance. Our research leverages an array of mathematical methods, including multi-wave analysis, periodic wave solutions, lump soliton dynamics, breather wave phenomena, homoclinic breathers, M-shaped waveforms, and rogue wave analysis. Additionally, our investigation encompasses the exploration of single kink and double kink configurations, interactions between periodic and kink waves, interaction between M shaped with kink and rogue, interaction between M shaped with one kink, interaction between M shaped with kink and periodic, interaction between M shaped with two kinks as well as periodic wave interactions with lump waves. To further emphasize the structure of solutions derived from particular parameter choices, we include three-dimensional, two-dimensional, streamplot, and contour graphs.

Fluorescence capturing behaviour of cyanobacterial resilience: Insights into UV-exposed ecosystems and its environmental applications.

Cyanobacteria are resilient microorganisms and thrive in environments exposed to UV radiation, ranging from ocean surfaces to scorching hot springs and dry expanses. 'Cyanobacterial Resilience' refers to their ability to withstand UV radiation, revealing intricate genomic secrets and adaptive mechanisms ensuring survival. These mechanisms include metabolic adaptations, robust DNA repair systems and UV-protective compounds such as Scytonemin and Mycosporine, vital for shielding against UV radiation survival. Cyanobacteria are crucial pioneers in UV-exposed ecosystems, highlighting their resilience and adaptability. Some cyanobacteria exhibit luminescence, emitting blue-green light due to phycobiliproteins, while bioluminescence in cyanobacteria, if it occurs, involves different compounds rather than luciferins and luciferase enzymes. This luminescence holds promise for various biotechnological applications, such as biosensors, imaging probes and carbon sequestration, for participating in photocatalytic processes for water purification and CO2 conversion, and contributes to solar simulation studies to advance photosynthesis and renewable energy technologies. The versatile applications of these materials highlight their ecological importance and potential in addressing global challenges. In conclusion, 'Cyanobacterial Resilience' highlights the remarkable adaptation strategies of cyanobacteria in UV-exposed environments. It emphasises their role as pioneers and innovators in biological and technological domains, providing insights into their enduring impact on ecosystems and scientific advancement.

Comprehensive analysis of organic dye doped metal-coordinated single crystals: A study of structural, optical, thermal, dielectric, piezoelectric and mechanical characteristics

Organic dye can induce positive changes in optical, mechanical and electrical properties of semi-organic single crystals. Single crystals of pure and 0.01 mol% SSY dye doped ZTS are grown from a slow evaporation solution technique. The structural properties of pure and SSY-dyed ZTS crystals are characterized using SCXRD and PXRD, confirming the maintenance of crystallinity and incorporation of dye molecules within the host lattice of ZTS crystal. Both ZTS crystals belong to orthorhombic crystal system with space group Pca21. The refined lattice parameters and cell volume of present pure ZTS crystal are a = 11.1723 Å, b = 7.7989 Å, c = 15.5357 Å, α = β = γ = 90°, V = 1353.6762 Å3. The intensity of all Raman peaks in ZTS is enhanced by the incorporation of dye. Optical spectroscopic UV–visible and photoluminescence techniques highlight the comparative studies of light absorption, energy bandgap, optical constants and fluorescence emission. An increment of 0.05 eV in optical bandgap is observed in dyed ZTS. The dielectric properties with variation in frequency range (23 Hz–10 MHz) at different temperature range (30–100 °C) are evaluated through impedance spectroscopy, showing an increase in dielectric constant around 15 % and reduction in dielectric loss, indicating potential for improved performance in electronic applications. A significant enhancement is observed in the piezoelectric coefficient (d33) in dyed ZTS single crystals. The d33 value of ZTS is increased from 2.75 to 2.95 pC/N by the doping of organic dye. Vickers micro-hardness tester is used to know the mechanical strength through micro-indentation, which illustrated an increase in the hardness nature of dyed ZTS single crystals. These findings provide a foundation for the future development of ZTS-based materials for technological applications, offering a promising route for the advancement of functional materials in the domain of piezoelectric and optoelectronic technologies.

Third- and fifth-order nonlinearities in quantum dot hybrid systems: Influence of graphene and metal nanoparticles

This work examines the third-order and fifth-order nonlinear susceptibilities in a hybrid system including a semiconductor quantum dot, metallic nanoshell, and graphene nanodisk. The research uses the density matrix method to examine the dipole–dipole interaction that occurs due to the applied field. It assumes that there is an increase in the strength of a continuous-wave electromagnetic field inside the quantum dot. The inclusion of the graphene nanodisk greatly amplifies the nonlinear optical reaction of the quantum dot in this combined system, which is governed by the dipole–dipole interaction. The findings indicate a notable disparity in the nonlinear optical reaction of the semiconductor quantum dot when the parameters are modified. The possible applications of this concept include optical sensors, photonic devices, quantum computing, medical imaging, energy harvesting, and high-density data storage. Additionally, it has the potential to progress terahertz technology and simplify the development of new materials with customized optical properties. The substantial rise in nonlinearities inside the hybrid system presents prospects for advancements in diverse technological and scientific domains.

Highly Efficient Near-Infrared Luminescent Radicals with Emission Peaks over 750 nm.

Purely organic molecules exhibiting near-infrared (NIR) emission possess considerable potential for applications in both biological and optoelectronic technological domains, owing to their inherent advantages such as cost-effectiveness, biocompatibility, and facile chemical modifiability. However, the repertoire of such molecules with emission peaks exceeding 750 nm and concurrently demonstrating high photoluminescence quantum efficiency (PLQE) remains relatively scarce due to the energy gap law. Herein, we report two open-shell NIR radical emitters, denoted as DMNA-Cz-BTM and DMNA-PyID-BTM, achieved through the strategic integration of a donor group (DMNA) onto the Cz-BTM and PyID-BTM frameworks, respectively. We found that the donor-acceptor molecular structure allows the two designed radical emitters to exhibit a charge-transfer excited state and spatially separated electron and hole levels with non-bonding characteristics. Thus, the high-frequency vibrations are effectively suppressed. Besides, the reduction of low-frequency vibrations is observed. Collectively, the non-radiative decay channel is significantly suppressed, leading to exceptional NIR PLQE values. Specifically, DMNA-Cz-BTM manifests an emission peak at 758 nm alongside a PLQE of 55%, whereas DMNA-PyID-BTM exhibits an emission peak at 778 nm with a PLQE of 66%. Notably, these represent the pinnacle of PLQE among metal-free organic NIR emitters with emission peaks surpassing 750 nm.

Highly Efficient Near‐Infrared Luminescent Radicals with Emission Peaks over 750 nm

Purely organic molecules exhibiting near‐infrared (NIR) emission possess considerable potential for applications in both biological and optoelectronic technological domains, owing to their inherent advantages such as cost‐effectiveness, biocompatibility, and facile chemical modifiability. However, the repertoire of such molecules with emission peaks exceeding 750 nm and concurrently demonstrating high photoluminescence quantum efficiency (PLQE) remains relatively scarce due to the energy gap law. Herein, we report two open‐shell NIR radical emitters, denoted as DMNA‐Cz‐BTM and DMNA‐PyID‐BTM, achieved through the strategic integration of a donor group (DMNA) onto the Cz‐BTM and PyID‐BTM frameworks, respectively. We found that the donor‐acceptor molecular structure allows the two designed radical emitters to exhibit a charge‐transfer excited state and spatially separated electron and hole levels with non‐bonding characteristics. Thus, the high‐frequency vibrations are effectively suppressed. Besides, the reduction of low‐frequency vibrations is observed. Collectively, the non‐radiative decay channel is significantly suppressed, leading to exceptional NIR PLQE values. Specifically, DMNA‐Cz‐BTM manifests an emission peak at 758 nm alongside a PLQE of 55%, whereas DMNA‐PyID‐BTM exhibits an emission peak at 778 nm with a PLQE of 66%. Notably, these represent the pinnacle of PLQE among metal‐free organic NIR emitters with emission peaks surpassing 750 nm.

A robust hybrid approach with product context-aware learning and explainable AI for sentiment analysis in Amazon user reviews

AbstractIn the ever-changing world of business, gaining valuable insights from customer perspectives is crucial. Consumer evaluations are crucial performance indicators for businesses seeking to enhance their impact. Cyberspace is expanding with an increasing volume of reviews, making it challenging to extract relevant information for desired products. This research explores sentiment analysis for Amazon product reviews in the domain of communication technology, utilizing four publicly available datasets. Sentiment analysis is frequently employed to support E-Commerce platforms in monitoring customer feedback on their products and striving to understand customer needs and preferences. Acknowledging that solely relying on user reviews is insufficient to achieve the best performance, we enhance our approach by incorporating additional context from product titles and headlines for a more comprehensive understanding of the learning algorithm. This paper utilizes three distinct embedding methods, including TF-IDF, Word2Vec, and FastText. FastText outperformed other embeddings when stacked with XGBoost and CatBoost, resulting in the FastXCatStack model. This model achieved accuracy scores of 0.93, 0.93, and 0.94 on mobile electronics, major appliances, and personal care appliances datasets respectively, and linear SVM showed an accuracy score of 0.91 on software reviews when combined with FastText. This research study also provides a comprehensive analysis of deep learning-based models, including approaches like LSTM, GRU, and convolutional neural networks as well as transformer-based models such as BERT, RoBERTa, and XLNET. In the concluding phase, interpretability modeling was applied using Local Interpretable Model-Agnostic Explanations and Latent Dirichlet Allocation to gain deeper insights into the model’s decision-making process.

Bayesian estimation of some reliability characteristics for Nakagami model using adaptive progressive censoring

The focus of this research is to clarify both conventional and Bayesian parametric estimation methods for the Nakagami distribution making use of adaptive progressive Type II censored data. From a classical estimation perspective, two estimation methods are considered: maximum likelihood and least squares estimations. Along with the model parameters, three reliability metrics are estimated using point and interval estimation. Bayes estimates with gamma and inverse gamma priors are investigated by employing the squared error loss function. The Bayes computations are created using the Markov Chain Monte Carlo technique. Moreover, the classical and Bayesian intervals are also taken into consideration. For evidence of the effectiveness of the given methodologies, a simulation study and three applications from the physics, chemistry, and engineering domains are explored. Lastly three optimality criteria are applied to the stated data sets to pick the best progressive censoring strategy.

An Adaptive Spiral Strategy Dung Beetle Optimization Algorithm: Research and Applications.

The Dung Beetle Optimization (DBO) algorithm, a well-established swarm intelligence technique, has shown considerable promise in solving complex engineering design challenges. However, it is hampered by limitations such as suboptimal population initialization, sluggish search speeds, and restricted global exploration capabilities. To overcome these shortcomings, we propose an enhanced version termed Adaptive Spiral Strategy Dung Beetle Optimization (ADBO). Key enhancements include the application of the Gaussian Chaos strategy for a more effective population initialization, the integration of the Whale Spiral Search Strategy inspired by the Whale Optimization Algorithm, and the introduction of an adaptive weight factor to improve search efficiency and enhance global exploration capabilities. These improvements collectively elevate the performance of the DBO algorithm, significantly enhancing its ability to address intricate real-world problems. We evaluate the ADBO algorithm against a suite of benchmark algorithms using the CEC2017 test functions, demonstrating its superiority. Furthermore, we validate its effectiveness through applications in diverse engineering domains such as robot manipulator design, triangular linkage problems, and unmanned aerial vehicle (UAV) path planning, highlighting its impact on improving UAV safety and energy efficiency.

Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using Gene Expression Programming (GEP)

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.

A systematic literature review on the mathematical underpinning of model‐based systems engineering

AbstractThe International Council on Systems Engineering (INCOSE) has initiated a Future of Systems Engineering (FuSE) program that includes a stream for advancing the theoretical foundations of the discipline of Systems Engineering (SE). A near‐term goal of FuSE is to assess the adequacy of current theoretical foundations of SE. The discipline of SE is converging toward model‐based practices (i.e., MBSE) that have not yet reached the maturity of model‐based practices in other engineering domains. For example, finite element analysis and computational fluid dynamics are grounded in mathematical theory, while, generally, MBSE is not. However, some attempts have been made to underpin MBSE with theoretical richness. This article presents a systematic literature study that surveyed state of the art on providing MBSE with mathematical foundations. Our protocol collected over 2000 publications that were reviewed for inclusion/exclusion, categorized, and analyzed. We provide insights to the type of mathematical theories used, domains of applications, and areas of SE to which the math was applied to, among other analysis. We also provide a synthesized discussion about the field moving forward, emphasizing positive trends along with the negatives and areas of concern. Overall, we found the field to be nascent.