Engineering Models Research Articles

Geometric stability and optimization in reaction–diffusion models for tissue engineering: A case study with hydrogel scaffolds

This paper presents a thorough qualitative analysis of coupled reaction–diffusion equations, with an emphasis on the careful selection of boundary conditions that govern the interactions between stem cell and nanozyme concentrations within hydrogel scaffolds. These scaffolds, widely applied in cartilage tissue repair, possess unique attributes such as tunable mechanical strength and high water content, which are captured within the model through carefully chosen parametric constraints. The precision in defining boundary conditions is particularly crucial, as stem cell proliferation within these scaffolds is highly sensitive to external parameters, directly influencing their behavior. Our approach employs a geometric framework to analyze the stability of the coupled system, with a focus on discerning the delicate balance between stem cell populations and nanozyme concentrations. The goal is to ensure that the scaffold sustains optimal mechanical integrity, achieves uniform cell distribution, and maintains controlled degradation rates. Through a rigorous stability and bifurcation analysis of the reaction–diffusion equations, we explore the system’s equilibrium points and establish conditions under which these equilibria are stable or unstable. This analysis serves as a theoretical foundation for optimizing the design of hydrogel scaffolds, particularly in the context of ensuring consistent mechanical properties and efficient cellular integration. In addition to the qualitative geometric methods, we integrate Optuna, an advanced optimization algorithm, to fine-tune the model parameters. This computational tool enhances the accuracy of the parameter selection process by efficiently navigating the solution space, thereby refining the predictions of scaffold performance. The combination of geometric stability analysis with the state-of-the-art optimization provides a novel framework for addressing challenges in tissue engineering, offering new insights into the dynamics of scaffold design and the development of experimental protocols. These findings not only elucidate the stability conditions of the system but also contribute in establishing rigorous guidelines for the control of stem cell populations and nanozyme delivery rates in practical applications.

Impact of parameter selection on seismic loss and recovery time estimates: A variance-based sensitivity analysis

Probabilistic models in performance-based earthquake engineering propagate uncertainties from key input parameters to output performance measures. Although these models integrate important sources of uncertainty, several model parameters are deterministic and remain constant despite the difficulty in defining them with high confidence based on empirical or theoretical arguments. This study employs variance-based sensitivity analysis to investigate how uncertainty in (1) demands, (2) fragility functions, (3) building replacement consequences and (4) impeding factor delays impact seismic loss and recovery time estimates. The results indicate that the size of modeling uncertainty added to the simulated demand distribution has the most significant impact on the variance in seismic losses at all, but the highest hazard level, that is, 2475-year. At low hazard levels, that is, 100 and 475 years, the uncertainty in the capacity of structural components (e.g. slab–column connections) and nonstructural components (e.g. elevator) are the main contributors to variance in downtime to re-occupancy and functional recovery, respectively. At the 2475-year intensity level, the uncertainty in building replacement cost and replacement time becomes the primary contributor of the variance in the seismic loss and recovery time outputs due to the high probability of irreparable damage. The analyses presented in this article offer valuable insights into which parameters deserve more attention when conducting seismic loss and recovery time assessments.

Enhancing Engineering and Architectural Design Through Virtual Reality and Machine Learning Integration

This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. Traditional clash detection methods in Building Information Modeling (BIM) systems are predominantly reactive, identifying discrepancies only after their occurrence, leading to costly and time-consuming design revisions. By integrating ML algorithms with VR-driven BIM, our approach proactively identifies and resolves clashes, as demonstrated across 28 diverse engineering projects. The results indicate a reduction in design clashes by 16% and iterative revisions by 15%, culminating in a 12% decrease in overall project timelines. This research underscores the transformative impact of combining VR and ML on additive manufacturing (AM) workflows, significantly improving efficiency and reducing the iterative nature of traditional methods. The findings highlight the framework’s scalability and adaptability, promising substantial advancements in engineering and architecture practices.

Beyond the fourth paradigm of modeling in chemical engineering

Beyond the fourth paradigm of modeling in chemical engineering

Advances in Topological Methods for Solving Nonlinear Partial Differential Equations

Higher order nonlinear partial differential equations (PDEs) are very difficult to compare or analyze and therefore there is restriction towards analytical or numerical appraisal. The following research aims at solving the problem of the solutions of Nonlinear PDEs through the application of topological fixed-point theory and degree theory, as well as the application of variational methods towards improving the existence, uniqueness, and stability of solutions to PDEs under all possible boundary conditions. The purpose of the study is to investigate the effectiveness of these methods in multifaceted and multi-grained systems. We used both fixed-point analyses to check the convergence of solutions and degree-theoretic methods for checking multiplicity of solutions, and variational structure for critical points of solutions. Newton-Raphson and finite element techniques were used in solving solutions with much ease for increased efficiency. A selection of findings shows that topological techniques are an efficient means of handling nonlinearity in PDEs; degree theory uncovers the multiplicity of solutions while variational techniques unveil the stability characteristics. This research advances the field of nonlinear analysis beyond the prior art by broadening the scope of topology in new domains and providing enhanced methods of computation of PDEs where it matters most: where traditional linear methods fail to apply. In the context of physics, engineering, and environmental studies nonlinear modeling is vital and the practical implication highlighted herein is useful for all. Possible future work might apply these methods to more general boundary conditions as well as combined analytical-numerical approaches.

Exploration of Three-dimensional Modeling by Unmanned Aerial Vehicles for Application in Practical Engineering

Citing actual engineering cases, this paper aims to enhance the application of the model in actual engineering by describing the experience gained in the process of generating 3D stereo models generated by UAV aerial surveys, as well as the characteristics of the aerial photography and model generation process.

Application of 3D Virtual Digital Visualization Technology in the Simulation and Modeling of Cross-Sea Network Engineering

This paper analyzes the importance of cross-sea interconnection projects and delves into the application of 3D virtual digital visualization technology in the simulation of these projects. The system employs OpenGL technology to render 3D images and supports environmental configuration through dynamic link libraries. The internal design of the system is clear, featuring functionalities such as multi-scene dynamic focusing, multi-data port linkage, BIM information display, event tracing, and process simulation. Compared with traditional construction techniques, 3D virtual visualization technology exhibits significant advantages in graphic rendering, scheme selection, collaboration, and construction methods. The research presented in this paper provides valuable references for optimizing cross-sea interconnection projects and other major infrastructure constructions, demonstrating the immense potential of 3D virtual visualization technology in enhancing construction efficiency and reliability.

An Upscaling Machine Learning Method Based On Physical Information

Objective: The objective of this study is to investigate a machine learning methodology based on the physics of dynamic fluid flow, with the aim of incorporating the knowledge of physical laws into learning algorithms to estimate precise results with lower computational cost. Theoretical Framework: This section presents the main concepts and theories that underpin the research. Key theories include nonlinear partial differential equations (PDEs), Darcy's Law, and the conservation of mass and energy, providing a solid foundation for understanding the research context. Method: The methodology adopted for this research involves modeling a one-dimensional two-phase fluid flow problem in a porous medium, governed by a first-order hyperbolic nonlinear equation. Data collection was conducted through numerical simulations, using micro and macro grids to evaluate permeability and boundary conditions. Results and Discussion: The results revealed that considering learning only in absolute permeability yielded good estimates of reservoir pressures. However, small discrepancies were observed in the estimation of saturations. The discussion contextualizes these results in light of the theoretical framework, highlighting the identified implications and relationships, as well as the study's limitations. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of reservoir modeling and fluid flow simulations. These implications may include optimizing oil extraction processes and improving predictive models in reservoir engineering. Originality/Value: This study contributes to literature by presenting an innovative approach that integrates physical laws into machine learning algorithms, resulting in more accurate and efficient estimates. The relevance and value of this research are evidenced by the potential application of the results in optimizing industrial processes and improving computational simulations.

Interpretable machine learning models for COPD ease of breathing estimation.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitoring has been shown to improve quality of life and reduce exacerbations, but remains an ongoing area of research. We introduce a novel method for estimating changes in ease of breathing for COPD patients, using obstructed breathing data collected via wearables. Physiological signals were recorded, including respiratory airflow, acceleration, audio, and bio-impedance. By comparing patient-specific measurements, this approach enables non-intrusive remote monitoring. We analyze the influence of signal selection, window parameters, feature engineering, and classification models on predictive performance, finding that acceleration signals are most effective, complemented by audio signals. The best model achieves an F1-score of 0.83. To facilitate clinical adoption, we incorporate interpretability by designing novel saliency map methods, highlighting important aspects of the signals. We adapt local explainability techniques to time series and introduce a novel imputation method for periodic signals, improving faithfulness to the data and interpretability.

Sensitivity Analysis and Application of the Shanghai Model in Ultra-Deep Excavation Engineering

In deep foundation pit engineering, the soil undergoes a complex stress path, encompassing both loading and unloading phases. The Shanghai model, an advanced constitutive model, effectively accounts for the soil’s deformation characteristics under these varied stress paths, which is essential for accurately predicting the horizontal displacement and surface settlement of the foundation pit’s enclosure structure. This model comprises eight material parameters, three initial state parameters, and one small-strain parameter. Despite its sophistication, there is a scarcity of numerical studies exploring the correlation between these parameters and the deformation patterns in foundation pit engineering. This paper initially establishes the superiority of the Shanghai model in ultra-deep circular vertical shaft foundation pit engineering by examining a case study of a nursery circular ultra-deep vertical shaft foundation pit, which is part of the Suzhou River section’s deep drainage and storage pipeline system pilot project in Shanghai. Subsequently, utilizing an idealized foundation pit engineering model, a comprehensive sensitivity analysis of the Shanghai model’s multi-parameter values across their full range was performed using orthogonal experiments. The findings revealed that the parameter most sensitive to the lateral displacement of the underground continuous wall was κ, with an increase in κ leading to a corresponding increase in displacement. Similarly, the parameter most sensitive to surface subsidence outside the pit was λ, with an increase in λ resulting in greater subsidence. Lastly, the parameter most sensitive to soil uplift at the bottom of the pit was also κ, with an increase in κ leading to more significant uplift.

Multiset Grammars as a Basic Knowledge Representation Model for Intelligent Systems Enabling Chemical Reactions Engineering

Application of multiset grammars as a knowledge representation model for intelligent systems, enabling analysis, optimization and design of complex chemical reactions, is proposed. This approach makes it possible to implant chemical knowledge bases into knowledge bases of intelligent systems, implementing smart planning and scheduling in cyberphysical industry, and thus to move to integrated end-to-end smart control of vast heterogeneous industrial clusters.

Las políticas educativas de la ingeniería civil en México. Una estratigrafía histórica

As a result of the social approach adopted in recent years by the Mexican Federal Government, a new strategy has emerged to promote higher education, focusing on the most marginalized individuals and communities, with the goal of fostering their inclusion in professional training at this level. This research presents the philosophical foundations of this initiative, based on the concept of “episteme”; it includes a review of the evolution of higher education in Mexico from the perspective of civil engineering, utilizing a stratigraphic model of knowledge. In particular, the creation of the Benito Juárez García System of Universities for Welfare and the Civil Engineering School in Texcoco, State of Mexico, is described. The latter serves as an alternative to address the consequences of educational policies implemented under the so-called neoliberal framework, representing a potential option for improved social participation in the development of specialized technical capacities. The results and conclusions demonstrate that the educational reforms implemented do not follow a continuous discursive line but rather emerge in response to the episteme or discursive stratum of the moment. As a case study, the current higher education model in civil engineering is presented. In the same way, the results of the new model of the University of Civil Engineering are presented.

3D bioprinting approaches for enhancing stem cell-based neural tissue regeneration.

Three-dimensional (3D) bioprinting holds immense promise for advancing stem cell research and developing novel therapeutic strategies in the field of neural tissue engineering and disease modeling. This paper critically analyzes recent breakthroughs in 3D bioprinting, specifically focusing on its application in these areas. We comprehensively explore the advantages and limitations of various 3D printing methods, the selection and formulation of bioink materials tailored for neural stem cells, and the incorporation of nanomaterials with dual functionality, enhancing the bioprinting process and promoting neurogenesis pathways. Furthermore, the paper reviews the diverse range of stem cells employed in neural bioprinting research, discussing their potential applications and associated challenges. We also introduce the emerging field of 4D bioprinting, highlighting current efforts to develop time-responsive constructs that improve the integration and functionality of bioprinted neural tissues. In short, this manuscript aims to provide a comprehensive understanding of this rapidly evolving field. It underscores the transformative potential of 3D and 4D bioprinting technologies in revolutionizing stem cell research and paving the way for novel therapeutic solutions for neurological disorders and injuries, ultimately contributing significantly to the advancement of regenerative medicine. STATEMENT OF SIGNIFICANCE: This comprehensive review critically examines the current bioprinting research landscape, highlighting efforts to overcome key limitations in printing technologies-improving cell viability post-printing, enhancing resolution, and optimizing cross-linking efficiencies. The continuous refinement of material compositions aims to control the spatiotemporal delivery of therapeutic agents, ensuring better integration of transplanted cells with host tissues. Specifically, the review focuses on groundbreaking advancements in neural tissue engineering. The development of next-generation bioinks, hydrogels, and scaffolds specifically designed for neural regeneration complexities holds the potential to revolutionize treatments for debilitating neural conditions, especially when nanotechnologies are being incorporated. This review offers the readers both a comprehensive analysis of current breakthroughs and an insightful perspective on the future trajectory of neural tissue engineering.

Computational Approaches to Solving Some Partial Differential Equations and Neutrosophic Partial Differential with Variable Coefficients Using the Laplace Residual Power Series Method

We employ the Laplace Residual Power Series Method to approximate analytical solutions for differential equations and neutrosophic differential equations with associated parameters, including non-homogeneous equations and fractional formulas in partial differential equations (PDEs). This approach showcases the method's simplicity, effectiveness, and robustness in deriving analytical series solutions for PDEs that involve associated parameters, especially in the context of fractional differential equations. Several practical uses of LRPSM with an emphasis on non-homogeneous and partial differential equations and neutrosophic equations with fractions (PDEs). These applications are significant in a variety of scientific and engineering domains that simulate complicated dynamic system such as anomalous diffusion in physics, viscoelastic material modeling in engineering and signal processing.

Exploring the expertise of large language models in materials science and metallurgical engineering

Exploring the expertise of large language models in materials science and metallurgical engineering

Engineering models of turbulence in the Venus atmosphere: Application to DAVINCI and other missions

Engineering models of turbulence in the Venus atmosphere: Application to DAVINCI and other missions

Nanoliter Hydrogel Array for Cell Screening and Cell Spheroid Sorting

AbstractThe transition from two‐dimensional (2D) to physiologically relevant three‐dimensional (3D) cell models has revolutionized biomedical research. Hydrogels are frequently used to produce 3D models for tissue engineering, disease modeling, and high‐throughput screenings (HTS). However, integrating 3D cultures into HTS workflows presents challenges, including automation compatibility and cost constraints. Addressing these challenges requires innovative approaches that enable miniaturization, automation, and cost reduction while maintaining experimental fidelity. The Droplet Microarray platform, based on hydrophilic‐superhydrophobic surface patterning, facilitates the formation of nanoliter‐hydrogel arrays containing cells or spheroids. This method allows dispensing of hundreds of nanoliter‐hydrogel droplets with precise control over volume and cell density, reducing reagent consumption and offering high‐throughput applications. Here, we demonstrate stable nanoliter‐hydrogel arrays on a chip, enabling experimental procedures such as washing and medium immersion. Our approach demonstrates that spheroid‐containing droplets can be gelled at any point of the experiment, allowing for the fixation of cell structures on the surface. The selective gelation of individual droplets enables spheroid sorting by stabilizing desired droplets while pooling the others. This method holds the potential for HTS and miniaturized workflows in 3D microenvironments, thereby advancing research in different fields such as cell, cell spheroid, or organoid screenings, drug screenings, and precision medicine.

Use of AI Methods with Large Language Models in Requirements and Test Engineering

Use of AI Methods with Large Language Models in Requirements and Test Engineering

Residual power series scheme treatments for fractional Klein-Gordon problem arising in soliton theory.

The Klein-Gordon problem (KGP) is one of the interesting models that appear in many scientific phenomena. These models are characterized by memory effects, which provide insight into complex phenomena in the fields of physics. In this regard, we propose a new robust algorithm called the confluent Bernoulli approach with residual power series scheme (CBCA-RPSS) to give an approximate solution for the fractional nonlinear KGP. The convergence, uniqueness and error analysis of the proposed method are discussed in detail. A comparison of the numerical results obtained by CBCA-RPSS with the results obtained by some well-known algorithms is presented. Numerical simulations using base errors indicate that CBCA-RPSS is an accurate and efficient technique and thus can be used to solve linear and nonlinear fractional models in physics and engineering. All the numerical results for the studied problems were obtained through implementation codes in Matlab R2017b.

Assessing STEM Outreach for historically marginalized groups in engineering: STEM/engineering identity and role models

Evaluating outreach efforts can include measuring changes in factors such as Engineering or STEM Identity. STEM/Engineering Identity involves an internal feeling of belonging and recognition from others (e.g., family, teachers). Strong STEM/Engineering Identity is associated with entering STEM careers. This pilot study examines one large outreach program to determine: are there changes in STEM or Engineering Identity after a weeklong day-camp? Are there differences associated with gender, age, race, or intersectional identities, and/or having role models or parents in engineering? Elementary campers (grades 4-7), high school campers and volunteer Junior Instructors (both grades 8-12) were surveyed pre- and post-camp. We measured STEM Identity for elementary campers and Engineering Identity for teenagers through validated survey measures. Teenagers were also asked about role models, having parents in engineering, and career goals. While there were small increases in STEM/Engineering Identity, participants generally started with high STEM/Engineering Identity and frequently had role models in engineering.