Engineering Models Research Articles

Microfluidic systems for modeling digestive cancer: a review of recent progress.

Purpose. This review aims to highlight current improvements in microfluidic devices designed for digestive cancer simulation. The review emphasizes the use of multicellular 3D tissue engineering models to understand the complicated biology of the tumor microenvironment (TME) and cancer progression. The purpose is to develop oncology research and improve digestive cancer patients' lives.Methods. This review analyzes recent research on microfluidic devices for mimicking digestive cancer. It uses tissue-engineered microfluidic devices, notably organs on a chip (OOC), to simulate human organ function in the lab. Cell cultivation on modern three-dimensional hydrogel platforms allows precise geometry, biological components, and physiological qualities. The review analyzes novel methodologies, key findings, and technical progress to explain this field's advances.Results. This study discusses current advances in microfluidic devices for mimicking digestive cancer. Micro physiological systems with multicellular 3D tissue engineering models are emphasized. These systems capture complex biochemical gradients, niche variables, and dynamic cell-cell interactions in the tumor microenvironment (TME). These models reveal stomach cancer biology and progression by duplicating the TME. Recent discoveries and technology advances have improved our understanding of gut cancer biology, as shown in the review.Conclusion. Microfluidic systems play a crucial role in modeling digestive cancer and furthering oncology research. These platforms could transform drug development and treatment by revealing the complex biology of the tumor microenvironment and cancer progression. The review provides a complete summary of recent advances and suggests future research for field professionals. The review's major goal is to further medical research and improve digestive cancer patients' lives.

Modeling the impact of terrain surface deformation on drag force using discrete element method and empirical formulation

Understanding motion-induced terrain deformation is essential for improving predictive models in geotechnical engineering, infrastructure development, and robotics. The existing analysis assumes a uniform and unchanged environment and lacks a description of the terrain deformation induced by motion, which is particularly significant when the grains are loosely packed. To address this gap, we performed numerical investigations to mathematically model the changes in the surrounding environment and their corresponding effects on resistance. Specifically, we examined the response of granular materials and the energy variation of the particles during motion. Increased energy in the direction of motion was categorized as influences above or below the medium surface. Aligned with the influencing factors, two variables were considered to quantify the impact on the drag force. Building upon the energy analysis and the framework of Resistive Force Theory, we proposed a drag force model for buried motion in loose granular terrain. Compared with the experimental data, the average predictive error decreased from 17.8% to less than 2.7%. Our study provides a feasible approach for incorporating the effects of terrain deformation into a drag force model, thereby enhancing the accuracy and contributing to the development of granular locomotion.

Utilizing language models for advanced electrocardiogram analysis

Electrocardiography (ECG) signals are often referred to as the language of the heart and have been widely utilized for diagnosing various heart ailments, particularly arrhythmias. Consequently, numerous machine learning models have been employed to automatically detect heart disorders using ECG signals. In this research, the primary objective is to detect arrhythmias using a center-symmetric self-organized textual pattern. A novel feature engineering model has been introduced, which includes the following components: (i) multilevel feature extraction using the proposed center-symmetric self-organized textual pattern (CSSOTP), (ii) iterative neighborhood component analysis (INCA), and (iii) classification using k-nearest neighbors (kNN) with 10-fold cross-validation. During the feature extraction phase, a multilevel feature extraction model incorporating maximum absolute pooling (MAP) and the proposed CSSOTP was applied. The CSSOTP was designed to select the most appropriate pattern for the given data block. A large language model (LLM) was leveraged to generate text for creating patterns, and ChatGPT was used to assist in text generation. To identify the most informative features, the INCA feature selector was employed, and the selected features were subsequently classified using the kNN classifier. An impressive 96.20 % classification accuracy was achieved by the CSSOTP-based feature engineering model when tested on an ECG dataset containing 17 classes. Furthermore, comparisons with state-of-the-art feature engineering models were conducted, demonstrating that the proposed model has superior classification capabilities.

Photoelectrochemistry and Photochemistry for Sustainable Organic Synthesis

To address the sustainability issues of the existing industrial processes for producing fine chemicals and pharmaceuticals, we should develop alternative methods that raise energy efficiency, atom economy and eliminate toxic chemicals.1 One way to do that is to take inspiration from biological systems - Nature itself offers paradigms for sustainability. For decades, the natural systems, and in particular photosynthesis, have provided the principal model for solar-energy science and engineering. Therefore, electron and charge transfer are not only at the core of life-sustaining biological processes (photosynthesis), but also at the core of modern sustainable technology (solar cells, energy storage, energy conversion).2 Regarding sustainable organic synthesis chemists also realized the potential of electron and charge transfer. Electrochemistry3 together with photochemistry4 are one of the greenest ways for driving chemical transformations, since their application improves atom economy, efficiency and prevents generation of toxic wastes.Both of them have given rise to new and efficient access to some of the most reactive intermediates,that are challenging or impossible to generate by traditional chemical methods. Our main efforts concern development of new methods for an ecological and sustainable organic synthesis. On one hand, our work describe the application of NHC-Cu three-coordinate complexes as photocatalysts in energy and electron transfer reactions. Taking into account photophysical properties of NHC-Cu(I) complexes along with the fact that they are straightforward in synthesis, non-toxic, inexpensive and abundant, we envisioned that their application as photocatalysts could provide a general alternative for toxic, expensive and rare Ir- and Ru-based complexes.5 On the other hand, the constructive merge of electrochemistry and photochemistry offers the potential to overcome the drawbacks of one method through the complementary advantages of the other. One of the effective ways for their merge is interfacial photoelectrochemistry (iPEC), where the reaction occurs at photoelectrode surface.6 Our work in this area describes mild and efficient electrochemical methods for [2+1], [2+2] and [2+4] cycloaddition reactions of alkene radical cations.7 Anodic oxidation of olefins at the photoanode surface produces electrophilic alkene radical cations, which further react with nucleophiles in a formal [2+1], [2+2] and [2+4] cycloaddition towards synthesis of 3-,4-,6-membered rings. The advantages of the application of photoelectrodes in organic synthesis include high selectivity, the use on reusable materials, non-precious metal catalysts and lost but not least significant energy savings.[1] R. Gorini at all Energy Strateg. Rev. 2019, 24, 38 [2] Y. Wang, H. Suzuki, J. Xie, O. Tomita, D. J. Martin, M. Higashi, D. Kong, R. Abe, J. Tang Chem. Rev. 2018, 118, 5201 [3] a) S. Waldvogel at all Chem. Sci. 2020, 11, 12386; b) K. D. Moeller Tetrahedron 2000, 56, 9527; c) P. S. Baran at all Chem. Rev. 2017, 117, 13230; [4] a) D.W.C. MacMillan at all J. Org. Chem. 2016, 81, 6898; b) P. Melchiorre at all Angew. Chem. Int. Ed. 2019, 58, 3730; c) T. P. Yoon at all Chem. Rev. 2016, 116, 10035; d) M. Rueping at all Chem. Sci. 2020, 11, 4051; e) F. Glorius at all Chem. Soc. Rev. 2018, 47, 7190. [5] K. Rybicka-Jasińska at all unpublished results; [6] K. Rybicka-Jasińska, V. I. Vullev Charge Transfer & Organic Photoelectrochemistry ACS in focus 2023 DOI: 10.1021/acsinfocus.7e7025 [6] K. Rybicka-Jasińska et all unpublished results

Research on Automated Modeling Technology of Sluice Gate Project Based on BIM Technology.

In order to improve the efficiency and accuracy of the modeling and design work of the sluice gate project, this paper proposes an automatic generation template of the sluice gate project with customized semantics and project layout scheme, aiming at realizing the rapid assembling of all kinds of components of the sluice gate project. In the construction process, this paper first starts from basic parametric modeling and proposes constraints as the basis of modeling. Subsequently, a template library framework is developed based on the constraints to ensure that the generated templates have a high degree of standardization and consistency. Finally, an efficient and flexible template library is successfully constructed by using the customized classes and functions of Revit API, which provides powerful technical support for the modeling and design work of sluice gate engineering. This achievement helps to promote the informationization and intelligent development of the water conservancy engineering industry, and its versatility and scalability also make it have a wide range of application prospects in other water conservancy engineering fields.

A novel scale-bridging method for MSMA linking continuum thermodynamics constitutive formulations to lumped system-level models

This work introduces a novel scale-bridging method between a continuum thermodynamics constitutive model and a lumped system-level model for magnetic shape memory alloys (MSMA). With this method, system models for real-time operations are generated based on virtual experiments using the constitutive model. The proposed method addresses the fact that, while constitutive models for MSMA typically only require small sets of parameters as input, their evaluation is still computationally expensive. System models for control engineering, however, require extensive experimental parameterization, while their evaluation is highly time-efficient. The proposed scale-bridging method has the potential to combine a small parameterization effort and a low computational cost of the real-time system model. Additionally, the constitutive model is utilized to investigate whether it can determine the individual behavior of MSMA samples. This is important since the inherent model parameters, while valid for ideal single crystals, deviate for non-ideal MSMA sample behavior. To this end, the MSMA constitutive model, based on a global variational principle originally proposed by Kiefer et al is supplemented by various extensions, including a more robust algorithmic treatment. A parameter identification procedure is introduced to optimize the constitutive model parameters based on an outer hysteresis curve for a particular load case. By conducting virtual experiments with the constitutive model, data sets are generated to parameterize Preisach hysteresis models as numerical approximations of the constitutive models. The resulting hysteresis models are compared with physical experiments using an MSMA test bench for different load cases. It is shown that the proposed scale-bridging method successfully generates hysteresis models derived from constitutive models. While maintaining accuracy comparable to strictly phenomenological models across various load cases (as validated through physical MSMA test bench experiments), these models require significantly less parameterization effort than classical system models. This translates to faster model creation and broader applicability.

3D Printed PLA Porous Scaffolds with Engineered Cell Size and Porosity Promote the Effectiveness of the Kelvin Model for Bone Tissue Engineering

AbstractIn this study, the aim is to investigate the effect of engineering the cell size and porosity of 3D‐printed poly lactic acid (PLA) porous scaffolds from the Kelvin model for bone tissue engineering applications. The Kelvin model is used as a bone tissue scaffold with different cell sizes and porosities. PLA, as a biodegradable and biocompatible polymer, is used to fabricate these scaffolds using the FDM technique. A compression test is used to evaluate the mechanical properties of scaffolds. The MTT assay has been used to investigate cell viability. For osteogenic differentiation studies, ALP activity and ARS assays are used. Increasing the porosity reduces the mechanical properties of the scaffold. While increasing the cell size at constant porosity increases the Young's modulus and yield stress in the samples, it is also observed that, in high porosities, the increase in cell size weakens the mechanical properties. Also, Kelvin model scaffolds help the proliferation and osteogenic differentiation of cells and have no toxic effect. It is demonstrated that this approach promotes the effectiveness of the Kelvin architecture for bone tissue engineering. As a result, designing the most suitable model based on cell size and porosity for the treatment process in the targeted area could be promising.

Stagnant rings and uniform film analysis of Phan-Thien Tanner fluid film flow on a vertically upward moving tube

Analyzing Phan-Thien Tanner fluid film behavior on a vertically upward moving tube can help predictive models in engineering, notably in coating and lubrication operations. This paper provides a theoretical analysis of the dynamics of stagnant rings and uniform Phan-Thien Tanner fluid film adhered to a vertically upward moving tube. Formulated ordinary differential equations are solved to get exact analytic expressions for velocity, flow rate, average velocity, shear stress components, and stagnant rings. Highly nonlinear algebraic equations are solved using Newton's method in MAPLE to find the linear and exponential Phan-Thien Tanner film thicknesses. The uniform film thickness widens with increasing constant tube velocity, while it decreases with increasing Deborah number, Stokes number, and elongation parameter. The analysis delineates that stagnant rings shrink around the tube as the Stokes number, Deborah number, and elongation parameter increase. The minimal Stokes condition for the existence of a realistic stagnant ring is also determined. It has been established that whenever the Stokes number is less than the minimal Stokes condition, stagnant rings do not form. A comparison between the exponential Phan-Thien Tanner, linear Phan-Thien Tanner, upper convected Maxwell, and Newtonian fluids is also provided for stagnant rings and fluid film thickness. Following the application of an efficient approximation on tube geometry, plate geometry is approximated, and the outcomes are consistent with existing literature. The results of this research are significant for a wide range of biofluid applications, including agrochemical uses, paint and surface coating flow behavior, thin films on the cornea and lungs, and chemical and nuclear reactor design.

Risk early warning model of tunnel engineering based on computer vision and CNN

Abstract Safety management in tunnel construction is important to ensure the safety of construction personnel and equipment. This paper proposes a tunnel engineering risk early warning model based on computer vision and convolutional neural network (CNN), aiming at realizing real-time monitoring and risk early warning of the construction site using modern image processing technology and deep learning algorithm. Many image data of tunnel engineering construction sites were collected, and the data were preprocessed and marked. Then, the paper designs a depth CNN model for feature extraction and risk identification of construction site images. Through the performance evaluation of the model on the training set and test set, the effectiveness and feasibility of the model in risk early warning are verified. The experimental results show that the proposed model achieves high accuracy, recall, and F1 score on the test set, which is superior to the traditional method based on image features and artificial rules. At the same time, we analyzed the prediction results of the model on the actual construction site images. We found that the model can effectively identify different risk factors and has strong generalization ability and practicability. To sum up, this study provides a new technical means and solution for the safety management of tunnel engineering, which has important theoretical and practical significance.

Fine-Scale Dietary Polyphenol Intake Is Associated with Systemic and Gastrointestinal Inflammation in Healthy Adults

BackgroundPolyphenols are dietary bioactive compounds, many of which have anti-inflammatory properties. However, information on the intake of dietary polyphenols at the class and compound levels and their associations with gastrointestinal (GI) and systemic inflammation is lacking. ObjectivesEstimate dietary polyphenol intake in healthy adults and examine its relationship with GI and systemic inflammation markers. MethodsHealthy adults (n = 350) completed the United States Department of Agriculture Nutritional Phenotyping Study, an observational, cross-sectional study balanced for age, sex, and body mass index. Dietary intake, assessed via multiple 24-h recalls, was ingredientized and mapped to FooDB, a comprehensive food composition database. Dietary polyphenol intake (total, class, compound) was estimated and examined for its relationship to GI and systemic inflammation markers using linear models and random forest regressions. ResultsMean total polyphenol intake was ∼914 mg/1000 kcal/d with flavonoids as the greatest class contributor (495 mg/1000 kcal/d). Tea, coffee, and fruits were among the largest food contributors to polyphenol intake. Total polyphenol intake was negatively associated with the GI inflammation marker, fecal calprotectin (β = −0.004, P = 0.04). At the class level, polyphenols categorized as prenol lipids (β = −0.94, P < 0.01) and phenylpropanoic acids (β = −0.92, P < 0.01) were negatively associated with plasma lipopolysaccharide-binding protein, a proxy for GI permeability. Food sources of these two classes included mainly olive products. We further detected a positive association between C-reactive protein and polyphenols in the “cinnamic acids and derivatives” class using hierarchical feature engineering and random forest modeling. ConclusionsEven in healthy adults, dietary polyphenol intake was negatively associated with GI inflammation and intake of prenol lipids and phenylpropanoic acids was negatively associated with GI permeability. Relationships between polyphenol intake and inflammatory outcomes varied with the resolution—total, class, compound—of polyphenol intake, suggesting a nuanced impact of polyphenols on GI and systemic inflammation.This trial was registered at clinicaltrials.gov as NCT02367287.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Water Conservancy Hub Geometric Modeling Based on Point Clouds

For hydraulic engineering, accurate geometric modeling is crucial for design, analysis, and maintenance. In recent years, point cloud technology, with its high precision and detailed data capture capabilities, has gradually become the preferred method for geometric modeling in hydraulic engineering. However, traditional point cloud processing methods exhibit slow data processing speeds and poor mesh quality when reconstructing 3D models. Therefore, we have studied the meshing method for the 3D reconstruction of large-scale discrete point cloud data and proposed a meshing method based on the Delaunay criterion and the growth method to achieve rapid meshing of discrete point cloud data. To demonstrate the efficiency and convenience of the proposed method, point cloud reconstruction experiments are conducted on various buildings. We compare our approach with the Poisson, BPA, and ONet methods. Additionally, we apply it to the Jiangsu Gaogang Water Conservancy Hub in China. The results show that our proposed method is superior in both reconstruction quality and efficiency, providing a promising solution for the reconstruction of 3D building models from point clouds.

Evaluation of Engineering Models for Large‐Scale Cluster Wakes With the Help of In Situ Airborne Measurements

Evaluation of Engineering Models for Large‐Scale Cluster Wakes With the Help of In Situ Airborne Measurements

Novel exact traveling wave solutions of Newton-Schrödinger system using Nucci reduction and Sardar sub-equation methods

The main theme of this piece of research is to tackle a coupled Newton-Schrödinger type model. Two analytical techniques namely, Nucci reduction method and Sardar sub-equation methods have been employed to scrutinize exact traveling wave solutions. Through the application of these approaches, various solitary and traveling wave solutions including bright, dark and singular periodic solitons, have been obtained. Further, we have not only discussed the physical depiction of specific solutions but have also visually presented them through two dimensional (2D), three dimensional (3D), and density plots utilizing relevant parameter values. The extracted solutions show that the proposed methods are effective, simple, and successful in pinpointing the exact solution of models in engineering, optics, and other nonlinear disciplines.

Emerging Advanced Technologies for Game Engineering

In this paper, the outcomes of the 8th International Workshop on Games and Software Engineering (GAS 2024)1 are reported. The one-day workshop has been held as part of the 46th International Conference on Software Engineering (ICSE 2024) in Lisbon, Portugal on April 14, 2024. The workshop programme includes two exciting keynotes discussing topics related to harnessing video game simulations to generate content and locate bugs, and the experience of maintaining a popular FOSS library, raylib. There are three research paper sessions. The first relates to automation in game engineering; the second explores testing and quality assurance; and the third discusses specification and quality of service. The conclusion of the workshop is anchored by a panel of four researchers, educators, and practitioners discussing the current strengths and limitations of large language models in game engineering.

The 13th International Workshop on Genetic Improvement(GI @ ICSE 2024)

The GI @ ICSE 2024 workshop, held 16 April, in addition to presentations, contained a keynote on how to use Genetic Improvement to control deep AI large language models in software engineering and a tutorial on a language independent tool for GI research. We summarise these, the papers, people, prizes, acknowledgements, discussions and hopes for the future.

3D printing assisted surface patterning process on acrylated hydrogels for contact guidance of fibroblasts

Generating stable and customizable topography on hydrogel surfaces with contact guidance potential is critical as it can direct/influence cell growth. This necessitates the development of new techniques for surface patterning of the hydrogels. We report on the design of a square grid template for surface patterning hydrogels. The template was 3-D printed and has the diameter of a well in a 24-well plate. Hyaluronic acid methacrylate (HA) hydrogel precursor solutions were cast on the 3D printed template's surface, which generated 3D square shape topographies on the HA hydrogel surface upon demolding. The 3D Laser Microscopy has shown the formation of a periodic array of 3D topographies on hydrogel surfaces. 3D Laser and Electron Microscopy Imaging have revealed that this new method has increased the surface area and exposed the underlying pore structure of the HA hydrogels. To demonstrate the method's versatility, we have successfully applied this technique to generate 3D topography on two more acrylate hydrogel formulations, gelatin Methacrylate and polyethylene glycol dimethacrylate. Human neonatal dermal fibroblast cells were used as a model cell line to evaluate the cell guidance potential of patterned HA hydrogel. Confocal fluorescence microscopy imaging has revealed that the 3D surface topographies on HA hydrogels can guide and align the actin filaments of the fibroblasts presumably due to the contact guidance mechanism. The newly developed methodology of 3D topography generation in acrylate hydrogels may influence the cell responses on hydrogel surfaces which can impact biomedical applications such as tissue engineering, wound healing, and disease modeling.

Bioengineered Silk Protein-Based 3D In Vitro Models for Tissue Engineering and Drug Development: From Silk Matrix Properties to Biomedical Applications.

3D in vitro model has emerged as a valuable tool for studying tissue development, drug screening, and disease modeling. 3D systems can accurately replicate tissue microstructures and physiological features, mirroring the in vivo microenvironment departing from conventional 2D cell cultures. Various 3D in vitro models utilizing biomacromolecules like collagen and synthetic polymers have been developed to meet diverse research needs andaddress the complex challenges of contemporary research. Silk proteins, bearing structural and functional similarities to collagen, have been increasingly employed to construct advanced 3D in vitro systems, surpassing the limitations of 2D cultures. This review examines silk proteins' composition, structure, properties, and functions, elucidating their role in 3D in vitro models. Furthermore, recent advances in biomedical applications involving silk-based organoid models are discussed. In particular, the unique physiological attributes of silk matrix constituents in in vitro tissue constructs are highlighted, providing a meticulous evaluation of their importance. Additionally, it outlines the current research hurdles and complexities while contemplating future avenues, thereby paving the way for developing complex and biomimetic silk protein-based microtissues.

Addressing Circularity Strategies by Reconfiguring Smart Products during Their Lifecycle

Considering circularity aspects during the engineering of smart products implies a significantly increased complexity of engineering processes. Especially technical reconfiguration of smart products, offered as services in availability-oriented business models, enables the integration of circular economy aspects in sustainable products and lifecycles, through realizing several aspects of the 9R strategies. This paper introduces an analysis of interdependencies between 9R strategies potential reconfiguration options, technical characteristics of smart products, different maturity levels of smart product and abilities for circularity. Partial engineering models managed in different product lifecycle management systems provide a technical basis for systematization and evaluation of circular abilities of reconfigurable smart products in different lifecycle phases. The approach aims to improve circularity-related decision making in systems engineering processes in the early development phases and during the reconfiguration of smart products during utilization phase. An industrial use case considering a microelectronic-centered smart product used in e-mobility solutions validates the approach.

Noise radiation from a partially opened and elevated tunnel-tube

Road or railway tunnels are considered in noise prediction programs generally by neglecting any emission from the tunnel structure between the openings and replacing each tunnel mouth by additional sources simulating the sound radiated from inside to outside through these openings. The sound pressure level inside the tunnel can be derived from the analog model of a room formed by a slice cut out of the extended tunnel with two perfect reflecting planes perpendicular to the tunnel axis. The paper presents a methodology for the case of an elevated tunnel tube with an upper ventilation gap running over the entire length of the tunnel to calculate the sound pressure levels in the surrounding area more approximately using engineering models like ISO 9613-2 or others and more detailed using a particle method developed to account for complete 3-dimensional propagation problems.