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Risk assessment studies of local asteroid hazards traditionally simulate the physics of meteors with engineering models tailored to analyze tens-of-millions of scenarios. However, these simplified approaches still need to solve time-dependent ODEs to model the entry process and the resulting ground damage. With a computational cost of O(0.01 CPU.s) per scenario, simulating these large numbers of potential entry conditions can take several days on local computers. To improve computational efficiency, we propose in this paper an orthogonal approach based on machine learning models to predict the size of damaged areas given a list of entry parameters. We train 5 machine learning methods and compare the predictions to the outputs of NASA's state-of-the-art Probabilistic Asteroid Impact Risk (PAIR) model. PAIR's remarkable capability to handle a broad spectrum of entry conditions and a high volume of scenarios establishes it as our reference model, with PAIR estimates assumed ground truth and used to train, test, and validate the machine learning models. The ML models, initially trained with PAIR's primitive input variables, are then further enhanced with engineered features to improve the performance of interpretable models. We find that complex models like neural networks are well-suited to estimate blast hazards, while simpler, interpretable linear models can accurately assess thermal damage. For both types of hazards, the radii of damaged areas can be predicted with less than 10% average relative errors for radii greater than 100 km. The CPU time is decreased by a factor O(1 0 3 ) compared to the PAIR model, enabling the simulation of millions of scenarios within minutes on a local computer. Beyond serving as surrogate models, we incorporate the machine learning models into a comprehensive Shapley sensitivity analysis, yielding a ranking of entry parameters based on their contributions to ground damages. These results offer valuable insights for prioritizing observation missions. • Machine learning models enable accurate and rapid simulations of asteroid hazards. • Data-driven methods can assess the radius of damaged areas with 10% average errors. • Models provide insights on the relative importance of entry parameters. • Risk studies can be performed on local machines instead of supercomputers. [ABSTRACT FROM AUTHOR]

Improving the reliability and efficiency of available electric propulsion schemes is one of the priority areas of space technology development. In particular, this should be done for ensuring the guaranteed removal of spacecraft to a disposal orbit at the end of their active life, which, in turn, will slow down the growth in the number of space debris objects in the most exploited regions of near-Earth space. The paper considers ways to improve the power and mass efficiency of one of the modern electric propulsion types - a radio-frequency ion thruster with an inductive radio-frequency discharge. Using an engineering physico-mathematical model, computational studies were performed to optimize the shape of the radio-frequency ion thruster main components, namely the discharge chamber and the grids of ion-extraction system. The thrust of the thruster was used as the optimization criterion; it was determined from the calculated ion density distributions in the radio-frequency discharge plasma. Such an approach to refine the performance improvement methods has not previously been applied to radio-frequency ion thruster. The ion density distributions, as well as the thruster integral characteristics determined from them, such as the thrust, total ion current from the thruster, propellant utilization factor, ionization cost and a number of others, were calculated for two actual standard sizes of radio-frequency ion thruster with the beam diameters of 80 mm and 160 mm, which can be used to solve the problems listed above. Besides, for the defined optimal configurations of thruster with two standard sizes the calculations were made to assess the possibility for increasing the thruster integral characteristics by applying an additional magnetostatic field in the radio-frequency discharge region. The study revealed the possibility of noticeable performance improvement compared to the basic radio-frequency ion thruster design with hemispherical discharge chamber and flat grids of the ion-extraction system. • The optimal shapes of the main radio-frequency ion thruster units were determined. • The influence of additional magnetic field on the thruster integral characteristics was studied. • The study revealed the possibility of improvement radio-frequency ion thruster performance. [ABSTRACT FROM AUTHOR]

Machine learning has an important role to create intelligent applications for Industry 4.0, and main objective of this paper is to contribute Industry 4.0 by using a sound signal dataset and a new feature engineering model to detect malfunctioning. This paper has evaluated a new sound classification model by using the comprehensive Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset. The model has three stages: (i) feature generation with the newly developed Directed Center Symmetric Local Binary Pattern (DCSLBP) and Unbalanced Tree-Based Multilevel Discrete Wavelet Transform (UTMDWT), (ii) feature selection using the Chi-squared (Chi2) function, and (iii) classification with a Bagged Tree (BT) classifier. The MIMII dataset contains recordings of clean and noisy sounds at -6 dB and 6 dB from four types of machinery (fan, pump, slider, and valve), allowing for the creation of five specific test cases. Each case achieved classification accuracies ¿90%, demonstrating the effectiveness of the method. The results indicate that the proposed model, while achieving high accuracy in tests and providing a practical, less complex solution, is suitable for adaptations in real-world applications. Continuous improvements are essential due to the diverse industrial noise and operational conditions. [ABSTRACT FROM AUTHOR]

Offshore wind energy is booming and floating offshore wind has been proved to be a promising clean energy. Wake effect is one of the biggest challenges in large-scale floating wind farm which results in power loss and increased fatigue load. In this paper, actuator line model (ALM) coupled with unsteady Reynolds-averaged Navier-Stokes (URANS) is adopted to investigate the wake characteristics and evolution of floating wind turbine under motion. Besides, modal analysis is adopted to extract typical features of turbine wake. Velocity fluctuation is the prominent characteristic of floating turbine wake. The increase of surge frequency results in the decrease of velocity fluctuating region and large surge amplitude leads to bigger velocity deficit fluctuations. In addition, the surge motion brings a faster recovery rate than fixed wind turbine. The induced velocity field generated by vortex ring structure is the key to understanding complex evolution of turbine wake and the vortex ring structure is analyzed in this paper. With the deep understanding of FOWT wake, an innovative vortex ring structure model is proposed in this paper. This work is the basic work of developing the engineering wake model of floating wind turbine, to cope with the wake effect challenge in the large-scale floating wind farm. • Wake characteristics and vortex structure is analyzed using actuator line method. • Wake evolution mechanism is clearly revealed through modal analysis. • Instability theory of helical vortices is introduced into the study of turbine wake. • A semi-empirical vortex structure model is proposed based on theoretical analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main principles and methods for measuring acoustic cavitation are discussed. • Acoustic wave and system parameters affecting US process efficiency are discussed. • US for agri-food applications improved product quality and extraction efficiency. • US can be combined with conventional agri-food processes to improve operations. • Innovative designs for large-scale US assisted food processing are described. Acoustic cavitation, an intriguing phenomenon resulting from the interaction of sound waves with a liquid medium, has emerged as a promising avenue in agri-food processing, offering opportunities to enhance established processes improving primary production of ingredients and further food processing. This comprehensive review provides an in-depth analysis of the mechanisms, design considerations, challenges and scale-up strategies associated with acoustic cavitation for agri-food applications. The paper starts by elucidating the fundamental principles of acoustic cavitation and its measurement, delving then into the diverse effects of different parameters associated with, the acoustic wave, mechanical design and operation of the ultrasonic system, along with those related to the food matrix. The technological advancements achieved in the design and set-up of ultrasonic reactors addressing limitations during scale up are also discussed. The design, engineering and mathematical modelling of ultrasonic equipment tailored for agri-food applications are explored, along with strategies to maximize cavitation intensity and efficiency in the application of brining, freezing, drying, emulsification, filtration and extraction. Advanced US equipment, such as multi-transducers (tubular resonator, FLOW:WAVE®) and larger processing surface areas through innovative designing (Barbell horn, CascatrodesTM), are one of the most promising strategies to ensure consistency of US operations at industrial scale. This review paper aims to provide valuable insights into harnessing acoustic cavitation's potential for up-scaling applications in food processing via critical examination of current research and advancements, while identifying future directions and opportunities for further research and innovation. [ABSTRACT FROM AUTHOR]

Quantifying the uncertainties of engineering systems modeled as nonlinear oscillators subject to random excitation is a theoretically complex and computationally demanding task. Consequently, finding an optimal design of such systems considering their life-cycle performance is prohibitive with Monte Carlo simulation methods. In this paper, an efficient performance-based design optimization approach is developed for finding the optimal parameters of engineering systems modeled as nonlinear/hysteretic oscillators subject to stationary and non-stationary excitation. This novel approach utilizes an arbitrary polynomial chaos expansion to estimate the expected cost of failure without performing a computationally expensive integration over the hazard levels. Moreover, we introduce a novel sequential heuristic optimization scheme based on simulated annealing to minimize the total expected cost over the structure life-cycle. Three examples are included in the paper to assess the developed optimization scheme. First, we use the developed framework to optimize a linear single-degree-of-freedom oscillator subject to broadband excitation. Second, a multi-degree-of-freedom oscillator with cubic nonlinearity in damping and stiffness, subject to stationary broadband excitation, is optimized to show the influence of the problem dimensionality in the optimization process. In the last example, a multi-story reinforced concrete shear building modeled as a multi-degree-of-freedom Bouc-Wen oscillator with stiffness and strength degradation and subject to multi-hazards modeled as stationary (wind excitation) and non-stationary (earthquake) stochastic processes, is optimized. • An efficient performance-based design optimization scheme is developed. • The best design of MDOF systems consider their life-cycle performance. • Statistical linearization is combined with time-variant reliability. • The monetary loss is estimated with an arbitrary polynomial chaos expansion. • A sequential simulated annealing optimization is employed in the framework. [ABSTRACT FROM AUTHOR]

The evaluation of space optical instruments under thermo-elastic loads is a complex and multidisciplinary process that requires integrating thermal, structural, and optical disciplines. This thorough analysis often requires substantial resources, leading small satellite projects to exclude it from their schedules. However, even though the instrument discussed in this paper is compact, its complex design and stringent dimensional stability requirements demand a comprehensive evaluation of its performance under thermal loads. The hyperspectral camera, which comprises 18 lenses, a grating, a slit, and a detector, is especially vulnerable to thermo-elastic distortions, as the deformation of even a single lens could significantly impact its performance. In this paper, we present the experimental validation of the STOP analysis applied to the HYPerspectral Small satellite for ocean Observation (HYPSO) Hypespectral Imager (HSI) model. Both the HSI Structural Thermal Optical Performance (STOP) numerical model and the HSI engineering model were subjected to identical thermal conditions in the simulations and in a Thermal and Vacuum Chamber (TVAC), and subsequently the optical results derived from simulations and the test campaign compared. To characterize the thermal field, an infrared camera and thermocouples were used. Moreover, to assess the thermal performance of the HSI, we measured the Full Width at Half Maximum (FWHM) of the main peaks in the intensity-wavelength spectra when the hyperspectral camera targeted a known spectral lamp. After individually calibrating the STOP models so that the FWHM and index of the intensity peaks are in close alignment with the experimentally measured FWHM and index, the lenses most sensitive for displacements were characterized. [ABSTRACT FROM AUTHOR]

Large language model (LLM) is an important direction in the development of AGI, but its technology is still in rapid change, and its capabilities still have obvious deficiencies and imbalances, with persistent problems such as hallucination, value non-alignment, weak specialization, and black-box effect. In this case, how to apply LLM to different professional fields and develop high-quality AIGC industry applications has become a great challenge for ISVs. Building AIGC industry applications based on LLM is not simply a matter of functional realization. Although researchers and open-source communities have proposed numerous application development frameworks or tool components, there is a lack of overall architecture design for systems engineering and a lack of discussion on theories and methods of LLM application development in large-scale industry domains, such as healthcare, government affairs, finance, and media. This paper analyzes the basic ideas of LLM industry applications development, defines the functional requirements and feature requirements of LLM industry applications, puts forward the concept of Large Language Model Systems Engineering (LLM-SE), and develops an AI assisted clinical risk prediction system for amyloidosis disease based on the architecture of LLM-SE, which adopt knowledge engineering, quality engineering, etc., and verifies the LLM-SE development architecture and methodology. • This paper proposes the concept of large language model systems engineering (LLM-SE). • This paper proposes and defines the feature requirements for AIGC industry applications. • This paper proposes a framework for large language model systems engineering. • This paper realizes a clinical risk prediction system for amyloidosis based on LLM-SE. [ABSTRACT FROM AUTHOR]

Precise evaluation of the frost heaving force and surrounding rock plastic zone is an important basis of the prevention of freezing damage in cold-region tunnels. The temperature gradient along the radial direction of cold-region tunnels surrounding rock results in the dual non-uniform frost heaving of surrounding rock, namely, the gradual change of frost heave ratio along the radial direction and the unequal frost heaving deformation in the circumferential and radial directions. A new elastoplastic solution of tunnel frost heaving force considering double non-uniform frost heave and intermediate principal stress was derived based on the elastoplastic mechanics theory, thick-cylinder theory, and unified strength theory to better guide the frost resistance design of tunnels in cold regions. The rationality and advantages of the unified plastic solution of tunnel frost heaving force were verified by model tests and engineering examples. Results showed that the theoretical calculation value of frost heaving force was in good agreement with the measured value. When the gradual change in the frost heave ratio of surrounding rock in the radial direction was ignored, the frost heaving force and radius of the surrounding rock plastic zone were large and conservative. The solution in this paper can accurately evaluate the tunnel frost heaving force and surrounding rock plastic zone. Finally, this paper studies the influences of intermediate principal stress coefficient, the frost heaving coefficient of unit temperature of frozen surrounding rock, non-uniform frost heaving coefficient in different directions, and maximum freezing depth on the evolutionary law of frost heaving force and plastic zone of cold-region tunnels are studied. • A dual non-uniform frost heaving model of cold region tunnels is proposed. • Use temperature gradient to describe the non-uniform frost heave of the surrounding rock. • An elasto-plastic solution for frost heave force based on dual non-uniform frost heave is derived. • Theoretical solutions, model test results and engineering tunnel cases were compared. [ABSTRACT FROM AUTHOR]

The specifications of a certain domain are addressed by a portfolio of software products, known as Software Product Line (SPL). Feature Model (FM) supports domain engineering by modeling domain knowledge along with variability among SPL. The quality of FM is one of the significant factors for the successful SPL in order to attain high quality software products. However, the benefits of SPL can be reduced due to defects in FM. Dead Feature (DF) is one of such defects. Several approaches exist in the literature to detect defects due to DF in FMs. But only a few can handle their sources and solutions which are cumbersome and difficult to understand by humans. An ontological knowledge-based method for handling defects due to DF in FMs is described in this paper. It specifies FM in the form of ontology-based knowledge representation. The rules based on first-order logic are created and implemented using Prolog to detect defects due to DF with sources as well as suggest solutions to resolve these defects. A case study of the product line available on SPLOT repository is utilized for illustrating the proposed work. The experiments are performed with real-world FMs of varied sizes from SPLOT and FMs created with the FeatureIDE tool. The results prove the efficiency, scalability (up to model with 32,000 features) and accuracy of the presented method. Therefore, reusability of DFs free knowledge enables deriving defect free products from SPL and eventually enhances the quality of SPL. [ABSTRACT FROM AUTHOR]

Wet joints are critical parts of prefabricated ultra-high-performance concrete (UHPC) light-weight rigid frame arch bridges (LWRFAGs), so their mechanical behavior is important to the structural design and applications of these arch bridges. To explore suitable joint configurations and reinforcement arrangements for connecting UHPC components or arch ribs of a practical UHPC LWRFAG, this paper experimentally investigated flexural behavior of several prefabricated UHPC joint beams with various kinds of wet joints through four-point bending tests. Firstly, four different UHPC joint beams with different joint configurations were designed, including (i) vertical joint; (ii) rhombus joint; (iii) vertical joint with top and bottom strips; and (iv) rhombus joint with top and bottom strips, and their flexural performance were compared with a complete UHPC beam. The experimental results revealed that UHPC joint beam employing the rhombus joint with top and bottom strips exhibited superior flexural behavior and cracking resistance among four UHPC joint beams. Subsequently, to achieve optimal and robust joint configurations, bending performance of one complete UHPC beam and three large-scale UHPC joint beams with rhombus joints but different reinforcement arrangements were experimentally studied. It is observed that the beam with the welded twist reinforcements (specimen V-4) shown optimal flexural performance and cracking resistance. Finally, to explore the load transfer mechanism and effects of reinforcement parameters on flexural behavior of specimen V-4, the parametric studies were comprehensively performed using finite element analyses (FEA). This study could serve as both the experimental and theoretical reference for flexural design of UHPC beams with the rhombus wet joints. • Flexural behavior of UHPC joint beams with various joint configurations was studied. • Flexural performance of three large-scale UHPC joint beams employing rhombus joints was investigated. • A calculation formula for flexural design of UHPC joint beams with rhombus wet joints was proposed. • Parametric studies regarding load transfer mechanism and effects of reinforcement parameters on flexural behavior of UHPC joint beams were performed. [ABSTRACT FROM AUTHOR]

• An improved adaptive Kriging method is proposed for possibility-based design optimization. • The enhanced expected improvement learning function is adopted to improve the efficiency of Kriging model. • Active possibility constraint is identified to improve efficiency of possibility-based design optimization. • The proposed method is applied to an aeroengine turbine disk. Possibility-based design optimization (PBDO) can provide theoretical basis for the structural optimal design of fuzzy uncertainty model in engineering, so as to obtain the optimal design variables. The genetic algorithm (GA) based on adaptive Kriging method for PBDO requires high precision in the whole design space, while the region far from the limit state surface (LSS) of the constraint function has little effect on PBDO, thus greatly affects the computational efficiency. In order to improve the efficiency of PBDO, an improved adaptive Kriging method combined with active possibility constraint (I-AK-AC) is proposed in this paper. In I-AK-AC, the enhanced expected improvement learning function is first put forward to promote the convergence efficiency of Kriging model by reducing the accuracy of the region far from the LSS of the constraint function. Whereafter, the active possibility constraint is identified, and only the boundaries of active possibility constraint need to be approximated precisely by Kriging model. By these ways, the convergence speed of Kriging model is further ameliorated without affecting the computational accuracy, and the efficiency of PBDO is significantly improved. Three test examples and an engineering application of aeroengine turbine disk illustrate the validity and accuracy of the proposed I-AK-AC. [ABSTRACT FROM AUTHOR]

Security is paramount in all mission-critical domains, including the aerospace industry. Cyber-attacks are increasing both in number and sophistication. Zero-trust is an emerging initiative that has proven very effective for enterprise systems in the Information Technology domain; however, research is lacking on applicable zero-trust mechanisms and their assurance for cyber–physical systems (CPS). We have already identified various zero-trust mechanisms in our previous work. In this paper, we present our zero-trust architecture design patterns and provide a methodology for the assurance of these mechanisms. Towards this objective, we have identified an initial set of assurance patterns covering individual zero-trust components in a system design. Our design and assurance patterns are made available to system engineers in pattern libraries. Engineers can model system architectures and utilize one or more of these patterns to provide design assurance based on individual zero-trust security requirements to improve the overall system cyber-security. To demonstrate our approach, we apply our assurance patterns to an unmanned aerial vehicle surveillance application. We discuss how our framework leverages the use of these patterns to develop zero-trust-enabled systems with different security requirements. Furthermore, our assurance patterns enable engineers to identify any design flaws and correct them during the initial system design phase, thus saving development time, effort, and cost. As a result, the overall approach can be utilized to design system models with specific zero-trust security requirements to improve the security posture of a CPS. [ABSTRACT FROM AUTHOR]

• A new optimization method without establishing and solving mathematical models. • The presented method avoids checkerboard and gray-scale element problems. • The presented method combines load-transfer paths for optimal material distribution. • The presented method resolves the issue of non-smooth boundary. To address the challenge of establishing and solving mathematical models for engineering structural optimization, a new topological optimization method that integrates load-transfer path theory with the engulfment algorithm is presented in this paper. The presented method applies the load-transfer path theory to identify the main load-bearing areas of the structure and utilizes the principle of concentrating more materials in relatively high-stress regions and fewer materials in relatively low-stress regions. An engulfment algorithm is introduced to optimize the material distribution. A comparative analysis between the presented and variable-density methods revealed that the path-engulfment method enhances the structural stiffness and strength while reducing its mass, confirming its precision and efficacy in structural optimization. The path-engulfment method was implemented on a truck crane frame, resulting in an optimized structure with increased stiffness and strength and reduced mass compared to the original design. Furthermore, this method eliminates the need for establishing and solving complex mathematical models while addressing issues related to checkerboards and gray-scale elements. A smooth boundary approach was introduced by leveraging the engulfment algorithm, enabling the direct application of the optimized structure for manufacturing purposes, particularly in engineering applications. [ABSTRACT FROM AUTHOR]

The promotion of intelligent control of engineering equipment requires extracting vital information from numerous original features in the monitoring data. Data-driven methods solely rely on data distribution to extract dimensional information, facing difficulties in comprehensively understanding and scrutinizing system behavior. To satisfy the control and optimization requirements of intelligent systems, this paper introduces a physics-based dimension reduction and modeling method. According to scientific principles, original features are transformed into causal variables with physical meaning. Then important causal variables are selected through regularization in machine learning. Finally, a concise prediction model with physical representation is established. An application to tunnel engineering demonstrates that the generated low-dimensional space reveals the essential laws between the control parameter and its main influencing factors (i.e., the geological conditions, operating status, and structural characteristics of the tunneling equipment), which can further provide a better prediction model balancing accuracy and dimensionality. • Physics-based machine learning method for exploring monitoring data is proposed. • Dimension reduction reveals explicit physical mechanism is achieved by this method. • Feature space with reduced dimension is first derived using a physical theorem. • Regularization is then adopted to reduce the dimensionality with physical meaning. • Model and generated space are verified with monitoring data of tunnel engineering [ABSTRACT FROM AUTHOR]

The stability of the tunnel face assumes a pivotal significance in tunnel construction safety. This paper introduces the material point method (MPM) to scrutinize the stability mode and failure characteristics of the tunnel face. Considering the impact of the unsupported length in the numerical model significantly enhances the applicability of the model in practical engineering. Firstly, the MPM model underwent validation through a comparison with model test results, demonstrating a harmonious accord in both magnitude, range, and evolution process. Secondly, 180 distinct simulation cases are conducted based on the MPM model, incorporating variables such as unsupported length (L), soil cohesion (c), and soil friction angle (φ). The results suggest that the stability of the tunnel face manifests in three modes: tunnel face stability (Mode Ⅰ), temporary stability of the tunnel face (Mode Ⅱ), and tunnel face instability (Mode Ⅲ). The failure pattern of Mode II is parabolic or elliptical + rectangular shape, whereas Mode III is elliptical + logarithmic spiral shape. For L less than 1.5 m, the failure pattern of the tunnel face assumes a parabolic shape in Mode II. Conversely, when L is 1.5 m or more, the failure pattern transitions to an elliptical + rectangular shape. For Mode III, variations in L do not influence the failure pattern observed on the tunnel face. The reduction in run-out distance fluctuates between 16.85 % and 30.77 % with each incremental 10 kPa augmentation in c , and 25.08 % and 45.03 % for each gradual 5° augmentation in the φ. For cases where c / D / γ ≤ 0.09 or φ ≤ 11°, the stability mode for tunnel faces of varying L uniformly aligns with Mode III. [ABSTRACT FROM AUTHOR]

• The single effect of deposited nanoparticles and the integral effect of deposition and suspension were quantitatively investigated under saturated pool boiling. • With a mass concentration ranging from 0.001 % to 0.005 % and a heat flux range of 60–130 kW/m2, a pool boiling model with error margin within 30 % was produced through the investigation of bubble dynamics. • The major influence of deposited nanoparticles on boiling heat transfer was determined based on the theoretical model. Currently, the majority of research indicates that nanofluids have superior heat transfer capabilities. On the impact of nanofluids on the boiling heat transfer, there is debate. It is still challenging to predict the boiling heat transfer of nanofluids. Based on bubble behavior, the impact of nanoparticles on boiling heat transfer is investigated in this paper. Furthermore, a comparison is made between the percentages of deposited and suspended nanoparticles that influence on boiling heat transfer. According to the findings, deposited nanoparticles improve wettability whereas suspended nanoparticles diminish it. The number of active nucleate sites and the frequency of bubble detachment increase as a result of the influence of suspended and deposited nanoparticles, whereas the diameter of the bubble decreases. This is similar to the trend of the effect of deposited nanoparticles on bubble behavior. Based on the effect of nanoparticles on wettability, predictive models for boiling heat transfer and bubble behavior were proposed, with a range of mass concentrations between 0.001 % and 0.005 % and heat flux between 60 and 130 kW/m2. Less than 30 % was found to be the error between the experimental data and the predictive equations. It was discovered that the influence of deposited nanoparticles on boiling heat transfer was usually dominant, according to the predictions made by theoretical models. [ABSTRACT FROM AUTHOR]

Mesh denoising is a crucial research topic in geometric processing, as it is widely used in reverse engineering and 3D modeling. The main objective of denoising is to eliminate noise while preserving sharp features. In this paper, we propose a novel denoising method called Attention Enhanced Dual Mesh Denoise (ADMD), which is based on a graph neural network and attention mechanism. ADMD simulates the two-stage denoising method by using a new training strategy and total variation (TV) regular term to enhance feature retention. Our experiments have demonstrated that ADMD can achieve competitive or superior results to state-of-the-art methods for noise CAD models, non-CAD models, and real-scanned data. Moreover, our method can effectively handle large mesh models with different-scale noisy situations and prevent model shrinking after mesh denoising. • We introduce a novel mesh denoising algorithm based on graph convolution. • We design a self-attention module to extract global features and a dual cross-attention module for feature fusion. • Some new techniques are proposed to improve the ability of the network. [ABSTRACT FROM AUTHOR]

The statistical study of droplets in spray evaporation tower has guiding significance for understanding spray evaporation and designing drying tower. The key to spray evaporation simulation is establishing a suitable single droplet evaporation model through experiment. In this paper, a visualized single droplet evaporation experiment platform was established. The mass, diameter, and temperature during the evaporation of 10 % NaCl solution droplets were obtained. The Reaction Engineering Approach model was developed and validated to describe the single droplet evolution process in saline water spray. A comparison calculation between the spray evaporation of pure and saline water was executed, and the distributions of residence time of droplets were obtained. It reveals that droplets with initial diameters less than 117 μm can fully evaporate, and for larger droplets, pure water droplets represent a higher evaporation ratio. About 50 % of the fully evaporated saline droplets stay in the backflow area. Contrast numerical experiments of saline water spray evaporation were performed, concluded that similar performance can be attained with the same gas-liquid ratio, and excessive inlet air will reduce the residence time of droplets. The influence of other operating parameters on the performance was also discussed, providing a comprehensive understanding of the regularity of spray drying. • A visualized single droplet evaporation experiment platform was established. • Effect of flow-induced vibration of cantilever was mitigated by low pass filtering. • REA model for NaCl solution droplets was first established and verified. • Salt particles trapped in circulation was observed in spray drying tower. [ABSTRACT FROM AUTHOR]

Testing of small-scale physical models of masonry structures can be useful both to study Soil Structure Interaction problems and to provide large enough datasets to statistically validate the global level assumptions of masonry numerical models. This paper proposes the use of a sand-based Binder Jet 3D printer to manufacture 1:10 scaled physical models of masonry walls, that can be used within a centrifuge. As such printers can only print one material, mortar is emulated by controlling the micro-geometry of the printed material at the position of the joints (i.e., by printing joints). Walls were printed and tested in compression and cyclic shear under fixed-fixed conditions and constant compressive load. Different notch geometries were tried. The tested specimens were found to behave similarly in compression and shear to full scale masonry walls. A numerical model using a concrete damage plasticity model was built in Abaqus. It captured the cyclic response of the masonry walls with a reasonable accuracy. Therefore, such small-scale models can be used to expand centrifuge modeling in structural engineering. • Novel approach for the physical modelling of masonry walls. • Scale 1:10, models to be used in the centrifuge. • Models were manufactured via 3D printers. • Compressive and shear tests showed that they behave similarly to true masonry. [ABSTRACT FROM AUTHOR]

An accurate and reliable 3D engineering geological model (expressed herein as a 3D litho-stratigraphic model) is essential for designing slopes, underground structures, and tunnels to reduce the risk of failure. Due to budget constraints, subsurface investigations with traditional methods (e.g., boreholes and geophysical prospecting) are often limited in quantity. Insufficient investigation often leads to inadequate litho-stratigraphic data that can degrade the reliability of the developed 3D litho-stratigraphic models. To this end, LiDAR, which can collect topographic data more efficiently and economically, may complement traditional geological investigations for developing litho-stratigraphic models. However, the task of integrating the subsurface geological data and LiDAR data to create 3D litho-stratigraphic models for engineering applications is challenging. This paper proposes a methodology that combines LiDAR-derived geological data (Type I data herein) and borehole data (Type II data herein) to develop realistic 3D litho-stratigraphic models. To demonstrate the methodology, we took a 1.2 km × 1.2 km area with sedimentary rocks in northern Taiwan as the study area. Under the assumption that the strata are parallel, we first fitted Type I data to a surface regression model (a polynomial mathematical model). Then, we incorporated Type II data into the regressive process to develop the desired 3D model. Comparing the former model (based on Type I data) with the latter model (based on Type I + II data) through a Root-Mean-Squared-Error (RMSE) analysis, it was found that the RMSE of the latter model decreased significantly, indicating a decrease in the error between the developed litho-stratigraphic model and the 'ground truth.' To verify this finding, we compared the newly developed 3D model with the 'known' geological cross-section of the Qidu Tunnel constructed nearby. The results indicated that the distribution of the primary sandstone strata uncovered from the previous Qidu Tunnel project and those projected by the newly developed 3D model was highly consistent. Thus, this study demonstrates that LiDAR data combined with a few borehole data has the potential to create reliable and accurate 3D geological models, providing an effective solution for obtaining 3D geological information at the scale of engineering applications with limited boreholes. Finally, the proposed modeling approach is further demonstrated through two hypothetical engineering application cases. • 3D litho-stratigraphic modeling using LiDAR and limited borehole data. • Modeling in a dip-slope terrain was verified through ground truth along a tunnel. • Combination of LiDAR and borehole data can greatly improve the model reliability. • Precise borehole data and extensive range of LiDAR data complement each other. [ABSTRACT FROM AUTHOR]

Specific structures that demand to maintain the shape of a local region under external loads, such as the injection surfaces of injection molded machine formwork structures, the outer surfaces of wings and engine blades, require homogeneous local deformation. Therefore, a topology optimization method that minimizes local relative displacement differences with stress constraints for such local area is proposed in this paper. The method presents a novel topology optimization model that employs the sum of squares of the discrepancies between local displacements and their mean values as the objective function. Additionally, it adopts a P-Normal aggregation function to handle global stress constraints effectively. Sensitive equations for the objective and stress constraint functions are derived via concomitant variables. Then, the algorithmic procedure of the proposed method is illustrated and validated with 2D and 3D examples by comparing the results with those of compliance minimization. The results demonstrate that the proposed method has obvious superiority and potential application value in the minimization of local relative displacement differences. Finally, the applicability and practicality of the proposed method in the field of engineering structures is verified by structural optimization of an engineering model in injection molding equipment. • A new topology optimization for local relative displacement difference minimization is presented. • A general formula for local relative displacement difference minimization is proposed. • The validity of the proposed method is tested by both 2D and 3D examples. • The proposed method is used to optimize an engineering structure of an injection molding machine. [ABSTRACT FROM AUTHOR]

Large organizations respond to huge volumes of Request for Proposals (RFP)/ Request for Information (RFI) every year. The process of developing a proposal for an RFP/ RFI is completely manual and time-, effort-, and intellect-intensive. While Model Driven Engineering (MDE) approaches have been popular in downstream Software Development Lifecycle (SDLC) phases to transform the design models into code, there has been a gap in leveraging model-based techniques in document-centric phases of proposal development, requirements analysis, etc. This paper presents an automated proposal development approach for a client-supplied RFP/RFI using a combination of model-based and AI-enabled techniques and describes the case study of its successful deployment to hundreds of presales users across multiple geographies. We explain the Proposal system and report on the experience of deploying the system in the industry, bring out its efficacy, and user feedback, and discuss the lessons learnt. [ABSTRACT FROM AUTHOR]

OnTrack is a tool that supports workflows for railway verification that has been implemented using model driven engineering frameworks. Starting with graphical scheme plans and finishing with automatically generated formal models set-up for verification, OnTrack allows railway engineers to interact with verification procedures through encapsulating formal methods. OnTrack is grounded on a domain specification language (DSL) capturing scheme plans and supports generation of various formal models using model transformations. In this paper, we detail the role model driven engineering takes within OnTrack and reflect on the use of model driven engineering concepts for developing domain specific formal methods toolsets. [ABSTRACT FROM AUTHOR]

Reservoir operations can induce massive landslides due to the regional hydraulic effects, where pile reinforcement is a widely used stabilization method. The 1-g physical modeling is one of the most common methods used to study reservoir landslides and their pile reinforcement, while some limitations have been presented in the method, slowed its development down, and adverse to novel results. This paper presents a systematic review and discussion of 1-g physical modeling relevant to pile-reinforced reservoir landslides. Various aspects, from the theoretical basis (i.e., principle of similitude) to experimental details (i.e., model construction, test technique, and data acquisition and interpretation), are investigated. The principle of similitude and application in the physical modeling of pile-reinforced reservoir landslides are discussed. Factors causing distortions in a 1-g physical landslide model are discussed, and measures for distortion control are proposed. The significance and limitations of the physical model construction and obtained data are analyzed, and some improvements are proposed. Based on previous work and our experience, the lessons learned in the technical aspects of 1-g physical modeling are presented along with recommendations for the physical modeling of landslides, especially those reinforced with piles. • A strategy of distortion compensation in 1-g physical modeling is provided as a supplement to the principle of similitude. • The lessons in technical aspects of 1-g physical modeling of reservoir landslides and pile reinforcement are discussed. • The displacement and pressure data limitations are analyzed and its improvements are proposed. [ABSTRACT FROM AUTHOR]

The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. This paper presents SMT 2.0, a major new release of SMT that introduces significant upgrades and new features to the toolbox. This release adds the capability to handle mixed-variable surrogate models and hierarchical variables. These types of variables are becoming increasingly important in several surrogate modeling applications. SMT 2.0 also improves SMT by extending sampling methods, adding new surrogate models, and computing variance and kernel derivatives for Kriging. This release also includes new functions to handle noisy and use multi-fidelity data. To the best of our knowledge, SMT 2.0 is the first open-source surrogate library to propose surrogate models for hierarchical and mixed inputs. This open-source software is distributed under the New BSD license. 2 2 https://github.com/SMTorg/SMT. • For engineering purposes, we propose a Python Open Source Toolbox for Surrogate Modeling named the Surrogate Modeling Toolbox 2.0. • SMT 2.0 is the most efficient Python modeling toolbox. • SMT had been extensively used for Engineering and Research modeling tasks with more than 200 citations already. • This toolbox can easily model black box problems for optimization purposes based on Kriging models (Gaussian Process). • The SMT 2.0 Toolbox provides meta/decreed/continuous mixed hierarchical (dimensional) variables for architectural modeling. • This 2.0 release also includes new functions to handle noisy and use multifidelity data. [ABSTRACT FROM AUTHOR]

In the realm of regenerative medicine, tissue engineering has introduced innovative approaches to facilitate tissue regeneration. Specifically, in pulp tissue engineering, both scaffold-based and scaffold-free techniques have been applied. Relevant articles were meticulously chosen from PubMed, Scopus, and Google Scholar databases through a comprehensive search spanning from October 2022 to December 2022. Despite the inherent limitations of scaffolding, including inadequate mechanical strength for hard tissues, insufficient vents for vessel penetration, immunogenicity, and suboptimal reproducibility—especially with natural polymeric scaffolds—scaffold-free tissue engineering has garnered significant attention. This methodology employs three-dimensional (3D) cell aggregates such as spheroids and cell sheets with extracellular matrix, facilitating precise regeneration of target tissues. The choice of technique aside, stem cells play a pivotal role in tissue engineering, with dental stem cells emerging as particularly promising resources. Their pluripotent nature, non-invasive extraction process, and unique properties render them highly suitable for scaffold-free tissue engineering. This study delves into the latest advancements in leveraging dental stem cells and scaffold-free techniques for the regeneration of various tissues. This paper offers a comprehensive summary of recent developments in the utilization of dental stem cells and scaffold-free methods for tissue generation. It explores the potential of these approaches to advance tissue engineering and their effectiveness in therapies aimed at tissue regeneration. • The three-dimensional model is classified into scaffold-free and scaffold-based approaches. • The scaffold-free approach applies cell units for tissue engineering. • Dental stem cells are the most accessible source for various tissue engineering. [ABSTRACT FROM AUTHOR]

The mechanical parameters of the surrounding rock of a tunnel are the premise and foundation of the supporting design in soft rock tunnel engineering. To obtain the mechanical parameters of the surrounding rock accurately and quickly, a composite inversion model is proposed, and different models are used to invert different mechanical parameters. In this paper, first, the Latin hypercube and Monte Carlo sampling methods are used to construct the mechanical parameter samples from the surrounding rocks. The numerical simulation of the water diversion project in central Yunnan is carried out by using FLAC3D, and the corresponding displacement data are obtained, three sets of effective inversion datasets are formed along with the mechanical parameter samples. Second, the adjusted coefficient of determination and the symmetrical mean absolute percentage error are used as the performance evaluation indices. The Bayesian 10-fold cross-validation iteration is used to optimize the five regression model hyperparameters for the three sets of inversion parameters. The base model of the best performance is selected for inverting the friction angle (φ) and cohesion (c). For the deformation modulus (E m) inversion parameters, LIME interpretability is used to optimize the weights and integrate the five models. Finally, by developing a calculation tool for the elastic modulus of tunnel support, an orthogonal experimental design is applied based on the verified engineering numerical model. The results show that the average error of the inversion results of the mechanical parameters of the surrounding rock by the proposed composite model is 9.32%, the minimum error is only 0.81%, and the error is within 15%. The inversion trend is similar to the actual displacement height, and the optimized support scheme provides a reference for the engineering site. [ABSTRACT FROM AUTHOR]

In shield tunnel digital twin modeling, constructing high-precision tunnel digital 3D models is essential but poses a challenging engineering problem that affects efficiency and accuracy. To address this, this paper presents a method based on 3D tunnel information models using image and point cloud data. A 3D Tunnel Information Model Construction Method (3DTIM) is proposed, based on vision and laser point cloud data, encompassing six core processing layers, and employing the YOLO-T tunnel inner wall image target recognition method alongside various point cloud data processing methods. Through this method, critical parameters can be rapidly and accurately extracted from images and point cloud data, enabling the direct creation of high-precision 3D tunnel models on the 3D modeling software with an accuracy better than 0.7 cm. The 3DTIM approach not only solves the problem of constructing high-precision tunnel 3D models, but also provides a valuable methodological reference for digital shield tunnel twin modeling, reducing the need for manual measurement and enhancing the efficiency of tunnel digital model construction. This research provides a new practical framework for future studies on tunnel 3D model construction and digital shield tunnel twin modeling based on image and point cloud data, forming a more valuable methodological reference. • Combined vision and laser point cloud data for tunnel modeling. • Parameter extraction and processing for 3D model accuracy. • High precision 3D base model for tunnel digital twin project. • Introduced a framework for building high-precision, centimeter-scale digital twin base models for tunnels. • Furthered the progression of digital twin modeling in tunnel engineering. [ABSTRACT FROM AUTHOR]