Search Results (1,843)

Additive manufacturing (AM) has revolutionized the production of complex metallic components by enabling the direct fabrication of intricate geometries from 3D model data. Despite its advantages in reducing material waste and customization of mechanical properties, AM faces challenges related to microstructural heterogeneity and mechanical property variability. This review highlights the structure–property relationships in additively manufactured metals, emphasizing how heterogeneous microstructure influences yield strength and fracture toughness. Phenomenological equations are provided based on the integration of neural networks and genetic algorithm-based models to predict mechanical properties from composition and microstructural features. We also outline key considerations such as acquiring high-fidelity datasets and understanding mathematical correlations within the data needed to formulate phenomenological equations. Full article

In this paper, we address the modeling, simulation, and control of a rotary inverted pendulum (RIP). The RIP model assembled via the MATLAB (Matlab 2021a)^®/Simulink (Simulink 10.3) Simscape (Simscape 7.3)™ environment demonstrates a high degree of fidelity in its capacity to capture the dynamic characteristics of an actual system, including nonlinear friction. The mathematical model of the RIP is obtained via the Euler–Lagrange approach, and a parameter identification procedure is carried out over the Simscape model for the purpose of validating the mathematical model. The usefulness of the proposed Simscape model is demonstrated by the implementation of a variety of control strategies, including linear controllers as the linear quadratic regulator (LQR), proportional–integral–derivative (PID) and model predictive control (MPC), nonlinear controllers such as feedback linearization (FL) and sliding mode control (SMC), and artificial intelligence (AI)-based controllers such as FL with adaptive neural network compensation (FL-ANC) and reinforcement learning (RL). A design methodology that integrates RL with other control techniques is proposed. Following the proposed methodology, a FL-RL and a proportional–derivative control with RL (PD-RL) are implemented as strategies to achieve stabilization of the RIP. The swing-up control is incorporated into all controllers. The visual environment provided by Simscape facilitates a better comprehension and understanding of the RIP behavior. A comprehensive analysis of the performance of each control strategy is conducted, revealing that AI-based controllers demonstrate superior performance compared to linear and nonlinear controllers. In addition, the FL-RL and PD-RL controllers exhibit improved performance with respect to the FL-ANC and RL controllers when subjected to external disturbance. Full article

Microfluidic devices offer promising solutions for automating various biological and chemical procedures. Epoxy resin, known for its excellent mechanical properties, chemical resistance, and thermal stability, is widely used in high-performance microfluidic devices. However, the poor printability of epoxy has limited its application in 3D printing technologies for fabricating epoxy-based microfluidic devices. In this study, fumed silica is introduced into epoxy resin to formulate a yield-stress fluid suspension as a support bath for embedded 3D printing (e-3DP). The study demonstrates that increasing the fumed silica concentration from 3.0% to 9.0% (w/v) enhances the yield stress from 9.46 Pa to 56.41 Pa, the compressive modulus from 19.79 MPa to 36.34 MPa, and the fracture strength from 148.16 MPa to 168.78 MPa, while reducing the thixotropic time from 6.58 s to 1.32 s, albeit with a 61.3% decrease in the transparency ratio. The 6.0% (w/v) fumed silica–epoxy suspension is selected based on a balance between yield stress, transparency, and mechanical performance, enabling high-fidelity filament formation. Two representative microfluidic devices are successfully fabricated, demonstrating the feasibility of a fumed silica–epoxy suspension for the customizable e-3DP of epoxy-based microfluidic devices. Full article

Pulse pile-up presents a significant challenge in nuclear radiation measurements, particularly in neutron-gamma pulse shape discrimination, as it causes pulse distortion and diminishes identification accuracy. To address this, we propose an optimized Support Vector Regression (SVR) algorithm for correcting pulse pile-up. Initially, the Dung Beetle Optimizer (DBO) and Whale Optimization Algorithm (WOA) are integrated to refine the correction process, with performance evaluated using charge comparison methods (CCM) for pulse shape discrimination. Leveraging prior knowledge from simulated data, we further analyze the relationships between various types of pulse pile-ups, including their combinations, inter-peak distances, and the accuracy of corrections. Extensive experiments conducted in a mixed neutron-gamma radiation field using plastic scintillators demonstrate that the proposed method effectively corrects pulse pile-up and accurately discriminates between neutron and gamma. Moreover, our approach significantly improves the fidelity of pulse shape discrimination and enhances the overall reliability of radiation detection systems in high-interference environments. Full article

The spread of airborne diseases such as COVID-19 underscores the need for effective indoor air quality control. This review focuses on ventilation strategies and portable air purifiers as key mitigation solutions. Ventilation systems, including natural and mechanical approaches, can reduce pathogen concentrations by improving airflow. However, combining ventilation with portable air purifiers, particularly those using HEPA filters, ESP filters, and UV-C radiation, can enhance Indoor air quality. While HEPA and ESP filters focus on trapping airborne particles, UV-C radiation can inactivate pathogens by disrupting their RNA. A review of UV air purifiers reveals a lack of studies on their efficacy and effectiveness in real-world settings. A thorough investigation into the performance of this mitigation solution is necessary, focusing on varying key factors, such as purifier placement, airflow dynamics, and UV dosage, to ensure optimal effectiveness. High-fidelity computational methods are essential in accurately assessing these factors, as informed by the physics of airborne transmission. Such advanced computations are necessary to determine the viability of portable UV air purifiers in mitigating airborne transmission in enclosed environments such as hospitals and public spaces. Integrating advanced air purification technologies with proper ventilation can improve safety in indoor environments and prevent future disease-related outbreaks. Full article

DNA polymerases from the hyperthermophilic Archaea have attracted considerable attention as PCR enzymes due to their high thermal stability and proofreading 3′ → 5′ exonuclease activity. This study is the first to report data concerning the purification and biochemical characteristics of the Tst DNA polymerase from Thermococcus stetteri. Both the wild type Tst(wt) DNA polymerase and its chimeric form containing the P36H substitution—which reduces the enzyme’s affinity for the U-containing template and dUTP—and the DNA-binding domain Sso7d from S. solfataricus were obtained and analyzed. It was shown that Tst(wt) could effectively amplify up to 6-kb DNA fragments, whereas TstP36H–Sso7d could amplify DNA fragments up to 15 kb. It was found that TstP36H–Sso7d has superior PCR efficiency compared to the commonly used DNA polymerase PfuV93Q–Sso7d. For the amplification of a 2-kb DNA fragment, TstP36H–Sso7d required less than 10 s of extension time, whereas for PfuV93Q–Sso7d, the extension time was no less than 30 s. Steady-state kinetic assays revealed that the dNTP-binding affinity K^dNTP_m was the same for TstP36H–Sso7d and PfuV93Q–Sso7d, whereas the maximum rate of dNTP incorporation, k_cat, was two orders of magnitude higher for TstP36H–Sso7d. Moreover, the incorporation of incorrect dNTP was not observed for TstP36H–Sso7d up to 56 °C, whereas for PfuV93Q–Sso7d, the extension of primer with incorrect dNTP was observed at 37 °C, supporting higher fidelity of TstP36H–Sso7d. The obtained data suggest that TstP36H–Sso7d may be a good candidate for high-fidelity DNA amplification. Full article

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment. Full article

This study investigated the ability of artificial neural networks (ANNs) to resolve the nonlinear dynamics inherent in the behavior of complex fluid flows, which often exhibit multifaceted characteristics that challenge traditional analytical or numerical methods. By employing flow profile pairs that are generated through high-fidelity numerical simulations, encompassing both the one-dimensional benchmark problems and the more intricate three-dimensional boundary layer transition problem, this research convincingly demonstrates that neural networks possess a remarkable capacity to effectively capture the discontinuities and the subtle wave characteristics that occur at small scales within complex fluid flows, thereby showcasing their robustness in handling intricate fluid dynamics phenomena. Furthermore, even in the context of challenging three-dimensional problems, this study reveals that the average velocity profiles can be predicted with a high degree of accuracy, utilizing a limited number of input profiles during the training phase, which underscores the efficiency and efficacy of the model in understanding complex systems. The findings of this study significantly underscore the immense potential that artificial neural networks, along with deep learning methodologies, hold in advancing our comprehension of the fundamental physics that govern complex fluid dynamics systems, while concurrently demonstrating their applicability across a variety of flow scenarios and their capacity to yield insightful revelations regarding the nonlinear relationships that exist among diverse flow parameters, thus paving the way for future research in this critical area of study. Full article

This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in this domain. By incorporating time-related attributes as conditioning information, our method ensures the preservation of chronological order and enhances data fidelity. A tailored learning scheme is implemented to capture the unique characteristics of solar power generation, particularly during sunrise and sunset. Comprehensive evaluations demonstrate the framework’s effectiveness in generating high-quality synthetic data, evidenced by a 79.58% improvement in the discriminative score and a 13.46% improvement in the predictive score compared to TimeGAN. Moreover, integrating the synthetic data into forecasting models resulted in up to 23.56% improvement in mean absolute error (MAE) for BIPV power generation predictions. These results highlight the potential of our framework to enhance prediction accuracy and optimize data utilization in renewable energy applications. Full article

Constructing scaffolds with the desired structures and functions is one of the main goals of tissue engineering. Three-dimensional (3D) bioprinting is a promising technology that enables the personalized fabrication of devices with regulated biological and mechanical characteristics similar to natural tissues/organs. To date, 3D bioprinting has been widely explored for biomedical applications like tissue engineering, drug delivery, drug screening, and in vitro disease model construction. Among different bioinks, photocrosslinkable bioinks have emerged as a powerful choice for the advanced fabrication of 3D devices, with fast crosslinking speed, high resolution, and great print fidelity. The photocrosslinkable biomaterials used for light-based 3D printing play a pivotal role in the fabrication of functional constructs. Herein, this review outlines the general 3D bioprinting approaches related to photocrosslinkable biomaterials, including extrusion-based printing, inkjet printing, stereolithography printing, and laser-assisted printing. Further, the mechanisms, advantages, and limitations of photopolymerization and photoinitiators are discussed. Next, recent advances in natural and synthetic photocrosslinkable biomaterials used for 3D bioprinting are highlighted. Finally, the challenges and future perspectives of photocrosslinkable bioinks and bioprinting approaches are envisaged. Full article

Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces; however, traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender leading to a dataset which we will make available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. Full article

This paper presents a comparative analysis of simplified and high-fidelity sonic boom prediction methods to assess their applicability in the conceptual design of supersonic aircraft. The high-fidelity approach combines Computational Fluid Dynamics (CFD) for near-field shock analysis with ray-tracing and the Augmented Burgers Equation for far-field propagation through a non-uniform atmosphere, whereas the simplified Carlson method uses analytical approximations for rapid predictions. The comparison across selected climb, cruise, and descent conditions for a supersonic reference aircraft shows that the Carlson method captures general trends in sonic boom behavior, such as changes in peak overpressure and signal duration with varying Mach number and altitude. However, significant deviations are noted under realistic atmospheric conditions, highlighting limitations in the simplified model’s accuracy. Common psycho-acoustic metrics were evaluated to assess the potential annoyance on the ground. The results demonstrate that while the simplified method is effective for early-stage design assessments, the high-fidelity model is essential for precise sonic boom characterization under realistic conditions, particularly for regulatory and community impact evaluations. Full article

The underwater imaging process is often hindered by high noise levels, blurring, and color distortion due to light scattering, absorption, and suspended particles in the water. To address the challenges of image enhancement in complex underwater environments, this paper proposes an underwater image color correction and detail enhancement model based on an improved Cycle-consistent Generative Adversarial Network (CycleGAN), named LPIPS-MAFA CycleGAN (LM-CycleGAN). The model integrates a Multi-scale Adaptive Fusion Attention (MAFA) mechanism into the generator architecture to enhance its ability to perceive image details. At the same time, the Learned Perceptual Image Patch Similarity (LPIPS) is introduced into the loss function to make the training process more focused on the structural information of the image. Experiments conducted on the public datasets UIEB and EUVP demonstrate that LM-CycleGAN achieves significant improvements in Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Average Gradient (AG), Underwater Color Image Quality Evaluation (UCIQE), and Underwater Image Quality Measure (UIQM). Moreover, the model excels in color correction and fidelity, successfully avoiding issues such as red checkerboard artifacts and blurred edge details commonly observed in reconstructed images generated by traditional CycleGAN approaches. Full article

 To overcome the problems in existing infrared remote sensing image generation methods, which make it difficult to combine high fidelity and high efficiency, we propose a High-Fidelity Infrared Remote Sensing Image Generation Method Coupled with the Global Radiation Scattering Mechanism and Pix2PixGAN (HFIRSIGM_GRSMP) in this paper. Firstly, based on the global radiation scattering mechanism, the HFIRSIGM_GRSMP model is constructed to address the problem of accurately characterizing factors that affect fidelity—such as the random distribution of the radiation field, multipath scattering, and nonlinear changes—through the innovative fusion of physical models and deep learning. This model accurately characterizes the complex radiation field distribution and the image detail-feature mapping relationship from visible-to-infrared remote sensing. Then, 8000 pairs of image datasets were constructed based on Landsat 8 and Sentinel-2 satellite data. Finally, the experiment demonstrates that the average SSIM of images generated using HFIRSIGM_GRSMP reaches 89.16%, and all evaluation metrics show significant improvement compared to the contrast models. More importantly, this method demonstrates high accuracy and strong adaptability in generating short-wave, mid-wave, and long-wave infrared remote sensing images. This method provides a more comprehensive solution for generating high-fidelity infrared remote sensing images.  Full article

Recent advancements in computational fluid dynamics (CFD) enable new and more complex analysis methods to be developed for early design stages. One such method is the isolated unsteady diffuser model, which seeks to reduce the computational cost of unsteady CFD when modeling diffusion systems in centrifugal compressors with vaned diffusers by isolating the diffuser from the computational domain and prescribing an unsteady and periodic inlet boundary condition. An initial iteration of this computational methodology was developed and validated for the Centrifugal Stage for Aerodynamic Research (CSTAR) at the High-Speed Compressor Laboratory at Purdue University. However, that work showed discrepancies in flow structure predictions between full-stage and isolated unsteady CFD models, and it also presented a narrow scope of only a single loading condition. Thus, this work addresses the need for improvement in the modeling fidelity. The original methodology was expanded by including a more accurate, non-uniform definition of turbulence at the diffuser inlet and modeling several loading conditions ranging from choke to surge. Results from isolated unsteady diffuser models with non-uniform turbulence modeling were compared with uniform turbulence isolated unsteady diffuser models and full-stage unsteady models at four loading conditions along a speedline. Flow structure predictions by the three methodologies were compared using 1D parameters and outlet total pressure and midspan velocity contours. The comparisons indicate a significant improvement in 1D parameter and flow structure predictions by the isolated unsteady diffuser models at all four loading conditions when including more accurate non-uniform turbulence, without a corresponding increase in computational cost. Additionally, both isolated diffuser methodologies accurately track trends in 3D flow structures along the speedline. Full article