Materials and Emerging Technologies for Sustainability

This paper presents a statistical framework for identifying circular flaws in structures using natural frequency data and Bayesian inference, explicitly addressing uncertainties arising from modeling errors and measurement noise. In this approach, the circular flaw is characterized by parameters such as the center coordinates and radius. The natural frequencies of the structure, measured under known boundary conditions, serve as the input data for the identification process. The smoothed finite element method (SFEM) forward model predicts the natural frequency shifts due to the presence of flaws and is integrated into the analysis. By combining observed frequency data with prior knowledge, Bayes’ theorem is employed to refine the probability distributions of the flaw parameters. The Markov chain Monte Carlo (MCMC) algorithm is utilized to sample from the posterior distributions of the parameters, ensuring robust uncertainty quantification. A numerical case study validates the proposed method, highlighting its accuracy and effectiveness in detecting and characterizing circular flaws.

This study presents a comprehensive investigation of bio-inspired rotating triply periodic minimal surface (RotTPMS) plates under thermo-mechanical loading conditions, advancing sustainable lightweight structural design through computational modeling. Three TPMS architectures including Primitive (P), I-Wrapped Package (IWP), and Gyroid (G) are analyzed using a quasi-3D higher-order shear deformation theory (HSDT) combined with isogeometric analysis (IGA). The research addresses critical thermal stability challenges in lightweight structures by examining thermal buckling and thermo-mechanical buckling behaviors across various crystalline rotations, relative densities, and temperature conditions. Results demonstrate that P-type RotTPMS structures exhibit superior thermal buckling resistance, with critical temperature rises up to 30% higher than other architectures, while maintaining optimal weight-to-performance ratios. The study reveals significant anisotropic characteristics in thermal responses, with crystalline rotations inducing variations of up to 300% in critical buckling loads at elevated temperatures. Temperature-dependent material properties substantially influence structural stability, reducing critical buckling temperatures by $15\%÷30\%$ compared to temperature-independent analyses. These findings provide necessary insights for designing thermally stable lightweight structures in sustainable engineering applications, particularly relevant for aerospace and mechanical systems. The developed computational framework and material design principles contribute to advancing additive manufacturing (AM) capabilities for sustainable structural solutions, aligning with growing demands for energy-efficient and eco-friendly engineering materials.

By developing photocatalyst systems that enhance the photocatalytic activity for the degradation of antibiotics, our interest currently lies in utilizing visible light to optimize the use of the solar light spectrum. Bismuth-based photocatalysts take advantage of nature’s narrow bandgap and wide range of compounds. In this study, the synthesis of bismuth-based heterojunctions from two visible light response Bi₂O₃ and Bi₂O₂S materials by hydrothermal method is used for the degradation of tetracycline. The highest removal efficiency belongs to the Bi₂O₃/Bi₂O₂S heterojunction hydrothermal in 6 h, which is 51.27%. The combination of Bi₂O₃ and Bi₂O₂S materials creates the heterojunction that enhances the visible light response up to 623 nm, reduces the recombination rate, increases carrier mobility, and tunes the energy level. In addition, the photocatalytic degradation of Bi₂O₃/Bi₂O₂S systems involves e${}^{-}$, h${}^{+}$, $\gamma$OH, and $\gamma$O${}_2^{-}$ in the degradation of tetracycline. The Bi₂O₃/Bi₂O₂S is a promising heterostructured photocatalyst in the removal of tetracycline from wastewater.

Collecting high-quality medical image data for machine learning applications remains a significant challenge due to data scarcity, privacy concerns, and high annotation costs. To address these issues, vision generative models, particularly Latent Diffusion Models (LDMs), have emerged as state-of-the-art solutions that reduce computational demands while maintaining superior performance in data generation tasks. In this study, we propose an enhanced LDM-based approach that integrates separable self-attention mechanisms within the diffusion process, positioned after residual blocks, to improve the capture of detailed features and maintain spatial consistency. This modification reduces memory usage by 82.94% and decreases the Fréchet Inception Distance (FID) by 25.01% compared to traditional self-attention models, all while preserving image quality. Our method addresses critical challenges such as data scarcity and computational efficiency in medical imaging by combining Variational Autoencoders (VAEs) for latent space mapping with U-Net for noise prediction. Evaluations on five datasets — PneumoniaMNIST, BloodMNIST, ChestMNIST, Dental4k, and HandMNIST — demonstrate significant improvements in computational efficiency, memory usage, and the quality of generated images, showcasing the potential of our approach for scalable and effective medical image synthesis.

Detection of retinal abnormalities is an interesting problem that has attracted the attention of several studies. Although many works have been published on this topic, they are still ineffective when applied to real data from medical facilities due to conditions and constraints that lead to low efficiency. To address this problem, this paper proposes an effective model to detect signs of retinal diseases, including glaucoma, cataracts, and diabetic retinopathy. The proposed model EfficientNet-TL combines transfer learning techniques with the EfficientNet network architecture to improve accuracy. In addition, EfficientNet-TL also applies the histogram of oriented gradients (HOG) technique to extract information about the structure and shape of abnormal regions on fundus images and the LBP technique to extract image texture features and recognize small changes on the retinal surface. Experimental results on the image dataset showed that the proposed model EfficientNet-TL achieved high efficiency with an accuracy of up to 93%, which is superior to the previous models.

This paper investigates the impact of various 3D printing parameters on two critical resources: printing time and plastic material consumption. Through a series of experiments, the paper aimed to optimize these resources by adjusting the relevant parameters. First, by incorporating multiple algorithmic models, our stacking artificial intelligence model achieved superior prediction accuracy and a more comprehensive evaluation of the parameters. This novel approach amalgamates machine learning models to identify the optimal configuration or leverage the strengths of various models. Second, the proposed method paves the way for developing automated tools that simplify the configuration of the printer parameters, making 3D printing more accessible. This innovation allows users with limited expertise to focus on essential aspects of 3D printing, reducing the process to a few elementary steps. Third, our findings enable 3D printing entities to offer advanced estimates of printing time and filament usage, particularly for large-scale projects. With a prediction accuracy of 96.19% based on the R² score, the proposed method enables users to plan uninterrupted printing sessions more effectively and optimize both print time and resource consumption according to their specific preferences. Lastly, mastering the printing parameters that affect time and material consumption is crucial to producing high-quality 3D models efficiently. This optimization reduces plastic waste and environmental pollution while increasing profits and operational efficiency. In general, through these contributions, our study advances the field of 3D printing by providing practical tools and insights that enhance both the efficiency and accessibility of the technology.