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The phenomenon of a time-dependent afterglow color change has huge potential to improve the security levels in information encryption and anti-counterfeiting. However, developing multiple triplet emission centers in long-lived afterglow materials is a formidable challenge. In this work, we developed a carbon dot (CDs) based composite material (SCD@BA1) with a time-dependent afterglow color change from yellow to green within 2 s (excited by 360 nm irradiation) by introducing sulfur into CDs. SCDs are embedded in BA/B2O3 matrix materials via covalent bonds, and the sulfur doping regulates the bandgap of the SCDs, and a new emission center with yellow room temperature phosphorescence (RTP) is generated on the CD surface. The rigid structure of the composite promotes the high-energy green RTP emission in the carbon core state and low-energy yellow RTP emission in the surface state simultaneously, activated under irradiation of approximately 360 nm light. The two triplet emissions with different colors and decay lifetimes lead to a phenomenon of a time-dependent afterglow color change. Simple concepts of advanced time-limited information encryption and anti-counterfeiting were developed to demonstrate the great potential of the SCD@BA1 composite for advanced security applications. [ABSTRACT FROM AUTHOR]

In this work, a Co–Ce@RM ozone catalyst was developed using red mud (RM), a by‐product of alumina production, as a support material, and its preparation process, catalytic efficiency, and tetracycline (TCN) degradation mechanism were investigated. A comprehensive assessment was carried out using the 3E (environmental, economic, and energy) model. The optimal production conditions for Co–Ce@RM were as follows: The doping ratio of Co and Ce was 1:3, the calcination temperature was 400°C, and the calcination time was 5 h, achieving a maximum removal rate of 87.91% of TCN. The catalyst was characterized using different analytical techniques. Under the conditions of 0.4 L/min ozone aeration rate, with 9% catalyst loading and solution pH 9, the optimal removal rates and chemical oxygen demand by the Co–Ce catalytic ozonation at RM were 94.17% and 75.27%, respectively. Moreover, free radical quenching experiments showed that superoxide radicals (O2−) and singlet oxygen (1O2) were the main active groups responsible for the degradation of TCN. When characterizing the water quality, it was assumed that TCN undergoes degradation pathways such as demethylation, dehydroxylation, double bond cleavage, and ring‐opening reactions under the influence of various active substances. Finally, the 3E evaluation model was deployed to evaluate the Co–Ce@RM catalytic ozonation experiment of TCN wastewater. Practitioner Points: The preparation of Co–Ce@RM provides new ideas for resource utilization of red mud.Catalytic ozonation by Co–Ce@RM can produce 1O2 active oxygen groups.The Co–Ce@RM catalyst can maintain a high catalytic activity after 20 cycles.The degradation pathway of the catalytic ozonation of tetracycline was fully analyzed.Catalytic ozone oxidation processes were evaluated by the "3E" (environmental, economic, and energy) model. [ABSTRACT FROM AUTHOR]

The uniform temperature distribution on the surface of the electromagnetic control roll (ECR) has a great impact on the quality of the strip; therefore, temperature control is essential. In order to study this issue, a two-dimensional volume of fluid (VOF) model was established using the simulation software FLUENT (2024 R1) to analyze the radial cooling capacity and surface temperature uniformity of the ECR under different process parameters, and an experimental validation was carried out at the same time. The error between the experiment and the model was less than 5% of the maximum temperature, proving the model is accurate. The results of the analysis show that the use of a controlled temperature mode has an effect on the cooling capacity and the speed has no effect on the cooling capacity. The temperature difference between the two sides of the ECR is too large, which will make the uniformity of the ECR surface temperature worse. While too high or too low, a roll speed and coolant injection speed will increase the non-uniformity of the ECR surface temperature; when the roll speed is 12 rad/s or coolant injection speed is 5 m/s, the ECR surface temperature distribution uniformity is the best. Properly adjusted process parameters can improve the cooling performance and ECR surface temperature uniformity. [ABSTRACT FROM AUTHOR]

Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of inverse processes of a reversible network and employs the same network to achieve embedding and extraction of watermarks. It is just that the information flow is in the opposite direction. Additionally, a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility of malicious attacks during transmission, ensuring the accurate extraction of watermarking information through the invertible network's inverse process. This paper evaluates the accuracy, robustness, and invisibility of MarkINeRV through multiple video datasets. The results demonstrate its efficacy in extracting watermarking information for copyright protection of NeRV. MarkINeRV represents a pioneering investigation into copyright issues surrounding NeRV. [ABSTRACT FROM AUTHOR]

As neural radiance fields continue to advance in 3D content representation, the copyright issues surrounding 3D models oriented towards implicit representation become increasingly pressing. In response to this challenge, this paper treats the embedding and extraction of neural radiance field watermarks as inverse problems of image transformations and proposes a scheme for protecting neural radiance field copyrights using invertible neural network watermarking. Leveraging 2D image watermarking technology for 3D scene protection, the scheme embeds watermarks within the training images of neural radiance fields through the forward process in invertible neural networks and extracts them from images rendered by neural radiance fields through the reverse process, thereby ensuring copyright protection for both the neural radiance fields and associated 3D scenes. However, challenges such as information loss during rendering processes and deliberate tampering necessitate the design of an image quality enhancement module to increase the scheme's robustness. This module restores distorted images through neural network processing before watermark extraction. Additionally, embedding watermarks in each training image enables watermark information extraction from multiple viewpoints. Our proposed watermarking method achieves a PSNR (Peak Signal-to-Noise Ratio) value exceeding 37 dB for images containing watermarks and 22 dB for recovered watermarked images, as evaluated on the Lego, Hotdog, and Chair datasets, respectively. These results demonstrate the efficacy of our scheme in enhancing copyright protection. [ABSTRACT FROM AUTHOR]

The neural radiance field (NeRF) has demonstrated significant advancements in computer vision. However, the training process for NeRF models necessitates extensive computational resources and ample training data. In the event of unauthorized usage or theft of the model, substantial losses can be incurred by the copyright holder. To address this concern, we present a novel algorithm that leverages the implicit neural representation (INR) watermarking technique to safeguard NeRF model copyrights. By encoding the watermark information implicitly, we integrate its parameters into the NeRF model's network using a unique key. Through this key, the copyright owner can extract the embedded watermarks from the NeRF model for ownership verification. To the best of our knowledge, this is the pioneering implementation of INR watermarking for the protection of NeRF model copyrights. Our experimental results substantiate that our approach not only offers robustness and preserves high-quality 3D reconstructions but also ensures the flawless (100%) extraction of watermark content, thereby effectively securing the copyright of the NeRF model. [ABSTRACT FROM AUTHOR]

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field (NeRF) models. We employ an embedding network to integrate the watermark into the images within the training set. Then, the NeRF model is utilized for 3D modeling. For copyright verification, a secret image is generated by inputting a confidential viewpoint into NeRF. On this basis, design an extraction network to extract embedded watermark images from confidential viewpoints. In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario, the verifier can extract the watermark from the confidential viewpoint to authenticate the model's copyright. The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks, thereby substantiating the effectiveness of our approach in safeguarding NeRF. [ABSTRACT FROM AUTHOR]