Image privacy protection relies on effective diffusion, yet existing methods still suffer from limited cross-plane interaction and insufficient perturbation propagation. To address this issue, this paper proposes a chaotic image privacy protection method based on Layer-Coupled Co-Evolutionary Cellular Automata (LCCE-CA). A diffusion-oriented Life-like CA rule optimization method is first designed by imposing finite-step balance and temporal-correlation constraints to obtain low-correlation and near-balanced diffusion rules. Then, a reversible LCCE-CA mechanism is constructed by coupling image bit-planes with auxiliary co-evolution planes, enabling explicit cross-plane feedback at each iteration. Based on this mechanism, a chaotic image privacy protection framework is developed and validated on standard grayscale image encryption experiments. Compared with the traditional 8th-order reversible CA, LCCE-CA reduces the early-stage mean balance deviation from 0.3003 to 0.0008, corresponding to a 99.7% reduction. At the 8th iteration, the cumulative perturbation propagation ratio increases from 0.2580 to 0.5609. Experiments on nine standard grayscale images with sizes of 256 × 256 and 512 × 512 show noise-like protected images, nearly uniform histograms, and strong spatial decorrelation, with a maximum absolute adjacent-pixel correlation coefficient of 0.00857. The scheme exhibits strong plaintext and ciphertext sensitivity, with the average Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) for plaintext sensitivity reaching 99.6075% and 33.4708%, respectively. These results indicate that LCCE-CA provides a practical reversible diffusion framework for secure image privacy protection.
Facial sketch synthesis is important for cross-modal face analysis and digital forensics, yet existing models often suffer from structural distortions and identity inconsistency under limited paired training data. Traditional methods, primarily based on generative adversarial networks, often suffer from training instability and insufficient detail reconstruction. To address these limitations, this study proposes a diffusion-based framework with a stage-wise multi-condition guidance mechanism that enhances both structural and textural fidelity. Specifically, (i) during the downsampling phase, semantic segmentation features are fused to guide the model with accurate structural information, such as facial part locations; and (ii) during upsampling, a hybrid cross-attention mechanism is employed to integrate coarse image textures with denoised noise, refining fine-grained details. Additionally, we incorporate the Vision Transformer within the U-Net backbone to better capture global contextual information in low-frequency regions, further enhancing image realism. Experiments on multiple benchmark datasets demonstrate strong overall performance in SSIM and FSIM, indicating improved structural similarity and feature-level fidelity under the evaluated settings. Moreover, our method obtains competitive LPIPS results, indicating improved perceptual similarity. We further compare with recent image-to-image translation and diffusion-based baselines and observe competitive performance in both visual coherence and identity preservation.
This study examines the impact of water availability on the urban land market by focusing on a large-scale inter-basin water transfer project in China. Employing a difference-in-differences framework and a unique parcel-level dataset, we find that the project increased average land prices in water-receiving areas by 8.47% while also stimulating urban land market activity, and ultimately promote urban economic growth and expansion. These effects are primarily driven by two channels: population growth and mobility, and accelerated industrial expansion—all induced by the large-scale water transfer. Additional analyses further reveal that the project’s influence on land price is almost entirely concentrated in residential and commercial land parcels, with negligible effects observed for industrial land. And the positive impacts are more pronounced along the project’s middle route and in arid counties. Notably, we find no empirical evidence of adverse effects on land markets in water-supplying areas. A back-of-the-envelope calculation suggests that the appreciation in land values could recoup over one-third of the project’s total investment, underscoring its substantial economic benefits.
This study determines the impacts of natural resource depletion, total resource rents, exports, and agricultural raw material imports on carbon emissions in the three most populated countries in the world China, India, and the United States, in the period–2000–2023. This study reveals the short- and long-term effects of trade and total natural resource rents on emissions using advanced panel econometric methods. The results indicate that unsustainable extraction and importation activities that increase carbon emissions can be reduced using resource rents, which, when properly managed, can be used to encourage environmental sustainability issues. This study contributes to the idea of climate action of united nation sustainable development goal 13 (SDG 13) by suggesting the relevance of trade patterns, and sustainable resource strategies for the design of carbon-cutting trajectories. This study provides policy-relevant data to inform the policies of macroeconomic activities pursuing domestic climate outcomes and international net-zero emissions.
This study aimed to explore the longitudinal relationships between game bullying victimization, self-critical rumination, and depressive emotions among junior high school students, and to reveal the potential mediating role of self-critical rumination. A cluster sampling method was adopted to conduct a three-wave longitudinal study over six months with 664 junior high school students (Mage = 12.82, 48.34
