Trace elements and isotopes (TEIs) are important to marine life and are essential tools for studying ocean processes1. Two different frameworks have arisen regarding marine TEI cycling: reversible scavenging favours water-column control on TEI distributions2–5, and seafloor boundary exchange emphasizes sedimentary imprints on water-column biogeochemistry6,7. These two views lead to disparate interpretations of TEI behaviours8–10. Here we use rare earth elements and neodymium isotopes as exemplar tracers of particle scavenging11 and boundary exchange6,7,12. We integrate these data with models of particle cycling and sediment diagenesis to propose a general framework for marine TEI cycling. We show that, for elements with greater affinity for manganese oxide than biogenic particles, scavenging is a net sink throughout the water column, contrary to a common assumption for reversible scavenging3,13. In this case, a benthic flux supports increasing elemental concentrations with water depth. This sedimentary source consists of two components: one recycled from elements scavenged by water-column particles, and another newly introduced to the water column through marine silicate weathering inside sediment8,14,15. Abyssal oxic diagenesis drives this benthic source, and exerts a strong influence on water-column biogeochemistry through seafloor geometry and bottom-intensified turbulent mixing16,17. Our findings affirm the role of authigenic minerals, often overshadowed by biogenic particles, in water-column cycling18, and suggest that the abyssal seafloor, often regarded as inactive, is a focus of biogeochemical transformation19,20.
Signature schemes are a fundamental component of cybersecurity infrastructure. While they are designed to be mathematically secure against cryptographic attacks, they are vulnerable to Rowhammer fault-injection attacks. Since all existing attacks are ad-hoc in that they target individual parameters of specific signature schemes, it remains unclear about the impact of Rowhammer on signature schemes as a whole. In this paper, we present Achilles, a formal framework that aids in leaking secrets in various real-world signature schemes via Rowhammer. Particularly, Achilles can be used to find potentially more vulnerable parameters in schemes that have been studied before and also new schemes that are potentially vulnerable. Achilles mainly describes a formal procedure where Rowhammer faults are induced to key parameters of a generalized signature scheme, called G-sign, and a post-Rowhammer analysis is then performed for secret recovery on it. To illustrate the viability of Achilles, we have evaluated six signature schemes (with five CVEs assigned to track their respective Rowhammer vulnerability), covering traditional and post-quantum signatures with different mathematical problems. Based on the analysis with Achilles, all six schemes are proved to be vulnerable, and two new vulnerable parameters are identified for EdDSA. Further, we demonstrate a successful Rowhammer attack against each of these schemes, using recent cryptographic libraries including wolfssl, relic, and liboqs.
Current antibiotic-resistant bacteria (ARB) disinfection techniques commonly rely on large dosages of oxidants, resulting in the presence of considerable amounts of residuals and toxic disinfection byproducts (DBPs) in water. Herein, we propose a highly effective ARB disinfection approach via activating an ultralow concentration (10 μM) of chlorite (ClO2–) by naturally abundant sunlight to generate various reactive species (i.e., HO•, Cl•, ClO•, and ClO2) with negligible generation of halogenated DBPs. Combining in situ characterization with theoretical calculations, we reveal that, in addition to the photolysis of ClO2– in the bulk solution, ClO2– ions electrostatically adsorbed on the positive local sites of lipids can boost light absorption and facilitate the in situ generation of reactive species upon sunlight irradiation, enabling more efficient attacks toward cell membranes and the intracellular antioxidant enzyme system. The intracellular antibiotic resistance genes (ARGs) are then released and further degraded, inhibiting horizontal ARG transfer. This approach can also achieve excellent ARB disinfection performance in real water matrices (e.g., lake and river water) in 1 L tanks and 500 mL plastic bottles with natural sunlight irradiation. Overall, this work presents an efficient, safe, and sustainable method to inactivate ARB with deep insights into disinfection mechanisms at the subcellular level.
In this paper, we develop a new adaptive hyperbolic-cross-space mapped Jacobi (AHMJ) method for solving multidimensional spatiotemporal integrodifferential equations in unbounded domains. By devising adaptive techniques for sparse mapped Jacobi spectral expansions defined in a hyperbolic cross space, our proposed AHMJ method can efficiently solve various spatiotemporal integrodifferential equations such as the anomalous diffusion model with reduced numbers of basis functions. Our analysis of the AHMJ method gives a uniform upper error bound for solving a class of spatiotemporal integrodifferential equations, leading to effective error control.
Due to the high cost and small scale of Image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent Blind IQA (BIQA) methods. Traditional deep learning-based methods emphasize visual information to capture quality features, while recent developments in Vision-Language Models (VLMs) demonstrate strong potential in learning generalizable representations through textual information. However, applying VLMs to BIQA poses three major Challenges: (1) How to make full use of the multi-modal information. (2) The prompt engineering for appropriate quality description is extremely time-consuming. (3) How to use mixed data for joint training to enhance the generalization of VLM-based BIQA model. To this end, we propose a Multi-modal BIQA method with prompt learning, named MMP-IQA. For (1), we propose a conditional fusion module to better utilize the cross-modality information. By jointly adjusting visual and textual features, our model can capture quality information with a stronger representation ability. For (2), we model the quality prompt's context words with learnable vectors during the training process, which can be adaptively updated for superior performances. For (3), we jointly train a linearity-induced quality evaluator, a relative quality evaluator, and a dataset-specific absolute quality evaluator. In addition, we propose a dual automatic weight adjustment strategy to adaptively balance the loss weights between different datasets and among various losses within the same dataset. Extensive experiments illustrate the superior effectiveness of MMP-IQA.
