Post-quantum cryptographic standardization is complete for the algorithms that matter most, yet adoption across financial infrastructure remains uneven and, in critical segments, largely absent. This paper argues that migration is no longer a research question but an execution question, and that the window for orderly transition is narrower than the commonly cited “Q-Day” horizon of 2030-2035 implies. Because adversaries can record encrypted traffic today and store it for decryption once cryptographically relevant quantum computers exist, the effective deadline for protecting data with multi-year confidentiality requirements has already passed. We review the NIST-approved suite — ML-KEM (FIPS 203), ML-DSA (FIPS 204), and SLH-DSA (FIPS 205) — alongside hybrid key-exchange constructions now deployed at internet scale, and assess their applicability to financial systems. We map the compliance landscape consolidating around 2030 and 2035, including NIST IR 8547, NSA CNSA 2.0, and the 2026 G7 Cyber Expert Group roadmap, alingside converging UK and EU timelines. Finally, we offer a practical migration framework organized around cryptographic inventory, exposure-window triage, and crypto-agility. Our central finding is that technical barriers to beginning migration are substantially lower than institutional inertia suggests: for many systems, post-quantum key agreement is a library upgrade rather than a redesign.
Generative artificial intelligence has become increasingly incorporated into digital media and more generally into production workflows with which the public frequently interacts. Current provenance standards and disclosure methods frequently rely on binary categorizations, differentiating only between entirely human-authored and AI-generated content, or depend on technical watermarking that lacks user-facing clarity. However, there are very different risks and outcomes depending on the different uses of AI in the creation and consumption of end products. Therefore, we propose the Reported Engagement with AI Level (REAL) Rating framework which addresses these concerns by introducing a six-tier scale (Levels 0-5) that discloses the extent of AI involvement in both product creation and user experience. This paper details the structural methodology of the REAL Rating system and the specifics of its application across text, image, audio, video, and software products.
When natural disasters strike, individuals, communities, and even entire countries can suffer. Researchers have studied the impacts of disasters on various factors of interest, from mental health, to poverty, to economic activity. However, the impact of disasters on the nonprofit sector is understudied despite the nonprofit sector's perhaps surprising role in local or national economies as well as its role in disaster response and recovery. Thus, we study the effect of natural disaster damage on different county-level nonprofit outcomes using a panel dataset spanning 1991 to 2021 and causal inference methods tailored to panel data. Contrary to prior work, which found small but positive associations between disaster damage and nonprofit revenue or assets, we find no evidence of a causal effect.
The phenomenon of religious, cultural, and increasingly complex religious, cultural, and life views challenges theological education to move beyond a doctrinal approach that is transmissive towards an inclusive, dialogical, and contextual learning model. However, various studies show that theological education still tends to be oriented towards cognitive aspects and has not fully integrated relational, affective, and transformative dimensions. Therefore, this article aims to reconstruct Jesus's pedagogy as a conceptual framework for developing inclusive theological learning in a pluralistic society. This study uses a qualitative approach based on a conceptual-analytical literature study with thematic content analysis of scientific publications (2021-2026). The results of the study show that Jesus' pedagogy is multidimensional, encompassing relational-inclusive, dialogical-participatory, and contextual-transformative principles. This pedagogy places love at the basis of relationships and encourages the development of ethical awareness across diversity. Theoretically, this research offers a conceptual synthesis that positions Jesus' pedagogy as an integrative paradigm, enriching contemporary theological education. Thus, Jesus' pedagogy is relevant as the basis for developing inclusive, reflective, and contextual learning in responding to the dynamics of a plural society.
Abstract Background Image noise in digital mammography (DM) can obscure image details and reduce the detectability of clinically relevant findings such as microcalcifications. Purpose To present and evaluate an extended model‐based (MB) image restoration pipeline for DM that incorporates quantum, electronic, and structural noise components, as well as spatial noise correlation, and to assess its effect on breast radiologists' detection of simulated microcalcification clusters in clinical mammograms. Methods A total of 120 clinical DM images acquired using a GE Senographe Pristina system were used. Simulated microcalcification clusters were inserted into locally dense fibroglandular regions at controlled contrast levels. The restoration pipeline was configured to target the signal and noise characteristics predicted by the model for an acquisition at approximately twice the original radiation dose. Six breast radiologists with different levels of experience, classified as Junior or Senior, participated in two human observer experiments: (i) a pilot study using original clinical images with simulated clusters and a four‐alternative forced‐choice (4AFC) protocol combined with a three‐down‐one‐up (3D1U) staircase to estimate reader‐specific contrast detection thresholds and achieve comparable baseline task difficulty across readers; and (ii) a main 4AFC observer study using individualized datasets generated according to each reader's estimated contrast threshold, in which microcalcification detection performance was compared between the original and restored image conditions based on the percentage of correct responses (PC). Two generalized linear mixed models (GLMMs) were used: the first evaluated the effect of image condition as a fixed effect, with reader and case as crossed random intercepts, and the second evaluated the effect of reader experience on detection performance in the original image condition. Results At the reader‐specific contrast thresholds estimated in the pilot study, the mean PC for the original images was 78.3%, within the prespecified target performance range. Mean PC increased to 84.3% for the restored images, corresponding to an absolute improvement of 6.0 percentage points. The primary GLMM indicated a significant restoration effect on detection performance (OR = 1.57, 95% CI: 1.15–2.14, ). Baseline mean PC was 78.7% for Junior readers and 78.0% for Senior readers, and the separate GLMM found no evidence of an experience‐group effect under the original image condition (). Conclusions The proposed extended MB restoration pipeline improved the detectability of simulated microcalcification clusters by breast radiologists under the controlled 4AFC conditions investigated. By targeting the signal and noise characteristics predicted by the model for an acquisition at approximately twice the original radiation dose without requiring additional radiation exposure, the method represents a promising approach for improving microcalcification detectability in DM.
