Atmospheric turbulence induces severe blurring and geometric distortions in facial imagery, critically compromising the performance of downstream tasks. To overcome this challenge, a lightweight conditional diffusion model was proposed for the restoration of single-frame turbulence-degraded facial images. Super-resolution techniques were integrated with the diffusion model, and high-frequency information was incorporated as a conditional constraint to enhance structural recovery and achieve high-fidelity generation. A simplified U-Net architecture was employed within the diffusion model to reduce computational complexity while maintaining high restoration quality. Comprehensive comparative evaluations and restoration experiments across multiple scenarios demonstrate that the proposed method produces results with reduced perceptual and distributional discrepancies from ground-truth images, while also exhibiting superior inference efficiency compared to existing approaches. The presented approach not only offers a practical solution for enhancing facial imagery in turbulent environments but also establishes a promising paradigm for applying efficient diffusion models to ill-posed image restoration problems, with potential applicability to other domains such as medical and astronomical imaging.
Climate change, which has intensified to a global governance crisis, demands adaptation strategies that are faster, precise, and more inclusive than ever before. Artificial intelligence (AI), increasingly positioned at the core of this transformation, is offering powerful tools for climate risk forecasting, disaster preparedness, energy optimization, agricultural efficiency, and business resilience. Yet the growing adoption of AI exposes a fundamental paradox: while it promises unprecedented analytical capacity, its benefits remain unevenly distributed across communities. The current study addressed this tension by presenting a comprehensive and governance-oriented analysis of AI-driven climate adaptation. Drawing on an extensive review of academic research and major institutional reports, this paper identified three interlinked challenges including methodological limitations, ethical and equity risks, as well as governance gaps which continuously undermine the effectiveness of AI-enabled adaptation. Predictive models struggled to incorporate complex social vulnerabilities; algorithmic opacity limited trust and accountability whereas persistent data inequality prevented low-income regions from leveraging advanced digital tools. In response, the study introduced a multi-layered governance framework encompassing technical capacity, regulatory and ethical infrastructure, and socially inclusive outcomes. The findings revealed that the contributions of AI to climate adaptation were fundamentally shaped by institutional quality, transparent data governance, equitable digital access, and participation of vulnerable populations in decision making. The paper concluded that AI held extraordinary potential to strengthen resilience, only if deployed within governance systems that prioritize fairness, accountability, transparency, ethics, and social inclusion. By aligning technological innovation with just and sustainable governance, AI becomes not only a predictive instrument but a transformative catalyst for equitable climate adaptation worldwide.
Accurate diagnosis of lung cancer brain metastasis is often hindered by incomplete magnetic resonance imaging (MRI) modalities, resulting in suboptimal utilization of complementary radiological information. To address the challenge of ineffective feature integration in missing-modality scenarios, a Transformer-based multi-modal feature fusion framework, referred to as Missing Modality Transformer (MMT), was introduced. In this study, multi-modal MRI data from 279 individuals diagnosed with lung cancer brain metastasis, including both small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), were acquired and processed through a standardized radiomics pipeline encompassing feature extraction, feature selection, and controlled data augmentation. The proposed MMT framework was trained and evaluated under various single-modality and combined-modality configurations to assess its robustness to modality absence. A maximum diagnostic accuracy of 0.905 was achieved under single-modality missing conditions, exceeding the performance of the full-modality baseline by 0.017. Interpretability was further strengthened through systematic analysis of loss-function hyperparameters and quantitative assessments of modality-specific importance. The experimental findings collectively indicate that the MMT framework provides a reliable and clinically meaningful solution for diagnostic environments in which imaging acquisition is limited by patient conditions, equipment availability, or time constraints. These results highlight the potential of Transformer-based radiomics fusion to advance computational neuro-oncology by improving diagnostic performance, enhancing robustness to real-world imaging variability, and offering transparent interpretability that aligns with clinical decision-support requirements.
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