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Abstract We investigate the occurrence patterns of SSEs along the shallow (15 km) portion of the Hikurangi subduction zone. First, we build a manual catalog constraining timing and length of 92 SSEs between 2006 and 2024. Then, we investigate SSE occurrence patterns by fitting a renewal process, using Bayesian inference to obtain the posterior distribution of model parameters. Our results show that SSE recurrence intervals vary along the Hikurangi margin; less frequent SSEs occur in the southern part of the margin. The periodicity of SSEs also changes along strike. SSEs in the northern part of the margin occur more regularly than those at the central part. Finally, we do not find conclusive evidence that 2016 7.8 Kaikōura earthquake had a lasting effect on SSE occurrence patterns. 

Abstract The prognosis of glioblastoma multiforme (GBM) remains dismal, despite standard treatment regimens. A key challenge in treating GBM is the persistence of glioma stem cells (GSCs) within the perivascular niche (PVN) – a protective tumor microenvironment (TME) that is often associated with inadequate drug penetration. Current preclinical models do not capture complexity of the human TME, particularly the vasculature and niche‐specific interactions that drive GBM progression. To overcome these limitations, an innovative 3Dex‐vivotumor‐on‐a‐chip (TOC) platform is engineered to accurately replicate the structural and functional characteristics of the PVN. Using this platform, this study demonstrates that monocyte membrane‐coated nanoparticles (MoNP) effectively target the abnormal tumor microvasculature, offering a promising approach to enhance drug delivery to these hard‐to‐reach GSCs. The results show that the therapeutic agent verteporfin, when delivered via MoNP, significantly inhibited GSC growth and invasiveness, while the free‐form drug showed minimal efficacy. Comprehensive transcriptomic profiling and cytokine analysis validated the TOC model's ability to reflect authentic GSC responses and confirmed that MoNP‐mediated verteporfin delivery effectively modulates key tumor‐related signaling pathways. This integrated TOC‐MoNP platform represents a clinically relevant tool that bridges the gap between traditional preclinical models and human disease, providing new opportunities for developing more effective GBM therapies. 

We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dB over a 5-span network under dynamic channel loading. 

Optical transmission systems require accurate modeling and performance estimation for autonomous adaption and reconfiguration. We present efficient and scalable machine learning (ML) methods for modeling optical networks at component- and network-level with minimized data collection.