In this study, nanoscale periodic surface structures were fabricated on polyimide (PI) films using a linearly polarized KrF excimer laser with a wavelength of 248 nm. The effects of laser energy density and pulse number on the morphology and surface roughness of laser-induced periodic surface structures (LIPSSs) were systematically investigated. When the pulse width was 20 ns, the repetition rate was 10 Hz, and the beam incidence angle was normal (90°), periodic ripples with a spatial period of approximately 200 nm formed within an energy density range of 7–18 mJ/cm² and pulse number range of 6000–18,000. The most uniform and well-defined structures were achieved at 14.01 mJ/cm² and 12,000 pulses, with a ripple depth of 60 nm and surface roughness ($R_{\mathrm{a}}$) approximately 26 times greater than that of pristine PI. The ripple orientation was consistently perpendicular to the laser polarization, consistent with low-spatial-frequency LIPSS (LSFL) formation mechanisms governed by interference-induced photothermal effects. In addition, surface wettability was found to be significantly enhanced due to changes in both surface chemistry and topography, with the water contact angle decreasing from 73.7° to 19.7°. These results demonstrate the potential of UV nanosecond laser processing for the scalable fabrication of functional nanostructures on polymer surfaces for applications in surface engineering and biointerfaces.

On August 24, 2023, Japan commenced the ocean discharge of treated nuclear-contaminated water from the Fukushima Daiichi Nuclear Power Plant, sparking widespread international concern. This study develops a tripartite evolutionary game model incorporating bounded rationality and time-delay effects to analyze strategic interactions among Japan, other countries, and the International Atomic Energy Agency (IAEA). Through the construction of payoff matrices and replicator dynamic equations with delay terms, the model identifies three evolutionarily stable strategies \(\gamma _4(0,0,1)\), \(\gamma _6(1,0,1)\), and \(\gamma _8(1,1,1)\) under specific cost and benefit conditions. Numerical simulations reveal that time delays significantly influence convergence speed and trajectory complexity, particularly for Japan and the IAEA. Parametric analyses show that Japan’s decision to discharge is sensitive to storage costs (\(C_{SJ}\)), discharge costs (\(C_{DJ}\)), export tax revenue loss (\(T_{RJ}\)), and reputational damage (\(I_J\)), while other countries’ responses are shaped by litigation compensation (\(C_{LC}\)) and aid to Japan (\(C_{HJ}\)). The model also aligns with real-world developments such as China’s conditional partial lifting of its seafood import ban in June 2025, exemplifying the stabilizing role of credible international oversight and transparency. This research contributes a novel delayed-replication framework to evolutionary game theory and offers actionable insights for managing transboundary environmental conflicts.

Multivariate time-series anomaly detection is important for server monitoring, industrial systems, and intelligent operation and maintenance. Existing point-wise and point-adjusted metrics often fail to show whether an anomalous segment is detected early enough for intervention. This paper proposes a lightweight dual-score enhancement for Anomaly Transformer. The method retains the original multiplicative anomaly score and adds window-normalized reconstruction error and association discrepancy terms, so weak devi- ations near anomaly onsets become more visible without changing the backbone network or using additional labels. Experiments are conducted on four public multivariate bench- marks, namelySMD,PSM,SMAP,andMSL,underthreerandomseeds. Comparedwiththe original Anomaly Transformer score, the proposed early-warning union mode improves Raw Event Recall by 0.3435, 0.2408, 0.0995, and 0.0278 on SMD, PSM, SMAP, and MSL, respectively, and reduces mean detection delay by 53.66%, 49.97%, 52.69%, and 39.74%. Additional range- and proximity-aware metrics further show that the score-level enhance- ment improves segment coverage and early-warning behavior, while AUPRC remains dataset-dependent. The results indicate that association-discrepancy score enhancement is a practical complement to existing Transformer-based anomaly detectors when early warning is more important than point-wise matching alone.