style: 按河南理工论文规范重排 — 封面/页眉页脚/标题字号/图表标题位置

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 \chapter*{摘要}
 \addcontentsline{toc}{chapter}{摘要}
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 随着全球气候变暖，极端高温事件频发且强度持续增加，对公共卫生构成日益严峻的挑战。老年群体（65岁及以上）因体温调节功能减退、慢性病患病率高以及社会隔离等因素，是高温热浪最脆弱的群体之一。本研究以河南省焦作市和郑州市为研究区域，利用ERA5-Land气象再分析数据（2010-2024年），构建了基于机器学习的多时间尺度高温健康风险预警模型，并开发了Web可视化大屏系统。

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 \chapter*{Abstract}
 \addcontentsline{toc}{chapter}{Abstract}
+\thispagestyle{abstract-en}

 Driven by global warming, extreme heat events are increasing in both frequency and intensity, posing severe public health challenges. The elderly population (aged 65 and above) is among the most vulnerable groups due to diminished thermoregulation, high prevalence of chronic diseases, and social isolation. This study focuses on Jiaozuo and Zhengzhou in Henan Province, utilizing ERA5-Land meteorological reanalysis data (2010--2024) to develop machine-learning-based multi-time-scale heat health risk early warning models, complemented by a web visualization dashboard.