Yuki Miura

Coastal areas face worsening storm-induced flooding due to climate change, threatening critical below-ground infrastructure like subway systems, as seen from Hurricane Sandy’s catastrophic impact on New York City (NYC)’s subway system in 2012. Stakeholders must urgently address these risks to protect infrastructure and assets. Simulating future flood scenarios is crucial for estimating flood risk and damage efficiently and for identifying reliable and effective protective measures. This article uses the GIS-based Subdivision-Redistribution (GISSR) methodology, a high-speed, physics-based flood estimation tool, that is now extended to model subway system flooding and associated economic impacts. It identifies flooded tunnels and stations and quantifies indirect economic losses from subway inoperability using an input–output model. The model is validated against observed subway flooding during Hurricane Sandy. Each analysis, covering both above- and below-ground flooding in Lower Manhattan, takes less than 90 s on a single 4-core Intel Core i7-13620H CPU machine. Scenario analyses were conducted with NYC stakeholders, incorporating sea level rise projections and various protective measures. Results, benchmarked against NYC’s ongoing resiliency projects, demonstrate the effectiveness of adaptation/protective strategies, particularly when subway system-specific and coastal measures are combined, highlighting the model’s value as a practical guide for stakeholders.

Building-level exposure data are critical to natural hazard risk modeling, yet most global inventories describe where buildings are located rather than what they are made of. Roof material is a critical but poorly documented attribute for assessing vulnerability to wildfires, wind hazards, urban heat, floods, and earthquakes. To address this gap, we introduce RoofNet, a global dataset that maps 49,662 georeferenced building instances from 101 countries to 14 key roofing material classes using Earth observation (EO) imagery (redistributed where permitted) and associated geospatial metadata. RoofNet contributes (1) climatographically and architecturally diverse coverage of roof material labels, (2) a scalable annotation pipeline combining SME-guided manual labeling with vision-language model (VLM)-assisted classification, rule-based validation, and human-in-the-loop verification, and (3) a resource for evaluating subtle, geographically variable material-level identification in EO imagery and its implications for material-aware hazard risk modeling. Evaluation on a manually labeled hold-out set shows that zero-shot Remote Contrastive Language-Image Pre-Training (RemoteCLIP) struggles with roof material classification, while fine-tuning with RoofNet improves top-1 accuracy from 4.9% to 47.7%. We use RoofNet in an illustrative hazard case study to demonstrate how material-aware exposure data can change vulnerability estimates relative to material-naive inventories. RoofNet provides a missing material layer for global building attribute mapping and scalable hazard risk assessment.

The International Panel on Climate Change’s assessment reports form the cornerstone for decarbonization and capacity building to combat the adverse effects of climate change at both international and national scales. However, the scope of the analysis in these reports is too broad for regional and city-level reforms. In response, the New York Panel on Climate Change (NPCC) was established in 2009 and codified into Local Law 42 of 2012 with the dual purpose of synthesizing the current understanding of the impacts of climate change on New York City and produce climate projections of record for infrastructure planning every three years. Since its inception, NPCC has published four assessments on topics ranging from climate science and projections to infrastructure resilience, equity and justice, health, and energy. The volunteer panelists and contributors, which include scientists and practitioners from a range of disciplines, co-produce the reports in collaboration with city agencies and private sector practitioners. In this presentation, we will introduce the framework used by the NPCC to produce local climate assessments for the largest city in the USA, share results from the most recent panel, and present the work of the 5th NPCC report.

現場オペレーションの効率化や，新しい番組表現の実現を目的として，AI等の新技術を誰でも簡単に扱えるビデオペンシステムを社内開発した．「リアルタイム選手追従CG」，「選手名CGの自動化」，「フィールド上の距離角度推定」などの機能を直感的な操作で実施可能なシステムである．またPC画面等の情報を囲むことで「得点データ入力の自動化」，「駅伝コースガイドの自動化」を実現させ，多くの番組制作シーンでの業務効率化を行った．さらに「試合のタイマと連動しAIが試合の盛り上がりを算出するコーナー」を企画し，スポーツのハイライトシーンに新たな軸を提供した．

“Demographic vulnerability to 100-year precipitation extremes. Panels ( A )–( C ) depict population exposure to heightened risks associated with 100-year precipitation extremes under varying temperature scenarios. In the baseline scenario, around 25 million individuals reside in high-risk areas, expected to double to 49.5 million with a 2 °C temperature increase and triple to 78.5 million under a 4 °C temperature increase. Panel ( D ) explores demographic characteristics by age groups, while Panel ( E ) provides an in-depth analysis of demographic data, including socio-economic status and disability. Current projections indicate that approximately 7 million individuals living below the poverty threshold are exposed to extreme precipitation events. Similar trends are noticeable in different groups of disabled population as well.”

As extreme precipitation intensifies under climate change, traditional risk models based on the '100-year return period' concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California. Exposure analysis indicates approximately 53 million residents currently reside in high-risk zones, potentially almost doubling and tripling under 2 °C and 4 °C warming. Drought frequency also rises, with over 37% of major farmland vulnerable to multi-year droughts, raising agricultural risks. Record 2023 sea surface temperature anomalies suggest an impending extreme El Niño event, demonstrating the need to account for natural climate variability. The insights gained aim to inform decision-makers in shaping adaptation strategies and enhancing the resilience of communities in response to evolving extremes.

It has been reported that visual statistical learning (VSL) is facilitated in skewed distributions. However, it remains unclear whether enhancement of VSL in Zipfian distributions is due to consciousness of the regularities presented at high frequency. This study addressed this issue. We measured participants' subjective confidence in regularities and awareness of regularities during familiarization by combining a previously reported procedure for VSL with a postdecision wagering task and posttest questionnaire. The results demonstrated that Zipfian distribution enhanced not only VSL but also metacognitive sensitivity, particularly for high-frequency regularities, as the effects of consciousness on VSL were limited to high-frequency regularities. Moreover, the results indicated that awareness during familiarization mediated VSL enhancement in the Zipfian distribution. These results suggest that VSL for events with high-frequency regularities plays an important role in the cognition of events with low-frequency regularities via awareness. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplementary ResultsGalvin et al. (2003) emphasized that assuming a normal distribution in Type II tasks is unreasonable.In addition, Type II d' is influenced by the performance of Type I tasks.Thus, we calculated meta-d' and meta-d'/d' according to Maniscalco and Lau (2012, 2014).Meta-d' is a measure of metacognitive sensitivity unaffected by the performance of Type I tasks.Meta-d'/d' reflects metacognitive efficiency.Meta-d'/d' = 1 signifies using all information from Type I tasks in Type II tasks; meta-d'/d' < 1 indicates a lack of information; meta-d'/d' > 1 refers to using additional information (Maniscalco & Lau ,2012).We used both trials2counts.m and fit_meta_d_MLE.mcodes to estimate meta-d' (Maniscalco & Lau, 2012, 2014).Data from three participants were excluded from the uniform and one from the Zipfian distribution conditions because of the following error.The message was not produced in the lowfrequency Zipfian distribution condition when estimating meta-d'.Error using barrier Objective function is undefined at initial point.fmincon cannot continue.Similar to the results of Type II d', a significant difference was detected in meta-d' between the uniform and Zipfian distribution conditions, t(62) = 2.18, p = .033,BF10 = 1.86, d = 0.54, 95% CI [0.05, 1.14].This difference indicated that meta-d' in the Zipfian distribution (mean = 1.73,SE = 0.21) was significantly larger than in the uniform distribution condition (mean = 1.13,SE = 0.17).However, meta-d' in the low-frequency Zipfian distribution (mean = 1.40,SE = 0.21) was similar to the uniform distribution condition t(63) = .976,p = .333,BF10 = 0.38, d = 0.24, 95% CI [-0.28, 0.81].Furthermore, meta-d'/d' was significantly higher than 1 in the Zipfian distribution condition, mean = 1.07 (SE = 0.14), t(32) = 3.47, p = .002,BF10 = 0.21, d = 0.59, 95% CI [1.30, 2.15].This value was marginally lower than 1 in the lowfrequency Zipfian distribution condition, mean = 0.97 (SE = 0.16), t(33) = 1.91, p = .065,BF10 = 0.19, d = 0.32, 95% CI [0.97, 1.82].However, meta-d'/d' in the uniform distribution condition was not significantly different from 1, mean = 0.97 (SE = 0.33), V = 295.00,p = .367,BF10 = 0.19, r = 0.16, 95% CI [0.78, 1.48]. 1 Note that Bayes factor values in one-sample tests were under 1 in all conditions.These results suggest that Zipfian distributions enhance metacognitive sensitivity to familiarity decisions.Moreover, all information was utilized in the post-decision wagering task across distribution conditions.