Urban structure reinforces attitudes towards tsunami evacuation.

Evacuation is a critical life-saving action, especially in devastating natural hazards such as near-field tsunamis. However, the development of effective evacuation measures remains challenging to the extent that a successful example has been referred to as a 'miracle'. Here we show that urban structures have the potential to reinforce attitudes towards evacuation and significantly influence the success of tsunami evacuation. Agent-based evacuation simulations revealed that a distinctive root-like urban structure formed in ria coasts reinforces positive evacuation attitudes by effectively gathering evacuation flows and leads to higher evacuation rates compared to typical grid-like urban structures, which can explain the regional differences in the number of casualties in the 2011 Tohoku tsunami. Although a grid-like structure reinforces negative attitudes under low evacuation tendencies, with leading evacuees, its dense feature helps to propagate positive attitudes and drastically improve evacuation tendencies. These findings pave the way for making successful evacuation inevitable through harmonised urban and evacuation plannings.

Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observation.

Unlike conventional crowd simulations for what-if analysis, agent-based crowd simulations for real-time applications are an emerging research topic and an important tool for better crowd managements in smart cities. Recent studies have attempted to incorporate the real-time crowd observations into crowd simulations for real-time crowd forecasting and management; however, crowd flow forecasting considering individual-level microscopic interactions, especially for large crowds, is still challenging. Here, we present a method that incorporates crowd observation data to forecast a large crowd flow, including thousands of individuals, using a microscopic agent-based model. By sequentially estimating both the crowd state and the latent parameter behind the crowd flows from the aggregate crowd density observation with the particle filter algorithm, the present method estimates and forecasts the large crowd flow using agent-based simulations that incorporate observation data. Numerical experiments, including a realistic evacuation scenario with 5000 individuals, demonstrated that the present method could successfully provide reasonable crowd flow forecasting for different crowd scenarios, even with limited information on crowd movements. These results support the feasibility of real-time crowd flow forecasting and subsequent crowd management, even for large but microscopic crowd problems.

Early forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networks.

Rapid and accurate hazard forecasting is important for prompt evacuations and reducing casualties during natural disasters. In the decade since the 2011 Tohoku tsunami, various tsunami forecasting methods using real-time data have been proposed. However, rapid and accurate tsunami inundation forecasting in coastal areas remains challenging. Here, we propose a tsunami forecasting approach using convolutional neural networks (CNNs) for early warning. Numerical tsunami forecasting experiments for Tohoku demonstrated excellent performance with average maximum tsunami amplitude and tsunami arrival time forecasting errors of ~0.4 m and ~48 s, respectively, for 1,000 unknown synthetic tsunami scenarios. Our forecasting approach required only 0.004 s on average using a single CPU node. Moreover, the CNN trained on only synthetic tsunami scenarios provided reasonable inundation forecasts using actual observation data from the 2011 event, even with noisy inputs. These results verify the feasibility of AI-enabled tsunami forecasting for providing rapid and accurate early warnings.

Antitumor effect of inhalatory lipopolysaccharide and synergetic effect in combination with cyclophosphamide.

BACKGROUND: Macrophage cells differentiate into killer macrophages, named M1 macrophages, that effectively eliminate cancer cells by generating cytokines. We examined the feasibility of a lung cancer therapy using lipopolysaccharide (LPS), which triggers this differentiation. It is expected that the delivery via inhalation of LPS directly into the lungs, where alveolar macrophages reside abundantly, would be effective at minimizing the possible toxic effect of LPS. MATERIALS AND METHODS: We determined the effects of intratracheal insufflation of LPS on (i) the exudation of lactate dehydrogenase, (ii) generation of tumor necrosis factor-α and interleukin-12, and (iii) tumor metastases in Lewis lung carcinoma-bearing C57BL/6 mice. RESULTS: Pulmonary insufflation of LPS resulted in a consistent accumulation of tumor necrosis factor-α, and transient increase in interleukin-12 without significant release of lactate dehydrogenase from the lung cells. In addition, a significant antitumor effect of LPS was observed; and this antitumor effect was potentiated by combination of LPS with the antitumor agent cyclophosphamide. CONCLUSION: Pulmonary inhalation of LPS combined with a chemotherapeutic agent is a promising approach to lung cancer therapy.

DMAP as an effective catalyst to accelerate the solubilization of waste fiber-reinforced plastics.

Waste fiber-reinforced plastics (FRPs), which are formidable composite plastics for chemical treatment, can be efficiently depolymerized by treatment with supercritical methanol in the presence of catalytic amounts of 4-(dimethylamino)pyridine (DMAP), which is a well-known catalyst for the formation of esters and amides. This novel depolymerization reaction also provides ready separation of the depolymerized products into three components: methanol-soluble oil, CHCl3-soluble solid, and an inorganic residue. Thus, almost complete decomposition of FRP as well as useful separation of the decomposed products was achieved with the present method, and the latter were ready for chemical recycling. Investigation of the reaction profile revealed that the reaction rate increased as the amount of DMAP increased. Unfortunately, DMAP employed in the reaction could not be recovered because of its decomposition catalyzed by dimethyl phthalate, a depolymerized product from waste FRP, under the reaction conditions employed.