Twitter has significant potential as a source of Volunteered Geographic Information (VGI), as its content is updated at high frequency, with high availability thanks to dedicated interfaces. However, the diversity of content types and the low average accuracy of geographic information attached to individual tweets remain obstacles in this context. The contributions in this paper relate to the general goal of extracting actionable information regarding the impact of natural hazards on a specific region from social platforms, such as Twitter. Specifically, our contributions describe the construction of a model classifying whether given spatio-temporal coordinates, materialized by raster cells in a remote sensing context, lie in a flooded area. For training, remotely sensed data are used as the target variable, and the input covariates are built on the sole basis of textual and spatial data extracted from a Twitter corpus. Our contributions enable the use of trained models for arbitrary new Twitter corpora collected for the same region, but at different times, allowing for the construction of a flooded area measurement proxy available at a higher temporal frequency. Experimental validation uses true data that were collected during Hurricane Harvey, which caused significant flooding in the Houston urban area between mid-August and mid-September 2017. Our experimental section compares several spatial information extraction methods, as well as various textual representation and aggregation techniques, which were applied to the collected Twitter data. The best configuration yields a F1 score of 0.425, boosted to 0.834 if restricted to the 10% most confident predictions.

It is crucial to analyze the tire under dynamic conditions to observe a performance close to realistic situations. Particularly, the temperature generated by the interaction of tire and pavement can provide useful information and understanding into how tire components can be optimized. However, in such a situation, data generation and measurement are challenging tasks. High-resolution thermal infrared imaging is a non-contact technology that transforms radiation patterns into a visible image and allows measuring the temperature changes on the surface of the tire. Therefore, the first step towards a systematic analysis of the performance of the tire is to segment the surface. To this end, we present an approach that combines traditional image processing methods with convolutional neural networks. We further investigate transfer learning techniques to improve the prediction of the proposed model on a different dataset. Our ultimate goal is to implement a robust network to segment a broad variety of tires. A segmentation accuracy >0.97 and a validation error <0.06 were achieved with transfer learning. The results have shown that our network can be extended to segment new data accurately.

Floods pose significant risks to human lives, infrastructure, and the environment. Timely and accurate flood forecasting plays a pivotal role in mitigating these risks. This study proposes a Digital Twin proof-of-concept framework aimed at improving flood forecasting and validated its effectiveness through a pilot study of the 2021 flood event in Luxembourg. The baseline forecasting method combines GloFAS ensemble streamflow forecasts with a high-resolution flood hazard datacube generated using a LISFLOOD-FP hydrodynamic model and then averaging among the member forecasts. To dynamically update the flood forecasts and improve their accuracy, the framework integrates satellite-based Earth observations (EOs)—specifically Sentinel-1-derived flood probability maps from the Global Flood Monitoring service—via a particle filter-based data assimilation (DA) process. As such, the simulations with more coherence with the observed Sentinel-1-derived flood probability maps are prioritized. This results in a Digital Twin capable of delivering daily flood depth forecasts, at detailed spatial resolution, up to 30 days ahead, with reduced prediction uncertainty. Using the 2021 flood event, we evaluate the performance of the Digital Twin in assimilating EO data to refine hydraulic model simulations and issue accurate flood forecasts. Although certain challenges persist—particularly the difficulty in quantifying the error structure of GloFAS discharge forecasts—the proposed approach demonstrates clear improvements in forecast accuracy compared to open-loop simulations. As a result, the approach reduces water level prediction errors by an average of 15–33% and increases the Nash–Sutcliffe Efficiency of discharge predictions by approximately 15–36%. Future work will aim to refine the flood hazard datacube and advance the characterization and modeling of uncertainties associated with both GloFAS streamflow forecasts and Sentinel-1-derived flood maps, thereby further enhancing the system’s predictive capability.

OBJECTIVES: Sutures require follow-up visits for favorable evolution. To improve postemergency wound care, we decided to include a standardized advice sheet for patients based on current recommendations. The objective is to assess its effectiveness on outpatients' compliance after being discharged from the emergency department (ED). METHODS: We performed a prospective, pre–post design trial in an ED of a teaching hospital. We included for two consecutive months all patients aged ≥16 years old and consulting for wounds that needed suturing, and we excluded chronic wounds, burns, and hand wounds since they all need special care. During the 1^st month, all patients received during ED visit usual verbal instructions concerning the postemergency care (Group A). During the 2^nd month, all patients received usual verbal instructions and a standardized written advice sheet that detailed postemergency wound care (Group B). We organized telephone follow-up after the suture removal date and asked about dressing changes, appearance of infection signs, and respect of suture removal date. We compared patients;' characteristics in the two groups and performed a multivariable logistic regression using compliance to discharge instructions as our endpoint. RESULTS: For 2 months, 509 patients consulted for wounds. 119 (23.4%) patients were included in the study and followed. Baseline characteristics of patients did not differ between the two groups. Patients who received the advice sheet (Group B) had a better compliance in postemergency care (91.7% vs. 72.9%; P = 0.01). Moreover, there were significantly less dressing changes in Group B than in Group A (5.3 [2.2] vs. 12.9 [7.7]; P < 0.01) and suture removal date was more in agreement with recommendations in Group B (83.9% vs. 66.7%; P = 0.03). Occurrence of infection was not significantly different between groups (9.7% vs. 13.7%; P = 0.37). CONCLUSION: For the management of wound care, discharge hospital process including a written advice sheet improves outpatients' compliance and postemergency care.