Whom should we call? Data policy for immediate impactors announcements

After the PR disaster of 1997 XF11 (March 1998), we started a crash research program on impact predictions. The difficulty was the chaotic motion of Earth-crossing asteroids (orbit uncertainty increases exponentially with time); it can be solved by replacing a real asteroid with a swarm of Virtual Asteroids. In 1999 we introduced Geometric Sampling to replace Monte-Carlo methods (see Milani, Chesley & Valsecchi, A&A 346, 1999). In November 1999 the first Impact Monitoring system CLOMON was operational. From 2002 the second generation systems CLOMON2 at Pisa and SENTRY at JPL are operational: critical cases are scanned for possible impacts in the next 90–100 years

Long-term impact risk for (101955) 1999 RQ36

The potentially hazardous asteroid (101955) 1999 RQ36 has the possibility of collision with the Earth in the latter half of the 22nd century, well beyond the traditional 100-year time horizon for routine impact monitoring. The probabilities accumulate to a total impact probability of approximately 10E-3, with a pair of closely related routes to impact in 2182 comprising more than half of the total. The analysis of impact possibilities so far in the future is strongly dependent on the action of the Yarkovsky effect, which raises new challenges in the careful assessment of longer term impact hazards. Even for asteroids with very precisely determined orbits, a future close approach to Earth can scatter the possible trajectories to the point that the problem becomes like that of a newly discovered asteroid with a weakly determined orbit. If the scattering takes place late enough so that the target plane uncertainty is dominated by Yarkovsky accelerations then the thermal properties of the asteroid,which are typically unknown, play a major role in the impact assessment. In contrast, if the strong planetary interaction takes place sooner, while the Yarkovsky dispersion is still relatively small compared to that derived from the measurements, then precise modeling of the nongravitational acceleration may be unnecessary.Comment: Reviewed figures and some text change

Computer‑aided craniofacial superimposition validation study: the identification of the leaders and participants of the Polish‑Lithuanian January Uprising (1863–1864)

In 2017, a series of human remains corresponding to the executed leaders of the “January Uprising” of 1863–1864 were uncovered at the Upper Castle of Vilnius (Lithuania). During the archeological excavations, 14 inhumation pits with the human remains of 21 individuals were found at the site. The subsequent identification process was carried out, including the analysis and cross-comparison of post-mortem data obtained in situ and in the lab with ante-mortem data obtained from historical archives. In parallel, three anthropologists with diverse backgrounds in craniofacial identification and two students without previous experience attempted to identify 11 of these 21 individuals using the craniofacial superimposition technique. To do this, the five participants had access to 18 3D scanned skulls and 14 photographs of 11 different candidates. The participants faced a cross-comparison problem involving 252 skull-face overlay scenarios. The methodology follows the main agreements of the European project MEPROCS and uses the software Skeleton-ID™. Based on MEPROCS standard, a final decision was provided within a scale, assigning a value in terms of strong, moderate, or limited support to the claim that the skull and the facial image belonged (or not) to the same person for each case. The problem of binary classification, positive/negative, with an identification rate for each participant was revealed. The results obtained in this study make the authors think that both the quality of the materials used and the previous experience of the analyst play a fundamental role when reaching conclusions using the CFS technique.CRUE-CSICSpanish Government Junta de Andalucia CONFIA 2021/C005/00141299 EXAISFI PID2021-122916NB-I00Centro de Investigacion de Galicia "CITIC" - Xunta de Galicia P18-FR-4262European Union (European Regional Development Fund-Galicia 2014-2020 Program)Ministry of Science, ICT & Future Planning, Republic of Korea ED431G 2019/01Universidade da Cor una/CISUG RYC2020-029454-

Automatic evolutionary medical image segmentation using deformable models

International audienceThis paper describes a hybrid level set approach to medical image segmentation. The method combines region-and edge-based information with the prior shape knowledge introduced using deformable registration. A parameter tuning mechanism, based on Genetic Algorithms, provides the ability to automatically adapt the level set to different segmentation tasks. Provided with a set of examples, the GA learns the correct weights for each image feature used in the segmentation. The algorithm has been tested over four different medical datasets across three image modalities. Our approach has shown significantly more accurate results in comparison with six state-of-the-art segmentation methods. The contributions of both the image registration and the parameter learning steps to the overall performance of the method have also been analyzed

Evidence evaluation in craniofacial superimposition using likelihood ratios

Financiado para publicación en acceso aberto: Universidad de Granada / CBUA[Abstract]: Craniofacial Superimposition is a forensic identification technique that supports decision-making when skeletal remains are involved. It is based on the analysis of the overlapping of a post-mortem skull with ante-mortem facial photographs. Despite its importance and wide applicability, the process remains complex and challenging. To address this, computerized methods have been proposed, but subjectivity and qualitative reporting persist in decision-making. This study introduces an evidence evaluation system proposal based on Likelihood Ratios, previously used in other forensic fields, such as DNA, voice, fingerprint, and facial comparison. We present a novel application of this framework to Craniofacial Superimposition. Our work comprises three experiments in which our LR system is trained and tested under distinct conditions concerning facial images: the first utilizes frontal facial photographs; the second employs lateral facial photographs; and the last one integrates both frontal and lateral facial photographs. In the three experiments, the proposed LR system stands out in terms of calibration and discriminating power, providing practitioners with a quantitative tool for evidence evaluation and integration. However, the lack of massive actual data obliged us to focus our study on synthetic data only. Therefore, it should be considered a proof of concept. Nevertheless, the resulting likelihood-ratio system offers objective decision support in Craniofacial Superimposition. Further studies are required to validate in a real scenario the conclusions achieved.This research has been developed within the R&D project CONFIA (grant PID2021-122916NB-I00), funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, and by grant FORAGE (B-TIC-456-UGR20) funded by Consejería de Universidad, Investigación e Innovación, both funded by “ERDF A way of making Europe”. Miss Martínez-Moreno is supported by grant PRE2022-102029 funded by MICIU/AEI/10.13039/501100011033 and the FSE+. Dr. Valsecchi's work is supported by Red.es under grant Skeleton-ID2.0 (2021/C005/00141299). Dr. Ibáñez's work is funded by the Spanish Ministry of Science, Innovation and Universities under grant RYC2020-029454-I and by Xunta de Galicia, Spain by grant ED431F 2022/21. The authors thank Pierre Guyomarc’h for providing us with the data used in this research. Funding for open access charge: Universidad de Granada / CBUA.Xunta de Galicia; ED431F 2022/21Junta de Andalucía; B-TIC-456-UGR2

Management of seizure on Postpartum Day 8: A case report of late postpartum eclampsia.

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Evidence evaluation in craniofacial superimposition using likelihood ratios

Craniofacial Superimposition is a forensic identification technique that supports decision-making when skeletal remains are involved. It is based on the analysis of the overlapping of a post-mortem skull with antemortem facial photographs. Despite its importance and wide applicability, the process remains complex and challenging. To address this, computerized methods have been proposed, but subjectivity and qualitative reporting persist in decision-making. This study introduces an evidence evaluation system proposal based on Likelihood Ratios, previously used in other forensic fields, such as DNA, voice, fingerprint, and facial comparison. We present a novel application of this framework to Craniofacial Superimposition. Our work comprises three experiments in which our LR system is trained and tested under distinct conditions concerning facial images: the first utilizes frontal facial photographs; the second employs lateral facial photographs; and the last one integrates both frontal and lateral facial photographs. In the three experiments, the proposed LR system stands out in terms of calibration and discriminating power, providing practitioners with a quantitative tool for evidence evaluation and integration. However, the lack of massive actual data obliged us to focus our study on synthetic data only. Therefore, it should be considered a proof of concept. Nevertheless, the resulting likelihood-ratio system offers objective decision support in Craniofacial Superimposition. Further studies are required to validate in a real scenario the conclusions achieved.R&D project CONFIA (grant PID2021-122916NB-I00), funded by MICIU/AEI/10.13039/ 501100011033 and by ERDF/EU - ‘‘ERDF A way of making Europe’’Grant FORAGE (B-TIC-456-UGR20) funded by Consejería de Universidad, Investigación e Innovación and by ‘‘ERDF A way of making Europe’’Miss Martínez-Moreno is supported by grant PRE2022-102029 funded by MICIU/AEI/10.13039/501100011033 and the FSE+Dr. Valsecchi’s work is supported by Red.es under grant Skeleton-ID2.0 (2021/C005/00141299)Dr. Ibáñez’s work is funded by the Spanish Ministry of Science, Innovation and Universities under grant RYC2020-029454-I and by Xunta de Galicia, Spain by grant ED431F 2022/21Funding for open access charge: Universidad de Granada / CBU

FacialSCDnet: A deep learning approach for the estimation of subject-to-camera distance in facial photographs

[Abstract]: Facial biometrics play an essential role in the fields of law enforcement and forensic sciences. When comparing facial traits for human identification in photographs or videos, the analysis must account for several factors that impair the application of common identification techniques, such as illumination, pose, or expression. In particular, facial attributes can drastically change depending on the distance between the subject and the camera at the time of the picture. This effect is known as perspective distortion, which can severely affect the outcome of the comparative analysis. Hence, knowing the subject-to-camera distance of the original scene where the photograph was taken can help determine the degree of distortion, improve the accuracy of computer-aided recognition tools, and increase the reliability of human identification and further analyses. In this paper, we propose a deep learning approach to estimate the subject-to-camera distance of facial photographs: FacialSCDnet. Furthermore, we introduce a novel evaluation metric designed to guide the learning process, based on changes in facial distortion at different distances. To validate our proposal, we collected a novel dataset of facial photographs taken at several distances using both synthetic and real data. Our approach is fully automatic and can provide a numerical distance estimation for up to six meters, beyond which changes in facial distortion are not significant. The proposed method achieves an accurate estimation, with an average error below 6 cm of subject-to-camera distance for facial photographs in any frontal or lateral head pose, robust to facial hair, glasses, and partial occlusion

Intensive monitoring of conventional and surrogate quality parameters in a highly urbanized river affected by multiple combined sewer overflows

Abstract The paper reports results of four intensive campaigns carried out on the Seveso River (Milan metropolitan area, Italy) between 2014 and 2016, during intense precipitation events. Laboratory analyses were coupled with on-site, continuous measurements to assess the impact of pollutants on water quality based on both conventional and surrogate parameters. Laboratory data included total suspended solids, caffeine, total phosphorus and nitrogen, and their dissolved forms. Screening of trace metals (Cr, Cu, Pb, Ni, Cd) and PBDEs (polybromodiphenylethers) was carried out. Continuous measurements included water level, physico-chemical variables and turbidity. Nutrient concentrations were generally high (e.g. average total phosphorus > 1,000 μg/L) indicating strong sewage contributions. Among monitored pollutants Cr, Cu, Pb, and Cd concentrations were well correlated to TSS, turbidity and discharge, being bound mostly to suspended particulate matter. A different behavior was found for Ni, that showed an early peak occurring before the flow peak, as a result of first flush events. PBDEs correlated well to nutrient concentrations, showing the highest peaks soon after activation of the combined sewer overflows, likely because of its accumulation in sewers. In addition to showing the existing correlations between quality parameters, the paper highlights the importance of surrogate parameters as indicators of anthropic pollution inputs

Pacemaker-detected severe sleep apnea predicts new-onset atrial fibrillation

Sleep apnea (SA) diagnosed on overnight polysomnography is a risk factor for atrial fibrillation (AF). Advanced pacemakers are now able to monitor intrathoracic impedance for automatic detection of SA events