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Resume Cette etude propose un retour d’experience sur les modalites de gestion et d’elimination des dechets contamines au Lutetium-177 (177Lu, Lu-177) instaurees dans le cadre des procedures de radiotherapie interne vectorisee au 177Lu-oxodotreotide (Lutathera®). Les traitements sont administres dans un box dedie du service de medecine nucleaire ou tous les dechets solides sont tries et collectes dans des contenants appropries. Des durees previsionnelles de stockage ont ete extrapolees a partir des resultats d’une campagne de mesures iteratives sur deux ans. Les effluents radioactifs des eviers chauds se deversent dans des cuves, dont l’activite volumique mesuree a la fermeture permet de calculer la duree d’entreposage avant vidange. L’activite du Lu-177 elimine par excretion renale dans les 6 heures qui suivent la fin de la perfusion du medicament radiopharmaceutique a ete evaluee a partir de l’analyse de prelevements urinaires. Les premieres mictions sont dirigees vers trois fosses tampons dont l’efficacite a ete estimee en comparant l’activite du Lu-177 qui entre dans le dispositif a celle qui en sort. Les resultats des controles semestriels, effectues par mesure en continu de l’activite volumique des effluents aux emissaires, ont permis d’evaluer la fraction de l’activite annuelle administree finalement rejetee dans les eaux usees et de definir des valeurs guides. L’impact des deversements radioactifs de l’etablissement sur l’exposition annuelle des travailleurs du reseau d’assainissement a ete quantifie avec l’outil de Calcul de l’Impact des Deversements Radioactifs dans les REseaux (CIDRRE) developpe par l’Institut de radioprotection et de surete nucleaire (IRSN).

The purpose of this work was to compare the measured red-cell volume (RCV) using sodium pertechnetate [RCV-99mTc] compared to the reference technique using sodium radiochromate [RCV-51Cr] and to assess the influence of technetium-99 elution on the RCV-99mTc value. Ten patients had simultaneous measurements of RCV-99mTc and RCV-51Cr. Elution of Tc-99m from red blood cells was 2.9% and led to an average overestimation of RCV-99mTc of 3.7%. The introduction of individual tracer elution rates in the RCV-99mTc calculation corrects this overestimation.

We present a novel method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future information of dynamic scenes. Our automated generation process creates groundtruth SOGMs from previous navigation data. We build on prior work to annotate lidar points based on their dynamic properties, which are then projected on time-stamped 2D grids: SOGMs. We design a 3D-2D feedforward architecture, trained to predict the future time steps of SOGMs, given 3D lidar frames as input. Our pipeline is entirely self-supervised, thus enabling lifelong learning for robots. The network is composed of a 3D back-end that extracts rich features and enables the semantic segmentation of the lidar frames, and a 2D front-end that predicts the future information embedded in the SOGMs within planning. We also design a navigation pipeline that uses these predicted SOGMs. We provide both quantitative and qualitative insights into the predictions and validate our choices of network design with a comparison to the state of the art and ablation studies.

A strategy for management of radioactive waste associated withWe conducted a prospective series of measurements of radioactive waste associated with the first 65 treatments administered. Sequential measurements of the first 45 vials used were performed on a dose calibrator to identify contaminants. Exposure rates in contact were monitored with a dose ratemeter on a 6-monthly basis for all waste stored: 46 empty vials, 19 vials partially used and 61 biohazard containers.Initial median activity of the first vials used was 118 MBq [4-4188 MBq]. For each vial, the decay curve of activity obtained was adjusted to a bi-exponential model. The major component, representing 99.7% of the activity, has a median half-life of 6.6 days [5.7-7.2 days] corresponding toAlthough only present as traces

Aim The increasing use of oral anticancer agents over the past years has necessitated changes in monitoring toxicities to ensure patients’ adherence and tolerance at home. The aim of this study was to describe nurses’ interventions and medical changes after alerts triggered by a web-based platform designed to support the management of oral anticancer agents-related toxicities. Methods This retrospective study included patients undergoing oral anticancer agents in a cancer center from September 2018 to September 2019 (excluding hormonal therapy). In this cancer center, the standard of care included symptoms’ collections for 1 month thanks to a web platform based on patient-reported outcomes. Patients had to fill a weekly questionnaire (Q1 to Q4). The web-based platform triggered orange alerts when patients reported moderate symptoms and red alerts when severe toxicities were declared. The rate of orange and red alerts, the rate of patients with medical changes consecutively to an orange or a red alert, and the different types of nurses’ interventions and medical changes were assessed. Results A total of 524 patients were extracted but the final number of 436 patients were included in this study and 1488 questionnaires were filled in. More than 90% of patients declared that they took their medication as prescribed. Up to 60% of patients recorded all grade symptoms, including 8% of patients who recorded Grades 3–4 symptoms during the month, mostly anorexia, fatigue, and diarrhea. The web platform system triggered 700 orange and 212 red alerts: 305/700 (44%) of orange alerts resulted in nurses’ interventions, most frequently phone counseling (78%), and 65/212 (31%) of red alerts resulted in medical changes, most frequent treatment interruptions (48%). Conclusion Implementing an e-health (electronic-health) system can be helpful for monitoring symptoms in patients under oral anticancer agents, enhancing that this organization should be a standard of care in every cancer centers.

In this paper, it proposes a low cost ball catching system which is called Doggy Drone. This system is composed of two main parts: using Xtion PRO to sense the position of objects and controlling the AR.Drone to a certain position precisely. Finally, our system achieves this real-time and challenging task.

International audience; The analysis and modeling of the failures bound to occur in today's large-scale production systems is invaluable in providing the understanding needed to make these systems fault-tolerant yet efficient. Many previous studies have modeled failures without taking into account the time-varying behavior of failures, under the assumption that failures are identically, but independently distributed. However, the presence of time correlations between failures (such as peak periods with increased failure rate) refutes this assumption and can have a significant impact on the effectiveness of fault-tolerance mechanisms. For example, the performance of a proactive fault-tolerance mechanism is more effective if the failures are periodic or predictable; similarly, the performance of checkpointing, redundancy, and scheduling solutions depends on the frequency of failures. In this study we analyze and model the time-varying behavior of failures in large-scale distributed systems. Our study is based on nineteen failure traces obtained from (mostly) production large-scale distributed systems, including grids, P2P systems, DNS servers, web servers, and desktop grids. We first investigate the time correlation of failures, and find that many of the studied traces exhibit strong daily patterns and high autocorrelation. Then, we derive a model that focuses on the peak failure periods occurring in real large-scale distributed systems. Our model characterizes the duration of peaks, the peak inter-arrival time, the inter-arrival time of failures during the peaks, and the duration of failures during peaks; we determine for each the best-fitting probability distribution from a set of several candidate distributions, and present the parameters of the (best) fit. Last, we validate our model against the nineteen real failure traces, and find that the failures it characterizes are responsible on average for over 50% and up to 95% of the downtime of these systems.