Dense and long-term monitoring of Earth surface processes with passive RFID -- a review

Billions of Radio-Frequency Identification (RFID) passive tags are produced yearly to identify goods remotely. New research and business applications are continuously arising, including recently localization and sensing to monitor earth surface processes. Indeed, passive tags can cost 10 to 100 times less than wireless sensors networks and require little maintenance, facilitating years-long monitoring with ten's to thousands of tags. This study reviews the existing and potential applications of RFID in geosciences. The most mature application today is the study of coarse sediment transport in rivers or coastal environments, using tags placed into pebbles. More recently, tag localization was used to monitor landslide displacement, with a centimetric accuracy. Sensing tags were used to detect a displacement threshold on unstable rocks, to monitor the soil moisture or temperature, and to monitor the snowpack temperature and snow water equivalent. RFID sensors, available today, could monitor other parameters, such as the vibration of structures, the tilt of unstable boulders, the strain of a material, or the salinity of water. Key challenges for using RFID monitoring more broadly in geosciences include the use of ground and aerial vehicles to collect data or localize tags, the increase in reading range and duration, the ability to use tags placed under ground, snow, water or vegetation, and the optimization of economical and environmental cost. As a pattern, passive RFID could fill a gap between wireless sensor networks and manual measurements, to collect data efficiently over large areas, during several years, at high spatial density and moderate cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references. 31 figures. 8 table

BMC Med

BACKGROUND: Overall survival (OS) is the gold standard endpoint to assess treatment efficacy in cancer clinical trials. In metastatic breast cancer (mBC), progression-free survival (PFS) is commonly used as an intermediate endpoint. Evidence remains scarce regarding the degree of association between PFS and OS. Our study aimed to describe the individual-level association between real-world PFS (rwPFS) and OS according to first-line treatment in female patients with mBC managed in real-world setting for each BC subtype (defined by status for both hormone-receptor [HR] expression and HER2 protein expression/gene amplification). METHODS: We extracted data from the ESME mBC database (NCT03275311) which gathers deidentified data from consecutive patients managed in 18 French Comprehensive Cancer Centers. Adult women diagnosed with mBC between 2008 and 2017 were included. Endpoints (PFS, OS) were described using the Kaplan-Meier method. Individual-level associations between rwPFS and OS were estimated using the Spearman's correlation coefficient. Analyses were conducted by tumor subtype. RESULTS: 20,033 women were eligible. Median age was 60.0 years. Median follow-up duration was 62.3 months. Median rwPFS ranged from 6.0 months (95% CI 5.8-6.2) for HR-/HER2 - subtype to 13.3 months (36% CI 12.7-14.3) for HR + /HER2 + subtype. Correlation coefficients were highly variable across subtypes and first-line (L1) treatments. Among patients with HR - /HER2 - mBC, correlation coefficients ranged from 0.73 to 0.81, suggesting a strong rwPFS/OS association. For HR + /HER2 + mBC patients, the individual-level associations were weak to strong with coefficients ranging from 0.33 to 0.43 for monotherapy and from 0.67 to 0.78 for combined therapies. CONCLUSIONS: Our study provides comprehensive information on individual-level association between rwPFS and OS for L1 treatments in mBC women managed in real-life practice. Our results could be used as a basis for future research dedicated to surrogate endpoint candidates

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

The Physics of the B Factories

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C

Suivi temporel d'un glissement de terrain à l'aide d'étiquettes RFID passives, couplé à l’observation de pluviométrie et de bruit sismique ambiant.

Landslide early-warning systems are based primarily on monitoring the displacement of the landslide. This work develops a new technique for monitoring these displacements, using radio-frequency identification (RFID) passive tags and phasebased location technique. This technique is deployed for the first time outdoors and for several months. Outdoor conditions revealed strong environmental influences due to temperature variations, moisture, snow and vegetation. These can cause a ±20 cm measurement uncertainty over a year, which is too large for landslide monitoring applications. The correction of these effects allows reaching the accuracy of ±1 cm under normal conditions, ±8 cm with snow and ±4 cm with dense high grass. The remaining effects due to snow and grass are explained by the influence of this material on the direct propagation, on the multipath interferences and on the antennas. This measurement system has been deployed on the Pont-Bourquin landslide for five months. The results validate the technique for landslide monitoring applications. The technique also shows the operational benefits of robustness to bad weather, easy maintenance and low-cost material, compared to conventional techniques (extensometer, GPS, total station)This thesis then studies two complementary monitoring methods that had recently been shown to provide precursors to landslide rupture. First, ambient seismic noise interferometry is used to detect a drop of shear-wave velocity prior to a rupture. The seismic method was studied in the literature to identify what must be developed to use this technique in an operational early-warning system. It requires getting rid of daily and seasonal environmental influences, choosing the processing parameters appropriate to the monitored landslide, and improving the temporal resolution below one day while keeping a stable enough signal. The other method consists of inverting an impulse response between rainfall and displacement rate, with a high resolution. It can shed light on complex infiltration processes (e.g. infiltration with two different delays at Pont-Bourquin) and detect their abnormal evolution across time. These developments should improve landslide operational monitoring with a low budget.La surveillance d’un glissement de terrain vise à anticiper sa rupture pour réduire le risque d’accident. Elle s'opère généralement en mesurant les déplacements du sol. Ce travail propose une nouvelle technique de mesure de déplacement de glissements, flexible et à bas coût, basée sur l’utilisation d’étiquettes d’identification radiofréquence (RFID). La méthode de localisation d’étiquettes par différence de phase à 866 MHz est explorée en conditions extérieures et sur de longues durées. Cette étude a montré une détérioration de la mesure causée par les variations de température, d’humidité, de neige et de végétation. Après application de corrections, la précision de mesure a été améliorée, passant de ±20 cm à ±1 cm en conditions extérieures courantes. Cette technique fonctionne également en conditions neigeuses et en présence d’herbes hautes, mais avec une incertitude de mesure plus élevée (±8 et 4 cm respectivement). Ces erreurs de mesure sont provoquées par des effets de propagation, d’interférence multitrajets, et de perturbations à proximité des antennes. Un système de mesure en continu a été déployé sur le glissement de terrain de PontBourquin, en Suisse, pendant cinq mois. Ce dispositif a validé l’efficacité de la technique en conditions réelles. De plus, la mesure résiste bien aux intempéries et le dispositif demande peu de maintenance, en comparaison avec les techniques conventionnelles (extensomètre, GPS, station totale).Deux méthodes de mesure complémentaires aux déplacements ont ensuite été étudiées. La méthode de corrélation de vibrations ambiantes est prometteuse, mais n’a pas encore été utilisée en surveillance opérationnelle. Une étude bibliographique souligne plusieurs verrous à lever, tels que la correction des variations saisonnières et journalières, l’augmentation de la résolution temporelle, et le choix des paramètres de traitement adaptés au site surveillé. La méthode qui consiste à inverser une fonction de transfert entre des données de pluie et de déplacements est ensuite étudiée. Une inversion haute résolution de cette fonction est proposée. Elle permet d’identifier des comportements hydrologiques complexes (ex : infiltration à deux vitesses sur le site de Pont-Bourquin) et de mesurer leur évolution. Les avancées de cette thèse vont permettre d’améliorer la surveillance opérationnelle tout en réduisant son coût, répondant aux besoins des collectivités territoriales

Monitoring landslide displacements with passive radio-frequency identification tags, coupled with ambient seismic noise and weather observations

La surveillance d’un glissement de terrain vise à anticiper sa rupture pour réduire le risque d’accident. Elle s'opère généralement en mesurant les déplacements du sol. Ce travail propose une nouvelle technique de mesure de déplacement de glissements, flexible et à bas coût, basée sur l’utilisation d’étiquettes d’identification radiofréquence (RFID). La méthode de localisation d’étiquettes par différence de phase à 866 MHz est explorée en conditions extérieures et sur de longues durées. Cette étude a montré une détérioration de la mesure causée par les variations de température, d’humidité, de neige et de végétation. Après application de corrections, la précision de mesure a été améliorée, passant de ±20 cm à ±1 cm en conditions extérieures courantes. Cette technique fonctionne également en conditions neigeuses et en présence d’herbes hautes, mais avec une incertitude de mesure plus élevée (±8 et 4 cm respectivement). Ces erreurs de mesure sont provoquées par des effets de propagation, d’interférence multitrajets, et de per-turbations à proximité des antennes. Un système de mesure en continu a été déployé sur le glissement de terrain de Pont-Bourquin, en Suisse, pendant cinq mois. Ce dispositif a validé l’efficacité de la technique en conditions réelles. De plus, la mesure résiste bien aux intempéries et le dispositif demande peu de maintenance, en comparaison avec les techniques conven-tionnelles (extensomètre, GPS, station totale).Deux méthodes de mesure complémentaires aux déplacements ont ensuite été étudiées. La méthode de corrélation de vibrations ambiantes est prometteuse, mais n’a pas encore été utilisée en surveillance opérationnelle. Une étude bibliographique souligne plusieurs verrous à lever, tels que la correction des variations saisonnières et journalières, l’augmentation de la résolution temporelle, et le choix des paramètres de traitement adaptés au site surveillé. La méthode qui consiste à inverser une fonction de transfert entre des données de pluie et de déplacements est ensuite étudiée. Une inversion haute résolution de cette fonction est proposée. Elle permet d’identifier des comportements hydrologiques complexes (ex : infiltration à deux vitesses sur le site de Pont-Bourquin) et de mesurer leur évolution. Les avancées de cette thèse vont permettre d’améliorer la surveillance opérationnelle tout en réduisant son coût, répondant aux besoins des collectivités territoriales.Landslide early-warning systems are based primarily on monitoring the displacement of the landslide. This work develops a new technique for monitoring these displacements, using radio-frequency identification (RFID) passive tags and phase-based location technique. This technique is deployed for the first time outdoors and for several months. Outdoor conditions revealed strong environmental influences due to temperature variations, moisture, snow and vegetation. These can cause a ±20 cm measurement uncertainty over a year, which is too large for landslide monitoring applications. The correction of these effects allows reaching the accuracy of ±1 cm under normal conditions, ±8 cm with snow and ±4 cm with dense high grass. The remaining effects due to snow and grass are explained by the influence of this material on the direct propagation, on the multipath interferences and on the antennas. This measurement system has been deployed on the Pont-Bourquin landslide for five months. The results validate the technique for landslide monitoring applications. The technique also shows the operational benefits of robustness to bad weather, easy maintenance and low-cost material, compared to conventional techniques (extensometer, GPS, total station).This thesis then studies two complementary monitoring methods that had recently been shown to provide precursors to landslide rupture. First, ambient seismic noise interferometry is used to detect a drop of shear-wave velocity prior to a rupture. The seismic method was studied in the literature to identify what must be developed to use this technique in an operational early-warning system. It requires getting rid of daily and seasonal environmental influences, choosing the processing parameters appropriate to the monitored landslide, and improving the temporal resolution below one day while keeping a stable enough signal. The other method consists of inverting an impulse response between rainfall and displacement rate, with a high resolution. It can shed light on complex infiltration processes (e.g. infiltration with two different delays at Pont-Bourquin) and detect their abnormal evolution across time. These developments should improve landslide operational monitoring with a low budget