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International audience; Tendon-driven continuum robots show promise for use in surgical applications as they can assume complex configurations to navigate along tortuous paths. However, to achieve these complex robot shapes, multiple segments are required as each robot segment can bend only with a single constant curvature. To actuate these additional robot segments, multiple tendons must typically be added on-board the robot, complicating their integration, robot control, and actuation. This work presents a method of achieving two curvatures in a single tendon-driven continuum robot segment through use of a novel magnetic locking mechanism. Thus, the need for additional robot segments and actuating tendons is eliminated. The resulting two curvatures in a single segment are demonstrated in two and three dimensions. Furthermore, the maximum magnetic field required to actuate the locking mechanism for different robot bending angles is experimentally measured to be 6.1 mT. Additionally, the locking mechanism resists unintentional unlocking unless the robot assumes a 0°bending angle and a magnetic field of 18.1 mT is applied, conditions which are not typically reached during routine use of the system. Finally, addressable actuation of two locking mechanisms is achieved, demonstrating the capability of producing multiple curvatures in a single robot segment.

International audience; The three zwitterionic di- and tricarboxylate ligands 1,1′-[(2,3,5,6-tetramethylbenzene-1,4-diyl)bis(methylene)]bis(pyridin-1-ium-4-carboxylate) (pL1), 1,1′-[(2,3,5,6-tetramethylbenzene-1,4-diyl)bis(methylene)]bis(pyridin-1-ium-3-carboxylate) (mL1), and 1,1′,1″-[(2,4,6-trimethylbenzene-1,3,5-triyl)tris(methylene)]tris(pyridin-1-ium-4-carboxylate) (L2) have been used as ligands to synthesize a series of 15 uranyl ion complexes involving various anionic coligands, in most cases polycarboxylates. [(UO2)2(pL1)2(cbtc)(H2O)2]·10H2O (1, cbtc4– = cis,trans,cis-1,2,3,4-cyclobutanetetracarboxylate) is a discrete, dinuclear ring-shaped complex with a central cbtc4– pillar. While [UO2(pL1)(NO3)2] (2), [UO2(pL1)(OAc)2] (3), and [UO2(pL1)(HCOO)2] (4) are simple chains, [(UO2)2(mL1)(1,3-pda)2] (5, 1,3-pda2– = 1,3-phenylenediacetate) is a daisy chain and [UO2(pL1)(pdda)]3·10H2O (6, pdda2– = 1,2-phenylenedioxydiacetate) is a double-stranded, ribbon-like chain. Both [UO2(pL1)(pht)]·5H2O (7, pht2– = phthalate) and [(UO2)3(mL1)(pht)2(OH)2] (8) crystallize as diperiodic networks with the sql topology, the latter involving hydroxo-bridged trinuclear nodes. [(UO2)2(pL1)(c/t-1,3-chdc)2] (9, c/t-1,3-chdc2– = cis/trans-1,3-cyclohexanedicarboxylate) and [UO2(pL1)(t-1,4-chdc)]·1.5H2O (10, t-1,4-chdc2– = trans-1,4-cyclohexanedicarboxylate) are also diperiodic, with the V2O5 and sql topologies, respectively. Both [(UO2)2(mL1)(c/t-1,4-chdc)2] (11) and [(UO2)2(pL1)(1,2-pda)2] (12, 1,2-pda2– = 1,2-phenylenediacetate) crystallize as diperiodic networks with hcb topology, and they display threefold parallel interpenetration. [HL2][(UO2)3(L2)(adc)3]Br (13, adc2– = 1,3-adamantanedicarboxylate) contains a very corrugated hcb network with two different kinds of cells, and the uncoordinated HL2+ molecule associates with the coordinated L2 to form a capsule containing the bromide anion. [(UO2)2(pL1)(kpim)2] (14, kpim2– = 4-ketopimelate) is a three-periodic framework with pL1 molecules pillaring fes diperiodic subunits, whereas [(UO2)2(L2)2(t-1,4-chdc)](NO3)1.7Br0.3·6H2O (15), the only cationic complex in the series, is a triperiodic framework with dmc topology and t-1,4-chdc2– anions pillaring fes diperiodic subunits. Solid-state emission spectra and photoluminescence quantum yields are reported for all complexes.

Magma-sediment mingling occurring in shallow porous sediments is mainly investigated through field observations of old exhumed rift basins. During the IODP Expedition 385 we have drilled through the shallow sills emplaced in the active rift of the Guaymas Basin, Gulf of California. The results of this expedition enable a pioneer study of the impact of recent magma-sediment mingling processes leading to peperite formation. Furthermore, it provides a present-day geological context to investigate and quantify the impact of this mingling process on the element cycles in subsurface sediments, and temporal evolution of the microbial habitat associated with epithermal hydrothermal fluid circulation. Our approach of exploring magma-sediment mingling processes includes laboratory experiments, numerical modelling, and identification of specific field analogues in addition to petrological and geochemical constraints.Using these modern techniques, we review here the discoveries made during the IODP Expedition 385 and present preliminary results from our post-cruise research from the perspective of the peperite formation. We report here petrographic and geochemical evidence of magma sediment hybridization indicative of an intense mingling process inferred to occur during the emplacement phase. The rheology of the soft, unconsolidated sediment controls and explains the various intrusion shapes and dimensions. Numerical simulation results indicate that heat dissipation in this context is much less efficient, which in turn considerably decreases the amount of thermogenic gas mobilized through thermal cracking in the contact aureole of sills. Additionally, we observe that hydrothermal pipe systems established during the cooling phase of sill emplacement can remain active at moderate- to low-temperature state after the heat of the sill has vanished. Using 2D seismic information and IODP drilling results, we were able to reconstruct the 3D structure of the sill at depth. It is funnel-shaped and roots in a depth where geothermal fluids can ascend from. The temperature found at these depths is consistent with the background geothermal gradient, suggesting that the large heat flow anomaly found at Hole U1548C is the mere expression of the active hydrothermal circulation fuelled by deeply sourced geothermal fluids.These potentially long-lasting hydrothermal systems provide preferable temperature and energetic conditions for microbial activity to thrive, with mildly degraded petroleum components from below and water recharge from above. Moreover, evidence indicates that the sill at Site U1547 is non-unique at the scale of the Guaymas Basin. How many of these catabolic reactors form at the early rifting phase? Can this process perhaps trigger peaks in subsurface biomass production associated with new continental margin formation? Our research heralds the dawn of a new paradigm. We suggest that in the context of a nascent ocean, sill emplacement in the first 500 m of sediments may power life instead of suppressing it.

Respecting the requirements of lightweight, small size, low power consumption and especially low cost, commercial off-the-shelf (COTS) analog-to-digital converters (ADCs) are gradually being used to replace specifically designed aerospace -grade ones in space-borne miniature interferometric fiber-optic gyroscopes (IFOGs). However, the total ionizing dose (TID) effect of $\gamma $ -ray radiation in the space environment may cause the damage of COTS ADCs, and lead to performance degradation and even the failure. In order to measure the safety of COTS ADCs in IFOGs under the TID effect, the radiation-induced degradation model and the corresponding reliability model are proposed in this work. First of all, the relationship between the degradation of COTS ADCs and the IFOG’s angular random walk (ARW) is deduced. Then, a radiation accelerated test is designed and performed, with a real-time, dynamic data acquisition method based on the first-input-first-output (FIFO) memories to determine the degradation of COTS ADCs. Finally, a degradation path model is established based on the degradation data, and the reliability of COTS ADCs used for space-borne IFOGs is further obtained by using the Monte Carlo method. An engineering application is introduced to show the effectiveness of the proposed model, and the results show that the reliability of the experimental COTS ADCs makes them usable for the space-borne miniature IFOGs in low-earth orbit (LEO) and geostationary earth orbit (GEO) space missions.

Nitrogen (N) is a major limiting factor affecting crop yield in unfertilized soil. Thus, cultivars with a high N use efficiency (NUE) and good grain protein content (GPC) are needed to fulfill the growing food demand and to reduce environmental burden. This is especially true for rice (Oryza sativa L.) that is cultivated with a high input of N fertilizer and is a primary staple food crop for more than half of the global population. Here, we report that rice asparagine synthetase 1 (OsASN1) is required for grain yield and grain protein contents under both N-sufficient (conventional paddy fields) and N-limiting conditions from analyses of knockout mutant plants. In addition, we show that overexpression (OX) of OsASN1 results in better nitrogen uptake and assimilation, and increased tolerance to N limitation at the seedling stage. Under field conditions, the OsASN1 OX rice plants produced grains with increased N and protein contents without yield reduction compared to wild-type (WT) rice. Under N-limited conditions, the OX plants displayed increased grain yield and protein content with enhanced photosynthetic activity compared to WT rice. Thus, OsASN1 can be an effective target gene for the development of rice cultivars with higher grain protein content, NUE, and grain yield under N-limiting conditions.

We develop general heterogeneous nonlocal diffusion models and investigate their connection to local diffusion models by taking a singular limit of focusing kernels. We reveal the link between the two groups of diffusion equations which include both spatial heterogeneity and anisotropy. In particular, we introduce the notion of deciding factors which single out a nonlocal diffusion model and typically consist of the total jump rate and the average jump length. In this framework, we also discuss the dependence of the profile of the steady state solutions on these deciding factors, thus shedding light on the preferential position of individuals.

Sulfate reduction is the quantitatively most important process to degrade organic matter in anoxic marine sediment and has been studied intensively in a variety of settings. Guaymas Basin, a young marginal ocean basin, offers the unique opportunity to study sulfate reduction in an environment characterized by organic-rich sediment, high sedimentation rates, and high geothermal gradients (100–958°C km−1). We measured sulfate reduction rates (SRR) in samples taken during the International Ocean Discovery Program (IODP) Expedition 385 using incubation experiments with radiolabeled 35SO42− carried out at in situ pressure and temperature. The highest SRR (387 nmol cm−3 d−1) was recorded in near-surface sediments from Site U1548C, which had the steepest geothermal gradient (958°C km−1). At this site, SRR were generally over an order of magnitude higher than at similar depths at other sites (e.g., 387–157 nmol cm−3 d−1 at 1.9 mbsf from Site U1548C vs. 46–1.0 nmol cm−3 d−1 at 2.1 mbsf from Site U1552B). Site U1546D is characterized by a sill intrusion, but it had already reached thermal equilibrium and SRR were in the same range as nearby Site U1545C, which is minimally affected by sills. The wide temperature range observed at each drill site suggests major shifts in microbial community composition with very different temperature optima but awaits confirmation by molecular biological analyses. At the transition between the mesophilic and thermophilic range around 40°C–60°C, sulfate-reducing activity appears to be decreased, particularly in more oligotrophic settings, but shows a slight recovery at higher temperatures.

Guaymas Basin is a young marginal rift basin in the Gulf of California characterized by active seafloor spreading and rapid sediment deposition, including organic-rich sediments derived from highly productive overlying waters and terrigenous sediments from nearby continental margins. The combination of active seafloor spreading and rapid sedimentation within a narrow basin results in a dynamic environment where linked physical, chemical, and biological processes regulate the cycling of sedimentary carbon and other elements. This continuum of interrelating processes from magma to microbe motivated International Ocean Discovery Program Expedition 385 and is reflected in its title, “Guaymas Basin Tectonics and Biosphere.”During IODP Expedition 385, organic-rich sediments with sill intrusions on the flanking regions and in the northern axial graben of Guaymas Basin (in eight sites) were drilled and core samples were recovered. Those cored samples were examined for their microbial cell abundance in a highly sensitive manner by density-gradient cell separation at the super clean room of Kochi Core Center, Japan, followed by direct counting on fluorescence microscopy. Cell abundance in surficial seafloor sediment (~109 cells/cm3) was roughly 1000 times higher than the bottom seawater (~106 cells/cm3) and gradually decreased with increasing depth and temperature. In contrast to the cell abundance profile observed at Nankai Trough (IODP Exp. 370), the gradual decrease of cell abundance was observed up to around 75ºC, and we detected microbial cells even at hot horizons above 100ºC.We will present the overview of the microbial cell distribution in the Guaymas Basin and discuss its relation to the current and past environmental conditions, e.g., temperature and sill-intrusion, etc.

Guaymas Basin is a young marginal rift basin in the Gulf of California characterized by active seafloor spreading and rapid deposition of organic-rich sediments derived from highly productive overlying waters and terrigenous sediments from nearby continental margins. Catalyzed by the steep geothermal gradient, sedimentary organic material of photosynthetic origin turns into a diverse spectrum of hydrocarbons that accumulate especially in deep, hot sediments, and might supply substrates for hydrocarbon-degrading microorganisms. The “from magma to microbe” perspective on these processes motivated International Ocean Discovery Program Expedition 385“Guaymas Basin Tectonics and Biosphere.”Hydrocarbon concentrations were determined in sediment samples selected from eight drilling sites on the flanking regions and in the northern axial graben of Guaymas Basin. Total petroleum hydrocarbon (C9-C44) concentrations increased from ca. 50-250 mg/kg towards >2000 mg/kg at in-situ temperatures above 80°C. A similar increase from ca. 10 mg/kg towards >100 mg/kg was observed for total saturated hydrocarbons. These gradients are shaped by abiotic hydrocarbon generation above 80°C at depth, and possibly by microbial hydrocarbon degradation at cooler temperatures in the upper sediment column. In a two-pronged approach, we are currently investigating the activity of bacterial/fungal consortia, isolated from Guaymas Basin surficial sediments, in the oxidation of selected alkanes and polyaromatics that occur at Guaymas Basin. In parallel, we explore the diversity, depth range and in-situ temperature range of bacteria, archaea and fungi in the Guaymas Basin subsurface sediments using PCR and metagenomic sequencing, to constrain microbial hydrocarbon cycling in the deep subsurface. Updates on these ongoing investigations will be presented.

In the oil and gas industry, empirical models are used to estimate the drift of pipes. These models encode pipe geometrical features, measured along the pipe length. If the estimated drift ...

International audience; Carbon materials have attracted increasing attention as supports for metal catalysts. Ironcontaining carbon nanotubes often promoted with copper have found application in Fischer-Tropsch synthesis, which provides an alternative way for conversion of renewable feedstocks to chemicals and fuels. In carbon nanotubes, the active phase can be nanoconfined inside the channels or localized on the outer surface. In most of previous work, the distribution of metal nanoparticles inside or outside carbon nanotubes is considered to be immobile during the catalyst activation or catalytic reaction. In this paper, we uncovered remarkable mobility of both iron and copper species in the bimetallic catalysts between inner carbon nanotube channels and outer surface, which occurs in carbon monoxide and syngas, while almost no migration of iron species proceeds in the monometallic catalysts. This mobility is enhanced by noticeable fragility and defects in carbon nanotubes, which appear on their impregnation with the acid solutions of metal precursors and precursor decomposition. Remarkable mobility of iron and copper species in bimetallic catalysts affects the genesis of iron active sites, and enhances interaction of iron with the promoter. In the bimetallic iron-copper catalysts, the major increase in the activity was attributed to higher reaction turnover frequency over iron surface sites located in a close proximity with copper.

A complete set of the logging data collected during the expedition is available at http://mlp.ldeo.columbia.edu/logdb/scientific_ocean_drilling. If you have problems downloading the data, wish to receive additional logging data, or have questions regarding the data, please contact Database Administrator, Bore

Extracting features for early failure detection in rotating machinery of nuclear power plants (NPPs) is difficult because in the early stages of failure the impact on the vibration signals is weak. To improve early fault detection in rotating machinery, a fault feature extraction method based on the combination of parameter-adaptive Variational Mode Decomposition (VMD) and Teager energy operator (TEO) is proposed in this paper. Firstly, we introduce the maximum weighted kurtosis index (WKI) as the objective function, and the Artificial Bee Colony (ABC) is used to optimize the VMD parameters. Then, the optimized VMD is used to decompose the vibration signal into multiple intrinsic mode functions (IMFs). Finally, TEO is used to demodulate the sensitive mode with the maximum WKI and identify the fault frequencies. Simulation and experiment show that the early fault features in vibration signals can be effectively extracted by the proposed method, and the comparisons with other three methods highlight the advantages of the proposed method.

Passive Safety Systems (PSSs) are increasingly employed in advanced Nuclear Power Plants (NPPs). Their safety performance is evaluated through computationally expensive Thermal-Hydraulic (T-H) simulations models and the identification of the operational conditions which lead to unsafe conditions (the so-called Critical failure Regions, CRs) may be challenging. In the present paper, a computational framework is proposed to identify the CRs of a generic passive Decay Heat Removal (DHR) system of a NPP. A time-demanding Best-Estimate Thermal-Hydraulic (BE-TH) model of the system is used to train a fast-running metamodel embedded within an adaptive sampling technique of literature, namely Adaptive Kriging Monte Carlo Sampling (AK-MCS), so as to provide increased accuracy in proximity of the failure threshold and identify which input values lead the PSS to failure. To the best authors’ knowledge this is the first time that the metamodel-based AK-MCS technique is applied for the identification of the CRs of a PSS of an NPP.

International audience; In this work, we apply a simulation-based framework that makes use of the Value of Information (VoI) for identifying the optimal spatial positioning of sensors on pressurized equipment. VoI is a utility-based Figure of Merit (FoM) which quantifies the benefits/losses of acquiring information. Sensors are typically positioned on pressurized equipment in line with specific recommendations based on operational experience, like UNI 11096 in Italy. We show that the recommendations in UNI 11096 are, indeed, justified and that, incidentally, relying on VoI for the optimization of the sensor positioning, one can achieve the same monitoring performance, as measured by VoI, where following UNI 11096, but with a reduced number of sensors. The proposed VoI-based approach can, thus, be used to confirm or revise recommendations coming from operational experience.

Sodium chlorate (NaClO3) is an achiral compound exhibiting supramolecular chirality at the solid state. It is historically the model compound used by Viedma to report his discovery of the symmetry breaking phenomenon now called Viedma ripening. In this paper, a temperature cycling induced deracemization (TCID) process – recently described as particularly efficient for organic compound deracemization – was developed for NaClO3 and the results were compared to the well-known Viedma ripening process. While the presence of chiral impurities usually improve of the efficacy of chiral compounds TCID, we observed that the voluntary addition of an inorganic impurity (sodium dithionate, Na2S2O6) had an opposite effect on the TCID process of NaClO3. Indeed, the process was strongly delayed by the presence of Na2S2O6. This phenomenon was explained by a suitable combination of crystal growth rate and secondary nucleation rate evaluations.

The article focuses on the design and application of an active reconfigurable controller that mitigates the effects of gust load and actuator faults on a flexible aircraft. A novel integrated adaptive output feedback scheme is investigated to address the actuator faults. The real-time fault values provided by the fault estimation module are considered in the reconfigurable control law to improve the fault-tolerant capability. The estimate values of faults and control gains are calculated by analyzing the stability of the overall system. The proposed controller is simulated using a flexible aircraft model with a discrete ‘1-cosine’ gust, and the results show that it can effectively mitigate the wing root moments and recover the flight maneuver stability after the aircraft suffered from gusts.

This work presents a data-driven method for identifying rare functional dependencies among components of different systems of Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. It is based on the representation of the alarm data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering rare dependencies among components of different systems and on the identification of groups of functionally dependent components. The proposed method is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected in the CTI of CERN (European Organization for Nuclear Research). The obtained results show the effectiveness of the proposed method.

We document the geometry of a massive sill at the root of an approximately 20-m high and 800 m-wide ring of hydrothermal formations, termed Ringvent, located 28.5 km off-axis on the northwestern flanking regions of the actively rifting Guaymas Basin (Gulf of California). Using petrophysical data collected during the IODP Expedition 385 and processed 2D seismic profiles, we present evidence on the mechanics of sill emplacement and how the related hydrothermal vent conduits were constructed. The currently active moderate-temperature hydrothermal vent field indicates that, despite being cold and crystallized, the magma plumbing system, is tapping into a deeper geothermal source of the basin. The vent system roots at the vertical end of the magma plumbing system with the top of the sill located at a depth range of 80 to 150 m below the seafloor. Our research aims at constraining how far deep the geothermal fluids are coming from, and identifying how close the hydrothermal system is from a steady-state condition, to draw implications for how frequently such a system may arise in nascent ocean basins.

International audience

The electrical power grid is one of the most critical infrastructures (CIs) in many developed and developing countries and must be planned, operated, maintained, and managed to ensure reliable, secure, and resilient service supply. Being coupled to the transport network, water distribution, Internet, food, etc., all are highly mutually dependent, including through information and communication technologies (the so-called cyber-based systems). We examine the threats to the electrical CIs of natural and man-made disasters that cause loss of power systems for several weeks over vast urban or statewide areas, affect many millions, and result in intersystems cascading failures. Key future questions include: how high and reliable to build flooding defenses; how to enhance backup power systems for all CI fail-safe interconnections; and establishing increased investment. What next must and can only be better systems, improved reliability, more effective emergency management, through decision-making guided by quantified risk and resilience of coupled CI. What we have learned from past real events discussed in this chapter has national as well as systems engineering implications.

International audience; Electrochemical synthetic route to sophisticated nano-objects has been considered as a promising and reliable strategy for diverse purpose in a wide range of applications. Especially, electrodeposition using nanoporous templates has attracted intensive research interests due to the potential for realizing diverse nanostructures and delicate composition control of the resulting fabricated materials. Unfortunately, the electrochemically filling of nanomaterials into nanoporous templates are still suffering from unexpected non-uniform formation of nanoobjects, originating from random nucleation in each nanopore and overgrowth from only a few nanopores. Here, we present a highly uniform, straightforward and controllable fabrication of Cu nano-objects via artificial nucleation-assisted electrodeposition principle using nanoporous AAO templates. Pre-electrodeposited Ni nanoparticles at the bottom of the nanopores of AAO successfully plays a role as a nucleus at the early stage of Cu electrodeposition process. The artificial nuclei enable highly uniform and stable Cu electrodeposition inside the nanopores of AAO templates by effectively suppressing the random overgrowth of Cu. Taking advantage of the stable electrodepositing behavior, we demonstrate precise control of the geometry of Cu nano-objects, uniformly dispersed over whole surfaces of substrates.

The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.

International audience; The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction.

Effective and comprehensive fault information can reduce the maintenance cost of a system. The selection of a proper test is important to obtain accurate information for fault detection and isolation (FDI) once a fault occurs. In real systems, the FDI task is made difficult by the correlations in the physical behaviors of the components and the possible coexistence of multiple faults. In this article, a new type of test selection model based on deep joint distribution is proposed, which takes into account the dependence and ambiguity groups. The optimal test can, then, be selected by an improved discrete binary particle swarm optimization (IBPSO) algorithm. The strengths of the proposed optimal test selection strategy are: 1) it can greatly improve the accuracy of test selection; 2) it considers the influence of multiple faults; 3) the dependence problem of the test outcomes can be addressed; and 4) it is computationally efficient and capable of selecting optimal test points meeting the requirements of real-time FDI. The application to a negative feedback circuit demonstrates the performance of the proposed method.

The COVID-19 pandemic has raised discussions on ethics for individual and social resilience. This paper dissects the cognitive biases and heuristics, affective influences and social and cultural factors that impact COVID-19-related risk judgments and compliance with protective behaviors. The analysis shows that these factors may distort people’s mental models of risk—including their individual and shared moral values—as well as their confidence and trust related to distinct stakeholders involved, such as government, science, the health system, and vaccine manufacturers. This has led us to an innovative articulation between risk education and communication on the one hand and the resilience of moral values, mental models of risk and trust on the other hand. The objective is to increase people’s understanding and acceptance of authorities’ recommendations to enhance social resilience, not only during the pandemic but also in view of future disasters. We conclude by discussing the need for a framework that integrates the resilience of ethics and ethics of resilience for disaster management, which should include cultural and societal aspects.

International audience; In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at cryogenic temperature by a Superconducting Magnet Cryogenic Cooling Circuit (SMCCC). To guarantee cooling, Loss-Of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, an approach to identify LOFA precursors (i.e., those component failures leading to a LOFA) is presented. The approach is based on a Spectral Clustering (SC) method using the Fuzzy C-Means (FCM) algorithm and is applied to the SMCCC of a single module of the ITER Central Solenoid (CS).

The COVID-19 pandemic is an opportunity to discuss ethics for social resilience. From early February 2020, the World Organization for Health has deplored the serious damage done by the abundant online misinformation and disinformation regarding the public behavior, especially the compliance with protective measures. Gonçalves-Sá (2020) has highlighted the underlying psychological aspect of that issue, namely, the human biases. Here, we dissect these biases, as well as other influences such as social factors, in order to demonstrate that they impact on behavioral decisions of compliance because they impact on risk perceptions of the COVID-19 disease and pandemic. Their implications for risk communication are then discussed, particularly regarding the resilience of ethics, that is, the necessity to change the citizens' moral and social values to increase their understanding and acceptance of the authorities' recommendations. We conclude by discussing the need for a framework that integrates the resilience of ethics and ethics of resilience for disaster management, which must include the culture and societal aspects.

International audience; In the last years, extreme weather events, including heat waves, have caused extensive and long-lasting interruptions in the electric power distribution systems of large urban areas. According to the current Performance-Based Regulation (PBR) approaches for incentives aimed at strengthening utility performance, these interruptions have to be quantitatively measured in network resilience metrics, rather than reliability metrics. In this distinction between resilience and reliability, a clear definition of the extreme events is required. This, however, is still lacking. To address the problem, in this work we propose a method to define heat waves, which relies on logistic regression with elastic-net penalization to quantitatively associate environmental and operating conditions of the network to significant increments of its failures. The methodology is validated by application to the medium voltage distribution network of the city of Milano, Italy.

International audience; Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

The objective of this paper is to develop an effective reliability approach to improve the numerical accuracy and computational efficiency of the low-cycle fatigue (LCF) damage prediction for turbine disks. The approach is called as distributed collaborative (DC) probabilistic substructure (PS)-based moving improved importance-sampling least-squares (MIIL), DC-PS-MIIL. In this approach, the MIIL model is firstly developed by combining an improved importance sampling (IIS) with moving least squares (MLS), to reduce the computation burden and improve the precision of surrogate model; then, the PS-MIIL comes up by integrating probabilistic substructure (PS) method into MIIL, to address the interface forces of turbine disks; finally, DC-PS-MIIL is proposed by incorporating distributed collaborative strategy with PS-MIIL. The comprehensive analysis procedure with DC-PS-MIIL is shown to simplify the probabilistic assessment of complex structures for improving numerical accuracy and simulation efficiency. Taking the LCF damage prediction of turbine disks as an example, DC-PS-MIIL is demonstrated to be an effective reliability approach. Also, numerical results show that the confidence levels, applied cycles and coefficients of variation (CVs) of random input variables have important impact on the LCF damage reliability for turbine disks.

International audience; The Reiner Gamma swirl is a prime location to investigate the lunar albedo patterns and their co‐location with magnetic anomalies. The precise relationship between impinging plasma and the swirl, and in particular, how these interactions vary over the course of a lunar day, remains an open issue. Here we use a fully kinetic particle‐in‐cell code, coupled with a magnetic field model based on orbital‐altitude observations, and simulate the interaction with the Reiner Gamma anomaly for all plasma regimes the region is exposed to along a typical orbit, including different solar wind incidence angles and the Moon's crossing through the terrestrial magnetosphere. Consistent with the hypothesis that swirls form as a result of plasma interactions with near‐surface magnetic fields, we show that the energy flux profile produces a pattern similar to Reiner Gamma's alternating bright and darkly colored bands, but only when integrating over the full lunar orbit. We additionally show that including He2+ as a self‐consistent plasma species improves the match.

International audience; Liquid drop impact dynamics depends on the liquid–substrate interaction. In particular, when liquid–solid friction is decreased, the spreading of the impacting drop lasts longer. We characterise this effect by using two types of superhydrophobic surfaces, with similar wetting properties but different friction coefficients. It is found that, for large enough impact velocities, a reduced friction delays the buildup of a viscous boundary layer, and leads to an increase of the time required to reach the maximal radius of the impacting drop. An asymptotic analysis is carried out to quantify this effect, and agrees well with the experimental findings. Interestingly, this novel description complements the general picture of drop impact on solid surfaces, and more generally addresses the issue of drag reduction in the presence of slippage for non-stationary flows.

The construction of large infrastructures (e.g., railways, gas pipelines or power grids) is increasingly facing widespread and violent opposition of radical environmentalist and ideological groups. Therefore, it is necessary to consider also the risk related to violent opposition actions when selecting construction sites. However, the classical paradigm of risk considering probability and impact hardly applies in this context, especially because of the difficulty of assessing probabilities, due to the lack of historical and/or reliable data. This paper develops a novel framework to support the selection of construction sites for infrastructure development, considering the risk of attacks by radical or ideologically motivated groups. The main originality is that the risk of attacks is evaluated considering the attractiveness of the different locations for the attackers, based on several criteria. Specifically, a novel multi-criteria decision model is introduced, which involves fourteen criteria related to the effort required to the opponents for performing the attack and to the expected outcomes of the attack in terms of potential damage to the targeted infrastructures and visibility of the action; moreover, the lifespan of the construction site and its influence on the possibility of an attack are taken into account. The criteria have been selected by a pool of forty-four Subject Matter Experts (SMEs), i.e., security managers and experts from the academia, industry and law enforcement. With such criteria, it is possible to construct a holistic index of attractiveness and quantify it by the Sparse Analytic Hierarchy Process (SAHP), based on the evaluations of the criteria provided by the pool of SMEs. The proposed methodology is applied to support the localization of the construction sites for the cross-border section of the Turin–Lyon High Speed and High Capacity Railway, including a new base-tunnel. Indeed, such an 8.6 billion Euro project has experienced strong opposition in Italy and, hence, security has become one of the main issues to be considered. The proposed model constitutes a decision aid tool to support the selection of construction plans, considering attractiveness and impact of attack.

Thermal discomfort is one of the main triggers for occupants’ interactions with components of the built environment such as adjustments of thermostats and/or opening windows and strongly related to the energy use in buildings. Understanding causes for thermal (dis-)comfort is crucial for design and operation of any type of building. The assessment of human thermal perception through rating scales, for example in post-occupancy studies, has been applied for several decades; however, long-existing assumptions related to these rating scales had been questioned by several researchers. The aim of this study was to gain deeper knowledge on contextual influences on the interpretation of thermal perception scales and their verbal anchors by survey participants. A questionnaire was designed and consequently applied in 21 language versions. These surveys were conducted in 57 cities in 30 countries resulting in a dataset containing responses from 8225 participants. The database offers potential for further analysis in the areas of building design and operation, psycho-physical relationships between human perception and the built environment, and linguistic analyses., Measurement(s)Natural Language • Demographics • humidity • response to temperature stimulus • temperature of air • geographic location • room temperature ambient airTechnology Type(s)Survey • sensorSample Characteristic - OrganismHomo sapiensSample Characteristic - EnvironmentbuildingSample Characteristic - LocationOman • Kingdom of Spain • Ecuador • Chile • Malaysia • Iraq • Poland • Nigeria • Iran • Italy • United States of America • Germany • Brazil • Jordan • Kingdom of the Netherlands • Greece • Sweden • Kingdom of Denmark • India • Sri Lanka • Uganda • South Korea • Republic of China • United Arab Emirates • Australia • French Republic • United Kingdom • Japan • Indonesia • China Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9805289

The Spectral Imaging of the Coronal Environment (SPICE) instrument is a high-resolution imaging spectrometer operating at extreme ultraviolet (EUV) wavelengths. In this paper, we present the concept, design, and pre-launch performance of this facility instrument on the ESA/NASA Solar Orbiter mission. The goal of this paper is to give prospective users a better understanding of the possible types of observations, the data acquisition, and the sources that contribute to the instrument's signal. The paper discusses the science objectives, with a focus on the SPICE-specific aspects, before presenting the instrument's design, including optical, mechanical, thermal, and electronics aspects. This is followed by a characterisation and calibration of the instrument's performance. The paper concludes with descriptions of the operations concept and data processing. The performance measurements of the various instrument parameters meet the requirements derived from the mission's science objectives. The SPICE instrument is ready to perform measurements that will provide vital contributions to the scientific success of the Solar Orbiter mission., Comment: A&A, accepted 19 August 2019; 26 pages, 25 figures

International audience; There is little known about the function of rice hexokinases (HXKs) in planta. We characterized hxk5-1, a Tos17 mutant of OsHXK5 that is up-regulated in maturing pollen, a stage when starch accumulates. Progeny analysis of self-pollinated heterozygotes of hxk5-1 and reciprocal crosses between the wild-type and heterozygotes revealed that loss of HXK5 causes male sterility. Homozygous hxk5-1, produced via anther culture, and additional homozygous hxk5-2, hxk5-3 and hxk5-4 lines created by CRISPR/Cas9 confirmed the male-sterile phenotype. In vitro pollen germination ability and in vivo pollen tube growth rate were significantly reduced in the hxk5 mutant pollen. Biochemical analysis of anthers with the mutant pollen revealed significantly reduced hexokinase activity and starch content, although they were sufficient to produce some viable seed. However, the mutant pollen was unable to compete successfully against wild-type pollen. Expression of the catalytically inactive OsHXK5-G113D did not rescue the hxk5 male-sterile phenotype, indicating that its catalytic function was responsible for pollen fertility, rather than its role in sugar sensing and signaling. Our results demonstrate that OsHXK5 contributes to a large portion of the hexokinase activity necessary for the starch utilization pathway during pollen germination and tube growth, as well as for starch biosynthesis during pollen maturation.

International audience; Developing an accurate and reliable multi-step ahead prediction model is a key problem in many Prognostics and Health Management (PHM) applications. Inevitably, the further one attempts to predict into the future, the harder it is to achieve an accurate and stable prediction due to increasing uncertainty and error accumulation. In this paper, we address this problem by proposing a prediction model based on Long Short-Term Memory (LSTM), a deep neural network developed for dealing with the long-term dependencies in time-series data. Our proposed prediction model also tackles two additional issues. Firstly, the hyperparameters of the proposed model are automatically tuned by a Bayesian optimization algorithm, called Tree-structured Parzen Estimator (TPE). Secondly, the proposed model allows assessing the uncertainty on the prediction. To validate the performance of the proposed model, a case study considering steam generator data acquired from different French nuclear power plants (NPPs) is carried out. Alternative prediction models are also considered for comparison purposes.

International audience; Defenders have to enforce defense strategies by taking decisions on allocation of resources to protect the integrity and survivability of cyber–physical systems (CPSs) from intentional and malicious cyber attacks. In this work, we propose an adversarial risk analysis approach to provide a novel one‐sided prescriptive support strategy for the defender to optimize the defensive resource allocation, based on a subjective expected utility model, in which the decisions of the adversaries are uncertain. This increases confidence in cyber security through robustness of CPS protection actions against uncertain malicious threats compared with prescriptions provided by a classical defend–attack game‐theoretical approach. We present the approach and the results of its application to a nuclear CPS, specifically the digital instrumentation and control system of the advanced lead‐cooled fast reactor European demonstrator.

International audience; Condition-Based Probabilistic Safety Assessment (CB-PSA) makes use of information on the components health conditions during operation to dynamically update the risk estimators(e.g., Core Damage Frequency (CDF)).In this paper, we perform aCB-PSA for controlling the risk of a Steam Line Break (SLB)-induced Steam Generator Tube Rupture (SGTR) accident scenario in a Pressurized Water Reactor (PWR)and use the outcome foroptimizing the maintenance.

This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).

In an integrated energy system (IES), the operating state of each energy subsystem changes relatively frequently, which can seriously threaten the security of IES operation. A systematic data-driven approach is proposed for detecting anomalies and analyzing the dynamics of IES vulnerability. Firstly, an anomaly detection method is introduced to determine whether there are anomalies in the system operation. The method can be set up even if the data labels for discriminating the anomalies are unknown, often the cause in practice. Secondly, a method of complex network phase theory is proposed to model information propagation among IES nodes representative of the IES physical entities. Complex network models can then be constructed to describe the system behavior in different operating conditions and over different time horizons. The degree centrality, betweenness centrality, and closeness centrality are used as indications to analyze changes in IES vulnerability. Finally, a method is proposed to identify the critical points of the IES from the point of view of its vulnerability. The new approach is applied to analyze the vulnerability of an IES in Spain. The results show that the proposed methods allow revealing system anomalies, vulnerability and weaknesses. Outcomes from an analysis by these methods can be used by managers to take defensive measures in advance for preventing and mitigating the impact of potential factors and threats on the IES.

Reliable and accurate wind speed forecasting (WSF) is fundamental for efficient exploitation of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant impact on the efficiency of wind power generation systems. Due to the non-stationarity and stochasticity of the wind speed (WS), a single model is often not sufficient in practice for the accurate estimation of the WS. Hybrid models are being proposed to overcome the limitations of single models and increase the WS forecasting performance. In this paper, a new hybrid WSF model is developed based on long short-term memory (LSTM) network and decomposition methods with grey wolf optimizer (GWO). In the pre-processing stage, the missing data is filled by the weighted moving average (WMA) method, the WS time series (WSTS) data are smoothed by WMA filtering and the smoothed data are used as model input after Z -score normalization. The forecasting model is formed by the combination of a single model, a decomposition method and an advanced optimization algorithm. Successively, the hybrid WSF model is developed by combining the LSTM and decomposition methods, and optimizing the intrinsic mode function (IMF) estimated outputs with a grey wolf optimizer (GWO). The developed non-linear hybrid model is utilized on the data collected from five wind farms in the Marmara region, Turkey. The obtained experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.

International audience; The classical paradigm for osmotic transport has long related the induced-flow direction to the solute membrane interactions, with the low-to-high concentration flow a direct consequence of the solute rejection from the semipermeable membrane. In principle, the same was thought to occur for the newly demonstrated membrane-free osmotic transport named diffusio-osmosis. Using a recently proposed nanofluidic setup, we revisit this cornerstone of osmotic transport by studying the diffusio-osmotic flows generated at silica surfaces by either poly(ethylene)glycol polymers or ethanol molecules in aqueous solutions. Strikingly, both neutral solutes yield osmotic flows in the usual low to high concentration direction, in contradiction with their propensity to adsorb on silica. Considering theoretically and numerically the intricate nature of the osmotic response that combines molecular-scale surface interaction and near-wall dynamics, these findings are rationalized within a generalized framework. These elements constitute a step forward toward a finer understanding of osmotically driven flows, at the core of rapidly growing fields ranging from energy harvesting to active matter.

International audience; Non-specific lipid transfer proteins (nsLTPs) are small, basic proteins that are characterized by an eight-cysteine motif. The biological functions of these proteins have been reported to involve plant reproduction and biotic or abiotic stress response. With the completion of the barley genome sequence, a genome-wide analysis of nsLTPs in barley (Hordeum vulgare L.) (HvLTPs) will be helpful for understanding the function of nsLTPs in plants. We performed a genome-wide analysis of the nsLTP gene family in barley and identified 70 nsLTP genes, which can be divided into five types (1, 2, C, D, and G). Each type of nsLTPs shares similar exon and intron gene structures. Expression analysis showed that barley nsLTPs have diverse expression patterns, revealing their various roles. Our results shed light on the phylogenetic relationships and potential functions of barley nsLTPs and will be useful for future studies of barley development and molecular breeding. (C) 2018 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.

International audience; Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging regression model and a distributed collaborative response surface method (DCRSM) for the reliability assessment of turbo-fan blades, considering the relevant uncertainty. Following a series of deterministic analyses, such as steady-state aerodynamic analysis, harmonic response analysis and Campbell diagram, and based on the assumption that vibration stress is mainly from aerodynamic load, the fatigue strength is calculated for turbo-fan blades under coupling aerodynamic forces, according to a modified Goodman curve of titanium-alloy. Giving consideration to the uncertainty of the resonance frequencies and material properties, the fatigue strength of the turbo-fan blade is evaluated, including probabilistic analysis and sensitivity analysis. In the case study analyzed, the conclusions are that the fatigue strength reliability reaches 96.808% with confidence level of 0.95 for the turbo-fan blade under the coupling aerodynamic forces, and the first three-order resonant frequencies are found to have important influence on the fatigue performance of turbo-fan blades.

International audience; In this paper, we develop a new quantitative method to assess the Strength of Knowledge (SoK) of a risk assessment. A hierarchical framework is first developed to conceptually represent the SoK in terms of three attributes (assumptions, data, phenomenological understanding), which are further broken down in sub-attributes and “leaf” attributes to facilitate their assessment in practice. The hierarchical framework, is, then, quantified in a top-down, bottom-up fashion for assessing the SoK. In the top-down phase, a reduced-order risk model is constructed to limit the complexity and number of basic elements considered in the SoK assessment. In the bottom-up phase, the SoK of each basic element in the reduced-order risk model is assessed based on predefined scoring guidelines and, then, aggregated using a weighted average of “leaf” attributes, where the weights are determined based on the Analytical Hierarchical Process (AHP). The strength of knowledge of the basic events is in turn, aggregated using a weighted average to obtain the SoK for the whole risk assessment model. The developed methods are applied to a real-world case study, where the SoK of the Probabilistic Risk Assessment (PRA) models of a Nuclear Power Plants (NPP) is assessed for two hazards groups, i.e., external flooding and internal events.

International audience; In this paper, a new data-driven auto associative bilateral kernel regression (AABKR) method based on weighted distance is proposed for the on-line monitoring of transient process operations. A bilateral approach to the kernel regression formulates a representative model that uses both the spatial and temporal information in the data, and a new weighted-distance algorithm captures temporal information. Moreover, an adaptive approach is proposed to dynamically compensate for faulty process inputs in the bilateral kernel evaluations, providing a robust model with little spillover. The proposed weighted-distance AABKR is first implemented using numerical process examples and then applied to the transient start-up operation of a nuclear power plant. Monte Carlo simulation results are provided by randomly assigning fault sensors and fault magnitudes. The results demonstrate the feasibility and efficiency of the proposed method.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems., Comment: Accepted in IEEE Communications Surveys and Tutorials, 2019

For nuclear power plants (NPPs) to have long lifetimes, ageing is a major issue. Currently, ageing management for NPP systems is based on correlations built from generic experimental data. However, each system has its own characteristics, operational history, and environment. To account for this, it is possible to resort to prognostics that predicts the future state and time to failure (TTF) of the target system by updating the generic correlation with specific information of the target system. In this paper, we present an application of particle filtering for the prediction of degradation in steam generator tubes. With a case study, we also show how the prediction results vary depending on the uncertainty of the measurement data. Keywords: Prognostics, Particle filtering, Model-based method, Steam generator tube rupture, Nuclear power plant