Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

The GPR (Ground Penetrating Radar) conference in Hong Kong year 2016 marked the 30th anniversary of the initial meeting in Tifton, Georgia, USA on 1986. The conference has been being a bi-annual event and has been hosted by sixteen cities from four continents. Throughout these 30 years, researchers and practitioners witnessed the analog paper printout to digital era that enables very efficient collection, processing and 3D imaging of large amount of data required in GPR imaging in infrastructure. GPR has systematically progressed forward from “Locating and Testing” to “Imaging and Diagnosis” with the Holy Grail of ’Seeing the unseen’ becoming a reality. This paper reviews the latest development of the GPR’s primary infrastructure applications, namely buildings, pavements, bridges, tunnel liners, geotechnical and buried utilities. We review both the ability to assess structure as built character and the ability to indicate the state of deterioration. Finally, we outline the path to a more rigorous development in terms of standardization, accreditation, and procurement policy.

A review of Ground Penetrating Radar application in civil engineering: A 30-year journey from Locating and Testing to Imaging and Diagnosis

We have produced ultrathin epitaxial graphite films which show remarkable 2D electron gas (2DEG) behavior. The films, composed of typically 3 graphene sheets, were grown by thermal decomposition on the (0001) surface of 6H-SiC, and characterized by surface-science techniques. The low-temperature conductance spans a range of localization regimes according to the structural state (square resistance 1.5 kOhm to 225 kOhm at 4 K, with positive magnetoconductance). Low resistance samples show characteristics of weak-localization in two dimensions, from which we estimate elastic and inelastic mean free paths. At low field, the Hall resistance is linear up to 4.5 T, which is well-explained by n-type carriers of density 10^{12} cm^{-2} per graphene sheet. The most highly-ordered sample exhibits Shubnikov - de Haas oscillations which correspond to nonlinearities observed in the Hall resistance, indicating a potential new quantum Hall system. We show that the high-mobility films can be patterned via conventional lithographic techniques, and we demonstrate modulation of the film conductance using a top-gate electrode. These key elements suggest electronic device applications based on nano-patterned epitaxial graphene (NPEG), with the potential for large-scale integration.

Ultrathin epitaxial graphite: 2D electron gas properties and a route toward graphene-based nanoelectronics

Nanotechnology innovations for the construction industry

Depending on their structure and order (individual, films, bundled, buckypapers, etc.), carbon nanotubes (CNTs) demonstrate different values of thermal conductivity, from the level of thermal insulation with the thermal conductivity of 0.1 W/mK to such high values as 6600 W/mK. This review article concentrates on analyzing the articles on thermal conductivity of CNT networks. It describes various measurement methods, such as the 3-ω method, bolometric, steady-state method and their variations, hot-disk method, laser flash analysis, thermoreflectance method and Raman spectroscopy, and summarizes the results obtained using those techniques. The article provides the main factors affecting the value of thermal conductivity, such as CNT density, number of defects in their structure, CNT ordering within arrays, direction of measurement in relation to their length, temperature of measurement and type of CNTs. The practical methods of using CNT networks and the potential directions of future research in that scope were also described.

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Thermal conductivity of carbon nanotube networks: a review

Regular and long-term infrastructures monitoring is fundamental for the assessment of road network conditions and the activation of adequate maintenance measures. When it comes to embedded sensing technologies for pavement monitoring much attention is paid to measurements accuracy and their understanding. Due to the viscoelastic behavior of the asphalt mix, the road pavement response depends on time and temperature. This paper aims at developing a methodology to adjust the strains measured within the pavement to a reference temperature. This methodology is based on the mechanical characteristics of the pavement and its sensitivity to temperature variations.

Procedure for Temperature Correction of Strains Measured in a Road Pavement

The invention relates to a device for analysing the microporosity of a given dual-phase material or comprises at least two phases, including at least one fluid phase, wherein said device includes a plurality of sensors (100) imbedded in said given material (M), the sensors each including one or more acoustic and capacitive CMUT transducers.

Method and device for the acoustic analysis of microporosities in a material such as concrete using a plurality of cmut transducers embedded in said material

An analyte sensor comprising at least two electronic components differentiated by their electrical properties, these components being formed from nanotubes, nanowires or graphene, sensitive to analytes. According to the invention, the two electronic components are connected together so as to constitute a logic inverter; the first electronic component being a field effect transistor; and the second electronic component being selected from a field effect transistor and a resistor.

Detector of analytes using a logic inverter constructed from two transistors nanotube / nanowire / graphene, and detection METHOD.

In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, a novel drift detection algorithm is presented in this work, exploiting the concept of rendez-vous between measuring instruments. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least. Note to Practitioners—In this paper, we investigate the problem of drift detection in sensor networks. This work was motivated by the fact that faulty nodes are rarely detected in existing in situ calibration algorithm prior to the correction of the instruments. Moreover, existing fault diagnosis algorithms for sensor networks do not specifically target drift and are often applicable to either (dense) static or mobile sensor networks but not both. We propose an algorithm designed for the detection of drift faults regardless of the type of sensor network and of the measurand. Specific attention is paid to the metrological quality of the measurement results used to carry out the diagnosis. The output of the algorithm provides information that can be exploited for the recalibration of faulty instruments. In future work, we will aim at providing tools and recommendations for the adjusment of the parameters of the diagnosis algorithm but also more elaborated approaches based on the results of our diagnosis algorithm to calibrate faulty nodes.

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

Societal demands in the field of air and water pollution monitoring require the ability to simultaneously detect a wide variety of chemical elements at very low concentrations in complex environments using compact and low-cost sensor devices. Although nanomaterial-based sensors have long been proposed as a solution to these exacting requirements, their detection accuracy is generally degraded in real-world conditions compared to laboratory conditions due to the effects of various interferents. To manage the related uncertainties and to associate confidence to the estimations, it seems natural to formulate the calibration-estimation problem in a probabilistic framework. This probabilistic formulation and its successful application to the monitoring of pH and active chlorine in drinking water are the main contributions of this work. While other tested calibration methods only allowed the monitoring of active chlorine, our solution enables the monitoring of both active chlorine and pH with quantified and reasonable uncertainties. Its success relies mainly on two adaptations of standard calibration methods: the consideration of the sensor inputs measurement errors (and not only the errors associated with the sensor outputs as it is generally done), and the introduction of two sources of model error (ME), one accounting for the approximate character of the calibration model, the other for unmeasured—and possibly unknown—interferents in the calibration environment.

Bérengère Lebental

Papers

Procedure for Temperature Correction of Strains Measured in a Road Pavement

Method and device for the acoustic analysis of microporosities in a material such as concrete using a plurality of cmut transducers embedded in said material

Detector of analytes using a logic inverter constructed from two transistors nanotube / nanowire / graphene, and detection METHOD.

Rendez-Vous Based Drift Diagnosis Algorithm for Sensor Networks Toward In Situ Calibration

Uncertainty-Based Calibration Method for Environmental Sensors—Application to Chlorine and pH Monitoring With Carbon Nanotube Sensor Array