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[Display omitted] The interaction of tris(6-methyl-2-pyridyl)phosphine oxide (L) with equimolar amounts of [Cu(MeCN) 4 ]PF 6 or AgClO 4 in acetonitrile produces scorpionate-like complexes [M(L)(MeCN)]X (M = Cu and Ag; X = PF 6 - and ClO 4 -). At ambient temperature, these compounds exhibit orange (λ max = 608 nm) or turquoise (λ max = 490 nm) photo-luminescence with a quantum yield up to 16% and lifetimes of microsecond order. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.04.005 Byline: Alexey V. Demyanov (a), Antonio Fuduli (b), Giovanna Miglionico (c) Keywords: Non-smooth optimization; Cutting planes; Bundle methods Abstract: We present a new bundle algorithm for minimizing convex not necessarily smooth functions. The novelty of our approach is based on a bundle modification strategy that we apply whenever the stability center is updated and which is aimed at substituting the points of the bundle by new points characterized by possibly better values of the objective function. Convergence of the algorithm is proved and numerical results are presented. Author Affiliation: (a) SISSA, Via Beirut 2-4, 34014 Trieste, Italy (b) Dipartimento di Matematica, Universita della Calabria, Via Pietro Bucci - Cubo 30B, 87036 Arcavacata di Rende (CS), Italy (c) Dipartimento di Elettronica Informatica e Sistemistica, Universita della Calabria, 87036 Rende (CS), Italy Article History: Received 4 March 2005; Accepted 3 April 2006

A series of Ag(I), Au(I), Ag(I)-Au(I) and Cu(I)-Au(I) complexes has been synthesized using tris(6-methyl-2-pyridyl)phosphine as an efficient P , N , N ′, N ″ - bridging ligand. Room temperature phosphorescence has been revealed for the obtained compounds. [Display omitted] Tris(6-methyl-2-pyridyl)phosphine (L) has been highlighted as a promising multidentate ligand for design of original clusters based on group 11 metals. The interaction of this ligand with Au(tht)Cl and Au(tht)Cl-KPF 6 system affords complexes [Au(L)Cl] and [AuL 2 ]PF 6 , in which a linear P–Au–P coordination is realized. Complex [AuL 2 ]PF 6 , upon treatment with AgPF 6 or CuPF 6 , unexpectedly forms penta-nuclear clusters [Au@Ag 4 L 4 ](PF 6) 5 and [Au@Cu 4 L 4 ](PF 6) 5 , respectively, showing Au-centered tetrahedral Ag 4 /Cu 4 kernel supported by four L ligands in P , N , N ′, N ″ - bridging manner. The structurally similar all-silver cluster [Ag@Ag 4 L 4 ](BF 4) 5 has been assembled by the direct reaction of L with AgBF 4. According to X-ray crystallography data and QTAIM study, the synthesized clusters {Au@Ag 4 }, {Au@Cu 4 } and {Ag@Ag 4 } feature the pronounced attractive metallophilic d10-d10 interactions between the central (Au/Ag) and peripherical (Cu, Ag) metal atoms. At ambient temperature, the title complexes exhibit room-temperature phosphorescence at λ max = 501–572 nm with emission lifetimes of microsecond order (τ = 0.86–20 µs) and the quantum yields of up to 23 %. [ABSTRACT FROM AUTHOR]

This paper presents an alternative way of simulating 3D facies models using conditional generative adversarial networks (GANs) by reconstructing the 3D volume upward with customised thickness from generated 2D slices. To mimic natural fluvial reservoirs, GANs need to learn the complicated facies' lateral and vertical sequences, connectivity patterns and spatial correlation, e.g. the distribution of channel deposits and lateral accretion packages, including channel lags and point bars in a meandering fluvial system. 3D GAN modelling remains challenging, despite some success in 3D, due to the increased data and GAN model complexity caused by the 3D training data. For example, a 3D facies model with a larger number of voxels introduces more complex geobody shapes and requires 3D convolutions that increases the computational burden compared to 2D convolutions. Our proposed conditional GAN-based framework predicts 2D upper layers slice by slice to build 3D facies models, mimicking the sedimentary process in a purely aggrading system, which is less computationally costly than generating the facies models used as training data and generating facies models directly in 3D from a GAN. Each upper layer is predicted conditionally to a lower layer that contains the facies distributions in all layers below the target using several unique training enhancements. All those methods help prevent the simulated 3D models from gradually losing realism and the preset sedimentary settings vertically while inferring the plausible evolution of the channel system. The results demonstrate that the trained conditional generator, a SPADE generator, can work as a simulator to produce unconditional 3D meandering fluvial facies models by inputting a user-defined spatial correlation strength between layers and the number of layers needed (thickness in metres) without post-processing or retraining. This framework's performance was evaluated using a connectivity metric towards the range of avulsion scenarios that represent a range of depositional uncertainty. • We propose a conditional GAN-based 3D reconstruction approach called FluvalGAN_3DR. • FluvalGAN_3DR allows customising the model thickness without re-training. • We developed a solution to tackle the decay of realism during 3D reconstruction. • We developed a novel way of avoiding GAN to have a biased sense of the avulsion rate. • FluvalGAN_3DR created varied avulsion scenarios covering various sand connectivity. [ABSTRACT FROM AUTHOR]

Assessing the change in uncertainty in reservoir production forecasts over field lifetime is rarely undertaken because of the complexity of joining together the individual workflows. This becomes particularly important in complex fields such as naturally fractured reservoirs. The impact of this problem has been identified in previous and many solutions have been proposed but never implemented on complex reservoir problems due to the computational cost of quantifying uncertainty and optimising the reservoir development, specifically knowing how many and what kind of simulations to run. This paper demonstrates a workflow that propagates uncertainty throughout field lifetime, and into the decision making process by a combination of a metric-based approach, multi-objective optimisation and Bayesian estimation of uncertainty. The workflow propagates uncertainty estimates from appraisal into initial development optimisation, then updates uncertainty through history matching and finally propagates it into late-life optimisation. The combination of techniques applied, namely the metric approach and multi-objective optimisation, help evaluate development options under uncertainty. This was achieved with a significantly reduced number of flow simulations, such that the combined workflow is computationally feasible to run for a real-field problem. This workflow is applied to two synthetic naturally fractured reservoir (NFR) case studies in appraisal, field development, history matching and mid-life EOR stages. The first is a simple sector model, while the second is a more complex full field example based on a real life analogue. This study infers geological uncertainty from an ensemble of models that are based on the carbonate Brazilian outcrop which are propagated through the field lifetime, before and after the start of production, with the inclusion of production data significantly collapsing the spread of P10-P90 in reservoir forecasts. The workflow links uncertainty estimation with the appropriate optimisation at appraisal, development and reservoir management stages to maximise oil recovery under uncertainty. [ABSTRACT FROM AUTHOR]

There is a continuous challenge in identifying and propagating geologically realistic features into reservoir models. Many of the contemporary geostatistical algorithms are limited by various modelling assumptions, like stationarity or Gaussianity. Another related challenge is to ensure the realistic geological features introduced into a geomodel are preserved during the model update in history matching studies, when the model properties are tuned to fit the flow response to production data. The above challenges motivate exploration and application of other statistical approaches to build and calibrate reservoir models, in particular, methods based on statistical learning. The paper proposes a novel data driven approach – Multiple Kernel Learning (MKL) – for modelling porous property distributions in sub-surface reservoirs. Multiple Kernel Learning aims to extract relevant spatial features from spatial patterns and to combine them in a non-linear way. This ability allows to handle multiple geological scenarios, which represent different spatial scales and a range of modelling concepts/assumptions. Multiple Kernel Learning is not restricted by deterministic or statistical modelling assumptions and, therefore, is more flexible for modelling heterogeneity at different scales and integrating data and knowledge. We demonstrate an MKL application to a problem of history matching based on a diverse prior information embedded into a range of possible geological scenarios. MKL was able to select the most influential prior geological scenarios and fuse the selected spatial features into a multi-scale property model. The MKL was applied to Brugge history matching benchmark example by calibrating the parameters of the MKL reservoir model parameters to production data. The history matching results were compared to the ones obtained from other contemporary approaches – EnKF and kernel PCA with stochastic optimisation. [ABSTRACT FROM AUTHOR]

In groundwater modeling, Monte Carlo Markov Chain (MCMC) simulations are often used to calibrate aquifer parameters and propagate the uncertainty to the quantity of interest (e.g., pollutant concentration). However, this approach requires a large number of flow simulations and incurs high computational cost, which prevents a systematic evaluation of the uncertainty in the presence of complex physical processes. To avoid this computational bottleneck, we propose to use an approximate model (proxy) to predict the response of the exact model. Here, we use a proxy that entails a very simplified description of the physics with respect to the detailed physics described by the “exact” model. The error model accounts for the simplification of the physical process; and it is trained on a learning set of realizations, for which both the proxy and exact responses are computed. First, the key features of the set of curves are extracted using functional principal component analysis; then, a regression model is built to characterize the relationship between the curves. The performance of the proposed approach is evaluated on the Imperial College Fault model. We show that the joint use of the proxy and the error model to infer the model parameters in a two-stage MCMC set-up allows longer chains at a comparable computational cost. Unnecessary evaluations of the exact responses are avoided through a preliminary evaluation of the proposal made on the basis of the corrected proxy response. The error model trained on the learning set is crucial to provide a sufficiently accurate prediction of the exact response and guide the chains to the low misfit regions. The proposed methodology can be extended to multiple-chain algorithms or other Bayesian inference methods. Moreover, FPCA is not limited to the specific presented application and offers a general framework to build error models. [ABSTRACT FROM AUTHOR]

A new method for calibration of channelized subsurface flow models is presented. The proposed method relies on the nested sampling algorithm and on adaptive construction of soft probability maps. Nested sampling (NS) is a Bayesian sampling algorithm for estimating the Bayesian evidence and obtaining samples from the posterior distribution of the unknown fields. NS utilizes a set of samples (active set) that evolves to high-likelihood regions. The sample evolution process is achieved by iteratively replacing the sample with the lowest likelihood within the active set by a new sample from the prior but with higher likelihood value (constrained sampling). For channelized models, drawing samples from the prior model based on a training image and only accepting the samples satisfying the likelihood constraint is computationally inefficient due to low acceptance rates. We develop an efficient constrained sampling step utilizing soft probability maps in addition to the training image (using the Tau model) to obtain samples from the prior satisfying the likelihood constraint. The soft probability map is constructed by averaging the samples within the active set and is shown to significantly increase the acceptance rate of the nested sampling algorithm. The proposed algorithm is applied for calibration of several channelized subsurface flow models. In addition, the NS algorithm is applied for prior model selection of a channelized model with different training images obtained by changing the orientation angles of a reference training image. The results show that selecting the prior model based on the data mismatch can be misleading. This highlights the need to evaluate the Bayesian evidence (estimated by the nested sampling algorithm) as a more reliable prior model selection statistics, especially when the amount of calibration data is limited. [ABSTRACT FROM AUTHOR]

Abstract: Benchmark problems have been generated to test a number of issues related to predicting reservoir behaviour (e.g. Floris et al., 2001, Christie and Blunt, 2001, Peters et al., 2010). However, such cases are usually focused on a particular aspect of the reservoir model (e.g. upscaling, property distribution, history matching, uncertainty prediction, etc.) and the other decisions in constructing the model are fixed by log values that are related to the distribution of cell properties away from the wells, fixed grids and structural features and fixed fluid properties. This is because all these features require an element of interpretation, from indirect measurements of the reservoir, noisy and incomplete data and judgments based on domain knowledge. Therefore, there is a need for a case study that would consider interpretational uncertainty integrated throughout the reservoir modelling workflow. In this benchmark study we require the modeller to make interpretational choices as well as to select the techniques applied to the case study, namely the geomodelling approach, history matching algorithm and/or uncertainty quantification technique. The interpretational choices will be around the following areas: [(1)] Top structure interpretation from seismic and well picks. [(2)] Fault location, dimensions and the connectivity of the network uncertainty. [(3)] Facies modelling approach. [(4)] Facies interpretations from well logs cutoffs. [(5)] Petrophysical property prediction from the available well data. [(6)] Grid resolution-choice between number of iterations and model resolution to capture the reservoir features adequately. A semi-synthetic study is based on real field data provided: production data, seismic sections to interpret the faults and top structures, wireline logs to identify facies correlations and saturation profile and porosity and permeability data and a host of other data. To make this problem useable in a manageable time period multiple hierarchically related gridded models were produced for a range of different interpretational choices. [Copyright &y& Elsevier]

Abstract: The paper is aimed at studying the peculiarities of dynamical behavior of tether in its deployment in low Earth orbit during YES2 experiment in Foton-M3 mission, and performing flight data analysis with account of these effects. The analysis in the first part of the paper uses as input a pre-provided tension profile for the mission (resulting from a simulation to be independently validated). With this input it then performs an open-loop simulation which explains the sensitivity to the initial parameters. For the actual flight design a feedback mechanism and algorithm was used in order to control the deployment speed along a nominal profile, minimizing sensitivity to conditions such as initial velocity and endmass value. The paper provides solutions accounting for final velocities of wave propagation in tether, which is especially important for such stages of the deployment as sharp changing of the velocity direction and intensive braking. Moreover the YES2 data is used to validate the theoretical derivations. [Copyright &y& Elsevier]

Abstract: We investigated failures in the global positioning system (GPS) performance produced by solar radio bursts with unprecedented radio flux density during the X6.5 and X3.4 solar flares on 6 and 13 December 2006, respectively. The effect of these events on GPS was compared to that of the X17.2 solar flare of 28 October 2003. Significant experimental evidence was found that high-precision GPS positioning on the Earth''s entire sunlit side was partially disrupted for more than 10–15min on 6 and 13 December 2006. The high level of phase slips and count omissions resulted from the wideband solar radio noise emission. Our results provide serious grounds for revising the role of space weather factors in the functioning of modern satellite systems and for considering these factors more carefully in practice. Similar failures in the operation of satellite navigation systems (GPS, GLONASS, and GALILEO) can be fatal for operating safety systems as a whole and lead to great financial losses. Another important conclusion of our investigation concerns the continuous calibrated monitoring of the level of the solar radio emission flux. This monitoring involved a large number of solar radio spectrographs and allowed us to estimate the solar radio noise level in the range of the GPS–GLONASS–GALILEO frequencies. [Copyright &y& Elsevier]

Abstract: Results of the investigation of transverse magnetoresistance temperature dependences in a single-crystal Cd specimen of orientation in the 8T magnetic field has lead to the discovery of the temperature breakdown effect in the direction. It has been established, that phonons with wave vector, which width is equal or larger than the minimal distance between the contingent Fermi surface sheets, are needed for the implementation of this effect. Realization of this condition is possible only in the area of the maximal contingence of a ‘packet’ and the ‘monster’ corrugated cylinder of the second Brillouin zone on the upper hexagonal AHL plane. [Copyright &y& Elsevier]

Using combined synthesis modes and optimized conditions for ultrasonic dispersion, we obtained a single-phase nanosized Sr 2 FeMoO 6–δ powder having a high degree of superstructural ordering of Fe and Mo cations (88 %) and an average grain size of 70.8 nm. The results of the Mössbauer spectroscopy and magnetic measurements show that the obtained nanosized strontium ferromolybdate powder is in a magnetically inhomogeneous state, consisting of superparamagnetic and ferrimagnetic phases. In comparison with the superparamagnetic component of magnetization, the ferrimagnetic component has higher values and rises smoothly until reaching saturation as the temperature decreases. Showing that there is no exchange magnetic interaction between the grains in the superparamagnetic phase, allowed us to estimate the critical sizes, of nanoparticles, d spm , in the single-domain state using the Néel-Brown model. The obtained value of d spm is smaller than the size of the single-domain particles, which confirms the absence of the frozen state in some of the superparamagnetic particles. These results are important for understanding the effects of nanosized grains on the magnetic properties and features of magnetization of the Sr 2 FeMoO 6–δ polydisperse powder, a promising material for spintronic device applications. [ABSTRACT FROM AUTHOR]

Abstract: Degradation of transionospheric radio signals and operation failures during ionospheric disturbances constitute a crucial factor of space weather influence on radio engineering satellite systems performance. We found that during the main phase of strong magnetic storms in 2000–2003 when the auroral oval expands into mid-latitudes, its southern boundary develops a region with intense small-scale electron density irregularities. Such irregularities may cause strong amplitude scintillations of GPS signals at both GPS operating frequencies. The another consequence of it was significant random GPS signal phase fluctuations, breaking-down of signal tracking, and sharp increasing of GPS positioning errors as a result. [Copyright &y& Elsevier]

Abstract: Self-organized silver nanostructures were grown in porous Si/SiO2 matrix fabricated by ion track technology. The different silver nanostructures with shapes like “sunflowers”, “azalea” or “corn” were realized by applying wet-chemical electroless deposition. We show that reproducible self-organized silver “sunflower” like nanostructures provide a high enhanced Raman signal of Nile blue dye molecules. Signal enhancement for a few or even just a single silver “sunflower” is demonstrated by analyzing the surface-enhanced Raman signature of Nile blue dye molecules. According to this, the silver nanostructures can act as efficient surfaces for surface enhanced Raman spectroscopy as well as (bio)-sensor applications. [Copyright &y& Elsevier]

Abstract: In this work, the magnetic properties and critical behavior around ferromagnetic–paramagnetic (FM–PM) phase transition in Ba1.7La0.3FeMoO6 compound have been investigated in detail. This compound exhibits a second-order magnetic phase transition with Curie temperature T C = 345 K. The critical exponents β, γ, and δ that are determined by using the modified Arrott plots (MAP), the Kouvel–Fisher (KF) and the critical isotherm analysis agree very well. Our results indicate a coexistence of short-range and long-range ferromagnetic (FM) interactions in Ba1.7La0.3FeMoO6 compound. The existence of long-range FM interactions in this compound can be associated with the crystal structure of materials with long-range Fe/Mo ordering parameter and strength of double-exchange interaction, whereas the existence of the short-range FM interactions can be explained by magnetic inhomogeneity and FM clusters. [Copyright &y& Elsevier]

We have studied a reaction between the reduced form of titanocene dichloride (Cp2TiCl2) and a group of organic halides: benzyl derivatives (4-XC6H4CH2Cl, X = H, NO2, CH3; 4-XC6H4CH2Br, X = H, NO2, PhC(O); 4-XC6H4CH2SCN, X = H, NO2) as well as three aryl halides (4-NO2C6H4Hal, Hal = Cl, Br; 4-CH3O-C6H4Cl). It has been shown that the electrochemical reduction of Cp2TiCl2 in the presence of these benzyl halides leads to a catalytic cycle resulting in the reductive dehalogenation of these organic substrates to yield mostly corresponding toluene derivatives as the main product. No dehalogenation has been observed for aryl derivatives. Based on electrochemical data and digital simulation, possible schemes of the catalytic process have been outlined. For non-substituted benzyl halides halogen atom abstraction is a key step. For the reaction of nitrobenzyl halides the complexation of Ti(III) species with the nitro group takes place, with the electron transfer from Ti(III) to this group (owing to its highest coefficient in LUMO of the nitro benzyl halide) followed by an intramolecular dissociative electron redistribution in the course of the heterolytic CHal bond cleavage.The results for reduced titanocene dichloride centers immobilized inside a polymer film showed that the catalytic reductive dehalogenation of the p-nitrobenzyl chloride does occur but with a low efficiency because of the partial deactivation of the film due to the blocking of the electron charge transport between the electrode and catalytic centers. [Copyright &y& Elsevier]

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model—machine learning (ML) residuals sequential simulations—MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. ML algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. [Copyright &y& Elsevier]

The formation of conducting nanotubules in etched tracks is reported in literature since about a decade. However, up to now precise production recipes are scarce. For this sake we present here a systematic study on some important factors that influence the formation of metallic nanotubules. In the case of chemical deposition, the first question to be answered is the choice of the activation technique to produce the required activation centers. Both the time of activation and the time of subsequent chemical deposition are crucial parameters in this connection. Finally, the maximum temperature is determined up to which thermal stability of the etched tracks and of the tubules therein is given. This study should allow one to predict better the efficiency of conducting nanotubule formation. [Copyright &y& Elsevier]