Studies on the nuclear magnetic resonance spectra of olefinic protons of conjugated fatty acid methyl esters

The NMR spectra of olefinic protons in the four representative conjugated fatty acid methyl esters, methylcis-9,trans-11-octadecadienoate, methyltrans-9,trans-11-octadecadienoate, methyl α eleostearate, and methyl β eleostearate, were studied. The chemical shift of each olefinic proton in these compounds was determined by considering their intramolecular environment. Coupling constants were also obtained as the results of spectral analysis.     

Unified reliability estimation and management of NoC based chip multiprocessors

We present a new architecture level unified reliability evaluation methodology for chip multiprocessors (CMPs). The proposed reliability estimation (REST) is based on a Monte Carlo algorithm. What distinguishes REST from the previous work is that both the computational and communication components are considered in a unified manner to compute the reliability of the CMP. We utilize REST tool to develop a new dynamic reliability management (DRM) scheme to address time-dependent dielectric breakdown and negative-bias temperature instability aging mechanisms in network-on-chip (NoC) based CMPs. Designed as a control loop, the proposed DRM scheme uses an effective neural network based reliability estimation module. The neural-network predictor is trained using the REST tool. We investigate how system’s lifetime changes when the NoC as the communication unit of the CMP is considered or not during the reliability evaluation process and find that differences can be as high as 60%. Full-system based simulations using a customized GEM5 simulator show that reliability can be improved by up to 52% using the proposed DRM scheme in a best-effort scenario with 2–9% performance penalty (using a user set target lifetime of 7 years) over the case when no DRM is employed.     

Anionic polymerization of styrene in 2-methyltetrahydrofuran: temperature dependence of the field effect

Kinetics of living anionic polymerizations of styrene was investigated for lithium, sodium, and potassium salts in 2-methyltetrahydrofuran at 35, 25, 0, and ?70°C in the presence and absence of a high intensity electric field. At 35 and 25°C, a field acceleration effect was observed whereas no effect was found at 0 and ?70°C. The propagation rate constant of ion pairs was almost independent of gegen ions and their temperature dependence was smaller than that of free anions. The free ions seem to be more strongly solvated as the temperature decreases.     

Fourier-transform speckle profilometry: three-dimensional shape measurements of diffuse objects with large height steps and/or spatially isolated surfaces

An interferometric technique for automated profilometry of diffuse objects has been proposed. It is based on the Fourier-fringe analysis of spatiotemporal specklegrams produced by a wavelength-shift interferometer with a laser diode as a frequency-tunable light source. Unlike conventional moiré techniques the proposed technique permits the objects to have discontinuous height steps and/or surfaces spatially isolated from one another. Experimental results are presented that demonstrate the validity of the principle.     

A low wall-loading DEMO reactor design with high priority for early and reliable realization of a tokamak fusion reactor over the cost performance

Based on scientific databases adopted for designing ITER plasmas and on the advancement of fusion nuclear technology from the recent R&D program, a low wall-loading DEMO fusion reactor has been designed, where high priority has been given to the early and reliable realization of a tokamak fusion plasma over the cost performance. Since the major radius of this DEMO reactor is chosen to be 10 m, plasma ignition is achievable with a low fusion power of 0.8 GW and an operation period of 4–5 hours is available only with inductive current drive. The low ignition power makes it possible to adopt a first wall with an austenitic stainless steel, for which significant databases and operating experience exists, due to its use in the presence of neutron irradiation in fission reactors. In step with development of advanced materials, a step-wise increase of the fusion power seems to be feasible and realistic, because this DEMO reactor has the potential to produce a fusion power of 5 GW.     

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress–strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes’ rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro–macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.     

Quantitative analysis of metabolic gases by multichannel Raman spectroscopy: use of a newly designed elliptic-spherical integration type of cell holder

We describe the quantitative analysis of some metabolic gases bymultichannel Raman spectroscopy. Raman spectra were measured forair, acetone, ammonia, carbon dioxide, and mixed gas consisting ofacetone, ammonia, and air. We designed a new elliptic-sphericalintegration type of cell holder to obtain the Raman spectra of gaseswith a high signal-to-noise ratio. Concentrations of acetone, ammonia, and carbon dioxide were determined by the peak intensities ofRaman bands at 2940, 3228, and 1385 cm(-1), respectively. To compensate for the fluctuations of Ramanintensities caused by several factors, such as the fluctuations oflaser power, the peak intensity of a band at 2324 cm(-1) dueto nitrogen gas was used as an internal intensity standard. Thecorrelation coefficient between the corrected Raman intensity at 2940cm(-1) and the concentration of acetone was calculated to be0.984 for a concentration range of 2-12 ppm. The detection limitof acetone gas was found to be 2 ppm.     

Prediction of starting performance of synchronous motor by DC decaytesting method with the rotor in any arbitrary position

This paper presents a novel method to predict the starting performance of synchronous motors. This proposed strategy is based on a simple standstill response testing method using a small-capacity DC power supply unit, tentatively named the DC decay testing method in this paper, and can be applied to standstill synchronous motors with the rotor in any arbitrary position. The proposed testing method is carried out on a 10 kW, 200 V, 31.9 A, 50 Hz four-pole laminated salient-pole synchronous motor with damper windings. The results measured by on-load test and those predicted by the proposed method on starting performance clearly show the validity of the proposed method     

Reorganizing knowledge in neural networks: an explanatory mechanismfor neural networks in data classification problems

We propose an explanatory mechanism for multilayered neural networks (NN). In spite of the effective learning capability as a uniform function approximator, the multilayered NN suffers from unreadability, i.e., it is difficult for the user to interpret or understand the "knowledge" that the NN has by looking at the connection weights and thresholds obtained by backpropagation (BP). This unreadability comes from the distributed nature of the knowledge representation in the NN. In this paper, we propose a method that reorganizes the distributed knowledge in the NN to extract approximate classification rules. Our rule extraction method is based on the analysis of the function that the NN has learned, rather than on the direct interpretation of connection weights as correlation information. More specifically, our method divides the input space into "monotonic regions" where a monotonic region is a set of input patterns that belongs to the same class with the same sensitivity pattern. Approximate classification rules are generated by projecting these monotonic regions.     

Chemical and electrochemical preparation of poly(9,10-dihydrophenanthrene-2,7-diyl) using nickel complexes and electrochemical properties of the polymer

Summary Dehalogenation polycondensations of 2,7-dibromo-9,10-dihydrophenanthrene with isolated zero-valent nickel complex and electrochemically generated zero-valent nickel complex afford -conjugated poly(9,10-dihydrophenanthrene-2,7-diyl). The electrochemically synthesized polymer is obtained as a thin film on electrode, and shows a reversible electrochemically doping-undoping cycle in an oxidation region. The polymer has essentially the same -conjugation system as that of poly(p-phenylene).