Electrical conductivity and domain structure in ferroelastic Tl2SeO4

We have carried out the optical observation, electrical conductivity and ²⁰⁵Tl NMR measurements, and subsequently investigated the origin of the large conductivity above ferroelastic phase transition temperature T_c (=661 K) on the basis of the domain structure and the crystal structure. Electrical conductivity exhibits the discontinuous increase around T_c with increasing temperature and becomes approximately 5 × 10⁻³ S m⁻¹ above T_c. Moreover, from the ²⁰⁵Tl NMR measurements, it is found that mobile Tl ions exist above T_c. Furthermore, from the analysis of the domain structure based on the crystal structure in the low-temperature ferroelastic phase, it is also found that the anomalously large fluctuations of SeO₄ tetrahedrons exist above T_c. It is deduced from these results that the high electrical conductivity above T_c is caused by the mobile Tl ions closely related to the anomalously large fluctuations of SeO₄ tetrahedrons accompanied by the ferroelastic phase transition.     

Stochastic Asset Pricing with Seismic Hazard Risk

Since cash flow of asset in the long run is generally subject to economic fluctuation, and also to potential damage due to seismic hazard risk, asset pricing is more relevantly evaluated, provided that uncertainties of both cash flow and seismic risk are properly taken into account. This study will propose a discounted cash flow method to evaluate asset pricing, in which incomes gained from asset are modeled as a Markov process and seismic loss is modeled due to only one earthquake event during the service time. A numerical example is demonstrated for an 11 story steel reinforced commercial building considering the service time of 50 years. Various risk–asset price curves are numerically obtained in the form of probability of excess versus asset price with discount rate as a parameter for cases with and without earthquake loss taken into account, and for a case in which earthquake strengthening measure is implemented. These curves contribute information to decision makers in charge of risk and investment management.     

Huge-scale molecular dynamics simulation of multibubble nuclei

We have developed molecular dynamics codes for a short-range interaction potential that adopt both the flat-MPI and MPI/OpenMP hybrid parallelizations on the basis of a full domain decomposition strategy. Benchmark simulations involving up to 38.4 billion Lennard-Jones particles were performed on Fujitsu PRIMEHPC FX10, consisting of 4800 SPARC64 IXfx 1.848 GHz processors, at the Information Technology Center of the University of Tokyo, and a performance of 193 teraflops was achieved, which corresponds to a 17.0% execution efficiency. Cavitation processes were also simulated on PRIMEHPC FX10 and SGI Altix ICE 8400EX at the Institute of Solid State Physics of the University of Tokyo, which involved 1.45 billion and 22.9 million particles, respectively. Ostwald-like ripening was observed after the multibubble nuclei. Our results demonstrate that direct simulations of multiscale phenomena involving phase transitions from the atomic scale are possible and that the molecular dynamics method is a promising method that can be applied to petascale computers.     

Energy Savings through Microwave Selective Heating of Pd/AC Catalyst Particulates in a Fixed‐Bed Reactor

Microwave radiation is a novel energy source to drive chemical reactions. In conventional reactors, however, the heat created either escapes through uninsulated reactor walls, or the microwave radiation is attenuated by insulated walls. Here, microwave selective heating of Pd catalyst particles supported on activated carbon particulates was examined in a fixed‐bed reactor using a novel vacuum‐filled Dewar‐like double‐walled continuous‐flow reactor. This reactor was developed toward energy savings in performing such organic reactions as the transformation of the hydride methyl cyclohexane to toluene.     

Slippage of 4He Films and Precursor Phenomenon of Superfluidity

We have carried out quartz crystal microbalance (QCM) experiments for ⁴He films on an exfoliated single-crystalline graphite using a 32 kHz tuning fork, and have measured the temperature dependence of the resonance frequency and the Q value for various areal densities and oscillation amplitudes. Comparing with the previous experiments for Grafoil, the decoupling of the films due to the slippage or the superfluidity was larger than that of Grafoil, and the competition between the slippage and the superfluidity was observed in three-atom thick films. Furthermore, it was found that the slippage is suppressed gradually at higher temperature than the superfluid onset T _c , and that the relaxation time decreases at low temperatures while it obeys the Arrhenius law at high temperatures. These results suggest a precursor to the superfluidity of ⁴He films.     

Study of melting and crystallization behavior of polyacrylonitrile using ultrafast differential scanning calorimetry

The thermal behavior of polyacrylonitrile (PAN) has been investigated using X-ray diffraction, differential scanning calorimetry (DSC), and ultrafast DSC. In conventional DSC, it is difficult to prevent the concurrent occurrence of the exothermic reactions of PAN with melting. However, in the ultrafast DSC curve, the exothermic peak due to these reactions disappears over the temperature range 0–400 °C at heating rates above 250 °C s⁻¹. Alternatively, the glass transition and the melting of PAN are observed over the temperature range 109–129 °C and 335–362 °C, respectively. Moreover, upon cooling from the molten state at a rate of −7500 °C s⁻¹, PAN crystallization is observed at 204 °C. These findings were observed repeatedly during heating and cooling measurements. From the extrapolation analysis, the zero-entropy-production melting temperature of PAN is found to lie in the temperature range 320–350 °C. Finally, the equilibrium melting temperature of PAN is estimated to be ca. 465 °C.     

Thermal Analysis for Package Cooling Technology Using Phase-Change Material by Using Thermal Network Analysis and CFD Analysis With Enthalpy Porosity Method

This paper describes a transient cooling technology for electronic equipments using phase-change material (PCM). The module is made of low-cost materials, yet it is designed to achieve a reasonably high level of heat transfer performance. Paraffin is used as the PCM. In previous our report, we can estimate the cooling performance of PCM by using a thermal network method, which cannot calculate melted PCM flow. In this paper, we consider the heat transfer phenomena of PCM module more deeply by using computational fluid dynamics (CFD) analysis with an enthalpy porosity method. By using this method, we can calculate phase-change phenomena and flow phenomena of melted PCM with CFD analysis. First, we briefly explain the results of the experiment and the thermal network analysis. Then we describe the details of CFD analysis with the enthalpy porosity method. In this calculation, melted PCM flow and heat absorption of latent heat can be analyzed. Therefore, we can discuss the reason why the thermal network analysis can estimate cooling performance of PCM module without dealing with melted PCM flow. The calculation results showed that natural convective flow of melted PCM affects the cooling performance of the PCM module. In the case where the PCM module is set vertically, high temperature and low temperature locations exist on the substrate. If several devices are cooled with the PCM module, device consuming the most power must be set in the lower part of the PCM module. From these results, we can conclude that no natural convective flow occurs in our experiment due to the shape of the PCM module.     

Exploration on efficient similar sentences extraction

Measuring the semantic similarity between sentences is an essential issue for many applications, such as text summarization, Web page retrieval, question-answer model, image extraction, and so forth. A few studies have explored on this issue by several techniques, e.g., knowledge-based strategies, corpus-based strategies, hybrid strategies, etc. Most of these studies focus on how to improve the effectiveness of the problem. In this paper, we address the efficiency issue, i.e., for a given sentence collection, how to efficiently discover the top-k semantic similar sentences to a query. The previous methods cannot handle the big data efficiently, i.e., applying such strategies directly is time consuming because every candidate sentence needs to be tested. In this paper, we propose efficient strategies to tackle such problem based on a general framework. The basic idea is that for each similarity, we build a corresponding index in the preprocessing. Traversing these indices in the querying process can avoid to test many candidates, so as to improve the efficiency. Moreover, an optimal aggregation algorithm is introduced to assemble these similarities. Our framework is general enough that many similarity metrics can be incorporated, as will be discussed in the paper. We conduct extensive experimental evaluation on three real datasets to evaluate the efficiency of our proposal. In addition, we illustrate the trade-off between the effectiveness and efficiency. The experimental results demonstrate that the performance of our proposal outperforms the state-of-the-art techniques on efficiency while keeping the same high precision as them.