A Novel KCaNi/α-Al2O3 Catalyst for CH4 Reforming with CO2

A potassium and calcium co-promoted nickel catalyst (KCaNi/-Al₂O₃) prepared by a direct impregnation method possessed a high activity, high stability and excellent coke resistance properties in CH₄ reforming with CO₂. XRD, XPS and H₂-TPR characterizations indicated that (i) Ca and K strengthened the interaction between Ni and -Al₂O₃ and promoted the formation of a unique NiAl₂O₄ phase on the surface of the catalyst and (ii) Ca and K increased the dispersion of Ni and retarded its sintering. Coking reactions (CH₄ temperature-programmed decomposition and O₂-TPO) disclosed that K reduced carbon formation via CH₄ decomposition.     

Discrimination of psychoactive fungi (commonly called "magic mushrooms") based on the DNA sequence of the internal transcribed spacer region

'Magic mushrooms' (MMs) are psychoactive fungi containing the hallucinogenic compounds, psilocin (1) and psilocybin (2). Since June 6, 2002, these fungi have been regulated by the Narcotics and Psychotropics Control Law in Japan. Because there are many kinds of MMs and they are sold even as dry powders in local markets, it is very difficult to identify the original species of the MMs by morphological observation. Therefore, we investigated the internal transcribed spacer (ITS) region in the ribosomal RNA gene of MMs obtained in Japanese markets to classify them by a genetic approach. Based on the size and nucleotide sequence of the ITS region amplified by PCR, tested MMs were classified into 6 groups. Furthermore, a comparison of the DNA sequences of the MMs with those of authentic samples or with those found in the databases (GenBank, EMBL and DDBJ) made it possible to identify the species of tested MMs. Analysis by LC revealed that psilocin (1) was contained at the highest level in Panaeolus cyanescens among the MMs, but was absent in the Amanita species.     

A novel anionic surfactant templating route for synthesizing mesoporous silica with unique structure

Anionic surfactants are used in greater volume than any other surfactants because of their highly potent detergency and low cost of manufacture. However, they have not been used as templates for synthesizing mesoporous silica. Here we show a templating route for preparing mesoporous silicas based on self-assembly of anionic surfactants and inorganic precursors. We use aminosilane or quaternized aminosilane as co-structure-directing agent (CSDA), which is different from previous pathways. The alkoxysilane site of CSDA is co-condensed with inorganic precursors; the ammonium site of CSDA, attached to silicon atoms incorporated into the wall, electrostatically interacts with the anionic surfactants to produce well-ordered anionic-surfactant-templated mesoporous silicas (AMS). These have new structures with periodic modulations as well as two-dimensional hexagonal and lamellar phases. The periodic modulations may be caused by the coexistence of micelles that differ in size or curvature, possibly owing to local chirality. These mesoporous silicas provide a new family of mesoporous materials as well as shedding light on the structural behaviour of anionic surfactants.     

Developmental word acquisition and grammar learning by humanoid robots through a self-organizing incremental neural network.

We present a new approach for online incremental word acquisition and grammar learning by humanoid robots. Using no data set provided in advance, the proposed system grounds language in a physical context, as mediated by its perceptual capacities. It is carried out using show-and-tell procedures, interacting with its human partner. Moreover, this procedure is open-ended for new words and multiword utterances. These facilities are supported by a self-organizing incremental neural network, which can execute online unsupervised classification and topology learning. Embodied with a mental imagery, the system also learns by both top-down and bottom-up processes, which are the syntactic structures that are contained in utterances. Thereby, it performs simple grammar learning. Under such a multimodal scheme, the robot is able to describe online a given physical context (both static and dynamic) through natural language expressions. It can also perform actions through verbal interactions with its human partner.     

Power generation of the touching Cu/Bi-Te/Cu composites under the periodically alternating temperature gradients

The thermo-emf ΔV of the touching p- and n-type Cu/Bi-Te/Cu composites with different thicknesses of t _Bi-Te and t _Cu was measured as a function of time by alternating the temperature difference ΔT at periods of T = 20, 60, 120, 240 and ∞ sec, where t _Bi-Te was varied from 0.1 to 2.0 mm and t _Cu from 0 to 4.0 mm. As a result, ΔV changes significantly with t _Bi-Te, t _Cu and T. The effective thermo-emf ΔV _eff increases significantly with an increase of 1/T and exhibited a local maximum at 1/T = 1/240 s⁻¹. The resultant | α | and the effective temperature difference ΔT _eff were increased significantly by optimizing t _Bi-Te and t _Cu at 1/T = 1/240 s⁻¹. The power generation ΔW _eff (= ΔV _eff²/4R _calc) estimated using the measured ΔV _eff and calculated R _calc also exhibited a local maximum at 1/240 s⁻¹ for an optimum combination of t _Bi-Te = 0.1 mm and t _Cu = 2.0 mm, so that the maxima ΔW _eff at 1/T = 1/240 s⁻¹ for the p- and n-type composites were 2.28 and 2.92 times higher than those obtained at 1/T = 0 s⁻¹. This significant increase in ΔW _eff is owing to both the increase in ΔT _eff and the increase in ZT due to the increase in |α|. The power generation was thus found to be enhanced significantly by imposing the alternating temperature gradients on touching Cu/Bi-Te/Cu composites.     

Dependence of Seebeck coefficient on a load resistance and energy conversion efficiency in a thermoelectric composite

The thermo-emf ΔV and current ΔI generated by imposing the alternating temperature gradients (ATG) at a period of T and the steady temperature gradient (STG) on a thermoelectric (TE) composite were measured as a function of t, where t is the lapsed time and T was varied from 60 to or ∞ s. The STG and ATG were produced by imposing steadily and alternatively a source voltage V in the range from 1.0 to 4.0 V on two Peltier modules sandwiching a composite. ΔT, ΔV, ΔI and V_P oscillate at a period T and their waveforms vary significantly with a change of T, where ΔV and V_P are the voltage drops in a load resistance R_L and in resistance R_P of two modules. The resultant Seebeck coefficient |α| = |ΔV|/ΔT of a composite under the STG was found to be expressed as |α| = |α₀|(1 − R_comp/R_T), where R_T is the total resistance of a circuit for measuring the output signals and R_comp is the resistance of a composite. The effective generating power ΔW_eff has a local maximum at T = 960 s for the p-type composite and at T = 480 s for the n-type one. The maximum energy conversion efficiency η of the p- and n-type composites under the ATG produced by imposing a voltage of 4.0 V at an optimum period were 0.22 and 0.23% at ΔT_eff = 50 K, respectively, which are 42 and 43% higher than those at ΔT = 42 K under the STG. These maximum η for a TE composite sandwiched between two Peltier modules, were found to be expressed theoretically in terms of R_P, R_T, R_L, α_P and α, where α_P and α are the resultant Seebeck coefficients of Peltier modules and a TE composite.     

A Gait Phase Measurement System Using Treadmill Motor Current

The article describes the development of a gait phase time-based split-belt treadmill measurement system. Conventional methods of measuring gait phase, such as the foot switch and force plate, require significant preparation and are costly. In this article, we propose a simple, cheap, and accurate gait phase measurement system that utilizes only the treadmill motor current value. Comparison of this algorithm with conventional methods reveals that the proposed algorithm is as accurate as the foot switch. Moreover, the proposed algorithm can estimate stance phase within a 0.2?s error of the measured value of the force plate in most cases (four out of five healthy subjects). This accuracy is higher than that of the foot switch which is widely used in the clinical field.     

A speeded-up online incremental vision-based loop-closure detection for long-term SLAM

An online incremental method of vision-only loop-closure detection for long-term robot navigation is proposed. The method is based on the scheme of direct feature matching which has recently become more efficient than the Bag-of-Words scheme in many challenging environments. The contributions of the paper are the application of hierarchical k-means to speed-up feature matching time and the improvement of the score calculation technique used to determine the loop-closing location. As a result, the presented method runs quickly in long term while retaining high accuracy. The searching cost has been markedly reduced. The proposed method requires no any off-line dictionary generation processes. It can start anywhere at any times. The evaluation has been done on standard well-known datasets being challenging in perceptual aliasing and dynamic changes. The results show that the proposed method offers high precision-recall in large-scale different environments with real-time computation comparing to other vision-only loop-closure detection methods.     

Flexible feature discovery and structural information control

In this paper, we propose a new information theoretic method called structural information control for flexible feature discovery. The new method has three distinctive characteristics, which traditional competitive learning fails to offer. First, the new method can directly control competitive unit activation patterns, whereas traditional competitive learning does not have any means to control them. Thus, with the new method, it is possible to extract salient features not discovered by traditional methods. Second, competitive units compete witheach other by maximizing their information content about input patterns. Consequently, this information maximization makes it possible to control flexibly competition processes. Third, in structural information control, it is possible to define many different kinds of information content, and we can choose a specific type of information according to a given objective. When applied to competitive learning, structural information can be used to control the number of dead or spare units, and to extract macro as well as micro features of input patterns in explicit ways. We first applied this method to simple pattern classification to demonstrate that information can be controlled and that different neuron firing patterns can be generated. Second, a dipole problem was used to show that structural information could provide representations similar to those by the conventional competitive learning methods. Finally, we applied the method to a language acquisition problem in which networks must flexibly discover some linguistic rules by changing structural information. Especially, we attempted to examine the effect of the information parameter to control the number of dead neurons, and thus to examine how macro and micro features in input patterns can explicitly be discovered by structural information.